About the Contributors

Total Page:16

File Type:pdf, Size:1020Kb

About the Contributors 395 About the Contributors Ying Dai received her BS and MS degrees from Xian Jiaotong University, China in 1985 and 1988, respectively. After some years working in the same university, she attended Department of Information Engineering, Shinshu University, Japan in 1992. She had a Dr. Eng degree from Shinshu University in 1996. She was granted JSPS Research Fellowships for Young Scientists from 1995 to 1997. She joined the Iwate Pref. University in 1998. She has been an associate professor in the Faculty of Software and Information Science, Iwate Pref. University since 2002. Her main research interests are in the area of Pattern Recognition, Image Understanding, Kansei Information Processing, and Soft Computing Tech- niques. She is the member of IEEE, IEICE (the Institute of Electronics, Information and Communication Engineers), and JSKE (Japan Society of Kansei Engineering). Basabi Chakraborty received B.Tech, M.Tech and PhD degrees in RadioPhysics and Electronics from Calcutta University, India. She worked in National Center for Knowledge based Computing Sys- tems and Technology affiliated to Indian Statistical Institute, Calcutta, India until 1990. From 1991 to 1993 she worked as a part time researcher in Advanced Intelligent Communication Systems Laboratory in Sendai, Japan. She received another PhD in Information Science from Tohoku University, Sendai in 1996. From 1996 to 1998, she worked as a post doctoral research fellow in Research Institute of Electri- cal Communication, Tohoku University, Japan (under Telecommunication Advancement Organization (TAO) fellowship for a period of 10 months). In 1998 she joined as a faculty in Software and Information Science department of Iwate Prefectural University, Iwate, Japan and currently an Associate Profes- sor in the same department. Her main research interests are in the area of Pattern Recognition, Image Processing, Soft Computing Techniques, Biometrics, Trust and Security in Computer Communication Network. She is a senior member of IEEE, member of ACM, Japanese Neural Network Society (JNNS) and Information Processing Society of Japan (IPSJ), executive committee member of IUPRAI (Indian Unit of Pattern Recognition and Artificial Intelligence), IEEE JC WIE (Women In Engineering) and ISAJ (Indian Scientists Association in Japan). Minghui Shi received his MS degree in 2002 and Dr. degree in 2008 respectively from Jiangnan University and Xiamen University, China. He is currently an assistant professor at Xiamen University. In addition to working as a teacher, he is also a research member of Artificial Intelligence Institute of Xiamen University. His research interests involve Soft Computing Techniques, Machine Learning, Pattern Recognition, Information Processing in TCM (Traditional Chinese Medicine), and Feature Selection. * * * Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. About the Contributors Sanghamitra Bandyopadhyay did her B Tech, M Tech and PhD in Computer Science from Cal- cutta University, IIT Kharagpur and ISI respectively. She is currently an associate professor at the Indian Statistical Institute, Kolkata, India. She has worked at the Los Alamos National Laboratory, Los Alamos, New Mexico, University of New South Wales, Sydney, Australia, University of Texas at Arlington, University of Maryland at Baltimore, Fraunhofer Institute, Germany, Tsinghua University, China, University of Rome, Italy and University of Heidelberg, Germany. She is the first recipient of the Dr. Shanker Dayal Sharma Gold Medal and also the Institute Silver Medal for being adjudged the best