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Editor-In-Chief Dr. Shiv Kumar Ph.D. (CSE), M.Tech. (IT, Honors), B.Tech. (IT) Senior Member of IEEE, Member of the Elsevier Advisory Panel Chairman: Lattice Science, Bhopal (MP), India CEO: Blue Eyes Intelligence Engineering and Sciences Publication, Bhopal (MP), India Specialization: Sound Processing, Image Processing and Recognition, Compression and Decompression

Editors Prof. Chandra Singh M.Tech, BE Member of IEEE, Elsevier, Springer Assistant Professor, Department of Electronics and Communication Engineering, Sahyadri College of Engineering & Management, Nashik (Maharashtra), India. Specialization: Database, Machine Learning, Blockchain, Cloud Computing, Wireless Sensor Networks

Prof. Aiswarya Kannan ME( VLSI), BE (EC) Assistant Professor, Department of Electronics and Communication Engineering, SRM TRP Engineering College, Irungalur (Tamil Nadu), India. Specialization: VlSI, Machine Learning, Deep Learning, IoT

Prof. Monesh Kumar Sharma M.Tech (Electrical Engineering), B.Tech (ECE) Member of ACM, Springer, PubMed Electrical Engineer, Department of Electrical, Aarvi Encon Private Limited Mumbai, Mumbai (Maharashtra), India. Specialization: Sprinklers System, Safety System, Building Electrification

Dr. Gunalan Kumariah Ph.D. (Electrical Engineering), M.E (Control and Instrumentation), B.E (EEE) Associate Professor, Department of Electrical and Electronics Engineering, R.M.K College of Engineering and Technology, Thiruvallur (Tamil Nadu), India. Specialization: Electrical and Electronics

Prof. Yenireddy Srinivasareddy M.Tech (Thermal Engineering), B.Tech (ME) Associate Professor, Department of Mechanical Engineering, CMR Engineering College, Hyderabad (Telangana), India. Specialization: Internal Combustion Engines, Solar Energy, Heat Exchangers

Prof. S. Menaga M.E (Wireless Communication), B.E (ECE) Associate Professor, Department of Electronics and Communication Engineering, Jai Shriram Engineering College, Avinashipalayam (Tamil Nadu), India. Specialization: Security implementation in Wireless Communication, Smart Device Development using IoT, Embedded Design

© All rights are reserved. For more details, please read ‘copyright Grants and Ownership Declaration’ from the journal website. Dr. Divya Midhunchakkaravarthy Ph.D (CS), MCA, B.Com (Computer) Associate Professor, Department of Computer Science, Lincoln University College, Bharu, Malaysia. Specialization: Cyber Security, IoT

Prof. Arjun K P M.Phil, MCA, B.Sc. (Computer Science) HOD, Department of Computer Science, MTM College of Arts, Science and Commerce, Veliyankode (Kerala), India. Specialization: Data Mining, Network Security

Prof. Girish Ramkrushna Talmale M.E (Embedded System & Computing), B.E(Computer Engineering) Member of IEE Assistant Professor, Department of Computer Science and Engineering, G H Raisoni College of Engineering, Nagpur (Maharashtra), India. Specialization: Data Mining, Network Security

Dr. Soumi Dutta Ph.D. (CSE), M.Tech (CSE), B.Tech (IT) Assistant Professor, Department of Computer Science and Application, Institute of Engineering & Management, Kolkata (West Bengal), India. Specialization: Data Mining, IoT, Machine Learning

Prof. Gnanasekaran V ME (Product Design), BE (ME) Assistant Professor, Department of Mechanical Engineering, AMET University, Chennai (Tamil Nadu), India. Specialization: Natural Composites, Friction Stir Welding

Dr. Vani A Ph.D, M.Tech, BE Member of Springer Professor and Head, Department of Electronics and Instrumentation Engineering, BMS College of Engineering, Bengaluru (Karnataka), India. Specialization: Image Processing and Machine Learning, VLSI Design and Embedded Systems, IoT

Dr. Sanjay Shekar N C Ph.D, M.Tech (Remote Sensing and GIS), BE(Construction Technology and Management) Associate Professor, Department of Civil Engineering, JSS Academy of Technical Education, Bengaluru (Karnataka), India. Specialization: Remote Sensing and GIS, Water Resources, RS and GIS Applications

© All rights are reserved. For more details, please read ‘copyright Grants and Ownership Declaration’ from the journal website. Prof. Kala Bharathi Kudikala MCA, BSc.(Computer Science) Assistant Professor, Department of Computer Science, St. Pious X Degree & PG College for Women, Secunderabad (Telangana), India. Specialization: Big Data and Cloud Computing

Dr. Prafulla Kumar Panda Ph.D, M.Tech, M.Sc. Assistant Professor, Department of Civil Engineering, Centurion University of Technology and Management, R.Sitapur (Odisha), India. Specialization: Remote sensing and GIS, Water Resource, Geology

Prof. S. Baskar ME, BE Member of Elsevier Assistant Professor, Department of Automobile Engineering, Vels Institute of Science, Technology and Advanced Studies, Chennai (Tamil Nadu), India. Specialization: Energy Heat Transfer

Dr. Niranjan Lal Ph.D (CSE), M.Tech, BE(IT) Member of IEEE, PubMed Assistant Professor, Department of Computer Science and Engineering, Mody University, Sikar (Rajasthan), India. Specialization: Database, Machine Learning, Blockchain, Cloud Computing, Wireless Sensor Networks

Author (s) Name Title of the Article Page No

M. Shireesha, Yasser Mirza Baig, Bio-Oil Extraction from the Shells of Cocos Nucifera – A C. Sarita, Syed Rashid Iqbal, 1-2 Source of Generating Renewable Energy and Its Analysis Caroline Wesley, N. Vaishnavi

Syed Altaf Hussain, Palani Optimization of Specific Energy Consumption in Turning of 2-3 Kumar.K, Md.Alamgir GFRP Composites using Particle Swarm Optimization

Nadia.Nawwar, Hani.Kasban, Deep Learning Model Based on Mobile-Net with Haar-like 3-4 May salama Algorithm for Masked Face Recognition at Nuclear Facilities

Reduce Artificial Intelligence Planning Effort by using Map- Mohamed Elkawkagy, Heba Elbeh 4-5 Reduce Paradigm

A.Nagesh Energy Audit System for Households using Machine Learning 6-6

Evaluation of Water Quality Impact Caused by Common D. Vasudevan, A.G. Murugesan Hazardous Waste Landfill Facility in Gummidipoondi, 7-8 Tamilnadu- India

Altaf Alaoui, Boris Olengoba Survey of Process of Data Discovery and Environmental Ibara, Badia Ettaki, Jamal 8-10 Decision Support Systems Zerouaoui

Ritika Singh, Nilansh Panchani, Analysis and Simulation of COVID-19 11-11 Aastha Bhatnagar

Kartik Khariwal, Rishabh Gupta, R-MFDroid: Android Malware Detection using Ranked 11-13 Jatin Singh, Anshul Arora Manifest File Components

Contribution of Mahapurush Srimanta Sankardeva to Abul Hussain 14-14 Assamese Literature and Culture

S.Logesh, R.Ramesh, I. Compatability Behaviour on Cold Formed Steel for I Section 14-15 Padmanaban and C Section in Variable Parameters

Pranav Andhyal, Karthik Applications of 5D CAD for Billing in construction using GIS 15-16 Nagarajan, Raju Narwade

Bhageerath Singh Kaurav, Karuna Modified Filter Equation with Improved Fuzzy Logic System 16-17 Markam, Pooja Sahoo Based Directional Median Filter for Mixed Noise

Construction of Video Management System Based on Remote Byeongtae Ahn 17-18 Education

Nishika Manira, Swelia Monteiro, Tashya Alberto, Tracy Niasso, Geo-Landmark Recognition and Detection 18-19 Supriya Patil

C. Achille Fumtchum , Pierre Tsafack, Florin Hutu, Guillaume A Survey of RF Energy Harvesting Circuits 19-21 Villemaud, Emmanuel Tanyi

J Paul Raja Singh, Sharmishtha User Reputation Calculation for Service-Oriented 21-21 Sen, Shreyes Prasad Environments

T.Venkatesh, K.Prathyush, Agriculture Crop Leaf Disease Detection using Image 22-22

Sandhya Sarma. K. N, Hemraj Securing Communication in the Iot- Based Power Shobharam Lamkuche, E.Chandra 22-25 Constrained Devices in Health Care System Blessie

Karthik Valliappan C, Vikram R Autonomous Indoor Navigation for Mobile Robots 25-25

G Manmadha Rao, Gade Chaitanya Prasad, K. Pavani, S Estimation of Glucose Levels in Blood Sample using A 26-26 Lakshaman Rao, B Prasanna Biosensor Kumar

F. Ajamah, P. Tsafack, E. Tanyi, An Assessment of Hydropower Potential for Electrical Energy 26-29 A. Cheukem, B. Ducharne Harvesting in Water Distribution Network in Buea-Cameroon

Manasi Bansode, Siddhi Pardeshi, Suyasha Ovhal, Pranali Shinde, Fake Review Prediction and Review Analysis 29-30 Anandkumar Birajdar

Deepa Sonal, Dina Nath Pandit, An Iot Based Model to Defend Covid-19 Outbreak 30-31 Md. Alimul Haque

Implementation of Industry 4.0 Revolution through Skill Amlan das Development– A Blessing for Local for Vocal in Covid-19 32-32 Pandemic

Dipak Kumar Patra, Sukumar Grammatical Fireworks Algorithm Method for Breast Lesion 33-34 Mondal, Prakash Mukherjee Segmentation in DCE-MR Images

© All rights are reserved. For more details, please read ‘copyright Grants and Ownership Declaration’ from the journal website. International Journal of Innovative Technology and Exploring Engineering (IJITEE) Volume-10 Issue-7, May 2021, ISSN: 2278-3075 (Online) Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP)

Author(s): M. Shireesha, Yasser Mirza Baig, C. Sarita, Syed Rashid Iqbal, Caroline Wesley, N. Vaishnavi Title of the Article: Bio-Oil Extraction from the Shells of Cocos Nucifera – A Source of Generating Renewable Energy and Its Analysis Abstract: Biomass is an important source of energy and fuel worldwide after coal, oil and natural gas. These fossil fuels do substantially more harm than renewable energy sources like biomass energy. Oil extracted from biomass is considered as an attractive option. In our project, we have specifically selected coconut shells as our feed as they are carbon-neutral, easy to store and abundantly available. Coconut shell also known as Cocos Nucifera shell in biological terms, once a discarded outer hardcover is now a product of great demand. Coconut shell charcoal is used as domestic and industrial fuel. This is obtained by various techniques. Initially, the shells are burned at high temperature and condensed to extract bio-oil using a series of unit operations and processes such as distillation, gas chromatography. These samples are then sent for analysis to compare them with the conventional fuel sources and then antimicrobial activity is examined. The medium-chain fatty acids in coconut oil have antimicrobial properties that can help protect against harmful microorganisms. Lauric acid and capric acid are known to have potent antimicrobial properties. Different bacterial cultures have been introduced later to test the ability of the oil to resist the harmful microorganisms and fungal cultures.Various analysis such as Infrared Spectroscopy, Gas-Mass Spectroscopy and Ultimate analysis are performed on the retrieved samples of oil extracted from the coconut shells. It is to be observed that the carbon content in the Cocos nucifera derived oil is less than the conventional diesel oil which makes it best for environmental uses. Keywords: Biomass; Cocos Nucifera; Coconut Shell; Distillation.

References: 1. Research and Prospects for the Development of Alternative Fuels in the Transport Sector in Poland: A Review. 2. Klass, Donald L. Biomass for renewable energy, fuels, and chemicals. Elsevier, 1998. 3. White, Leslie Paul, and L. G. Plaskett. Biomass as fuel. Academic Press Ltd., 1981. 4. Yerima, Ibrahim, and Mohammed Zanna Grema. "The potential of coconut shell as biofuel." The Journal of Middle East and North Africa Sciences 4.5 (2018): 11-15 5. Sundaram, E. Ganapathy, and E. Natarajan. "Pyrolysis of coconut shell: An experimental investigation." The Journal of Engineering Research [TJER] 6.2 (2009): 33-39. 6. Novita, Sri Aulia, et al. "Processing coconut fiber and shell to biodiesel." Advance Science Engineering Information Technology 4 (2014): 84-86. 7. Rout, Tanmaya Kumar. Pyrolysis of coconut shell. Diss. 2013. 8. Kozlov, A., Svishchev, D., Donskoy, I., Shamansky, V., & Ryzhkov, A. (2015). A technique proximate and ultimate analysis of solid fuels and coal tar. Journal of Thermal analysis and Calorimetry, 122(3), 1213-1220. 9. Alberto, J., Tsamba., Weihong Yang. and Wlodzimierz Blasia., 2006, "Pyrolysis Characteristics and Global Kinetics of Coconut and Cashew Nut Shells," Fuel Processing Technology, Vol. 87, pp. 523-550. 10. Demirbas, Ayhan. "Current technologies for the thermo-conversion of biomass into fuels and chemicals." Energy Sources 26.8 (2004): 715-730. 11. Fagernäs, Leena. "Chemical and physical characterisation of biomass-based pyrolysis oils. Literature view." (1995). 12. Sarkar, Jayanto Kumar, and Qingyue Wang. "Different pyrolysis process conditions of south Asian waste coconut Shell and characterization of gas, bio-char, and bio-oil." Energies 13.8 (2020): 1970. 13. Tsai, W. T., M. K. Lee, and Dan YM Chang. "Fast pyrolysis of rice straw, sugarcane bagasse and coconut shell in an induction-heating reactor." Journal of analytical and applied pyrolysis 76.1-2 (2006): 230-237. 14. Younis, Manar, et al. "Renewable biofuel production from biomass: A review for biomass pelletization, characterization, and thermal conversion techniques." International Journal of Green Energy 15.13 (2018): 837- 863. 15. Mostafazadeh, Ali Khosravanipour, et al. "A review of recent research and developments in fast pyrolysis and bio-oil upgrading." Biomass Conversion and Biorefinery 8.3 (2018): 739-773. 16. Hickman, K. C. D. "High-vacuum Short-path Distillation-A Review." Chemical Reviews 34.1 (1944): 51-106 17. Ali, Imtiaz, Haitham Bahaitham, and Raed Naebulharam. "A comprehensive kinetics study of coconut shell waste pyrolysis." Bio resource technology 235 (2017): 1-11. 18. Xiu, Shuangning, and Abolghasem Shahbazi. "Bio-oil production and upgrading research: A review." Renewable and Sustainable Energy Reviews 16.7 (2012): 4406-4414. 19. Pinheiro Pires, Anamaria Paiva, et al. "Challenges and opportunities for bio-oil refining: A review." Energy & Fuels 33.6 (2019): 4683- 4720. 20. Tao, Ling, and Andy Aden. "The economics of current and future biofuels." Biofuels (2011): 37- 69.

Souvenior of Volume – 10 Issue – 7 May 2021 Website: www.ijitee.org DOI: 10.35940/ijitee 1

International Journal of Innovative Technology and Exploring Engineering (IJITEE) Volume-10 Issue-7, May 2021, ISSN: 2278-3075 (Online) Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP)

21. Festel, Gunter W. "Biofuels–economic aspects." Chemical Engineering & Technology: Industrial Chemistry‐Plant Equipment‐Process Engineering‐Biotechnology 31.5 (2008): 715-720. 22. Sahu, Santosh. "Trends and Patterns of Energy consumption in India." (2008). 23. Archana, A., et al. "Coconut Shell as a Promising Resource for Future Biofuel Production." Biomass Valorization to Bioenergy. Springer, Singapore, 2020. 31-43.

Author(s): Syed Altaf Hussain, Palani Kumar.K, Md.Alamgir Title of the Article: Optimization of Specific Energy Consumption in Turning of GFRP Composites using Particle Swarm Optimization Abstract: In this paper, an attempt is made to optimize the control parameters for the minimization of specific energy consumption in turning GFRP composites using particle swarm optimization (PSO).Optimization of specific energy consumption in machining is helpful to evaluate the process energy characteristics and also facilitates choosing the best control parameters for energy saving. The control parameters considered are cutting speed, feed, depth of cut and fiber orientation angle. Experiments are planned and executed according to Taguchi's L25 orthogonal array in the design of experiments on an all geared lathe with PCD cutting tool insert. A quadratic predictive model was developed for specific energy consumption using RSM and the optimal combinations of control parameters were determined using PSO. The Predicted results from PSO show that there is an improvement in MRR by 46.44% and a reduction in SEC by 33.69%. From the confirmative experimental results, it is observed that PSO algorithm has a powerful global search ability to solve the optimization problem. Keywords: GFRP Composites, Taguchi Method, Turning, Specific Energy Consumption, PCD Tool Insert, Particle Swarm Optimization (PSO)

References: 1. S. Kalpakjian, S.Schmid, “Manufacturing processes for engineering materials”, Prentice-Hall, Englewood Cliffs, New Jersey; 2003. 2. E.M. Trent, P.K.Wright, “Metal cutting”Butterworth-Heinemann; 2000. 3. F. Draganescu, M. Gheorghe, C.V. Doicin, “Models of machine tool efficiency and specific consumed energy”, Int. J. of Mat, Proce.Tech Journal of Materials Processing Technology”, vol. 141, no. 1, pp. 9-15, 2003. 4. S. Jayaram, S.Kapoor, R. DeVor, “Estimation of the specific cutting pressures for mechanistic cutting force models”, Int. J. of Mat.Tool and Manuf, vol. 4, No.2, pp. 265-281, 2001. 5. T. Gutowski, J. Dahmus, A. Thiriez, “Electrical Energy Requirements for manufacturing processes” Proceedings of 13th CIRP International Conference on Life Cycle Eng, Leuven, 2006, pp. 623, 2006. 6. W Li, S. Kara, “An empirical model for predicting energy consumption of manufacturing processes: a case of turning process” Proce. of the Int. of Mech. Engineers Part B-J. of Engg. Manuf., vol. 225, pp. 1636-1646, 2011. 7. V.A. Balogun, P. T. Mativenga, “Impact of un-deformed chip thickness on specific energy in mechanical machining processes” J. of Clea.Prod.,vol. 69, pp.260-268, 2014. 8. F. Pusavec, P. Krajnik and J. Kopa, "Transitioning to sustainable production”, Part I: App. on Mach. Tech.J. of Clea.Prod, vol. 18, pp. 174-184, 2010 9. F. Pusavec, D. Kramar, P. Krajnik and J. Kopac "Transitioning to sustainable production - Part II: evaluation of sustainable machining technologies"J. of Clea.Prod, vol. 18, pp. 1211-1221, 2010. 10. Yu Su, Guoyong Zhao *, Yugang Zhao, Jianbing Meng and Chunxiao Li, “Multi-Objective Optimization of Cutting Parameters in Turning AISI 304 Austenitic Stainless Steel”, Metals, pp. 2-11, 2020. 11. Pengfei Hu, Xiaosong Zhao, “Particle Swarm Optimization for Multi-response Parameter Optimization Based on Desirability Functions” International Conference of Information Technology, Computer Engineering and Management Sciences;2011. 12. S. Panda, M. Mishra, B.B. Biswas, P. Nanda “ Optimization of Multiple Response characteristics of EDM process using Taguchi based Grey relational analysis and modified PSO”, J. of Adv. Manuf. Sys, vol.14, no. 3, pp. 123- 148, 2015. 13. Arindam Majunder, Pakaj Kumar Das, Abhishek Majumder, Moutushee Debnath “An Appraoch to optimize the EDM Process parameters using desirability based Multi- Objective PSO”, Prod & Manuf Res, vol.22, no.1, pp. 228-240, 2014. 14. Bobby Oedy Pramoedyo Soepangkat, Rachmadi Norcahyo, M. Khoirul Effendi, Bambang Pramujati "Multi- response optimization of carbon fiber reinforced polymer (CFRP) drilling using backpropagation neural network- particle swarm optimization (BPNN-PSO)" Engg. Sci. and Tech, an Int. J, vol. 23, pp. 700-713, 2020.

Souvenior of Volume – 10 Issue – 7 May 2021 Website: www.ijitee.org DOI: 10.35940/ijitee 2

15. Azmi, A. I., Syahmi, A. Z., Naquib, M., Lih, T. C., Mansor, A. F., & Khalil, A. N. M. “Effects of machining conditions on the specific cutting energy of carbon fibre reinforced polymer composites”. J. of Phy: Conf. Ser, 2017. 16. AB Marathe AB, and AM Javali “Effect of drilling parameters on specific cutting energy and delamination of a composite made of unsaturated polyester resin and chopped glass fibres, J of Thermo. Plas. Comp. Mat, vol.29,no. 9, pp. 1261-1281, 2014. 17. Rosario D, Manuel G and Maria R “Determination of Energy during the Dry Drilling of PEEK GF30 Considering the Effect of Torque”, Proc.Engg. vol.63, pp.687-693, 2013. 18. J. P. Davim, Mata, “Influence of cutting parameters on surface roughness using statistical analysis”. Ind Lub. Trib, vol. 56, No.5, 270-274, 2004. 19. Syed Altaf Hussain, Pandurangadu V,Palanikumar K, "Surface Roughness Analysis in the ma 20. chining of GFRP Composites using Carbide Tool(K20)”, Euro. J of Sci.Res, vol. 41, no. 1, pp. 84-98, 2010. 21. P.J. Ross, “Taguchi Techniques for Quality Engineering”, Tata McGraw Hill, Second Edition, 2005. 22. J. Kennedy, R. Eberhart, R. “Particle swarm optimization”. In Proc. IEEE Int. Conf. Neural Networks (ICNN‘95), Perth, Australia, 4; 1995, pp. 1942-1948. 23. Syed Altaf Hussain, K. Palanikumar, V. Pandurangadu and G.Venkata subbaiah, “ Optimal machining parameters for Minimizing the surface roughness in Turning of GFRP Composites by PCD tooling” Int. Jof App Engg. Res, vol. 5, no.13, pp. 2227-2239, 2010

Author(s): Nadia.Nawwar, Hani.Kasban, May salama Title of the Article: Deep Learning Model Based on Mobile-Net with Haar-like Algorithm for Masked Face Recognition at Nuclear Facilities Abstract: During the spread of the COVID-I9 pandemic in early 2020, the WHO organization advised all people in the world to wear face-mask to limit the spread of COVID-19. Many facilities required that their employees wear facemask. For the safety of the facility, it was mandatory to recognize the identity of the individual wearing the mask. Hence, face recognition of the masked individuals was required. In this research, a novel technique is proposed based on a mobile-net and Haar-like algorithm for detecting and recognizing the masked face. Firstly, recognize the authorized person that enters the nuclear facility in case of wearing the masked-face using mobile-net. Secondly, applying Haar-like features to detect the retina of the person to extract the boundary box around the retina compares this with the dataset of the person without the mask for recognition. The results of the proposed modal, which was tested on a dataset from Kaggle, yielded 0.99 accuracies, a loss of 0.08, F1.score 0.98. Keywords: COVID-19, Deep learning, Mobile-Net, and Haar-Like.

References: 1. M. Alsawwaf, Z. Chaczko, and M. Kulbacki, "In Your Face: Person Identification Through Ratios of Distances Between Facial Features," 2020, pp. 527-536. 2. A. Bhadani and A. Sinha, "A FACEMASK DETECTOR USING MACHINE LEARNING and PROCESSING TECHNIQUE," vol. 13, 11/09 2020. 3. Z. Zhu, T. Morimoto, H. Adachi, O. Kiriyama, T. Koide, and H. J. Mattausch, "Multi-view Face Detection and Recognition using Haar-like Features," 01/01 2004. 4. K. Hammoudi, A. Cabani, H. Benhabiles, and M. Melkemi, "Validating the Correct Wearing of Protection Mask by Taking a Selfie: Design of a Mobile Application “CheckYourMask” to Limit the Spread of COVID-19," Computer Modeling in Engineering \& Sciences, vol. 124, no. 3, pp. 1049--1059, 2020. 5. S. S. Zhou, S. Lukula, C. Chiossone, R. W. Nims, D. B. Suchmann, and M. K. Ijaz, "Assessment of a respiratory face mask for capturing air pollutants and pathogens including human influenza and rhinoviruses," (in eng), J Thorac Dis, vol. 10, no. 3, pp. 2059-2069, Mar 2018. 6. J. Angelico and K. Wardani, "Convolutional Neural Network Using Kalman Filter for Human Detection and Tracking on RGB-D Video," CommIT (Communication and Information Technology) Journal, vol. 12, p. 105, 10/31 2018. 7. G. Guo and N. Zhang, "A survey on deep learning based face recognition," Computer Vision and Image Understanding, vol. 189, p. 102805, 2019/12/01/ 2019. 8. U. Aiman and V. P. Vishwakarma, "Face recognition using modified deep learning neural network," in 2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT), 2017, pp. 1- 5. 9. H. Huang, D. Sun, R. Wang, C. Zhu, and B. Liu, "Ship Target Detection Based on Improved YOLO Network," Mathematical Problems in Engineering, vol. 2020, p. 6402149, 2020/08/17 2020.

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10. A. S. Joshi, S. S. Joshi, G. Kanahasabai, R. Kapil, and S. Gupta, "Deep Learning Framework to Detect Face Masks from Video Footage," in 2020 12th International Conference on Computational Intelligence and Communication Networks (CICN), 2020, pp. 435-440. 11. M. S. Ejaz, M. N. Islam, M. Sifatullah, and A. Sarker, "Implementation of Principal Component Analysis on Masked and Non-masked Face Recognition," 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), pp. 1-5, 2019. 12. M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L. Chen, "MobileNetV2: Inverted Residuals and Linear Bottlenecks," in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018, pp. 4510- 4520. 13. F. wu, J. Smith, W. Lu, and B. Zhang, Pose-robust Face Recognition by Deep Meta Capsule network-based Equivariant Embedding. 2021. 14. N. Damer, J. H. Grebe, C. Chen, F. Boutros, F. Kirchbuchner, and A. Kuijper, "The Effect of Wearing a Mask on Face Recognition Performance: an Exploratory Study," in 2020 International Conference of the Biometrics Special Interest Group (BIOSIG), 2020, pp. 1-6. 15. M. Ejaz and M. Islam, Masked Face Recognition Using Convolutional Neural Network. 2019, pp. 1-6. 16. H. Walid, Efficient Masked Face Recognition Method during the COVID-19 Pandemic. 2020. 17. J. Huang, Y. Shang, and H. Chen, "Improved Viola-Jones face detection algorithm based on HoloLens," EURASIP Journal on Image and Video Processing, vol. 2019, no. 1, p. 41, 2019/02/11 2019. 18. K. Zhang, Z. Zhang, Z. Li, and Y. Qiao, "Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks," IEEE Signal Processing Letters, vol. 23, no. 10, pp. 1499-1503, 2016. 19. Y. Xu, W. Yan, G. Yang, J. Luo, T. Li, and J. He, "CenterFace: Joint Face Detection and Alignment Using Face as Point," Scientific Programming, vol. 2020, p. 7845384, 2020/07/02 2020. 20. T. Meenpal, A. Balakrishnan, and A. Verma, "Facial Mask Detection using Semantic Segmentation," in 2019 4th International Conference on Computing, Communications and Security (ICCCS), 2019, pp. 1-5. 21. J. Lee, S. Lee, and S. Yang, "An Ensemble Method of CNN Models for Object Detection," in 2018 International Conference on Information and Communication Technology Convergence (ICTC), 2018, pp. 898-901. 22. J. Lee, S.-K. Lee, and S.-I. Yang. 23. T. H. Le, "Applying Artificial Neural Networks for Face Recognition," Advances in Artificial Neural Systems, vol. 2011, p. 673016, 2011/11/03 2011. 24. T. Mita, T. Kaneko, and O. Hori, "Joint Haar-like features for face detection," in Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1, 2005, vol. 2, pp. 1619-1626 Vol. 2. 25. P. Menezes, J. C. Barreto, and J. Dias, "Face tracking based on haar-like features and eigenfaces," IFAC Proceedings Volumes, vol. 37, no. 8, pp. 304-309, 2004/07/01/ 2004. 26. M. Rezaei, H. Ziaei Nafchi, and S. Morales, "Global Haar-Like Features: A New Extension of Classic Haar Features for Efficient Face Detection in Noisy Images," in Image and Video Technology, Berlin, Heidelberg, 2014, pp. 302-313: Springer Berlin Heidelberg. 27. M. Songyan and B. Lu, "A face detection algorithm based on Adaboost and new Haar-Like feature," in 2016 7th IEEE International Conference on Software Engineering and Service Science (ICSESS), 2016, pp. 651-654. 28. S. Du, N. Zheng, Q. You, Y. Wu, M. Yuan, and J. Wu, "Rotated Haar-Like Features for Face Detection with In- Plane Rotation," in Interactive Technologies and Sociotechnical Systems, Berlin, Heidelberg, 2006, pp. 128-137: Springer Berlin Heidelberg. 29. M. Jiang and X. Fan, RetinaMask: A Face Mask detector. 2020. 30. S.-K. Pavani, D. Delgado, and A. F. Frangi, "Haar-like features with optimally weighted rectangles for rapid object detection," Pattern Recognition, vol. 43, no. 1, pp. 160-172, 2010/01/01/ 2010. 31. Y. Li and F. Ren, "Light-Weight RetinaNet for Object Detection," CoRR, vol. abs/1905.10011, / 2019

Author(s): Mohamed Elkawkagy, Heba Elbeh Title of the Article: Reduce Artificial Intelligence Planning Effort by using Map-Reduce Paradigm Abstract: While several approaches have been developed to enhance the efficiency of hierarchical Artificial Intelligence planning (AI-planning), complex problems in AI-planning are challenging to overcome. To find a solution plan, the hierarchical planner produces a huge search space that may be infinite. A planner whose small search space is likely to be more efficient than a planner produces a large search space. In this paper, we will present a new approach to integrating hierarchical AI-planning with the map-reduce paradigm. In the mapping part, we will apply the proposed clustering technique to divide the hierarchical planning problem into smaller problems, so-called sub-problems. A preprocessing technique is conducted for each sub-problem to reduce a declarative hierarchical planning domain model and then find an individual solution for each so-called sub-problem sub-plan. In the reduction part, the conflict between sub- plans is resolved to provide a general solution plan to the given hierarchical AI-planning problem. Pre- processing

Souvenior of Volume – 10 Issue – 7 May 2021 Website: www.ijitee.org DOI: 10.35940/ijitee 4

phase helps the planner cut off the hierarchical planning search space for each sub-problem by removing the compulsory literal elements that help the hierarchical planner seek a solution. The proposed approach has been fully implemented successfully, and some experimental results findings will be provided as proof of our approach's substantial improvement inefficiency. Keywords: Artificial Intelligence Planning, Map-Reduce, Hadoop, Big Data.

References: 1. D, Nau, M. Ghallab, and P. Traverso, "automated planning: Theory and practice," Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 2004. 2. R. Fikes, and N. Nilsson, "STRIPS: a new approach to the application of theorem proving to problem-solving," Artificial Intelligence, 2, 1971, PP 189–208. 3. D. Holler, P. Bercher, G. Behnke, and S. Biundo, ¨HTN planning as heuristic progression search," Journal of artificial intelligence research (JAIR) 2020, vol. (67): PP 835– 880. 4. D. Wilkins, K. Mayers, "A multi-agent planning architecture," In Proceedings of AIPS, 1998. pp. 162. 5. J. Tonino, A. Bos, M. deWeerdt, C. Witteveen, "Plan coordination by revision in collective agent-based systems," Journal of Artificial Intelligence 142, 2. 2002, pp. 121–145. 6. M. Weerdt, C. Witteveen, "A resource logic for multi-agent plan merging," In Proceedings of the 20th Workshop of the UK Planning and Scheduling, 2003, PP. 244–256. 7. A. Mors, J. Valk, C. Witteveen, "Task coordination, and decomposition in multi-actor planning systems," In Proceedings of the Workshop on Software-Agents in Information Systems and Industrial Applications (SAISIA), 2006. pp. 83–94. 8. M. desJardins, M. Wolverton, "Coordinating a distributed planning system," Journal of AI Magazine, 20(4).1999, PP. 4553. 9. H. Hisashi, "Stratified multi-agent HTN planning in dynamic environments," In Proceedings KES-AMSTA, Pp. 189–198 (2007). 10. M. Elkawkagy, S. Buindo, "Hybrid Multi-agent Planning," In Proceedings of the German Conference on Multiagent System Technologies, 2011, pp. 16-28. 11. M. Elkawkagy, H. Elbeh, "Improving AI Planning using Map Reduce," International Journal of Innovative Technology and Exploring Engineering, 9(4). 2020. PP 615-618. 12. N. Maleki, A. Rahmani, M. Conti, " MapReduce: an infrastructure review and research insights," The Journal of Supercomputing, Springer Nature, 75(10),2019, PP. 1-69. 13. M. Elkawkagy, P. Bercher, B. Schattenberg, and S. Biundo, "Improving hierarchical planning performance by the use of landmarks," In Proceedings of the 26th National Conference on Artificial Intelligence (AAAI), 2012, pp. 1763–1769. 14. P. Bercher, D. Hller, G. Behnke, and S. Biundo, "User-centered planning - a discussion on planning in the presence of human users," In Proceedings of the International Symposium on Companion Technology, 2015. 15. M. Elkawkagy, B. Schattenberg, S. Biund, "Landmarks in hierarchical planning," In Proceedings of ECAI, 2010, pp. 229–234. 16. M. Elkawkagy, P. Bercher, B. Schattenberg, S. Biundo, "Exploiting landmarks for hybrid planning," In proceedings of 25th PuK Workshop Planen, Scheduling und Konfigurieren, Entwerfen, 2010. 17. M. Elkawkagy, P. Bercher, B. Schattenberg, S. Biundo, S., "Landmark-aware strategies for hierarchical planning," In Proceedings of the 3rd Workshop on Heuristics for Domain-independent Planning, 2011, PP 73-79. 18. M. Elkawkagy, "Improving the performance of hybrid planning," International Journal of Artificial Intelligence, 14 (2), 2016, 98-116. 19. P. Bercher, D. H oller,̈ G. Behnke, and S. Biundo, "On implications of preconditions and effects of compound HTN planning tasks" In Proceedings of the 22nd European Conference on Artificial Intelligence (ECAI), (2016) pp. 225–233, IOS Press 20. G. Behnke, D. Holler, and S. Biundo, "Finding opti- ¨ mal solutions in HTN planning – A SAT-based approach," In Proc. of the 28th Int. Joint Conf. on AI (IJCAI), 2019, 5500–5508, IJCAI Organization. 21. R. Goldman, and U. Kuter, "Hierarchical task network planning in common Lisp: the case of SHOP3", In Proc. of the 12th European Lisp Symposium (ELS), 2019, 73–80. ACM. 22. D. Holler, P. Bercher, G. Behnke, and S. Biundo, "On guiding search in HTN planning with classical planning heuristics," In Proc. of the 28th Int. Joint Conf. on AI (IJCAI), (2019), 6171–6175, IJCAI Organization.

Souvenior of Volume – 10 Issue – 7 May 2021 Website: www.ijitee.org DOI: 10.35940/ijitee 5

Author(s): A.Nagesh Title of the Article: Energy Audit System for Households using Machine Learning Abstract: the growth in population and economics the global demand for energy is increased considerably. The large amount of energy demand comes from houses. Because of this the energy efficiency in houses in considered most important aspect towards the global sustainability. The machine learning algorithms contributed heavily in predicting the amount of energy consumed in household level. In this paper, a energy audit system using machine learning are developed to estimate the amount of energy consumed at household level in order to identify probable areas to plug wastage of energy in household. Each energy audit system is trained using one machine leaning algorithm with previous power consumption history of training data. By converting this data into knowledge, gratification of analysis of energy consumption is attained. The performance of energy audit Linear Regression system is 82%, Decision Tree system is 86% and Random Forest 91% are predicted energy consumption and the performance of learning methods were evaluated based on the heir predictive accuracy, ease of learning and user friendly characteristics. The Random Forest energy audit system is superior when compare to other energy audit system. Keywords: Energy Prediction, Linear Regression, Machine Learning, Decision Tree, Random Forest

References: 1. Gonzalez-Briones, A., Hernandez, G., Corchado, J. M., Omatu, S., & Mohamad, M. S. “Machine Learning Models for Electricity Consumption Forecasting:” A Review. 2019 2nd International Conference on Computer Applications & Information Security (ICCAIS). 2. Zhao, H.-x. and F. Magoulès, A review on the prediction of building energy 137 consumption. Renewable and Sustainable Energy Reviews, 2012. 16(6): p. 3586-138 3592. 3. E. Barbour and M. González, "Enhancing household level load forecasts using daily load profile clustering," in Proceedings of the 5th Conference on Systems for Built Environments, 2018, pp. 107-115: ACM. 4. K. Gajowniczek and T. Zbkowski, “Short term electricity forecasting using individual smart meter data,” Procedia Computer Science, vol. 35, pp. 589 – 597, 2014. 5. I.I. Attia and H. Ashour,Energy Saving Through Smart Home," The Online Journal on Power and Energy Engineering (OJPEE), vol. II, no. 3, July 2011. 6. Lombard, L., J. Ortiz, and C. Pout, A review on buildings energy consumption 129 information. Energy and buildings, 2008. 40(3): p. 394-398. 7. Jihoon Moon, Jinwoong Park, Eenjun Hwang, Forecasting power consumption for higher educational institutions based on machine learning. The Journal of Supercomputing August 2018; 74(8): 3778–3800 8. Chang, H.-C.; Kuo, C.-C.; Chen, Y.-T.;Wu,W.-B.; Piedad, E.J. Energy Consumption Level Prediction Based on Classification Approach with Machine Learning Technique. In Proceedings of the 4th World Congress on New Technologies (NewTech’18), Madrid, Spain, 19–21 August 2018; pp. 1–8. 9. K. Aurangzeb, "Short Term Power Load Forecasting using Machine Learning Models for energy management in a smart community," 2019 International Conference on Computer and Information Sciences (ICCIS), Sakaka, Saudi Arabia, 2019, 10. H. Zhao and F. Magoul`es. “Feature Selection for Predicting Building Energy Consumption Based on Statistical Learning Method”. In: Journal of Algorithms & Computational Technology 6.1 (2012), pp. 59–77. 11. Edwards, R.E., New, J., Parker, L.E. (2012). Predicting future hourly residential electrical consumption: A machine learning case study. Energy and Buildings, 49, 591–603 12. LAM, J. C., CHAN, R. Y. C. & LI, D. H. W. 2002. Simple Regression Models for Fully Air-conditioned Public Sector Office Buildings in Subtropical Climates. Architectural Science Review, 45, 361-369. 13. Yu, Z., F. Haghighat, B. C. Fung, and H. Yoshino (2010). A decision tree method for building energy demand modeling. Energy and Buildings 42 (10), 1637-1646. 14. Tso, G. K. and K. K. Yau (2007). Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks.Energy 32 (9), 1761-1768. 15. Ahmad, T. and H. Chen, Nonlinear autoregressive and random forest approaches to 198 forecasting electricity load for utility energy management systems. Sustainable Cities 199 and Society, 2019. 45: p. 460-473 16. Ahmad, M.W., J. Reynolds, and Y. Rezgui, Predictive modeling for solar thermal 192 energy systems: A comparison of support vector regression, random forest, extra 193 trees and regression trees. Journal of Cleaner Production, 2018. 203: p.810-821. 17. G. Dudek. “Short-Term Load Forecasting Using Random Forests”.In Intelligent Systems’2014: Proceedings of the 7th IEEE International Conference Intelligent Systems 2014, Warsaw, Poland 2 (2015), pp. 821–828

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Author(s): D. Vasudevan, A.G. Murugesan Title of the Article: Evaluation of Water Quality Impact Caused by Common Hazardous Waste Landfill Facility in Gummidipoondi, Tamilnadu- India Abstract: The aim of the study was to evaluate the water quality impact caused due to the operations of common hazardous waste landfill facility (CHWLF) in Gummidipoondi industrial estate, Tiruvallur district, Tamilnadu, India. The watershed area of the hazardous waste landfill facility was delineated using Arc-GIS tools and prediction of ground water flow direction was carried out using three-dimensional ground water flow model using VISUAL MODFLOW software. The water quality analysis was performed in the upstream and downstream directions of the project site and the results showed that all the tested parameters were within the BIS 10500:2012 drinking water limits, except pH which showed slightly acidic characteristics in certain locations. The tested water samples mostly belonged to the Ca + Mg-HCO3’ type as classified using the multivariate analysis method using piper diagram. Co-relation between the water quality parameters were determined using statistical analysis of Pearson's correlation coefficient (r) values. Keywords: Common Hazardous Waste Management Facility, Landfill, Water Pollution, MODFLOW, Impact Assessment, Ground Water Contamination.

References: 1. Abd El-Salam, M.M., Abu-Zuid, G.I., 2015. Impact of landfill leachate on the groundwater quality: A case study in Egypt. J. Adv. Res. 6, 579–586. https://doi.org/10.1016/j.jare.2014.02.003 2. Bagchi, A 1994. Design, Construction and Monitoring of Landfills. 2nd Ed. John Wiley Sons. Inc., New York. 3. Boyd, C.E., Tucker, C.S., Somridhivej, B., 2016. Alkalinity and Hardness: Critical but Elusive Concepts in Aquaculture. J. World Aquac. Soc. 47, 6–41. https://doi.org/10.1111/jwas.12241 4. Boyd, C.E., Tucker, C.S., Viriyatum, R., 2011. Interpretation of pH, acidity, and alkalinity in aquaculture and fisheries. N. Am. J. Aquac. 73, 403–408. https://doi.org/10.1080/15222055.2011.620861 5. Brisbane, S.M.P.&, 1996. Melbourne Regional Landfill Hydrogeological Assessment. Monit. landfill leachate. 6. Burge, H.A., Rogers, C.A., 2000. Outdoor allergens. Environ. Health Perspect. 108, 653–659. https://doi.org/10.1289/ehp.00108s4653 7. CPCB, 2016. “Treatment, Storage, and Disposal Facilities (TSDFs) data published by Central Pollution control Board, ENVIS Centre on Control of Pollution Water, Air and Noise, 2016, http://cpcbenvis.nic.in/tsdf.html#. 8. Chao-Shi Chen, Chia-Huei Tu , Shih-Jen Chen and Cheng-Chung Chen , Int. J. Environ. Res. Public Health 2016, 13, 467; doi:10.3390/ijerph13050467 9. Chofqi A, Younsi A, KbirLhadi E, Mania J, Mudry J, Veron A, 2004, Environmental impact of an urban landfill on a coastal aquifer (El Jadida, Morocco), Journal of African Earth Sciences, 39 (3–5), 509-516. https://doi.org/10.1016/j.jafrearsci.2004.07.013. 10. Dongo, K., Tiembre, I., Kone, B.A., Zurbrugg, C., Odermatt, P., Tanner, M., Zinsstag, J., Cisse, G., 2012. Exposure to toxic waste containing high concentrations of hydrogen sulphide illegally dumped in Abidjan, Cote d’Ivoire. Environ. Sci. Pollut. Res. Int. 19, 3192–3199. https://doi.org/10.1007/s11356-012-0823-2. 11. Et alfy and Merkel, 2006, Hadrochemical relationships and geochemical modelling of ground water in Al Arish area, North Sinai, Egypt, Journal of American Institute of Hydrology, 22, 47-62. 12. Fazzo, L., Minichilli, F., Santoro, M., Ceccarini, A., Della Seta, M., Bianchi, F., Comba, P., Martuzzi, M., 2017. Hazardous waste and health impact: a systematic review of the scientific literature. Environ. Heal. 16, 107. https://doi.org/10.1186/s12940-017-0311-8 13. Hassan A.H and Mohamed H.R, 2005, Assessment of sanitary landfill leachate characterizations and its impacts on groundwater at Alexandria, The Journal of Egyptian Public Health Association, 80 (1-2),27- 49. 14. Harbaugh, A.W.; Banta, E.R.; Hill, M.C.; McDonald, M.G. MODFLOW-2000, the U.S. Geological Survey Modular Ground-Water Model—User Guide to Modularization Concepts and the Ground-Water Flow Process; USGS Open-File Report 00-92; U.S. Geological Survey: Reston, VA, USA, 2000. 43. 15. Jothivenkatachalam J, Nithya A and Chandra Mohan S, 2010, Correlation analysis of drinking water quality in and around Perur Block of Coimbatore District, Tamil Nadu, India, Rasayan 3 (4), 649-654. 16. Kubal, M., Kočí, V., Švagr, A., Kuraš, M., Kuraš, M., 2003. Environmental impact of hazardous waste landfill breakdown, in: WIT Transactions on Biomedicine and Health. WIT Press, pp. 93–102. https://doi.org/10.2495/EHR030101. 17. Kumar, D and Alappat, B.J. 2005, Analysis of leachate pollution index and formulation of sub-leachate pollution indices. Waste Management & Research, 23, 230 - 239. 18. Kumari P, Amarjeet K and Gupta N C, 2018, Extent of groundwater contamination due to leachate migration adjacent to unlined landfill site of Delhi, Environmental Claims Journal, DOI: 10.1080/10406026.2018.1543825

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19. Krishna Kumar S, Logeshkumaran L, Magesh N.S, Prince S. Godson, Chandrasekar N, 2015, Hydro- geochemistry and application of water quality index (WQI) for groundwater quality assessment, Anna Nagar, part of Chennai City, Tamil Nadu, India, Appl Water Sci, 5, 335–343. 20. Maiti, S.K., De, S., Hazra, T., Debsarkar, A., & Dutta, A., 2016, Characterization of leachate and its impact on surface and groundwater quality of a closed dumpsite – A Case Study at Dhapa, Kolkata, India. Procedia environmental sciences, 35, 391-399. 21. Manoj K., Ghosh S. and Padhy P.K, 2013. Characterization and Classification of Hydrochemistry using Multivariate Graphical and Hydrostatistical Techniques, Res. J. Chem. Sci. 3(5), 32-42. 22. McDonald, M.G.; Harbaug, A.W. A Modular Three-Dimensional Finite-Difference Ground-Water Flow Model; USGS TWRI Chapter 6-A1; United States Government Printing Office: Reston, VA, USA, 1988; 586p. Available online: http://pubs.usgs.gov/twri/twri6a1/ (accessed on 24 January 2019). 23. Misra, V., Pandey, S.D., 2005. Hazardous waste, impact on health and environment for development of better waste management strategies in future in India. Environ. Int. 31, 417–431. https://doi.org/10.1016/j.envint.2004.08.005 24. MOEF, 2016. Hazardous and Other Wastes - Ministry of Environment and Forests. The gazzate of India 1981, 1– 68. 25. Monavari, S.M., Tajziehchi, S., Rahimi, R., 2013. Environmental Impacts of Solid Waste Landfills on Natural Ecosystems of Southern Caspian Sea Coastlines. J. Environ. Prot. (Irvine,. Calif). 04, 1453–1460. https://doi.org/10.4236/jep.2013.412167 26. Mariappan N V, Princeton, P., 2012. Environment assessment of industrial landfill site in Tamil nadu. Int. J. Curr. Res 4, 252–256. 27. Narendra Singh Bhandari, Kapil Nayal, 2008, Correlation study on physico-chemical parameters and quality assessment of Kosi river water, Uttarakhand, E-Journal of Chemistry, 5 (2) 342-346. 28. Piper, A.M., 1944. A graphic procedure in the geochemical interpretation of water-analyses. Eos, Transactions American Geophysical Union, 25(6), 914–928. 29. Press, O., 2016. Living near a landfill could damage your health, https://www.sciencedaily.com/releases/2016/05/160524211817.htm. 30. Qianlin Zheng, Teng Ma, Yanyan Wang, Yani Yana, Lu Liua, 2017, 31. Hydrochemical characteristics and quality assessment of shallow groundwater in Xincai River Basin, Northern China, Procedia Earth and Planetary Science, 17, 368 – 371. 32. Sana’aOdat, 2015, Cluster and Factor Analysis of Groundwater in Mafraq Area, Jordan, Current World Environment, 10(2), 422-431, http://dx.doi.org/10.12944/CWE.10.2.06. 33. Singh, U., Kumar, M., Chauhan, R., Jha, P.K., Ramanathan, A., & Subramanian, V, 2008. Assessment of the impact of landfill on groundwater quality: A case study of the Pirana site in western India. Environmental Monitoring and Assessment, 141, 309-321. 34. Slack Rebecca, Gronow Jan, Hall David and Voulvoulis Nikolaos, 2007, Household Hazardous Waste Disposal to Landfill: Using Landsim to Model Leachate Migration. Environmental pollution (Barking, Essex : 1987). 146. 501-9. 10.1016/j.envpol.2006.07.011. 35. Sunilkumar Srivastava, Ramanathan, A.L,2008, Geochemical assessment of groundwater quality in vicinity of Bhalswa landfill, Delhi, India, using graphical and multivariate statistical methods, Environ Geol, 53:1509–1528. 36. Vasudevan, D, Murugesan A. G , 2017. Hazardous Waste Management in India : Current Scenario and Future Opportunities, International Journal of Scientific Research in Science and Technology 3, 118–127. 37. Vrijheid, M., 2000. The developing brain and the environment: An introduction. Environ. Health Perspect. 108. 38. Weiner, E., 2000. National Recommended Water Quality Criteria. Applied Environmental. Chemistry. https://doi.org/10.1201/9781420032963.axb 39. Yang Y, Jiang YH, Lian XY, 2016, Risk-Based Prioritization Method for the Classification of Groundwater Pollution from Hazardous Waste Landfills, Environ Manage.58(6):1046-1058. doi:10.1007/s00267-016-0749-4. 40. Ying Li, Li J, Chen S, Diao W. 2012, Establishing indices for groundwater contamination risk assessment in the vicinity of hazardous waste landfills in China. Environ Pollut.;165:77-90. doi:10.1016/j.envpol.2011.12.042

Author(s): Altaf Alaoui, Boris Olengoba Ibara, Badia Ettaki, Jamal Zerouaoui Title of the Article: Survey of Process of Data Discovery and Environmental Decision Support Systems Abstract: The process of data discovery is an approach to extracting knowledge, valid, and usable information from large amounts of data, using automatic or semi-automatic methods. This article is an inventory of the different information extraction processes encountered in the literature for different fields of application and for the development of environmental informatics. Following an analysis between the different models, we can summarize the existing models with a proposal for a process that exploits the strengths of the different processes.

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Keywords: Knowledge Discovery in Databases, KDDM, Environmental Decision Support Systems, GIS, KBS, EDSS.

References: 1. Fayyad, U. M., G. Piatetsky-Shapiro, P. Smyth, and Ft. Uthurusamy, 1996. Advances in Knowledge Discovery and Data Mining, (AKDDM), AAAI/MIT Press. 2. Fayyad, U.M., Haussler, D. and Stolorz, Z. 1996. KDD for Science Data Analysis; Issues and Examples. Proc. 2nd Int. Conj. on Knowledge Discovery and Data Mining (KDD-96), Menlo Park, CA: AAAI Press. 3. Fayyad, U.M., Piatetsky-Shapiro, G., and Smyth, P. 1996. From Data Mining to Knowledge Discovery: An Overview, in AI(DDM, AAAI/MIT Press, pp. 1-30 4. D. A. Swayne, R. Denzer, L. Lilburne, M. Purvis, N. W. T. Quinn, and A. Storey, “?,” in Environmental Software Systems, vol. 39, R. Denzer, D. A. Swayne, M. Purvis, and G. Schimak, Eds. Boston, MA: Springer US, 2000, pp. 259–268. 5. U. Baizyldayeva, O. K. Vlasov, A. A. Kuandykov, and T. B. Akhmetov, “Multi-Criteria Decision Support Systems. Comparative Analysis,” 2013. 6. Shearer, C., The CRISP-DM model: The new blueprint for data mining. Journal of Data Warehousing, 5(4), 13– 22, 2000. 7. Adriaans. P and Zantinge.D, Data mining, Addison-Wesley, 1999. 8. Berry, M. J., & Gordon, L., Data mining techniques: For marketing, sales, and customer support. New York, NY: Wiley, 1997. 9. SAS Enterprise Miner – SEMMA. SAS Institute. Accessed from http://www.sas.com/technologies/analytics/datamining/miner/semma.html, on May 2008 10. Cabena, P., Hadjinian, P., Stadler, R., Verhees, J., & Zanasi, A., Discovering data mining: From concept to implementation. Upper Saddle River, NJ: Prentice Hall, 1998. 11. Hirji, K. K., Exploring data mining implementation. Communications of the ACM, 44(7), 87–93. doi:10.1145/379300.379323, 2001. 12. Anand, S. S., Bell, D. A., & Hughes, J. G., The role of domain knowledge in data mining. In Proceedings of the 4th International Conference on Information and Knowledge Management (pp. 37-43), 1995. 13. Anand, S.S., Büchner, A.G., Decision Support through Data Mining, FT Pitman Publishers, 1998. 14. Buchheit, RB, Garrett, JH, Jr, Lee, SR and Brahme, R, A knowledge discovery framework for civil infrastructure: a case study of the intelligent workplace. Engineering with Computers 16(3–4), 264–274, 2000. 15. Jensen, S., Mining medical data for predictive and sequential patterns: PKDD 2001. In Proceedings of the 5th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2001 Discovery Challenge on Thrombosis Data, 2001. 16. Butler, S., An investigation into the relative abilities of three alternative data mining methods to derive information of business value from retail store-based transaction data. BSc thesis, School of Computing and Mathematics, Deakin University, Australia, 2002. 17. Blockeel, H. and Moyle, S., Collaborative data mining needs centralized model evaluation. In Proceedings of the ICML-2002 Workshop on Data Mining Lessons Learned, pp.21–28, 2002. 18. Silva, E.M., Do Prado, H.A. and Ferneda, E., Text mining: crossing the chasm between the academy and the industry. Management Information Systems 6, 351–361, 2002. 19. Jensen, S., Mining medical data for predictive and sequential patterns: PKDD 2001. In Proceedings of the 5th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD2001 Discovery Challenge on Thrombosis Data, 2001. 20. Butler, S., An investigation into the relative abilities of three alternative data mining methods to derive information of business value from retail store-based transaction data. BSc thesis, School of Computing and Mathematics, Deakin University, Australia, 2002. 21. Blockeel, H. and Moyle, S., Collaborative data mining needs centralized model evaluation. In Proceedings of the ICML-2002 Workshop on Data Mining Lessons Learned, pp.21–28, 2002. 22. Silva, EM, Do Prado, HA and Ferneda, E., Text mining: crossing the chasm between the academy and the industry. Management Information Systems 6, 351–361, 2002. 23. Hipp, J and Lindner, G., Analyzing warranty claims of automobiles. An application description following the CRISP-DM data mining process. In Proceedings of 5th International Computer Science Conference, Hong Kong, China, pp.31–40, 1999. 24. Gersten, W., Wirth, R. and Arndt D., Predictive modeling in automotive direct marketing: tools, experiences and open issues. In Proceeding of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 398–406, 2000.

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25. Moyle, S., Bohanec, M. and Ostrowski, E., Large and tall buildings: a case study in the application of decision support and data mining. In Proceedings of the ECML/PKDD’02 workshop on Integrating Aspects of Data Mining, Decision Support and Meta-Learning, pp.88–99, 2002. 26. Li, S-T and Shue, L-Y, Data mining to aid policy making in air pollution management. Expert Systems with Applications 27(3), 331–340, 2004. 27. De Abajo, N, Lobato, V, Diez, AB and Cuesta, SR., ANN quality diagnostic models for packaging manufacturing: an industrial Data Mining case study. In Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 799–804, 2004. 28. Cios, K, Teresinska, A, Konieczna, S, Potocka, J and Sharma, S., Diagnosing myocardial perfusion from PECT bull’s-eye maps—a knowledge discovery approach. IEEE Engineering in Medicine and Biology Magazine, Special issue on Medical Data Mining and Knowledge Discovery 19(4), 17–25, 2000. 29. Cios, K and Kurgan, L, Trends in data mining and knowledge discovery. In Pal, N and Jain, L (eds) Advanced Techniques in Knowledge Discovery and Data Mining. Springer, pp.1–26, 2005. 30. Sacha, J, Cios, K and Goodenday, L., Issues in automating cardiac SPECT diagnosis. IEEE Engineering in Medicine and Biology Magazine, Special issue on Medical Data Mining and Knowledge Discovery 19(4), 78–88, 2000. 31. Kurgan, L, Cios, K, Tadeusiewicz, R, Ogiela, M and Goodenday, L., Knowledge discovery approach to automated cardiac SPECT diagnosis. Artificial Intelligence in Medicine 23(2), 149–169, 2001. 32. Goh, KG, Hsu, W, Lee, ML and Wang, H., ADRIS: an automatic diabetic retinal image screening system. In Cios, K (ed.) Medical Data Mining and Knowledge Discovery, pp. 181–207, 2001. 33. Shalvi, D and DeClaris, N., A data clustering and visualization methodology for epidemiological pathology discoveries. In Cios, K (ed.) Medical Data Mining and Knowledge Discovery, pp. 129–151, 2001. 34. Cios, K (ed.) 2001, Medical Data Mining and Knowledge Discovery. Springer-Verlag. 35. Maruster, L, Weijters, T, De Vries, G, Van den Bosch, A and Daelemans, W, 2002, Logistic-based patient grouping for multi-disciplinary treatment. Artificial Intelligence in Medicine 26(1–2), 87–107. 36. Ganzert, S. Guttmann, J, Kersting, K, Kuhlen, R, Putensen, C, Sydow, M and Kramer, S., Analysis of respiratory pressure–volume curves in intensive care medicine using inductive machine learning. Artificial Intelligence in Medicine 26(1–2), 69–86, 2002. 37. Perner, P., Perner, H. and Muller, B., Mining knowledge for HEp-2 cell image classification. Artificial Intelligence in Medicine 26(1–2), 161–173, 2002. 38. Hofer, J. and Brezany P., Distributed Decision Tree Induction within the Grid Data Mining Framework GridMiner-Core. GridMiner TR2004–04, Institute for Software Science, University of Vienna, 2004. 39. Kurgan, L, Cios, K, Sontag, M and Accurso, F., Mining the cystic fibrosis data. In Zurada, J and Kantardzic, M (eds) Next Generation of Data-Mining Applications. IEEE Press and Wiley, pp. 415–444, 2005. 40. Han, J. and Kamber, M., Data Mining: Concepts and Techniques. Morgan Kaufmann, 2001. 41. Edelstein, H., Data mining: let’s get practical. DB2 Magazine 3(2), summer, 1998. 42. Klosgen, W and Zytkow, J, 2002, The knowledge discovery process. In Klosgen, W and Zytkow, J (eds) Handbook of Data Mining and Knowledge Discovery. Oxford University Press, pp.10–21. 43. Haglin, D, Roiger, R, Hakkila, J and Giblin, T, 2005, A tool for public analysis of scientific data. Data Science Journal 4(30), 39–53. 44. Haagsma I.G. and Johanns R.D., “Decision support systems: An integrated approach,” in Environmental Systems, edited by P. Zannetti, vol. II, pp. 205–212, 1994. 45. Gabaldo ń C., Ferrer J., Seco A., and Marzal P., “A software for the integrated design of wastewater treatment plants,” Environmental Modelling and Software, vol. 13, no. 1, pp. 31– 44, 1998. 46. Guariso G. and Page B. (Eds.), “Computers support for environmental impact assessment,” in IFIP, North- Holland, ISBN 0-444-81838-3, 1994. 47. Okubo T., Kubo K., Hosomi M., and Murakami A., “A knowledge-based decision support system for selecting small- scale wastewater treatment processes,” Water Science Technol- ogy, vol. 30, no. 2, pp. 175–184, 1994. 48. Serra P., Lafuente J., Moreno R., de Prada C., and Poch M., “Development of a real-time expert system for wastewater treatment plants control,” Control. Eng. Practice, vol. 1, no. 2, pp. 329–335, 1993. 49. Aarts R.J., Knowledge-based Systems for Bioprocesses, Tech- nical Research Centre of Finland, vol. 120, 1992. 50. Fox, M. S., & Smith, S. F. (1984). ISIS?a knowledge-based system for factory scheduling. Expert Systems, 1(1), 25–49. 51. Mar-Ortiz, J., Gracia, M. D., & Castillo-García, N. (2018). Challenges in the Design of Decision Support Systems for Port and Maritime Supply Chains. In Exploring Intelligent Decision Support Systems (pp. 49-71). Springer, Cham.

Souvenior of Volume – 10 Issue – 7 May 2021 Website: www.ijitee.org DOI: 10.35940/ijitee 10

Author(s): Ritika Singh, Nilansh Panchani, Aastha Bhatnagar Title of the Article: Analysis and Simulation of COVID-19 Abstract: India is facing a severe second wave of COVID-19 which is much worse than the first wave. It is spreading much faster. India has now surpassed U.S. in terms of daily COVID-19 cases. This paper aims to analyze the trend of COVID 19 and examine why second wave happened and why it is so bad by simulating a simple SEIR model. Which is a compartmental model based on 4 compartments Susceptible, Exposed, Infectious, Recovered. Keywords: Covid-19, SIR model, SEIR Model, Compartmental Models, Data Analytics, Data Visualization.

References: 1. Klaus Dietz, J.A.P. Heesterbeek (2002) “Daniel Bernoulli’s epidemiological model revisited,” Mathematical Biosciences, 180 (June):1-21 2. FredBrauer (2017) “Mathematical epidemiology: Past, present, and future,” Infectious Disease Modelling, 2(2) (May): 113-127 3. William Ogilvy Kermack, A. G. McKendrick (1927) “A contribution to the mathematical theory of epidemics,” Proceedings of the Royal Society A, 115 (772) (Aug): 700–721 4. Li Q, Guan X, Wu P, Wang X, Zhou L, Tong Y, Ren R, Leung KSM, Lau EHY, Wong JY, Xing X, Xiang N, Wu Y, Li C, Chen Q, Li D, Liu T, Zhao J, Liu M, Tu W, Chen C, Jin L, Yang R, Wang Q, Zhou S, Wang R, Liu H, Luo Y, Liu Y, Shao G, Li H, Tao Z, Yang Y, Deng Z, Liu B, Ma Z, Zhang Y, Shi G, Lam TTY, Wu JT, Gao GF, Cowling BJ, Yang B, Leung GM, Feng Z. (2020) “Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus-Infected Pneumonia,’ N Engl J Med. 26;382(13) (Mar):1199-1207. 5. Dilip Kumar Bagal, Arati Rath, Abhishek Barua, Dulu Patnaik (2020) “Estimating the parameters of susceptible- infected-recovered model of COVID-19 cases in India during lockdown periods,” Chaos, Solitons & Fractals,Volume 140 (Nov) 6. He, S., Peng, Y. & Sun K. (2020) “SEIR modeling of the COVID-19 and its dynamics.” Nonlinear Dyn 101 (Jun), 1667–1680 7. Rajesh Ranjan, Aryan Sharma, Mahendra K. Verma (2021) “Characterization of the Second Wave of COVID-19 in India,” medRxiv (April) 8. Howard (Howie) Weiss (2013) “The SIR model and the Foundations of Public Health,” MATerials MATemàtics: 1-17 9. Aschwanden C. (2021) “Five reasons why COVID herd immunity is probably impossible.” Nature.591(7851) (Mar):520-522 10. Leung Kathy, Shum Marcus HH, Leung Gabriel M, Lam Tommy TY, Wu Joseph T. (2021) “Early transmissibility assessment of the N501Y mutant strains of SARS-CoV-2 in the United Kingdom, October to November 2020,” Euro Surveill 26(1) (Jan) 11. David Smith and Lang Moore (2004) "The SIR Model for Spread of Disease," JOMA (Dec)

Author(s): Kartik Khariwal, Rishabh Gupta, Jatin Singh, Anshul Arora Title of the Article: R-MFDroid: Android Malware Detection using Ranked Manifest File Components Abstract: With the increasing fame of Android OS over the past few years, the quantity of malware assaults on Android has additionally expanded. In the year 2018, around 28 million malicious applications were found on the Android platform and these malicious apps were capable of causing huge financial losses and information leakage. Such threats, caused due to these malicious apps, call for a proper detection system for Android malware. There exist some research works that aim to study static manifest components for malware detection. However, to the best of our knowledge, none of the previous research works have aimed to find the best set amongst different manifest file components for malware detection. In this work, we focus on identifying the best feature set from manifest file components (Permissions, Intents, Hardware Components, Activities, Services, Broadcast Receivers, and Content Providers) that could give better detection accuracy. We apply Information Gain to rank the manifest file components intending to find the best set of components that can better classify between malware applications and benign applications. We put forward a novel algorithm to find the best feature set by using various machine learning classifiers like SVM, XGBoost, and Random Forest along with deep learning techniques like classification using Neural networks. The experimental results highlight that the best set obtained from the proposed algorithm consisted of 25 features, i.e., 5 Permissions, 2 Intents, 9 Activities, 3 Content Providers, 4 Hardware Components, 1 Service, and 1 Broadcast Receiver. The SVM classifier gave the highest classification accuracy of 96.93% and an F1-Score of 0.97 with this best set of 25 features.

Souvenior of Volume – 10 Issue – 7 May 2021 Website: www.ijitee.org DOI: 10.35940/ijitee 11

Keywords: Android Security, Machine Learning, Malware Detection, Manifest File Components, Mobile Malware, Static Solution.

References: 1. Desktop vs Mobile vs Tablet Market Share Worldwide, Available Online. https://gs.statcounter.com/platform641 market-share/desktop-mobile-tablet/. 2. Android dominates 81% of the world smartphone market, Available Online. https://www.cnet.com/news/android643 dominates-81-percentof-world-smartphone-market/. 3. Critical Warning Issued Regarding 10 Million Samsung Phone Updates, Available On line. https://www.forbes.com/sites/daveywinder/2019/07/05/critical-warning-issued-regarding-10-million-samsung- phone-updates/. 4. Hundreds of Malicious Apps are showing up on the Google Play Store, disguised as legitimate Applications, Available Online.https://us.norton.com/internetsecurity-emerging-threats-hundreds-of-android-apps-containing- dresscode-malware-hiding-in-google-play-store.html/. 5. Development of new Android malware worldwide from June 2016 to May 2019, Available Online. https://www.statista.com/statistics/680705/global-android-malware-volume/. 6. 45,000 Android devices infected by new unremovable xHelper malware, Available On line. https://thenextweb.com/security/2019/10/30/45000-android-devices-infected-by655 new-unremovable-xhelper- malware/. 7. A. Feizollah et al., ”A review on feature selection in mobile malware detection”, Digital Investigation, vol. 13, pp. 22-37, 2015. 8. M. Grace, W. Zhou, X. Jiang, and A. Sadeghi, ”Unsafe exposure analysis of mobile in-app advertisements”, 5th ACM WiSec, 2012. 9. W. Enck, M. Ongtang, and P. McDaniel, ”On Lightweight Mobile Phone Application Certifi661 cation”, 16th ACM CCS, 2009. 10. K. Talha, D. Alper, and C. Aydin, ”APK Auditor: Permission-based Android malware detection system”, Digital Investigation, vol. 13, pp. 1-14, 2015. 11. V. Moonsamy, J. Rong, and S. Liu, ”Mining permission patterns for contrasting clean and malicious android applications”, Future Generation Computer Systems, vol. 36, pp. 122-132, 2014. 12. F. Idrees, and M. Rajarajan, ”Investigating the Android Intents and Permissions for Malware detection”, 7th International Workshop on Selected Topics in Mobile and Wireless Computing, 2014. 13. R. Taheri, M. Ghahramani, R. Javidan, M. Shojafar, Z. Pooranian, and M. Conti, "Similarity-based Android malware detection using Hamming distance of static binary features", Future Generation Computer Systems, vol. 105, pp. 230-247, 2020. 14. J. Qiu et al., "A3CM: Automatic Capability Annotation for Android Malware," IEEE Access, vol. 7, pp. 147156- 147168, 2019. 15. H. Bai, N. Xie, X. Di and Q. Ye, "FAMD: A Fast Multifeature Android Malware Detection Framework, Design, and Implementation," IEEE Access, vol. 8, pp. 194729-194740, 2020. 16. D. Arp, M. Spreitzenbarth, M. Hubner, H. Gascon, and K. Rieck, ”DREBIN: Effective and Explainable Detection of Android Malware in Your Pocket”,NDSS,2014. 17. M. Varsha, P. Vinod, and K. Dhanya, "Identification of malicious android app using manifest and opcode features", Journal of Computer Virology and Hacking Techniques, vol. 13, pp. 125–138, 2017. 18. A. Mahindru, and A. Sangal, "FSDroid:- A feature selection technique to detect malware from Android using Machine Learning Techniques", Multimedia Tools and Applications, 2021. 19. V. Dharmalingam, and V. Palanisamy, "A novel permission ranking system for android malware detection—the permission grader", Journal of Ambient Intelligence and Humanized Computing, 2020. 20. A. Arora, S. K. Peddoju and M. Conti, "PermPair: Android Malware Detection Using Permission Pairs," IEEE Transactions on Information Forensics and Security, vol. 15, pp. 1968-1982, 2020. 21. K. Khariwal, J. Singh and A. Arora, "IPDroid: Android Malware Detection using Intents and Permissions," 4th World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4), London, United Kingdom, pp. 197-202, 2020. 22. C. Li, K. Mills, D. Niu, R. Zhu, H. Zhang and H. Kinawi, "Android Malware Detection Based on Factorization Machine," IEEE Access, vol. 7, pp. 184008-184019, 2019. 23. R. Sato, D. Chiba, and S. Goto, "Detecting Android Malware by Analyzing Manifest Files", Proceedings of the Asia-Pacific Advanced Network, vol. 36, pp. 23-31, 2013. 24. Q. Han, V. S. Subrahmanian and Y. Xiong, "Android Malware Detection via (Somewhat) Robust Irreversible Feature Transformations," IEEE Transactions on Information Forensics and Security, vol. 15, pp. 3511-3525, 2020.

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25. K. Elish et al., ”Profiling user-trigger dependence for Android malware detection”, Computers & Security, vol. 49, pp. 255-273, 2015. 26. M. Zhang et al., ”Semantics-Aware Android Malware Classification Using Weighted Contextual API Dependency Graphs”, ACM CCS, 2014. 27. H. Zhang, S. Luo, Y. Zhang and L. Pan, "An Efficient Android Malware Detection System Based on Method- Level Behavioral Semantic Analysis," IEEE Access, vol. 7, pp. 69246-69256, 2019. 28. M. Y. -Azar, L. Hamey, V. Varadharajan and S. Chen, "Byte2vec: Malware Representation and Feature Selection for Android," The Computer Journal, vol. 63, no. 1, pp. 1125-1138, 2020. 29. Y. Zhang and B. Li, "Malicious Code Detection Based on Code Semantic Features," IEEE Access, vol. 8, pp. 176728-176737, 2020, 30. V.M. Afonso et al., ”Identifying Android malware using dynamically obtained features”, Journal of Computer Virology and Hacking Techniques, vol. 11, pp.9-17,2015. 31. P. Feng, J. Ma, C. Sun, X. Xu and Y. Ma, "A Novel Dynamic Android Malware Detection System With Ensemble Learning," IEEE Access, vol. 6, pp. 30996-31011, 2018. 32. M. Jaiswal, Y. Malik and F. Jaafar, "Android gaming malware detection using system call analysis," 6th International Symposium on Digital Forensic and Security (ISDFS), Antalya, pp. 1-5, 2018. 33. S. Iqbal and M. Zulkernine, "SpyDroid: A Framework for Employing Multiple Real-Time Malware Detectors on Android," 13th International Conference on Malicious and Unwanted Software (MALWARE), Nantucket, MA, USA, pp. 1-8, 2018. 34. R. Feng, S. Chen, X. Xie, G. Meng, S. -W. Lin and Y. Liu, "A Performance-Sensitive Malware Detection System Using Deep Learning on Mobile Devices," IEEE Transactions on Information Forensics and Security, vol. 16, pp. 1563-1578, 2021. 35. I. Bibi, A. Akhunzada, J. Malik, J. Iqbal, A. Musaddiq and S. Kim, "A Dynamic DL-Driven Architecture to Combat Sophisticated Android Malware," IEEE Access, vol. 8, pp. 129600-129612, 2020. 36. R. Surendran, T. Thomas and S. Emmanuel, "On Existence of Common Malicious System Call Codes in Android Malware Families," IEEE Transactions on Reliability, vol. 70, no. 1, pp. 248-260, 2021. 37. S. Wang, et al., ”Detecting Android Malware Leveraging Text Semantics of Network Flows”, IEEE Transactions On Information Forensics And Security, vol. 13, pp. 1096-1109, 2018. 38. J. Feng, L. Shen, Z. Chen, Y. Wang and H. Li, "A Two-Layer Deep Learning Method for Android Malware Detection Using Network Traffic," IEEE Access, vol. 8, pp. 125786-125796, 2020. 39. I. J. Sanz, M. A. Lopez, E. K. Viegas and V. R. Sanches, "A Lightweight Network-based Android Malware Detection System," IFIP Networking Conference (Networking), Paris, France, pp. 695-703, 2020. 40. A. Arora, S. Garg, and S.Peddoju,”Malware detection using network traffic analysis in android based mobile devices”, 8th IEEE NGMAST,2014. 41. A. Arora, and S. Peddoju, ”Minimizing Network Traffic Features for Android Mobile Malware Detection”, 18th ACM ICDCN, 2017. 42. S. Imtiaz, S. Rehman, A. Javed, Z. Jalil, X. Liu, and W. Alnumay, "DeepAMD: Detection and identification of Android malware using high-efficient Deep Artificial Neural Network", Future Generation Computer Systems, vol. 115, pp. 844 – 856, 2021. 43. A. Mahindru, A. Sangal, "MLDroid—framework for Android malware detection using machine learning techniques", Neural Computing & Applications, 2020. 44. A. Mehtab et al., "AdDroid: Rule-Based Machine Learning Framework for Android Malware Analysis", Mobile Networks and Applications, vol. 25, pp. 180–192, 2020. 45. H. Zhu et al., "HEMD: a highly efficient random forest-based malware detection framework for Android," Neural Computing & Applications, vol. 30, pp. 3353–3361, 2018. 46. A. Arora, and S. Peddoju, ”NTPDroid: A Hybrid Android Malware Detector Using Network Traffic and System Permissions”, 17th IEEE TrustCom, 2018. 47. A. Arora, S. Peddoju, V. Chauhan, and A. Chaudhary, ”Hybrid Android Malware Detection by Combining Supervised and Unsupervised Learning”, 24th ACM MobiCom, 2018. 48. M. Alhanahnah et al., "DINA: Detecting Hidden Android Inter-App Communication in Dynamic Loaded Code," IEEE Transactions on Information Forensics and Security, vol. 15, pp. 2782-2797, 2020. 49. Y. Zhou, and X. Jiang, ”Dissecting android malware: Characterization and evolution”, IEEE Symposium on Security and Privacy, 2012. 50. Koodous Malware Dataset, ”www.koodous.com”. 51. A. Taha, and S. Malebary, "Hybrid classification of Android malware based on fuzzy clustering and the gradient boosting machine", Neural Computing and Applications, 2020.

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Author(s): Abul Hussain Title of the Article: Contribution of Mahapurush Srimanta Sankardeva to Assamese Literature and Culture Abstract: Mahapurusha Srimanta Sankardeva was an Assamese saint-scholar. Study on his life and works is of great academic importance in Assam. The tutorial, cultural and literature contribution by him still influences the fashionable creative works. The ideas, cultural contribution and philosophy of Srimanta Sankardeva became an integral an area of the lifetime of Assamese people. Therefore, the investigators have felt the requirement to review about the contribution of Mahapurusha Srimanta Sankardeva within the sphere of Assamese literature and culture in relevancy its educational significanceto uplift the moral, spiritual, value based thought, character building and personality development of the long run generation of the people. the foremost objectives of the study are to review the Contribution of Mahapurusha Srimanta Sankardeva within the sphere of Assamese literature and culture and to review the tutorial significance of the Contribution of Mahapurusha Srimanta Sankardeva within the sector of Assamese literature and culture. Keywords: Assamese Literature, Educational Significance, Mahapurush Srimanta Sankardev, Cultural Contribution. References: 1. B. Hazarika, Neo-Vaishnavite Satras of Assam in 21stCentury (Problemsand 2. Prospects),Jagaran Press, Guwahati, 2013. 3. B. Hazarika, Neo-Vaishnavite Satras of Assam in 21stCentury (Problemsand 4. Prospects),Jagaran Press, Guwahati, 2013. 5. B. Hazarika, Neo-Vaishnavite Satras of Assam in 21stCentury (Problemsand 6. Prospects),Jagaran Press, Guwahati, 2013. 7. B. Hazarika, Neo-Vaishnavite Satras of Assam in 21stCentury (Problemsand 8. Prospects),Jagaran Press, Guwahati, 2013. 9. B. Hazarika, Neo-Vaishnavite Satras of Assam in 21stCentury (Problemsand 10. Prospects),Jagaran Press, Guwahati, 2013. 11. B. Hazarika, Neo-Vaishnavite Satras of Assam in 21stCentury (Problemsand 12. Prospects),Jagaran Press, Guwahati, 2013. 13. Best, J. W. & Kahn, J. V. (2007). Research in Education. New Delhi: Dorling Kindersley Publishers (India) Pvt. Ltd. 14. Borkakoti, S. K. (2006).Unique Contributions of Srimanta Sankardeva in Religion and Culture, Nagaon, Srimanta Sankardeva Sangha, p.82. 15. Deka, P. (2017). Srimanta Sankardeva and his Philosophy. EPRA International Journal of Socio-Economic and Environmental Outlook. Vol. 3. Retrieved from https://eprawisdom.com/jpanel/upload/articles/753pm15.Priti Deka.pdf. 16. Hazarika, B. (2013). Neo-Vaishnavite Satras of Assam in 21st Century (Problems and Prospects). Jagaran Press, Guwahati, Assam. 17. Kalita, S. (2017). Philosophy of Srimanta Sankardeva and His Neovaishnavism: A Philosophical Study. IOSR Journal of Humanities and science (IOSR-JHSS). Vol. 22, Issue 10, Ver.VI, PP. 36-40. Retrieved from http://www.iosrjournals.org/iosr-jhss/papers/Vol. 22 Issue10/Version-6/E2210063640.pdf.

Author(s): S.Logesh, R.Ramesh, I. Padmanaban Title of the Article: Compatability Behaviour on Cold Formed Steel for I Section and C Section in Variable Parameters Abstract: This Study represents compatibility on Cold formed steel in I-Section beams and C-section beams with variable length parameters was 1000mm, 1500mm, 2000 mm under simply supported end condition subjected to uniformly distributed loading. The Cold formed steel is of shell type in Numerical simulation is carried out using the Software ABAQUS. For validation the series of parameters studies have been carried out using the numerical model of different parameters, such as the effect of length, width, thickness. CFS I-Section steel in various thickness of 1mm, 2mm, 3mm and 4mm with same loading conditions. CFS C-Section steel in various uneven flange width such as 500mm at the top flange and bottom flange of different width such as 400mm, 300mm, 200mm respectively in variable lengths with various loading conditions and with the thickness of about 1mm. For both I-Section and C-Section Beams the Effective Length ranges, MISES(max and min) and deflections(max and min) were taken for the analyse of the Sections. This study gives the way of finding the effective Section by the analysis of behaviour of I-Section beam and C-Section beam through the deflection results in various length variations in the beam Section using the ABAQUS software for finding the Structural behaviour in the more accuracy manner by applying meshing more finer for the Element Section in the Analyse of beam. The loading condition and the supporting condition applied to the beam

Souvenior of Volume – 10 Issue – 7 May 2021 Website: www.ijitee.org DOI: 10.35940/ijitee 14

section in different loading for getting the effective Section. For further stability in effective section we can use different types of connection. Keywords: Cold formed Steel, Beams, ABAQUS, deflection. References: 1. Analysis of Cold Formed Steel Member in Compression using Abaqus/mahendra mane,et al. 2. Simulation of flexural behaviour and design of cold-formed steel closed built-up beams composed of two sigma sections for local buckling/M. Anbarasu. 3. Torsion in thin-walled cold-formed steel beams/B.P. Gotluru,et al. 4. Beam tests of cold-formed steel built-up sections with web perforations/Liping Wang 5. Behaviour of Cold-Formed Steel Built-Up Sections with Intermediate Stiffeners under Bending. I: Tests and Numerical Validation/Liping Wang, et al. 6. Effect of web opening on the bending behaviour of cold formed steel built-up ‘I’ SECTION/Srinath T,et al. 7. Analytical Investigation On Cold-Formed Steel Built-Up Section Under Flexure/V.Raghul. 8. Experimental investigations of cold-formed steel beams of corrugated web and built-up section for flanges/Dan dubina,et al. 9. Experimental investigations of buckling of lipped, cold-formed thin-walled beams with I-section/P. Paczos. 10. Local–global interactive buckling of built-up I-beam sections/Amin Mohebkhah,et al. 11. Analysis of Cold-Formed C-Beam and Built Up Beam/ G. Jenitha, V. Gokul Nath and N. Ganesh Kumar. 12. IS 801-1975 13. IS 811-1987

Author(s): Pranav Andhyal, Karthik Nagarajan, Raju Narwade Title of the Article: Applications of 5D CAD for Billing in construction using GIS Abstract: A Construction project involves project management and financial planning at various stages right from the concept stage to the execution stage. This involves a large number of people working on different aspects of the project adhering to their specific job roles in collaboration with the others. These members not only work on the different aspects but also work on different software’s and platforms in order to create a holistic working plan to ensure timely and flawless construction activities. But these software’s only provide specific information feeded to it. A single program which would provide information of all these software’s collectively on one platform would not only make it convenient for sharing data but also help in reducing the delay and eliminating errors. A 5D model can be created linking the schedule of the project and the cost involved in it to the drawings on a GIS platform. In this research a 5D model of a Residential cum Commercial project Located in Prabhadevi, Mumbai, Maharashtra, India has been generated. This model includes the data related to the Schedule and Cost of the project, which can help in making decisions related to monetary aspects, Men & Material preparedness, verification of bills & Billing Audits. A 5D model holds Spatial data such as Project Schedule, Itemized Element costs and Quantities along with the 3D model of the structure. The conclusion of the study states that a GIS Model can serve as a real time data base for all the parties involved in the project at every level of its progression. Keywords: CAD, Project Management, Billing, GIS, 5D Model. References: 1. Karthik Nagarajan, Raju Narwade, Poonam Tiwari, Heena Pande and Divyashree Yadav (2020): “Automatic Urban Road Extraction from High-Resolution Satellite Data Using Object-Based Image Analysis: A Fuzzy Classification Approach”, Journal of Remote Sensing & GIS, (Dec-2019), ISSN: 2469-4134, Volume-09, Issue-4,pp-1-8, (Aug 2020). DOI:10.35248/2469-4134.20.9.279 2. Karthik Nagarajan, Raju Narwade and Shrenik Shah (2020): "Assessment Of Urban Utilities For Mumbai City Using 3d Modeling Techniques", International Journal of Civil Engineering and Technology (IJCIET), (May 2020) ISSN Print: 0976-6308 and ISSN Online: 0976-6316, Volume-11, Issue-5 3. Karthik Nagarajan, Raju Narwade and Panaskar S (2019): “Analysis of Changes in LULC of Western Ghat by Comparing NDVI and NDWI “ Journal of Remote Sensing & GIS, (Dec-2019), ISSN: 2469-4134, Vol-08, Issue-04, pp:01-07, DOI : 4. Karthik Nagarajan, Raju Narwade and Arya Vijayan (2019): “Real-Time Water Leakage Monitoring System Using IoT Based Architecture “ International Journal for Research in Engineering Application & Management (IJREAM), (Nov-2019), ISSN: 2454-9150, Vol-05, Issue-08, pp.24-30.

Souvenior of Volume – 10 Issue – 7 May 2021 Website: www.ijitee.org DOI: 10.35940/ijitee 15

5. Karthik Nagarajan, Raju Narwade, Biradar Shilpa, Prof. Gayatri Deshpande,(2019): “E-Waste: An Alternative to Partial Replacement of Coarse Aggregate in Concrete “, International Journal Of Engineering Research & Technology (IJERT), (July-2019) ISSN: 2278-0181, Volume 08, Issue 07 (July 2019), pp.993-999 6. Karthik Nagarajan, RajuNarwade and Chowdary Mohanlal (2019): “Applications of 4D GIS Model in Construction Management “, International Journal of Innovative Technology and Exploring Engineering (IJITEE), (July-2019) ISSN: 2278-3075, Volume-8 Issue-9, July 2019, pp.2597-2608 7. Karthik Nagarajan, Raju Narwade, Tejaswini D. N., Mahesh S. Singh (2019): “Factors affecting the labor productivity of brickwork and analyzing them using RII method “ International Journal of Advanced Technology and Engineering Exploration, (IJATEE), (May-2019), ISSN (Print): 2394-5443 ISSN (Online): 2394-7454, Vol-06, Issue-54, pp.143-151. 8. Karthik Nagarajan, Raju Narwade and Pallavi Patil (2019): “ Resource Management of Infrastructural Project for Future Cities: A Re Modified Minimum Moment method “ International Journal of Management, Technology And Engineering (IJMTE), (Feb-2019), ISSN NO: 2249-7455, Vol:9, Issue:2, pp.1094-1099 9. Karthik Nagarajan, Raju Narwade and Shobana Jadhav (2019): “Best Feasible Transportation ), Route Analysis for Delivering Ready Mixed Concrete (RMC) - A Geographic Information System (GIS) Approach “ International Research Journal of Engineering and Technology (IRJET)", (Feb-2019) Vol:6, Issue:2, e-ISSN: 2395-0056, p-ISSN: 2395-0072, pp.2401-2405 10. Karthik Nagarajan, RajuNarwade and Aditya Shatri (2019): “Integrated Land-Use Zoning, Using Topographical Data: Optimizing Vacant Space For Urbanization At Akole Taluka, Maharashtra, India “ International Journal of Advanced Research in Engineering and Technology (IJARET), Volume 10, Issue 1, ( Jan-Feb 2019 ), pp. 188-199, IAEME Publication, Article ID: IJARET_10_01_018, Print: 0976-6480 and ISSN Online: 0976-6499 11. Karthik Nagarajan and Shrikant Charhate (2016): "Smart Modal Analysis of Multistoried Building Considering the Effect of Infill Walls.", International Journal of Global Technology Initiatives(IJGTI) 5.1 (March 2016), Vol:5, Issue:1,p-ISSN: 2277-6591,e-ISSN: 2320-1207 pp.C20-C27. 12. V.K. Bansal,” Potential application areas of GIS in preconstruction planning,” Technical note, Journal of professional issues in engineering education and practice, 2015, 1-7. 13. S. Bhandari, D. Bhandari, and M. Kumari,”Application of geographical information system in progress monitoring of construction project,” International Journal of Scientific & Engineering Research, 2013, 4(12), 100-107. 14. K.T. Chang, Introduction to Geographic Information Systems, Tata McGraw-Hill, 2006, New Delhi, India. 15. M.Y. Cheng, and O’Connor,” ArcSite: Enhanced GIS for construction site layout,” Journal of Construction Engineering and Management, 1996, 329-336. 16. M. Ebrahim, I. Mosly and I. Elhafez,” Building construction information system using GIS,” Arab Journal of Science and Engineering, 2015, 41: 3827-3840. 17. M. Fischer and Bonsang Koo, “Feasibility Study of 4D CAD in Commercial Construction.” Journal of Construction Engineering and Management, Vol 126, Issue 4-July 2000. 18. M. Fischer and F. Aalami, “Cost loaded production model for planning and control.” Construction Informatics Digital Library, paper w78-1999-2813 19. J. Irizarry and E.P. Jalaei,” Integrating BIM and GIS to improve the visual monitoring of construction supply chain management,” Automation in Construction, 2013, 241-254. 20. Changming Kim, Hyojoo Son, Changwan Kim, “Automated construction progress measurement using a 4D building information model and 3D data” Automation in construction 31 (2013) 75-82 21. V. Kolagotla,” Geographic information system and its application to project management in construction industry,” 10th ESRI India user Conference, 2009, 1-12. 22. A.C. Kumar and T. Reshma,” 4D applications of GIS in construction management,” Advances in Civil Engineering, Volume 2017, Article ID 1048540, 1-9. 23. V.R. Kumar and T. Navneethakrishnan,”4D model through GIS for planning and scheduling of residential construction projects,” Research Journal of Applied Sciences, 2012, 7(4), 222-228. 24. R.A.R. Mansoori,” Application of primavera & GIS for effective project management,” International Journal of Engineering Research, 2016, 5(1), 140-142. 25. O’Brien William, “Towards 5D CAD-Dynamic Cost and Resource Planning for Specialist Contractors” Construction Congress VI, Feb 20-22,2000

Author(s): Bhageerath Singh Kaurav, Karuna Markam, Pooja Sahoo Title of the Article: Modified Filter Equation with Improved Fuzzy Logic System Based Directional Median Filter for Mixed Noise

Souvenior of Volume – 10 Issue – 7 May 2021 Website: www.ijitee.org DOI: 10.35940/ijitee 16

Abstract: DWM (Directional weighted median) filter is very popular in filtering digital image and remove mixed noise. Fuzzy logic is implemented with median filters to improve its performance. In the previous work, fuzzy logic system is implemented with switching median filter and gives better performance than directional median filter as well as switching median filter. Experimenting directional median filter with same fuzzy logic system didn’t yield to better results therefore fuzzy logic parameters has been changes as per strong points of directional weighted median filter and a constant has been included in the filtering equation to improve the results. So in this proposed work, we have successfully implemented directional weighted median filter with fuzzy logic system which is proving better results than DWM and FSMF (Fuzzy Switching Median Filter). PSNR (Peak Signal to Noise Ratio)is used for qualitative analysis of results. Keywords: DWM (Directional Median Filter), FSMF (Fuzzy Switching Median Filter),Mixed noise, Gaussian noise, Salt&pepper noise, Fuzzy logic rules, Membership functions, PSNR (Peak signal to noise ratio),Fuzzification. References: 1. Jagrati Gupta, Sandeep Kumar Agrawal, “Fuzzy Logic Gain Factor Based Improved Switching Median Filter for Mixed Noise”, International Conference on Advance Computation and Telecommunication (ICACAT), sponsored by IEEE, 2018. 2. Rita Gupta, AkanshaYadav, “Fuzzy Logic based Switching Median Filter for Mixed Noise in Digital Image”, IJSRD - International Journal for Scientific Research & Development, Vol. 5, Issue 07, 2017. 3. Vahid Kiani, Iran Abbas Zohrevand, “A Fuzzy Directional Median Filter for Fixed-value Impulse Noise Removal”, Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS), Jan 2019. 4. Zong Chen and Li Zhang, “Multi-stage Directional Median Filter”, World Academy of Science, Engineering and Technology 59 2009. 5. Sweety Deswal, Surbhi Singhania, Shailender Gupta and Pranjal Garg, “An Optimised Fuzzy Approach to Remove Mixed Noise from Images”, International Journal of Signal Processing, Image Processing and Pattern Recognition Vol.9, No.4 (2016). 6. Chung-Chia Kang, Wen-June Wang, “Fuzzy reasoning-based directional median filter design”, Signal Processing, Volume 89, Issue 3, March 2009. 7. Bogdan Smolka, Damian Kusnik, “Robust local similarity filter for the reduction of mixed Gaussian and impulsive noise in color digital images”,SIViP, springer, Oct 2015. 8. D. V. Murugan, R. Balasubramanian, “An Efficient Gaussian Noise Removal Image Enhancement Technique for Gray Scale Images“, International Journal of Computer, Electrical, Automation, Control and Information Engineering Vol 9, No 3, 2015. 9. Mahesh Prasanna K and Dr. Shantharama Rai C, “Applications of Fuzzy Logic in Image Processing – A Brief Study “, international journal of advanced computer technology, (Volume 4, Issue 3), March-2015. 10. Kenny Kal Vin Toh, Haidi Ibrahim and Muhammad Nasiruddin Mahyuddin, “Salt-and-Pepper Noise Detection and Reduction Using Fuzzy Switching Median Filter”, IEEE Transactions on Consumer Electronics, Vol. 54, No. 4, NOVEMBER 2008. 11. Naga shettappa Biradar, M.L.Dewal, Manoj Kumar Rohit, “SPECKLE NOISE REDUCTION USING HYBRID TMAV BASED FUZZY FILTER”, International Journal of Research in Engineering and Technology, Volume 3, Special Issue 3, May 2014. 12. M. A. P. Chamikara, A. A. C. A Jayathilake, S. R. Kodituwakku, Fuzzy Based Statistical Method for Image Noise Filtering, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 4, Issue 10, October 2014.

Author(s): Byeongtae Ahn Title of the Article: Construction of Video Management System Based on Remote Education Abstract: For effective remote education using multimedia, it is necessary to develop efficient management techniques of video information. This requires real-time processing of video information which should be managed and retrieved in a compressed forms. The main technology of compressing video is currently MPEG-2. This implies that it is very important to manage and retrieve video compressed in MPEG-2, and then to process the video in real-time for the remote education environment using multimedia. This paper is to develop the management system of video information which is one of the most critical requirements in remote education systems for managing and retrieving MPEG-2 video. Keywords: Video, Remote Education, Mpeg, Semantic, Image.

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References: 1. A. Ono, M. Amano, M. Hakaridani, T. Satou, and M. Sakauchi, "A Flexible Content-Based Image Retrieval System with Combined Scene Description Keyword," IEEE, pp. 201-208, June 2019. 2. B. Bruegge, J. Blythe, J. Jackson and J. Shufelt, " Object-Oriented System Modeling with OMT," OOPSLA '92, 27(10), PP. 414-427, Oct. 2019. 3. D. Gall, "MPEG: A Video Compression Standard for Multimedia Applications," CACM, 34(4), pp. 47-58, April 2019. 4. E. Oomoto and K. Tanaka, "OVID: Design and Implementation of a Video-Object Database System," IEEE Trans. on Knowledge and Data Engineering, 5(4), pp. 62-72, 2019. 5. H. Aoki, S. Shimotsuji, and O. Hori, "A Shot Classification Method of Selecting Effective Key-Frames for Video Browsing," ACM Multimedia 96, pp. 1-10, 2019. 6. H. Frater and D. Paulissen, Multimedia Mania, Abacus, 2019. 7. I. Center, "Query by Image and Video Content: The QBIC System," IEEE Computer, 28(9), pp. 23-32, Sept. 2019. 8. J. Monaco, "How to Read a Film," The Art, Technology, Language, History and Theory of Film and Media, Oxford University Press, 2019. 9. J. Meng and S. Chang, "CVEPS - A Compressed Video Editing and Parsing System," ACM Multimedia 96, pp. 43-53, 2020. 10. J. Rumbaugh, M. Blaha, W. Premerlani, F. Eddy, and W. Lorensen, Object-Oriented Modeling and Design, Prentice-Hall, 2020. 11. J. Smith and S. Chang, "Searching for Images and Videos on the World-Wide Web," Technical Report #459-96- 25, Center for Telecommunications Research, Columbia University, New York, August 2020. 12. J. Smith and S. Chang, "VisaulSEEK: a Fully Automated Content-based Image Query System," ACM Multimedia '96, Nov. 2020. 13. K. Hirata and T. Kato, "Query by Visual Example Content-based Image Retrieval," Advances in Database Technology(EDBT '92), pp. 56-71, 2020. 14. M. Davis, "Media Streams: Representing Video for Retrieval and Repurposing," Ph. D. Thesis, Massachusetts Institute of Technology, 2020. 15. R. and J. Gray, "Similar-shape Retrieval in Shape Data Management," IEEE Computer, pp. 57-62, Sept. 2020. 16. R. Hjelsvold, "Video Information Contents and Architecture," In Proceedings of the 4th International Conference on Extending Database Technology, pp. 28-31, March 2020. 17. R. Hjelsvold and R. Midtstraum, "Modelling and Querying Video Data," Proceedings of the 20th VLDB Conference, 2020. 18. [18] S. Smoliar and H. Zhang, "Content-Based Video Indexing and Retrieval," IEEE MultiMedia, pp. 62-72 2020. 19. S. Stevens, "Next Generation Network and Operating System Requirements for Continuous Time Media," In Proceedings of the Second International Workshop for Network and Operating System Support for Digital Audio and Video, November 2020. 20. T. Smith, "If You Could See What I Mean... Descriptions of Video in An Anthropologist's Notebook," Master's Thesis, MIT, 2020. 21. V. Kobla and D. Doermann, "Compressed Domain Video Indexing Techniques Using DCT and Motion Vector Information in MPEG Video," SPIE, Vol. 3022, pp. 200-211, 2020. 22. V. Ogle and M. Stonebraker, "Chabot: Retrieval From a Relational Database of Images," IEEE Computer, 28(9), pp. 40-48, 2020.

Author(s): Nishika Manira, Swelia Monteiro, Tashya Alberto, Tracy Niasso, Supriya Patil Title of the Article: Geo-Landmark Recognition and Detection Abstract: The widespread use of smartphones and mobile data in the present-day society has exponentially led to the interaction with the physical world. The increase in the amount of image data in web and mobile applications makes image search slow and inaccurate. Landmark recognition, an image retrieval task, faces its challenges due to the uncommon structure it possesses, such as, buildings, cathedrals, castles or museums. These are shot from various angles which are often different from each other, for instance, the exterior and interior of a landmark. This paper makes use of a Convolutional Neural Networks (CNN) based efficient recognition system that serves in navigation, to organize photo collections, identify fake reports and unlabeled landmarks from historical data. It identifies landmarks correctly from a variety of images taken at different viewpoints as well as distances. An appropriate CNN architecture helps to provide the best solution for the currently selected dataset.

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Keywords: Convolutional Neural Networks (CNN), Faster Region Based CNN (Faster RCNN), Histogram of Oriented Gradients (HOG), Rectified Linear Unit (ReLU), Region of Interest (RoI), Region Proposal Network (RPN), Residual Networks (ResNet), Visual Geometry Group (VGG). References: 1. Andrew Crudge, Will Thomas and Kaiyuan Zhu, Article ‘Landmark Recognition Using Machine Learning’ 2015: http://cs229.stanford.edu/proj2014/Andrew%20Crudge,%20Will%20Thomas,%20Kaiyuan%20Zhu,%20Landm ark%20Recognition%20Using%20Machine%20Learning.pdf 2. Maxence Dutreix, Nathan Hatch, Raghav Kuppan, Pranav Shenoy Kasargod Pattanashetty, Anirudha Sundaresan ‘Google Landmark Recognition and Retrieval Challenges’ Article. April 25, 2018 : https://nhatch.github.io/files/landmarks_report.pdf 3. ‘Google Landmark Recognition using Transfer Learning’ Article by Catherine McNabb, Anuraag Mohile, Avani Sharma, Evan David, Anisha Garg : https://towardsdatascience.com/google-landmark- recognition-using-transfer-learning-dde35cc760e1 4. Blog on end to end Image Classification in fastai.: https://rajaskakodkar.github.io/blog/deep%20learning/2020/10/07/image-classification.html 5. https://medium.com/@abhinaya08/google-landmark-recognition-274aab3c71ae 6. https://en.wikipedia.org/wiki/Deep_learning 7. Introduction to transfer learning: https://builtin.com/data-science/transfer-learning 8. Guide to CNN, by Daphne Cornelisse. April 24, 2018: https://www.freecodecamp.org/news/an-intuitive-guide- to-convolutional-neural-networks-260c2de0a050/ 9. Basics of Convolution Neural Networks, February 25, 2019: https://towardsdatascience.com/covolutional- neural-network-cb0883dd6529 10. CNN working by Derrick Mwiti, May 8, 2018: heartbeat.fritz.ai/a-beginners-guide-to-convolutional-neural- networks-cnn-cf26c5ee17ed 11. Fast-ai: https://www.fast.ai/ 12. ‘Understanding AlexNet’ by Sunita Nayak : https://learnopencv.com/understanding-alexnet/amp/ 13. ‘VGGNet Architecture’ by Prabin Nepal, July 30, 2020: https://medium.com/analytics-vidhya/vggnet- architecture-explained-e5c7318aa5b6 14. Residual Networks (ResNet): https://www.geeksforgeeks.org/residual-networks-resnet-deep-learning/ 15. ‘Detailed guide to understand and implement ResNets’ by Ankit Sachan: https://cv- tricks.com/keras/understand-implement-resnets/ 16. https://www.researchgate.net/figure/ResNet-50-architecture-26-shown-with-the-residual-units-the-size-of-the- filters-and_fig1_338603223 17. Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun, ‘Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks’ paper published on 6th January 2016: https://arxiv.org/pdf/1506.01497.pdf

Author(s): C. Achille Fumtchum , Pierre Tsafack, Florin Hutu, Guillaume Villemaud, Emmanuel Tanyi Title of the Article: A Survey of RF Energy Harvesting Circuits Abstract: The aim of this work is, on one hand, to review the state of the art of the architectures and diodes used in radio-frequency energy harvesting systems, the idea here is to review the most recent works, as well as their characteristics, which include frequency, type of diode used, topology, maximum efficiency and corresponding power, and on the other hand to carry out simulations to determine the most appropriate case for any further work in the field. After having determined the most common topologies, we used the main known radio-frequency diodes to characterize them in a first step, clearly a process of comparing the results of the simulations of the different topologies is done by initially considering an identical frequency. and afterward determine the effect of frequency band on their conversion efficiency. Keywords: RF Energy Harvesting Topologies; Conversion Efficiency; Wireless Power Transfer; Telecommunications; Circuits And Systems. References: 1. R. K. Sidhu, J. Singh Ubhi and A. Aggarwal, "A Survey Study of Different RF Energy Sources for RF Energy Harvesting," 2019 International Conference on Automation, Computational and Technology Management (ICACTM), London, UK, 2019, pp. 530-533, doi: 10.1109/ICACTM.2019.8776726.

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2. K. O'Brien, G. Scheible and H. Gueldner “Analysis of Wireless Power Supplies for Industrial Automation Systems”, IECON’03. 29th Annual Conference of the IEEE Industrial Electronics Society (IEEE Cat. No.03CH37468), Roanoke, VA, USA, 2003 pp. 367 – 372, doi: 10.1109/IECON.2003.1280008. 3. Minhong Mi, Mickle, M. H., Capelli, C., & Swift, H. “RF energy harvesting with multiple antennas in the same space”. IEEE Antennas and Propagation Magazine, 47(5), 100–106, 2005, doi: 10.1109/MAP.2005.1599171. 4. Thamer S. Almoneef, “Design of a rectenna array without a matching network”; IEEE Access, vol. 8, Jun. 2020, doi: 10.1109/ACCESS.2020.3001903. 5. H. Tafekirt, Jose Pelegri-Sebastia, Adel Bouajaj, and Britel Mohammed Reda, “Sensitive triple-band rectifier for energy harvesting applications”, IEEE Access, vol. 8, May 2020, doi: 10.1109/ACCESS.2020.2986797. 6. Ryan Reed, Fariborz Lohrabi Pour, and Dong Sam Ha, “An efficient 2.4 GHz differential rectenna for radio frequency energy harvesting”, Int. Midwest Symp. on Circuits and Systems (MWSCAS), Aug. 2020, doi: 10.1109/MWSCAS48704.2020.9184600. 7. Jincheng Zhao, Guru Subramanyam, Hailing Yue, “A Dual-Band Rectifying Antenna Design for RF Energy Harvesting”, Int. Midwest Symp. on Circuits and Systems (MWSCAS), Aug. 2020, doi: 10.1109/MWSCAS48704.2020.9184606. 8. Si Ce Wang, Min Jun Li, and Mei Song Tong, “A Miniaturized High-Efficiency Rectifier with Extended Input Power Range for Wireless Power Harvesting”, IEEE Microwave and Wireless Components Letters, vol. 30, pp. 617 – 620, June 2020, doi: 10.1109/LMWC.2020.2990534. 9. Mengfan Wang, Jianing Chen, Xinwang Cui, and Long Li “Design and Fabrication of 5.8GHz RF Energy Harvesting Rectifier” Cross Strait Quad-Regional Radio Science and Wireless Technology Conference (CSQRWC), July 2019, doi: 10.1109/CSQRWC.2019.8799201. 10. Mohamed M. Mansour, and H. Kanaya, “High-Efficient Broadband CPW RF Rectifier for Wireless Energy Harvesting”, IEEE Microwave and Wireless Components Letters, Vol. 29, pp. 1 – 3, April 2019, doi: 10.1109/LMWC.2019.2902461. 11. Kapil Bhatt, Sandeep Kumar, Pramod Kumar Member, and Chandra Charu Tripathi, “Highly Efficient 2.4 & 5.8 GHz Dual Band Rectenna for Energy Harvesting Applications”, IEEE Antennas and Wireless Propagation Letters, Vol.18, pp. 2637 - 2641, Dec. 2019, doi: 10.1109/LAWP.2019.2946911. 12. Shanpu Shen, Yujie Zhang, Chi-Yuk Chiu, and Ross Murc, “An Ambient RF Energy Harvesting System Where the Number of Antenna Ports Is Dependent on Frequency”, IEEE Transactions on Microwave Theory and Techniques Vol. 67, pp. 3821 - 3832, Sept. 2019, doi: 10.1109/TMTT.2019.2906598. 13. Shunsuke Hatanaka, and Haruichi Kanaya, “Wireless Micro Energy Harvesting Circuit for Sensor System”, IEEE Electronics Packaging Technology Conference (EPTC), Dec. 2019, doi: 10.1109/EPTC47984.2019.9026709. 14. Chih-Hsi Lin; Chien-Wen Chiu; Jian-Yuan Gong. “A Wearable Rectenna to Harvest Low-Power RF Energy for wireless healthcare applications”, 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), 2018, doi: 10.1109/CISP-BMEI.2018.8633222. 15. Mohamed M. Mansour, and Haruichi Kanaya, “Compact and Broadband RF Rectifier With1.5 Octave Bandwidth Based on a Simple Pairof L-Section Matching Network,” IEEE Microwave and Wireless Components Letters, Vol. 28, pp. 335 – 337, April 2018. 16. Qasim Awais, Yang Jin, Hassan Tariq Chattha, Mohsin Jamil, Qiang He, and Bilal A. Khawaja, “A compact rectenna system with high conversion efficiency for wireless energy harvesting”, in IEEE Access, vol. 6, pp. 35857-35866, 2018, doi: 10.1109/ACCESS.2018.2848907. 17. M. Aboualalaa, I. Mansour, M. Mansour, A. Bedair, A. Allam, M. Abo-Zahhad, H. Elsadek, K. Yoshitomi, and R. K. Pokharel, "Dual-band Rectenna Using Voltage Doubler Rectifier and Four-Section Matching Network," 2018 IEEE Wireless Power Transfer Conference (WPTC), Montreal, QC, Canada, 2018, pp. 1-4, doi: 10.1109/WPT.2018.8639451. 18. A. Mouapi, N. Kandil, and G. V. Kamani, “A Miniature Rectifier Design for Radio frequency Energy Harvesting applied at 2.45 GHz,” IEEE International Conference on Environment and Electrical Engineering and Commercial Power System Europe (EEEIC / I&CPS), pp.1–5, Jun. 2018, doi: 10.1109/EEEIC.2018.8493844. 19. A. Biswas, S. B. Hamidi, C. Biswas, P. Roy, D. Mitra and D. Dawn, "A novel CMOS RF energy harvester for self- sustainable applications," 2018 IEEE 19th Wireless and Microwave Technology Conference (WAMICON), Sand Key, FL, 2018, pp. 1-5, doi: 10.1109/WAMICON.2018. 20. C. Li, M. Yu and H. Lin, "A Compact 0.9-/2.6-GHz Dual-Band RF Energy Harvester Using SiP Technique," in IEEE Microwave and Wireless Components Letters, vol. 27, no. 7, pp. 666-668, July 2017, doi: 10.1109/LMWC.2017.2711506. 21. M. ur Rehman, W. Ahmad and W. T. Khan, "Highly efficient dual band 2.45/5.85 GHz rectifier for RF energy harvesting applications in ISM band," 2017 IEEE Asia Pacific Microwave Conference (APMC), Kuala Lumpar, 2017, pp. 150-153, doi: 10.1109/APMC.2017.8251400. 22. A. Eid, J. Costantine, Y. Tawk, A. H. Ramadan, M. Abdallah, R. ElHajj, R. Awad, I. B. Kasbah, "An efficient RF

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energy harvesting system," 2017 11th European Conference on Antennas and Propagation (EUCAP), Paris, 2017, pp. 896-899, doi: 10.23919/EuCAP.2017.7928573. 23. M. M. Mansour and H. Kanaya, "Compact RF rectifier circuit for ambient energy harvesting," 2017 IEEE International Symposium on Radio-Frequency Integration Technology (RFIT), Seoul, 2017, pp. 220-222, doi: 10.1109/RFIT.2017.8048256. 24. B. Merabet, H. Takhedmit, B. Allard, L. Cirio, F. Costa, O. Picon, and C. Vollaire, “Low-cost converter for harvesting of microwave electromagnetic energy,” IEEE Energy Conversion Congress and exposition, pp. 2592 – 2599, 2009, doi: 10.1109/ECCE.2009.5316093. 25. A. Fumtchum, F. Hutu, P. Tsafack, G. Villemaud and E. Tanyi, "High Efficiency Rectifier for a Quasi-Passive Wake-up Radio," 2019 International Symposium on Signals, Circuits and Systems (ISSCS), Iasi, Romania, 2019, pp. 1-4, doi: 10.1109/ISSCS.2019.8801754.

Author(s): J Paul Raja Singh, Sharmishtha Sen, Shreyes Prasad Title of the Article: User Reputation Calculation for Service-Oriented Environments Abstract: All the cloud based applications work on service- oriented architectures and collaborate with multiple components from other services to execute discreet application logic. In this environment there are a lot of Web services facilitated to the customer to make the systems. As the potential of the same Web service will change with respect to users' needs.On an average a user will be heavily relied on tools to aid their activities on the internet vice versa the Service provider are also dependent on the users profile and what services are being used in the system. A User Reputation model offers a solution to the Service providers in supporting their service decision based on the User Profile. This model takes usage ratings as data and produces a personalised score. We suggest a new Cumulative separation on the basis of Tags and popularity estimation method and showcase its enhanced filtration ability. Keywords: Collaborative filtering, Feedback rating, Matrix factorization, Quality of Service (QoS), Reputation, Service-Oriented Architectures. References: 1. OPRC: An Online Personalized Reputation Calculation Model in Service-Oriented Computing Environments ,Date of publication June 28, 2019, 2. Changsheng Zhu Chi-Tsun Cheng,Yindong Chen. MeURep: A novel user reputation calculation approach in personalized cloud services PLoS One. 2019; 14(6): e0217933. Published online 2019 Jun 21. 3. Guang Ling, Irwin King, Michael R. Lyu A Unified Framework for Reputation Estimation in Online Rating Systems in Proc. IJCAI, 2013, pp. 26702676. 4. YUYU YIN,(Member, IEEE), LU CHEN, YUESHEN XU, JIAN WAN.IEE Location-Aware Service Recommendation With Enhanced Probabilistic Matrix Factorization E Int. Conf. Web Services (ICWS), Jun. 2017 5. Junwei Zhang , Deyu Li ,and Xiaoqin Fan A Customer-Centric Trust Evaluation Model for Personalized Service Selection, .Hindawi Scientific Programming Volume 2018, Article ID 4819195, 13 pages 6. Mohammad Azzeh ,Online Reputation Model Using Moving Window International Journal of Advanced Computer Science Applications,Vol.8,No.4,2017 7. Rui LI and Xin ZHANG, A Tag-based Recommendation Algorithm Integrating Short-term and Long-term Interests of Users School of Information Science and Engineering, Hunan University, Changsha, 410082 China ,2017 2nd International Conference on Software, Multimedia and Communication Engineering (SMCE 2017) .ISBN: 978-1-60595-458-5 8. Mohamed Amine Chatti, Simona Dakova, Hendrik Thüs and Ulrik Schroeder Tag-Based Collaborative FilteringRecommendation in Personal LearningEnvironments IEEE TRANSACTIONS ON LEARNING TECHNOLOGIES 9. Jiang D, Xue J, Xie W. A reputation model based on hierarchical bayesian estimation for web services. In: Computer Supported Cooperative Work in Design (CSCWD), 2012 IEEE 16th International Conference on. IEEE; 2012. p. 88–93. 10. Y. Wu, C. Yan, Z. Ding et al., “A novel method for calculating service reputation,” IEEE Transactions on Automation Science and Engineering, vol. 10, no. 3, pp. 634–642, 2013

Souvenior of Volume – 10 Issue – 7 May 2021 Website: www.ijitee.org DOI: 10.35940/ijitee 21

Author(s): T.Venkatesh, K.Prathyush, S.Deepak, U.V.S.A.M.Preetham Title of the Article: Agriculture Crop Leaf Disease Detection using Image Processing Abstract: As we all know that the Agriculture plays an important role in the Indian economy and majority of the individuals depends upon it and offers huge amount of the crops through the worldwide. The Illnesses in these crops are generally on the leaf's influences on the decrease of both quality and number of horticultural items. We should know the disease of the crop correctly to solve the problem. There will be a huge loss if we do not find the disease and treat properly. The view of natural eye isn't so a lot more grounded in order to watch minute variety in the contaminated piece of leaf. In this report, we are giving a programming answer for naturally identify and arrange plant leaf diseases. In this we are utilizing picture preparing methods to characterize alignments and rapidly finding can be completed according to infection. This methodology will upgrade the efficiency of yields in a efficient way and can get us the accurate disease which helps us to find the solution for the diseased crop. It observes a few stages with the help of these pictures obtaining, picture pre-handling, division, highlights extraction and genetic algorithm-based grouping. Relating to the cultivation of land, efficiency is something on which economy exceptionally depends. This is the one of the reasons that sickness identification in plants assumes a significant job in the agriculture business field, as having the illness in plants are very normal. In an event that legitimate consideration isn't taken here, at that point it causes true consequences for plants and because of which quality of each and every item, amount or efficiency is being influenced. The recognition of plant infections through some programmed step is gainful as it avoids a huge work of checking in huge homesteads of harvests. At the beginning of the crop harvesting step itself, it shows the side effects or the symptoms of the diseases. This proposed method surfaces into a new programmed manner by distinguishing the effects of the crop plant diseases. We are using some image processing techniques for the identification of the disease. Additionally, it watches the review on the various diseases order strategies which also can be utilized for plant leaf alignment. Picture division, which is a significant viewpoint for sickness identification in a plant leaf alignment, is finalized by the input RGB mask images. Keywords: This Methodology Will Upgrade The Efficiency Of Yields In A Efficient Way And Can Get Us The Accurate Disease Which Helps Us To Find The Solution For The Diseased Crop. References: 1. Moran, M. Susan. "Image-based remote sensing for agricultural management– Perspectives of image providers, research scientists and users." In Proceedings of the Second International Conference on Geospatial Information in Agriculture and Forestry, vol. 1. 2000. 2. Brugger, Fritz. "Mobile applications in agriculture." Syngenta Foundation (2011): 1- 38. 3. Laudien, Rainer, Georg Bareth, and Reiner Doluschitz. "Comparison of remote sensing based analysis of crop diseases by using high resolution multispectral and hyperspectral data–case study: Rhizoctonia solani in sugar beet." Geoinformatics 7 (2004): 670-676. 4. Pilli, S.K., Nallathambi, B., George, S.J. and Diwanji, V., 2015, February. eAGROBOT—A robot for early crop disease detection using image processing. In 2015 2nd International Conference on Electronics and Communication Systems (ICECS) (pp. 1684-1689). IEEE. 5. DEVI P, I. N. D. I. R. A. "Pesticides in agriculture-a boon or a curse? A case study of Kerala." Economic and Political Weekly (2010): 199-207. 6. Al-Hiary, Heba, Sulieman Bani-Ahmad, M. Reyalat, Malik Braik, and Zainab Alrahamneh. "Fast and accurate detection and classification of plant diseases." International Journal of Computer Applications 17, no. 1 (2011): 31- 38.

Author(s): Sandhya Sarma. K. N, Hemraj Shobharam Lamkuche, E.Chandra Blessie Title of the Article: Securing Communication in the Iot- Based Power Constrained Devices in Health Care System Abstract: One of the most appealing IoT application areas is medical care and health care. This promising technology is reshaping current health-care service that comply with treatment and mediation at home. The core part of IoT constitutes sensors and various devices for diagnosis and imaging. Now-a-days sensors are becoming smaller, allowing them to be worn without interfering with daily activities.. To make sensors wearable and wireless, it should be small in dimensions and also the energy, memory, and processing power available also matters. Health services dependent on the Internet of Things are supposed to minimise cost, enhance the user's experience and improve their quality of life. IoT has many hurdles in its implementation, security is the most important. This paper throws light on the different methods of securing the medical sensitive data through the network. Keywords: Internet of Things, Cloud Computing, Healthcare cloud, Security, Sensors.

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References: 1. Haiping Huang,Tianhe Gong,Ning Ye, Ruchuan Wang, Yi Dou,” Private and secured Medical Data Transmission and Analysis for wireless Sensing Health care System” , IEEE Trans.Industrial Informatics.,vol 13, No 3, June 2017. 2. Shu-Di Bao, Meng Chen, Guang-Zhong,“A Method of Signal Scrambling to Secure Data storage for Healthcare Applications.” IEEE Trans.Biomed. Health Informatics., vol.21,No.6,Nov 2017 3. Hadeal Abdulaziz Al Hamid, Sk Md Mianur Rahman, M. Shamim Hossain, Ahmad almogren, Atif Alamri, “A Security Model for Preserving the Privacy of Medical Big Data in a Healthcare Cloud Using a Fog Computing Facility With Pairing Based Cryptography.” , IEEE Access.,vol 5, 2017. 4. Abid Mehmood, Iynkaran Natgunanathan, Yog Xiang, Howard Poston, Yushu Zhang, “Anonymous Authentication Scheme for Smart Cloud Based Healthcare Application”, IEEE Access.,vol 6, 2018. 5. Mohamed Elhoseny, Gustavo Ramirez-Gonzalez, Osama M, Abu- Elnasr, Shihab A Shawkat, ArunKumar N, Ahmed Farouk, “Secure Medical Data Transmission Model for IoT – Based Healthcare Systems”, IEEE Access Information Security,Telecommunication Applications.,vol 6, 2018. 6. Mukhtar M e Mahmoud, Joel J P C Rodrigues, Syed Hassan Ahmed, Sayed Chhattan Shah, Jalal F Al-Muhtadi “Enabling Technologies on Cloud of Things for Smart Healthcare”, IEEE Access cyber threats, Countermeasures in Healthcare sector,vol 6, 2018. 7. Ankur Limaye, Tosiron Adegbija,”HERMIT: A Benchmark Suite for the Internet of Medical things.”, IEEE Trans, Internet of Things,vol 5, October 2018. 8. Jingwei Liu, Huifang Tang, Rong Sun, Xiaojiang Du,Mohsen Guizani, “Ligtweight and Privacy – Preserving Medical Service Access for Healthare Cloud . LPP-MSA.”, IEEE Access.,vol 7, August, 2019. 9. Leila Ismail, Huned Materwala, Sherali Zeadally,“Lightweight Blockchain for Healthcare”, IEEE Access.,vol 7, October, 2019. 10. Engwei Wang, Hui Zhu, Ximeng Liu, Rongxing Lu, Jiafeng Hua, Hui Li,“Privacy-Preserving Collaborative Model Learning Scheme for E- Healthcare”, IEEE Access.,vol 7, November, 2019. 11. Yang Yang, Ximeng Liu, Robert H. Deng, Yingjiu Li,“Light weight Shareable and Traceable Secure Mobile Heath System”, IEEE Trans, Dependable and Secure Computing,vol 17, January 2020. 12. Y.Yin,Y. Zeng, X. Chen, andY. Fan, ``The Internet of Things in healthcare: An overview,'' J. Ind. Inf. Integr., vol. 1, pp. 3_13, Mar. 2016. 13. Sandip Roy, Ashok Kumar Das, Santanu Chatterjee, Neeraj Kumar, Samiran Chattopadhyay, Joel J.P.C.Rodrigues, “Provably Secure Fine- Grained Data Access Control Over Multiple Cloud Servers I Mobile Cloud Computing Based Healthcare Applications”, IEEE Trans, Industrial Informatics, vol 15, No 1, January 2019. 14. Huansheng Ning, Hong Liu, Laurence T . Yang, “Cybernity Security in the Internet of Things”. 15. Owusu-Agyemang Kwabena, Zhen Qin, Tianming Zhuang, Zhiguang Qin, “MSCryptoNet: MultiScheme Privacy – Preserving Deep Learning in Cloud Computing”, IEEE Access.,vol 7, March , 2019. 16. Hadi Habibzadeh, Karthik Dinesh, Omid Rajabi Shisvan, Andrew Boggio-Dandry, Gaurav Sharma, “A survey of Healthcare Internet of Things(HIoT): a Clinical Perspetive”, IEEE Trans, Internet of Things,vol 17, January 2020. 17. S.M.Riazul Islam, Daehan Kwak, MD. Humaun Kabir, Mahmud Hossain, Kyung-SUP Kwak, “The Internet of Things for Health Care: A comprehensive Survey, IEEE Access,vol 13, January 2015Raman Dugyala, N Hanuman Reddy, N Chandra Sekhar Reddy, J Phani Prasad, “A Roadmap to Security in IoT”, IJAE Reseach ISSN 0973- 4562, vol 12,2017. 18. Anoma Abade ,” Security in Internet of things Using Attribute Based Encryption”, JASC, ISSN NO: 0076-5131, vol 5, July 2018. 19. Rodrigo Roman, Pablo ajera, Javier Lopez, “Securing the Internet of Things”, IEEE Computer society, September 2011. 20. Stephanie B Baker, Wei Xiang, Ian Atikson, “Internet of things for Smart Healthcare: Technologies, Challenges, and Opportunities”, IEEE Access,vol 5, 2017. 21. AbdulAziz Shebab, Mohamed Elhoseny, Khan Muhammad, Arun Kumar Sangaiah, Po Yang, Haojun Huang, Guolin Hou, “Secure and Robust Fragile Watermarking Scheme for Medical Images”, IEEE Access,vol 6, 2018. 22. Hao Jin,, Yan Luo, eilong Li, Jomol Mathew, “ A Review of Secure and Privacy-Preserving Medical Data Sharing”, IEEE Access,vol 7, 2019. 23. Ashraf Darwish Aboul Ella Hassanien, Mohamed Elhoseny, Arun Kumar Sangiah, Khan Muhammad, “The impact of the hybrid platform of internet of things and cloud computing on healthcare system: opportunities, challenges, and open problems”, Journal of Ambient Intelligence and Humanized Computing, December 2017. 24. Al-Dahhan, R. R., Shi, Q., Lee, G. M., & Kifayat, K. (2019). Survey on revocation in ciphertext-policy attribute- based encryption. In Sensors (Switzerland) (Vol. 19, Issue 7, pp. 1–22). https://doi.org/10.3390/s19071695 25. Ali, M., Sadeghi, M.-R., & Liu, X. (2020). Lightweight Revocable Hierarchical Attribute-Based Encryption for

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Internet of Things. IEEE Access, 8, 23951–23964. https://doi.org/10.1109/access.2020.2969957 26. Bansal, S., & Kumar, Di. (2019). IoT Application Layer Protocols: Performance Analysis and Significance in Smart City. 2019 10th International Conference on Computing, Communication and Networking Technologies, ICCCNT 2019, 1–6. https://doi.org/10.1109/ICCCNT45670.2019.8944807 27. Bellemou, A. M., García, A., Castillo, E., Benblidia, N., Anane, M., Álvarez-Bermejo, J. A., & Parrilla, L. (2019). Efficient implementation on low-cost SoC-FPGAs of TLSv1.2 protocol with ECC_AES support for secure IoT coordinators. Electronics (Switzerland), 8(11), 1–18. https://doi.org/10.3390/electronics8111238 28. Chen, X., Liu, Y., Chao, H. C., & Li, Y. (2020). Ciphertext-Policy Hierarchical Attribute-Based Encryption against Key-Delegation Abuse for IoT-Connected Healthcare System. IEEE Access, 8, 86630–86650. https://doi.org/10.1109/ACCESS.2020.2986381 29. Fontaine, C. (2019). About Homomorphic Encryption Implementation Progresses and Challenges. 30. Goodman, N., Zwick, A., Spicer, Z., & Carlsen, N. (2020). Public engagement in smart city development: Lessons from communities in Canada’s Smart City Challenge. The Canadian Geographer / Le Géographe Canadien, June. https://doi.org/10.1111/cag.12607 31. Hung, C. W., & Hsu, W. T. (2018). Power consumption and calculation requirement analysis of AES for WSN IoT. Sensors (Switzerland), 18(6). https://doi.org/10.3390/s18061675 32. Jaloudi, S. (2016). Open source software of smart city protocols current status and challenges. 2015 International Conference on Open Source Software Computing, OSSCOM 2015. https://doi.org/10.1109/OSSCOM.2015.7372690 33. Jawhar, I., Mohamed, N., & Al-Jaroodi, J. (2018). Networking architectures and protocols for smart city systems. Journal of Internet Services and Applications, 9(1). https://doi.org/10.1186/s13174-018-0097-0 34. Kalmeshwar, M., & K S, A. P. D. N. P. (2017). Internet Of Things: Architecture,Issues and Applications. International Journal of Engineering Research and Applications, 07(06), 85–88. https://doi.org/10.9790/9622- 0706048588 35. Khan, M. A., Sargento, S., & Luís, M. (2018). Data collection from smart-city sensors through large-scale urban vehicular networks. IEEE Vehicular Technology Conference, 2017-Septe(August 2019), 1–6. https://doi.org/10.1109/VTCFall.2017.8288308 36. Kölsch, J., Heinz, C., Ratzke, A., & Grimm, C. (2019). Simulation-based performance validation of homomorphic encryption algorithms in the internet of things. Future Internet, 11(10). https://doi.org/10.3390/FI11100218 37. Leveugle, R., Mkhinini, A., & Maistri, P. (2018). Hardware support for security in the internet of things: From lightweight countermeasures to accelerated homomorphic encryption. Information (Switzerland), 9(5). https://doi.org/10.3390/info9050114 38. Liao, T. L., Lin, H. R., Wan, P. Y., & Yan, J. J. (2019). Improved attribute-based encryption using chaos synchronization and its application to MQTT security. Applied Sciences (Switzerland), 9(20). https://doi.org/10.3390/app9204454 39. Narayanaswamy, S., & Kumar, A. V. (2019). Application layer security authentication protocols for the internet of things: A survey. Advances in Science, Technology and Engineering Systems, 4(1), 317–328. https://doi.org/10.25046/aj040131 40. Peralta, G., Cid-Fuentes, R. G., Bilbao, J., & Crespo, P. M. (2019). Homomorphic encryption and network coding in IoT architectures: Advantages and future challenges. Electronics (Switzerland), 8(8), 1–14. https://doi.org/10.3390/electronics8080827 41. Rasori, M., Perazzo, P., & Dini, G. (2020). A lightweight and scalable attribute-based encryption system for smart cities. Computer Communications, 149(May 2019), 78–89. https://doi.org/10.1016/j.comcom.2019.10.005 42. Shahrokni, H., & Brandt, N. (2013). Making sense of smart city sensors. Urban and Regional Data Management, UDMS Annual 2013 - Proceedings of the Urban Data Management Society Symposium 2013, May 2013, 117–127. https://doi.org/10.1201/b14914-15 43. Suryadevara, N. K., & Biswal, G. R. (2019). Smart plugs: Paradigms and applications in the smart city-and-smart grid. In Energies (Vol. 12, Issue 10, pp. 1–20). https://doi.org/10.3390/en12101957 44. Talari, S., Shafie-Khah, M., Siano, P., Loia, V., Tommasetti, A., & Catalão, J. P. S. (2017). A review of smart cities based on the internet of things concept. In Energies (Vol. 10, Issue 4, pp. 1–23). https://doi.org/10.3390/en10040421 45. Toma, C., Alexandru, A., Popa, M., & Zamfiroiu, A. (2019). IoT solution for smart cities’ pollution monitoring and the security challenges. Sensors (Switzerland), 19(15). https://doi.org/10.3390/s19153401 46. Tsai, K. L., Huang, Y. L., Leu, F. Y., You, I., Huang, Y. L., & Tsai, C. H. (2018). AES-128 based secure low power communication for LoRaWAN IoT environments. IEEE Access, 6, 45325–45334. https://doi.org/10.1109/ACCESS.2018.2852563 47. Tsai, K. L., Leu, F. Y., You, I., Chang, S. W., Hu, S. J., & Park, H. (2019). Low-Power AES Data Encryption Architecture for a LoRaWAN. IEEE Access, 7, 146348–146357. https://doi.org/10.1109/ACCESS.2019.2941972

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48. Wang, L., Li, J., & Ahmad, H. (2016). Challenges of fully homomorphic encryptions for the internet of things. IEICE Transactions on Information and Systems, E99D(8), 1982–1990. https://doi.org/10.1587/transinf.2015INI0003 49. Weize, Y., & Kose, S. (2017). A Lightweight Masked AES Implementation for Securing IoT Against CPA Attacks. IEEE Transactions on Circuits and Systems I: Regular Papers, 64(11), 2934–2944. https://doi.org/10.1109/TCSI.2017.270209

Author(s): Karthik Valliappan C, Vikram R Title of the Article: Autonomous Indoor Navigation for Mobile Robots Abstract: An autonomous navigation system for a robot is key for it to be self-reliant in any given environment. Precise navigation and localization of robots will minimize the need for guided work areas specifically designed for the utilization of robots. The existing solution for autonomous navigation is very expensive restricting its implementation to satisfy a wide variety of applications for robots. This project aims to develop a low-cost methodology for complete autonomous navigation and localization of the robot. For localization, the robot is equipped with an image sensor that captures reference points in its field of view. When the robot moves, the change in robot position is estimated by calculating the shift in the location of the initially captured reference point. Using the onboard proximity sensors, the robot generates a map of all the accessible areas in its domain which is then used for generating a path to the desired location. The robot uses the generated path to navigate while simultaneously avoiding any obstacles in its path to arrive at the desired location. Keywords: Autonomous, Self-Reliant, Localization, Expensive, Image Sensor, Simultaneously. References: 1. H. Huang, d. Sun, r. Wang, c. Zhu, and b. Liu, "ship target detection based on improved yolo network," mathematical problems in engineering, vol. 2020, p. 6402149, 2020/08/17 2020. 2. T. H. Le, "applying artificial neural networks for face recognition," advances in artificial neural systems, vol. 2011, p. 673016, 2011/11/03 2011. 3. K. Zhang, z. Zhang, z. Li, and y. Qiao, "joint face detection and alignment using multitask cascaded convolutional networks," ieee signal processing letters, vol. 23, no. 10, pp. 1499-1503, 2016 4. Jie zhou, ying cao, xuguang wang, peng li, and wei xu. Deep recurrent models with fast-forward connections for neural machine translation. Corr, abs/1606.04199, 2016. 5. A. Graves and j. Schmidhuber. Framewise phoneme classification with bidirectional lstm networks. In proc. Int. Joint conf. On neural networks ijcnn 2005, 2005. 6. Klaus greff, rupesh kumar srivastava, jan koutník, bas r. Steunebrink, and jürgen schmidhuber. Lstm: a search space odyssey. Corr, abs/1503.04069, 2015. 7. O. Abdel-hamid, L. Deng and D. Yu, Exploring Convolutional Neural Network Structures and Optimization Techniques for Speech Recognition, pp. 3366-3370, August 2013. 8. C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, et al., Going Deeper with Convolutions, 2014. 9. A. Buyval, I. Afanasyev, E. Magid. “Comparative analysis of ROS-based monocular slam methods for indoor navigation”, in Proc. SPIE. 10. M. Sokolov, O. Bulichev and I. Afanasyev, “Analysis of ROS-based Visual and Lidar Odometry for a Teleoperated Crawler-type Robot in indoor environment”, in Proc. Int. Conf. on Informatics in Control, Automation and Robotics (ICINCO), Madrid, Spain, 2017. 11. Magid, E., Tsubouchi, T.: Static balance for rescue robot navigation: discretizing rotationalmotion within random step environment. In: International Conference on Simulation, Model-ing, and Programming for Autonomous Robots, pp. 423–435. Springer, Berlin (2010) . 12. J. Engel, J. Stuckler and D. Cremers, "Large-scale direct slam with stereo cameras", Proc. IEEE/RSJ Intelligent Robots and Systems (IROS), pp. 1935-1942, 2015. 13. A.J. Davison, I.D. Reid, N.D. Molton and O. Stasse, "MonoSLAM: Real-time single camera SLAM", IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 29, no. 6, pp. 1052-1067, 2007. 14. 14.W. Hess, D. Kohler, H. Rapp and D. Andor, "Real-Time Loop Closure in 2D LIDAR SLAM", IEEE Int. Conf. on Robotics and Automation (ICRA), pp. 1271-1278, 2016 15. A. Buyval, I. Afanasyev and E. Magid, "Comparative analysis of ROS-based monocular slam methods for indoor navigation", Proc. SPIE 10341 of Int. Conf. on Machine Vision (ICMV), 2016.

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Author(s): G Manmadha Rao, Gade Chaitanya Prasad, K. Pavani, S Lakshaman Rao, B Prasanna Kumar Title of the Article: Estimation of Glucose Levels in Blood Sample using A Biosensor Abstract: This paper discusses about estimation of glucose concentration in blood using a Triple pole Complementary split ring resonator (TP-CSRR) antenna. Glucose concentration in blood is the direct indicator of Diabetes disease. The designed microstrip antenna operates in range of 2-5 Ghz and has a resonance frequency of 3.35 Ghz when simulated. When the antenna is excited, blood acts as dielectric load to it. Hence the glucose concentration of blood affects the resonant frequency and amplitude at resonant frequency of the s21 parameter of the antenna. Using this information, we can estimate the glucose concentration of blood sample. Debye model was used to model the blood. It is effective in detecting glucose concentration of Type-2 diabetes (70-120 mg/dL). The amplitude sensitivity is 0.58 dB(mg/ml) and frequency sensitivity is 583 Mhz/(mg/ml). Keywords: Complementary Spilt Ring Resonator, Debye Model, Permittivity, Hyperglycemia.. References: 1. “Non-Invasive Real-Time Monitoring of Glucose Level Using Novel Microwave Biosensor Based on Triple-Pole CSRR”. IEEE Transactions on Biomedical Circuits and Systems (Volume: 14, Issue: 6, Dec. 2020). 2. WHO, “WHO World Health Day 2016_WHO calls for global action to halt rise in and improve care for people with diabetes.pdf,” World Health Org., Geneva, Switzerland, Tech. Rep., 2016. 3. “Microwave-Based Noninvasive Concentration Measurements for Biomedical Applications”. IEEE Transactions on Microwave Theory and Techniques (Volume: 61, Issue: 5, May 2013) 4. “Noninvasive blood glucose measurement using microwave resonators” IEEE trans. Antennas propag., vol. 51, no. 10, pp. 2572–2581, oct. 2003. 5. “Design and analysis of split ring resonator based microstrip patch antenna for x-band applications”. ICTACT journal on microelectronics, January 2019, volume: 04.

Author(s): F. Ajamah, P. Tsafack, E. Tanyi, A. Cheukem, B. Ducharne Title of the Article: An Assessment of Hydropower Potential for Electrical Energy Harvesting in Water Distribution Network in Buea-Cameroon Abstract: Significant amount of energy is consumed in water supply systems resulting in reduced sustainability of these systems. Measures to reduce their energy demand are strongly needed. In this study, an estimation of the intrinsic hydro energy potential of the water supply system of a Cameroon municipality was made in order to propose an energy- potential map useful to identify the most interesting sites where excess energy in the network can be harvested to improve the energy efficiency of the network. A geodatabase to store network data was developed using Geographic Information Systems. The shapefiles resource data were explored and the hydraulic simulator EPANET software was used to create a model. Calculations were performed to determine the energy recovery values at different locations in the network. The resulting digital map presented 18 candidate sites which show a total annual energy potential of 635 MWh, realizable at capacity factor and efficiency of 41 % and 65 % respectively. This potential can offset the energy footprint of the network by about 34 % while 127 tons of carbon-dioxide emission reductions are achieved. The results of this investigation highlight that development of renewable energy resource on water supply network infrastructure is an innovative technology that can contribute significantly to improve the energy efficiency, economic and environmental sustainability of the water supply system. Keywords: Water supply system, Hydro Energy Potential, Energy potential map, Carbon dioxide, Energy efficiency, Sustainability. References: 1. United nations,Transforming our world,the 2030 agenda for sustainable development,2015.https://sustainabledevelopment.un.org/post2015/transformingourworld 2. United Nations World water Assessment programme,Nature-Based Solutions for water,2018.https://www.unwater.org/publications/world-water-development-report-2018/ 3. International Energy Agency,Water energy nexus,2016. https://www.iea.org/topics/energy-and-water 4. Irene Fernández García,David Ferras,Aonghus Mc Nabola, Potential of Energy Recovery and Water Saving Using Micro-Hydropower in Rural Water Distribution,J. Water Resour. Plann. Manage.45(3)(2019)05019001.https://DOI: 10.1061/(ASCE)WR.1943-5452.0001045

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5. Baba Adamu, Balgah Sounders N., Pamboudam Nfondoum C,Patterns of pipe-borne water supply in Buea Municipality:Challenges and way forward, International Journal of English literature and Social Sciences (IJELS) 4(3)(May-June 2019)718-731.https://dx.doi.org/10.22161/ijels.4.3.25 6. Innocent Basupia , Zoran Kapelana,David Butlera ,Reducing life-cycle carbon footprint in the (re)design of water distribution systems using water demand management interventions,Urban water journal 11(2)(2014) 91-107 .https://doi.org/10.1080/1573062X.2012.750374 7. G. Venkatesh, Helge Brattebø, Sveinung Sægrov, Kourosh Behzadian,Zoran Kapelan, Metabolism-modelling approaches to long term sustainability assessment of urban water services,Urban Water Journal 14(1) (2015)11- 22.http://dx.doi.org/10.1080/1573062X.2015.1057184 8. Kelly Twomey Sanders, The energy trade-offs of adapting to a water-scarce future: case study of Los Angeles, International Journal of Water Resources Development.32(3)(2015)362-378 http://dx.doi.org/10.1080/07900627.2015.1095079 9. Armando Carravetta,Oreste Fecarotta, Marco Sinagra,Tullio Tucciarelli,Cost-Benefit Analysis for Hydropower Production in Water Distribution Networks by a Pump as Turbine. Journal of Water Resources Planning and Management 140(6)(2014)04014002. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000384 10. N. Fontana,M.,Giugni,L. Glielmo, G. Marini,R. Zollo, Operation of a Prototype for Real Time Control of Pressure and Hydropower Generation in Water Distribution Networks, Water Resources Management 33(2018)697-712. https://doi.org/10.1007/s11269-018-2131-1 11. N. Fontana, M.ASCE,M. Giugni,L. Glielmo,G. Marini, M.ASCE,R. Zollo5, Real-Time Control of pressure for Leakage Reduction in Water Distribution Network:Field Experiments, J. Water Resour. Plann. Manage.144(3)( 2018) 04017096.https://doi.org/10.1061/(ASCE)WR.1943-5452.0000887 12. Javier Almandoz,Enrique Cabrera, M.ASCE,Francisco Arregui,Enrique Cabrera Jr.,Ricardo Cobacho, Leakage Assessment through Water Distribution Network Simulation, J. Water Resour. Plann. Manage. 131(2005)458-466. https://doi.org/10.1061/(ASCE)0733-9496(2005)131:6(458) 13. Tucciarelli, T., Criminisi, A.,Termini, D. Leak Analysis in Pipeline Systems by Means of Optimal Valve Regulation. Journal of Hydraulic Engineering 125(3) (1999)277–285. doi:10.1061/(asce)0733- 9429(1999)125:3(277) 14. Walski, T., Bezts, W., Posluszny, E. T., Weir, M., & Whitman, B. E.,Modeling leakage reduction through pressure control. Journal - American Water Works Association 98(4) (2006)147–155. doi:10.1002/j.1551- 8833.2006.tb07642.x 15. Prescott, S. L., Ulanicki, B. Improved Control of Pressure Reducing Valves in Water Distribution Networks. Journal of Hydraulic Engineering 134(1) (2008)56–65. doi:10.1061/(asce)0733-9429(2008)134:1(56) 16. Irene Fernández García, Aonghus Mc Nabola, Maximizing Hydropower Generation in Gravity Water Distribution Networks: Determining the Optimal Location and Number of Pumps as Turbines, J. Water Resour. Plann. Manage.,146(1)(2020)04019066.https://doi.org/10.1061/(ASCE)WR.1943-5452.0001152 17. Majidi, M.,Etezadi-Amoli M,Recapturing wasted energy in water pressure reducing valves via in-conduit hydropower generators, Measurement 123(2018)62–68. doi:10.1016/j.measurement.2018.03.061 18. Lucy Corcoran,Aonghus McNabola,Paul Coughlan, Energy Recovery Potential of the Dublin Region Water Supply Network, World congress on water, climate and Energy, Dublin, 2012. https://scholar.google.com/citations?user=JRCn_u8AAAAJ&hl=en 19. Armando Carravetta,Oreste Fecarotta, Marco Sinagra,Tullio Tucciarelli, Cost-Benefit Analysis for Hydropower Production in Water Distribution Networks by a Pump as Turbine, J. Water Resour. Plann. Manage. 140(2014)04014002. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000384 20. Gallagher,I.M. Harris, A.J. Packwood, A. McNabola, A.P. Williams, A strategic assessment of micro-hydropower in the UK and Irish water industry: Identifying technical and economic constraints, Renewable Energy 81(September 2015)808-815. http://dx.doi.org/10.1016/j.renene.2015.03.078 21. Justyna Czemiel Berndtsson,Kenji Jinno,Sustainability of urban water system: examples from Fukuoka, Japan, Water Policy10(2008)501-513. https://doi.org/doi: 10.2166/wp.2008.064 22. ]Martin Rygaard, Philip J. Binning, Hans-Jørgen Albrechtsen,Increasing urban water self-sufficiency: New era, new challenges, Journal of Environmental Management 92 (2011) 185-194. https://doi.org/10.1016/j.jenvman.2010.09.009 23. Helena M.Ramos,Kaloyan N.Kenov,FilipeVieira,Environmentally friendly hybrid solutions to improve the energy and hydraulic efficiency in water supply systems,Energy for Sustainable Development 15(4)(December 2011)436- 442. https://doi.org/10.1016/j.esd.2011.07.009 24. .Theophilus Gaiusobaseki,Hydropower opportunities in the water industry,international journal of environmental sciences 1(3)(2010) 392-402.http://www.ipublishing.co.in/jesvol1no12010/EIJES1036.pdf 25. A.McNabola,P.Coughlan,A.P.Williams,Energy Recovery Potential of the Dublin Region Water Supply Network RE&PQJ,1(9)(May 2011)1123-1124. https://doi.org/10.24084/repqj09.569

Souvenior of Volume – 10 Issue – 7 May 2021 Website: www.ijitee.org DOI: 10.35940/ijitee 27

26. M.Patelis,V.Kanakoudis,K.Gonelas, Combining pressure management and energy recovery benefits in a water distribution system installing PATs, Journal of Water Supply: Research and Technology—AQUA (8 June 2017).https://doi: 10.2166/aqua.2017.018 27. Armando Carravetta,Giuseppe de, Giudice,Oreste Fecarotta,Helena M. Ramos, PAT Design Strategy for Energy Recovery in Water Distribution Networks by Electrical Regulation, Energies 6(1)(2013)411- 424.https://doi.org/10.3390/en6010411 28. Irene Samora,Pedro Manso,MárioJ.Franca, AntonJ.Schleiss,Helena M.Ram,Energy Recovery Using Micro- Hydropower Technology in Water Supply Systems:The Case Study of the City of Fribourg. Water8(8)(2016)344. https://doi.org/10.3390/w8080344 29. A Lambert, M Fantozzi, J Thornton,Practical approaches to modelling leakage and pressure management in distribution systems-Progress since 2005, CCWI 12th International Conference on Computing and control for the water Industry, 2013.https://www.semanticscholar.org/paper/Practical-approaches-to-modeling-leakage-and-in- Lambert-Fantozzi/f74b9452b8a0457af3c466867ea63b00df6d37dd 30. S.Fellini,R.Vesipa,F.Boano,L.Ridolfi, Multipurpose Design of the Flow-Control System of a Steep Water Main, J.Water Resour. Plann. Manage.144(2)(2018)05017018. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000867 31. Miguel Crespo Chacón,Juan Antonio Rodríguez‐Díaz,Jorge García Morillo, John Gallagher,Paul Coughlan,Aonghus McNabola, Potential Energy Recovery Using Micro‐Hydropower Technology in Irrigation Networks: Real‐World Case Studies in the South of Spain,www.mdpi.com/journal/proceedings 2(679)2018.doi:10.3390/proceedings2110679 32. J. D. D. F. R. A. P. P. D. C.,Laura Monteiro,Assessment of the potential for energy recovery in water trunk mains, Researchgate.net/publication,2016 .https://www.researchgate.net/publication/316657735 33. Gideon Johannes,Bonthuysa,Marco van Dijkb,Giovanna Cavazzinic,Leveraging water infrastructure asset management for energy recovery and leakage reduction,Sustainable Cities and Society 46(2019)101434. https://doi.org/10.1016/j.scs.2019.101434 34. I Loots, M van Dijk, S J van Vuuren, J N Bhagwan, A Kurtz, Conduit-hydropower potential in the City of Tshwane water distribution system: A discussion of potential applications, financial and other benefits,Journal of the South African Institution of Civil Engineering 56(3)(October 2014)2–13. http://www.scielo.org.za/pdf/jsaice/v56n3/01.pdf 35. Aonghus McNabolaa,Paul Coughlanb,Lucy Corcorana,Christine Powera,A.Prysor Williamsc,Ian Harrisc,John Gallagherc,David Stylesc,Energy recovery in the water industry using micro-hydropower: an opportunity to improve sustainability,Water Policy 16 (2014)168–183.doi:10.2166/wp.2013.164 36. Ilker T.Telci,Mustafa M.Aral,Optimal Energy Recovery from Water Distribution Systems Using Smart Operation Scheduling,Water10(10)(2018)1464 .https://doi.org/10.3390/w10101464 37. BUCREP, Troisieme Recensements General de Population et de l’Habitat. Rapport de Presentation des Resultats definitifs.Bureau Central des Recensements et des Etudes de population,2005. http://www.bucrep.cm/index.php/fr/recensements/3eme-rgph/20-3eme-rgph/presentation (last aceessed:22-05- 2020) 38. Buea-Council, Gestion de l’eau de la commune BUEA.Rapport de l'amission ECT1 de Juin 2008, Buea,2008. 39. Kedia, A.C. Remote Sensing Unit, Faculty of Science, Department of Geology, University of Buea,2015. 40. A. McNabola,P.Coughlan,A.P.Williams,Energy recovery in the water industry: an assessment of the potential of micro-hydropower,Water and Environment Journal 28 (2014) 294–304. https://doi.org/10.1111/wej.12046 41. N. Gray,2010.Water Technology:An introduction for Environmental Scientists and Engineers, 3rd edn éd., Oxford: ButterworthHeinemann. https://b-ok.cc/book/1191384/404b73 42. S. Kucukali,Water Supply Lines as a Source of Small Hydropower in Turkey: A Case Study in Edremit,World Renewable Energy Congress-Sweden Linköping;Sweden.04(057)(May 2011)8- 13.DOI:10.3384/ecp110571400 43. Jure Margeta,A framework for application of renewable energy in urban water system,Scientific paper / Znanstveni rad 16(2018)1-10. https://doi.org/10.13167/2018.16.1 44. S. P. Archana Tamrakari,Hydro Power Opportunity in the Sewage Waste Water,American International Journal of Research(2015) 179 https://www.semanticscholar.org/paper/Hydro-Power-Opportunity-in-the-Sewage-Waste-Water-Tamrakar- Pandey/31480ee672374946e4761109f41d6298836b56ce

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45. Belay,Amdework,Hydraulic Network Modeling and upgrading of Legedadi subsystem water supply, Addis Ababa,March 2012 http://localhost:80/xmlui/handle/123456789/622 46. N.Fontana,M. Giugni,L Glielmo,G.Marini,R.Zollo,Operation of a Prototype for Real Time Control of Pressure and Hydropower Generation in Water Distribution Networks,Water Resources Management,10 November 2018. https://doi.org/10.1007/s11269-018-2131-1 47. T.Tucciarelli,A.Criminisi,D.Termini,leak analysis in pipeline systems by means of optimal valve regulation, J.Hydraul.Eng.125(1999)277-285. https://doi.org/10.1061/(ASCE)0733- 9429(1999)125:3(277) 48. Simon L. Prescott,Bogumil Ulanicki,Improved Control of Pressure Reducing Valves in Water Distribution Networks,J. Hydraul. Eng.134(2008)56-65. https://doi.org/10.1061/(ASCE)0733-9429(2008)134:1(56) 49. Tom walski, William bezts, Emanuel t. posluszny, Mark howard weir, modeling leakage reduction through pressure control,journal - american water works association 98(4)(April 2006)147- 155.doi:10.1002/j.1551-8833.2006.tb07642.x 50. Irene Fernández García, Daniele Novara,Aonghus Mc Nabola,A Model for Selecting the Most Cost-Effective Pressure Control Device for More Sustainable Water Supply Networks Water11(2019)1297 doi:10.3390/w11061297 51. A.McNabola, P.Coughlan, A.P.Williams,The Technical & Economic Feasibility of Energy Recovery in Water Supply Networks,RE&PQJ.1(9)(May 2011) https://doi.org/10.24084/repqj09.569 52. Irene Fernández García,Aonghus Mc Nabola,Maximizing Hydropower Generation in Gravity Water water Distribution Networks:Determining the optimal Location and Number of Pumps as Turbines, J. Water Resour. Plann. Manage.146(1)( 2020) 04019066. https://doi.org/10.1061/(ASCE)WR.1943-5452.0001152 53. Laura Monteiro,João Delgado,David Figueiredo,Rita Alves,Pedro Póvoa, Dídia Covas,Assessment of the potential for energy recovery in water trunk mains,Conference Paper,November 2016. https://www.researchgate.net/publication/316657735 54. Javier Almandoz,Enrique Cabrera, M.ASCE,Francisco Arregui,Enrique Cabrera Jr.,Ricardo Cobacho,Leakage Assessment through Water Distribution Network Simulation,J.Water Resour. Plann.Manage.131(2005)458-466 https://doi.org/10.1061/(ASCE)0733-9496(2005)131:6(458) 55. Jude .N. Kimengsi,Amawa Sani G,Water Rationing in the Buea Municipality: Patterns,Effects and Coping Strategies,African Journal of Social Sciences 6(3)2015.https://www.researchgate.net/publication/289184428 56. I.Kougias, T.Patsialis,A.Zafirakou,N.Theodossiou,Exploring the potential of energy recovery using micro hydropower systems in water supply systems,Water Utility Journal 7(2014)25-33. https://www.ewra.net/wuj/pdf/WUJ_2014_07_03.pdf

Author(s): Manasi Bansode, Siddhi Pardeshi, Suyasha Ovhal, Pranali Shinde, Anandkumar Birajdar Title of the Article: Fake Review Prediction and Review Analysis Abstract: Online reviews can be deceptive or manipulative evaluations of services and products which are often carried out deliberately for manipulation strategy to mislead the readers. Identifying such reviews is an important but challenging problem. There are even some associations in the merchandise industry who are hiring professionals to write fake reviews so that they can promote their products or defame rivals products. Hence we aim to develop a method which will detect fake reviews and remove them. The proposed method classifies users' reviews into suspicious, fake, positive and negative categories by phase-wise processing. In this paper, we are processing hotel reviews by using different data mining techniques. Moreover the reviews obtained from users are being classified into positive or negative which can be used by a consumer to select a product. Organizations providing services can monitor customer sentiments by scrutinizing and understanding what the customers are thinking about products through reviews. This can help buyers to purchase valuable products and spend their money on quality products. Also in our model end users see star ratings based on reviews for each hotel. Keywords: Countvectorizer, Deceptive reviews, K- Nearest Neighbor (KNN), Logistic Regression, Multinomial Naïve Bayes,Random Forest Classifier, Support Vector Machine (SVM), Stochastic Gradient Descent (SGD), Stemming.

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References: 1. Random Forest Approach for Sentiment Analysis in Indonesian Language,Muhammad Ali Fauzi,Brawijaya University,October 2018. 2. Fake Review Detection using Data Mining, Md Forhad Hossain, Missouri State University, [email protected], Summer 2019 . 3. An Empirical Study on Detecting Fake Reviews Using Machine Learning Techniques, Elshrif Elmurngi, Abdelouahed Gherbi,2017. 4. Fraud Detection in Online Reviews using Machine Learning Techniques,Kolli Shivagangadhar, Sagar H, Sohan Sathyan, Vanipriya C.H. , 2015. 5. Random Forest Approach for Sentiment Analysis in Indonesian Language Article in Indonesian Journal of Electrical Engineering and Computer Science, M. Ali Fauzi Faculty of Computer Science, Brawijaya University, Malang, Indonesia· October 2018 6. Fake Review Detection using Opinion Mining, Dhairya Patel, Aishwarya Kapoor, Sameet Sonawane, 2018. 7. Classifiers Ensemble for Fake Review Detection,Harish Baraithiya, R. K. Pateriya(2019) 8. [8] Design and Implementation of Web Application for Review Classification,Siddhi Pardeshi, Suyasha Ovhal, Pranali Shinde,Manasi Bansode, Anandkumar Birajdar,2021 9. https://www.kaggle.com/jiashenliu/515k-hotel-reviews-data-in-europe 10. https://www.kaggle.com/rtatman/deceptive-opinion-spam-corpus https://www.kaggle.com/naveedhn/yelp-review-with-sentiments-and-features

Author(s): Deepa Sonal, Dina Nath Pandit, Md. Alimul Haque Title of the Article: An Iot Based Model to Defend Covid-19 Outbreak Abstract: Covid-19 is a pandemic that has swept the globe since the end of 2019. Scientists are working around the clock to create a vaccine to combat the Coronavirus. People are now monitored using smart-phone and web-based software. The Internet of Things (IoT) refers to items that have sensors embedded in them. To check the spread of Covid-19, the IoT can be used. Social Distancing breaks the chain of spreading. It has an effect not only on healthcare spending but also on the speed at which infected patients recover. IoT can be used efficiently for maintaining social distance. As a result, the current research aims to define, analyze and highlight the inclusive applications of the IoT philosophy by providing a perspective roadmap to combat the COVID-19 pandemic by maintaining social distancing. Reviewing the literature, a real-time detecting and alerting method for the COVID-19 condition monitoring is proposed. Keywords: Internet of Things, Monitoring, Alerting, COVID-19 outbreak, Social Distancing. References: 1. Ajit Kumar, S. (2020). Applications of IoT in Agricultural System. International Journal of Agricultural Science and Food Technology, 6(1), 041–045. https://doi.org/10.17352/2455-815x.000053 2. Haque, M. A., Haque, S., Sonal, D., Kumar, K., & Shakeb, E. (2021). Security Enhancement for IoT Enabled Agriculture. Materials Today: Proceedings. https://doi.org/10.1016/j.matpr.2020.12.452 3. Haque, M. A., Sonal, D., Haque, S., Nezami, M. M., & Kumar, K. (2020). An IoT-Based Model for Defending Against the Novel Coronavirus (COVID-19) Outbreak. Solid State Technology, 592–600. 4. Allam, Z., & Jones, D. S. (2020). On the Coronavirus (COVID-19) Outbreak and the Smart City Network: Universal Data Sharing Standards Coupled with Artificial Intelligence (AI) to Benefit Urban Health Monitoring and Management Healthcare. https://doi.org/10.3390/healthcare8010046 5. Alshraideh, H., Otoom, M., Al-Araida, A., Bawaneh, H., & Bravo, J. (2015). A Web Based Cardiovascular Disease Detection System. Journal of Medical Systems. https://doi.org/10.1007/s10916- 015-0290-7 6. Cvjetkovic, V. M., & Matijevic, M. (2016). Overview of Architectures with Arduino Boards as Building Blocks for Data Acquisition and Control Systems. International Journal of Online Engineering (IJOE), 12(07), 10. https://doi.org/10.3991/ijoe.v12i07.5818 7. Darwish, A., Hassanien, A. E., Elhoseny, M., Sangaiah, A. K., & Muhammad, K. (2019). The impact of the hybrid platform of internet of things and cloud computing on healthcare systems: opportunities, challenges, and open problems. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-017-0659- 1 8. Din, S., & Paul, A. (2019). Smart health monitoring and management system: Toward autonomous wearable sensing for Internet of Things using big data analytics. Future Generation Computer Systems. https://doi.org/10.1016/j.future.2017.12.059 9. Fatima, S. A., Hussain, N., Balouch, A., Rustam, I., Saleem, M., & Asif, M. (2020). IoT

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enabled Smart Monitoring of Coronavirus empowered with Fuzzy Inference System. International Journal of Advance Research, Ideas and Innovations in Technology. Hamidi, H. (2019). An approach to develop the smart health using Internet of Things and authentication based on biometric technology. Future Generation Computer Systems https://doi.org/10.1016/j.future.2018.09.024 10. Hlaing, Nopparatjamjomras, T. R., & Nopparatjamjomras, S. (2018). Digital technology for preventative health care in Myanmar. Digital Medicine. https://doi.org/10.4103/DIGM.DIGM_25_18 11. Kelly, M. P. (2016). Digital Technologies and Disease Prevention. In American Journal of Preventive Medicine. https://doi.org/10.1016/j.amepre.2016.06.012 12. Luigi A., Antonio I., G. M. (2010). The Internet of Things: A survey. Science Direct Journal of Computer Networks, Volume 54, 2787–2805. 13. Maghdid, H. S., Ghafoor, K. Z., Sadiq, A. S., Curran, K., Rawat, D. B., & Rabie, K. (2020). A Novel AI-enabled Framework to Diagnose Coronavirus COVID 19 using Smartphone Embedded Sensors: Design Study. http://arxiv.org/abs/2003.07434 14. Mohammed, M. N., Syamsudin, H., Al-Zubaidi, S., Sairah, A. K., Ramli, R., & Yusuf, E. (2020). “Novel covid-19 detection and diagnosis system using iot based smart helmet” ,International Journal of Psychosocial Rehabilitation. https://doi.org/10.37200/IJPR/V24I7/PR270221 15. Mônica Vitalino de Almeida, S., Cleberson Santos Soares, J., Lima dos Santos, K., Emanuel Ferreira Alves, J., Galdino Ribeiro, A., Trindade Tenório Jacob, Í., Juliane da Silva Ferreira, C., Celerino dos Santos, J., Ferreira de Oliveira, J., Bezerra de Carvalho Junior, L., & do Carmo Alves de Lima, M. (2020). COVID-19 therapy: what weapons do we bring into battle? Bioorganic & Medicinal Chemistry. https://doi.org/https://doi.org/10.1016/j.bmc.2020.115757 16. Otoom, M., Alshraideh, H., Almasaeid, H. M., López-De-Ipiña, D., & Bravo, J. (2015). Real-Time Statistical Modeling of Blood Sugar. Journal of Medical Systems. https://doi.org/10.1007/s10916-015- 0301-8 17. Rath, M., & Pattanayak, B. (2019). “Technological improvement in modern health care applications using Internet of Things (IoT) and proposal of novel health care approach.” International Journal of Human Rights in Healthcare. https://doi.org/10.1108/IJHRH-01-2018-0007 18. Srinivasa Rao, A. S. R., & Vazquez, J. A. (2020). Identification of COVID-19 can be quicker through artificial intelligence framework using a mobile phone–based survey when cities and towns are under quarantine. Infection Control & Hospital Epidemiology, 41(7), 826–830. https://doi.org/10.1017/ice.2020.61 19. T.T. Nguyen. (2020). Artificial Intelligence in the Battle against Coronavirus (COVID-19): A Survey and Future Research Directions. https://doi.org/10.13140 20. Ting, D. S. W., Carin, L., Dzau, V., & Wong, T. Y. (2020). Digital technology and COVID-19. In Nature Medicine. https://doi.org/10.1038/s41591-020-0824-5 21. Ajit Kumar, S. (2020). Applications of IoT in Agricultural System. International Journal of Agricultural Science and Food Technology, 6(1), 041–045. https://doi.org/10.17352/2455-815x.000053 22. Bleibtreu, A., Bertine, M., Bertin, C., Houhou-Fidouh, N., & Visseaux, B. (2020). Focus on Middle East respiratory syndrome coronavirus (MERS-CoV). Medecine et Maladies Infectieuses, 50(3), 243–251. https://doi.org/10.1016/j.medmal.2019.10.004 23. Cascella, M., Rajnik, M., Cuomo, A., Dulebohn, S. C., & Di Napoli, R. (2020). Features, Evaluation and Treatment Coronavirus (COVID-19). In StatPearls. 24. Chamola, V., Hassija, V., Gupta, V., & Guizani, M. (n.d.). SPECIAL SECTION ON DEEP LEARNING ALGORITHMS FOR INTERNET OF MEDICAL THINGS A Comprehensive Review of the COVID-19 Pandemic and the Role of IoT, Drones, AI, Blockchain, and 5G in Managing Its Impact. https://doi.org/10.1109/ACCESS.2020.2992341 25. Coronavirus disease (COVID-19). (n.d.). Retrieved May 8, 2021, from https://www.who.int/emergencies/diseases/novel-coronavirus-2019 26. Haque, M. A., Sonal, D., Haque, S., Nezami, M. M., & Kumar, K. (2020). An IoT-Based Model for Defending Against the Novel Coronavirus (COVID-19) Outbreak. Solid State Technology, 592–600. 27. Lee, J. (n.d.). The Future of Service Post-COVID-19 Pandemic , Volume 1 Rapid Adoption of Digital (Vol. 1). 28. Nasajpour, M., Pouriyeh, S., Parizi, R. M., Dorodchi, M., Valero, M., & Arabnia, H. R. (2020). Internet of Things for Current COVID-19 and Future Pandemics: an Exploratory Study. Journal of Healthcare Informatics Research, 4(4), 325–364. https://doi.org/10.1007/s41666-020-00080-6

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Author(s): Amlan Das Title of the Article: Implementation of Industry 4.0 Revolution through Skill Development– A Blessing for Local for Vocal in Covid-19 Pandemic Abstract: We are amidst a noteworthy change with respect to the manner in which we make items, because of the digitization of assembling. This change is convincing to the point that it is being called Industry 4.0 to speak to the fourth insurgency that has happened in assembling. Industry 4.0 is flagging an adjustment in the conventional assembling scene. Otherwise called the Fourth Industrial Revolution, Industry 4.0 envelops three mechanical patterns driving this change: network, insight and adaptable robotization. Industry 4.0 portrays the developing pattern towards computerization and information trade in innovation and cycles inside the assembling business, including: The Internet of Things (IoT), The Industrial Internet of Things (IIoT), Cyber-physical Systems (CPS), Smart Manufacturing, Smart Factories, Cloud Computing, Additive Manufacturing, Big Data, Robotics, Cognitive Computing, Artificial Intelligence and Block chain and so forth. This mechanization makes an assembling framework whereby the machines in manufacturing plants are increased with remote network and sensors to screen and picture a whole creation cycle and settle on independent choices. In this paper we are worry about how aptitude and ability of human asset can be grown with the goal that we can conquer this pandemic circumstance effectively. Delicate abilities for taking care of these forthcoming new innovation inserted framework must be taken consideration and carefully instilled by human asset with the goal that simple smooth of efficiency just as hole crossing over of flexibly and request can be conceivable. Skill development should be considered as prioritizing factor for this. Keywords: Industry 4.0, cloud computing, cognitive computing, Cyber physical system, flexible automation., skill development References: 1. Crouch, C., Finegold, D., Sako, M. (2004). Are skills the answer? The political economy of skills creation in advanced industrial countries. Oxford, UK: Oxford University Press. 2. Cabral, C. and Dhar, R.L. (2019), "Skill development research in India: a systematic literature review and future research agenda", Benchmarking: An International Journal, Vol. 26 No. 7, pp. 2242-2266. 3. Cukier, W. (2019), "Disruptive processes and skills mismatches in the new economy: Theorizing social inclusion and innovation as solutions", Journal of Global Responsibility, Vol. 10 No. 3, pp. 211- 225. https://doi.org/10.1108/JGR-11-2018-0079 4. Babu, V. and Kinkhabwala, B. (2019), "Was an untapped “skilling” opportunity ignored? Integrating CSR initiatives to bridge the skilled manpower gap", Worldwide Hospitality and Tourism Themes, Vol. 11 No. 1, pp. 37-53. https://doi.org/10.1108/WHATT-10-2018-0059 5. The Fourth Industrial Revolution and the Libraries Delight Promise Udochukwu, Chidimma Agunwamba Examining the impact of industry 4.0 on academic libraries ISBN: 978-1-80043-657-2, eISBN: 978-1-80043-656- 5 6. Acioli, C., Scavarda, A. and Reis, A. (2021), "Applying Industry 4.0 technologies in the COVID–19 sustainable chains", International Journal of Productivity and Performance Management, Vol. ahead-of-print No. ahead-of- print. https://doi.org/10.1108/IJPPM-03-2020-0137 7. Choudhury, S. (2020), "Will the Pandemic Bring Industrial Revolution 4.0 Closer to Home?", Kumar, P., Agrawal, A. and Budhwar, P. (Ed.) Human & Technological Resource Management (HTRM): New Insights into Revolution 4.0, Emerald Publishing Limited, Bingley, pp. 157-166. 8. Will skills save us? Rethinking the relationships between vocational education, skills development policies, and social policy in South Africa. Volume 32, Issue 5, September 2012 9. https://doi.org/10.1016/j.ijedudev.2012.01.001 10. Action-Based Cognitive Remediation: Pairing Cognitive Training With Skill Development and CBT Principles Christopher Bowie, Maya Gupta, Michael Grossman, Michael Best, Katherine Holshausen in Schizophrenia BulletinSchizophrenia Bulletin, Volume 43, Issue suppl_1, March 2017, Page S109, https://doi.org/10.1093/schbul/sbx021.293 11. Skill development and business education, U.Somashekhar, DOI:10.36106/ijsr Can a Skill be Measured or Assessed? Level Skills Development Approach to Skill Assessment -Yuliya ShtaltovnaGJSD Vol. 1 No. 1 (2021) 12. Strategic role of HRD in employee skill development: An employer perspective Khalid Rasheed Memon Journal of Human Resource Management 2014; 2(1): 27-32 Published online April 10, 2014 (http://www.sciencepublishinggroup.com/j/jhrm) doi: 10.11648/j.jhrm.20140201.15

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Author(s): Dipak Kumar Patra, Sukumar Mondal, Prakash Mukherjee Title of the Article: Grammatical Fireworks Algorithm Method for Breast Lesion Segmentation in DCE-MR Images Abstract: For cancer detection and tissue characterization, DCE-MRI segmentation and lesion detection is a critical image analysis task. To segment breast MR images for lesion detection, a hard-clustering technique with Grammatical Fireworks algorithm (GFWA) is proposed in this paper. GFWA is a Swarm Programming (SP) system for automatically generating computer programs in any language. GFWA is used to create the cluster core for clustering the breast MR images in this article. The presence of noise and intensity inhomogeneities in MR images complicates the segmentation process. As a result, the MR images are denoised at the start, and strength inhomogeneities are corrected in the preprocessing stage. The proposed GFWA-based clustering technique is used to segment the preprocessed MR images. Finally, from the segmented images, the lesions are removed. The proposed approach is tested on 5 patients’ 25 DCE- MRI slices. The proposed method’s experimental findings are compared to those of the Grammatical Swarm (GS)- based clustering technique and the K-means algorithm. The proposed method outperforms other approaches in terms of both quantitative and qualitative results. Keywords: Breast Cancer, DCE-MRI, Clustering, Warm Programming, Grammatical Fireworks Algorithm. References: 1. Agner, S.C., Soman, S., Libfeld, E., Mcdonald, M., Thomas, K., Englander, S., Rosen, M.A., Chin, D., Nosher, J., Madabhushi, A.: Textural kinetics: A novel dynamic contrast- enhanced(dce)-mri feature for breast lesion classification. Journal of Digital Imaging 24, 446–63 (2011) 2. Arjmand, A., Meshgini, S., Afrouzian, R., Farzamnia, A.: Breast tumor segmentation using k-means clustering and cuckoo search optimization. IEEE (2019) 3. Azmi, R., Norozi, N.: A new markov random field segmentation method for breast lesion segmentation in mr images. Journal of Medical Signals Sensors 1, 156–164 (2011) 4. Balafar, M., Ramli, A., Mashohor, S.: A new method for mr grayscale inhomogeneity correction. Artif. Intell. Rev. 34, 195–204 (2010) 5. Bohare, M., Cheeran, A., Sarode, V.: Analysis of breast mri images using wavelets for detection of cancer. IJCA Special Issue on Electronics, Information and Communication Engineering 4, 1–3 (2011) 6. Boukerroui, D., Basset, O., Guerin, N., Baskurt, A.: Multiresolution texture based adaptive clustering algorithm for breast lesion segmentation. European Journal of Ultrasound 8, 135–144 (1998) 7. Bray, F., Ren, J.S., Masuyer, E., Ferlay, J.: Global estimates of cancer prevalence for 27 sites in the adult population in 2008. Int J Cancer 132(5), 1133—-45 (2013). DOI 10.1002/ijc.27711 8. Chang, Y.C., Huang, Y.H., Huang, C.S., Chang, P.K., Chen, J.H., Chang, R.F.: Classification of breast mass lesions using model-based analysis of the characteristic kinetic curve derived from fuzzy c-means clustering. Magnetic Resonance Imaging 30(3), 312–322 (2012) 9. Chen, W., Giger, M.L., Bick, U.: A fuzzy c-means (fcm)-based approach for computerized segmentation of breast lesions in dynamic contrast-enhanced mr images. European Journal of Ultrasound 1, 63–72 (2006) 10. Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., Tarbox, L., Prior, F.: The cancer imaging archive (tcia): Maintaining and operating a public information repository 11. Cui, Y., Tan, Y., Zhao, B., Liberman, L., Parbhu, R., Kaplan, J., Theodoulou, M., Hudis, C., Schwartz, L.: Malignant lesion segmentation in contrast-enhanced breast mr images based on the marker-controlled watershed. Medical physics 36, 4359–69 (2009) 12. Davies, D., Bouldin, D.: A cluster separation measure. IEEE Trans. Pattern Anal. Mach. Intell. 1(2), 224–227 (1979) 13. Derrac, J., Garc´la, S., Molina, D., Herrera, F.: A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm and Evolutionary Computation 1, 3–18 (2011) 14. Ferlay, J., Soerjomataram, I., Ervik, M.: Globocan 2012 v1.0, cancer incidence and mortality worldwide: Iarc cancerbase. 11. GLOBOCAN (2013) 15. Ferlay, J., Soerjomataram, I., Ervik, M., Dikshit, R., Eser, S., Mathers, C., Rebelo, M., Parkin, D., Forman, D., Bray, F.: Globocan 2012 v1.0, cancer incidence and mortality worldwide: Iarc cancer base. no. 11 [internet]. In: F. Lyon (ed.) International Agency for Research on Cancer 16. Hamy, V., Dikaios, N., Punwani, S., Melbourne, A., Latifoltojar, A., Makanyanga, J., Chouhan, M., Helbren, E., Menys, A., Taylor, S., Atkinson, D.: Respiratory motion correction in dynamic mri using robust data decomposition registration - application to dce-mri. Medical Image Analysis 18, 301–313 (2014)

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17. Hauth, E., Stockamp, C., Maderwald, S., Mu¨hler, A., Kimmig, R., Jaeger, H., Barkhausen, J., Forsting, M.: Evaluation of the three-time-point method for diagnosis of breast lesions in contrast-enhanced mr mammography. Clinic Imaging 30, 160–165 (2006) 18. Jayender, J., Gombos, E., Chikarmane, S., Dabydeen, D., Jolesz, F., Vosburgh, K.: Sta- tistical learning algorithm for in situ and invasive breast carcinoma segmentation. Com- puterized Medical Imaging and Graphics 37, 281– 292 (2013) 19. Khalvati, F., Ortiz, C.G., Balasingham, S., Martel, A.L.: Automated segmentation of breast in 3d mr images using a robust atlas. IEEE Transactions on medical imaging 34, 116–125 (2015) 20. Lingle, W., Erickson, B.J., Zuley, M.L., Jarosz, R., Bonaccio, E., Filippini, J., N., G.: Radiology data from the cancer genome atlas breast invasive carcinoma collection [tcga- brca] (2007) 21. MacQueen, J.: Some methods for classification and analysis of multivariate observations. p. 281–297. University of California Press (1967) 22. McClymont, D., Mehnert, A., Trakic, A., Kennedy, D., Crozier, S.: Fully automatic lesion segmentation in breast mri using mean-shift and graph-cuts on a region adjacency graph. J. Magn. Reson. Imaging 39, 795–804 (2014) 23. Milenkovi´c, J., Hertl, K., Koˇsir, A., Zˇibert, J., Tasiˇc, J.: Characterization of spatiotemporal changes for the classification of dynamic contrast-enhanced magnetic-resonance breast lesions. Artificial Intelligence in Medicine 58(2), 101–114 (2013) 24. Mlejnek, M., Ermes, P., Vilanova, A., Rijt, R., Bosch, H., Gerritsen, F., Gr¨oller, E.: Application-oriented extensions of profile flags (2006) 25. Mohan, J., Krishnavenib, V., Guo, Y.: A survey on the magnetic resonance image denoising methods. Biomedical Signal Processing and Control 9, 56–69 (2014) 26. Nie, K., Chen, J., Yu, H., Chu, Y., Nalcioglu, O., Su, M.: Quantitative analysis of lesion morphology and texture features for diagnostic prediction in breast mri. Acad. Radiol. 15, 1513–1525 (2008) 27. Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffussion. IEEE Trans. Pattern Anal. Mach. Intell. 12(7), 629–639 (1990) 28. Piantadosi, G., Marrone, S., Galli, A., Sansone, M., Sansone, C.: Dce-mri breast lesions segmentation with a 3tp u- net deep convolutional neural network. IEEE (2019) 29. Shetty, M.: Breast Cancer Screening and Diagnosis - A Synopsis. Springer Science + Business Media, New York (2015) 30. Si, T.: Grammatical evolution using fireworks algorithm. In: M. Pant, K. Deep, J. Bansal, A. Nagar, K. Das (eds.) Proceedings of Fifth International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol. 436. Singapore (2016) 31. Si, T., De, A., Bhattacharjee, A.: Brain mri segmentation for tumor detection using grammatical swarm based clustering algorithm. In: Proc. of IEEE International Conference on Circuits, Power and Computing Technologies (ICCPCT-2014), vol. 436. Nagercoil, India (2014) 32. Si, T., De, A., Bhattacharjee, A.: Grammatical swarm based segmentation methodology for lesion segmentation in brain mri. International Journal of Computer Applications 121(4), 1–8 (2015) 33. Sim, K., Chia, F., Nia, M., Tso, C., Chong, A., Abbas, S., Chong, S.: Breast cancer detection from mr images through an auto-probing discrete fourier transform system. Computers in Biology and Medicine 49, 46—59 (2014) 34. Tan, Y., Zhu, Y.: Fireworks algorithm for optimization. In: Y. Tan, Y. ShiKay, C. Tan (eds.) ICSI 2010, Part I, LNCS, vol. 6145, p. 355–364. Springer, Heidelberg (2010) 35. Tuncay, A.H., Akduman, I.: Realistic microwave breast models through t1-weighted 3-d mri data. IEEE Transactions on Biomedical Engineering 62(796305), 688–698 (2015) 36. Wang, Y., Morrell, G., Heibrun, M., Payne, A., Parker, D.: 3d multi-parametric breast mri segmentation using hierarchical support vector machine with coil sensitivity correction. Acad. Radiol. 20, 137–147 (2013) 37. Wei, C.H., Li, Y., Huang, P., Gwo, C.Y., Harms, S.: Estimation of breast density: An adaptive moment preserving method for segmentation of fibroglandular tissue in breast magnetic resonance images. European Journal of Radiology 81(4), e618–e624 (2012) 38. WHO https://www.who.int/cancer/prevention/diagnosis- screening/breast-cancer/en/. Accessed online: 05/12/2019 39. Wu, Q., Salganicoff, M., Krishnan, A., Fussell, D.S., Markey, M.K.: Interactive lesion segmentation on dynamic contrast enhanced breast mr using a markov model. Proc. SPIE (6144) (2006) 40. Yao, J., Chen, J., Chow, C.: Breast tumor analysis in dynamic contrast enhanced mri using texture features and wavelet transform. IEEE Journal of selected topics in signal processing 3, 94–100 (2009)

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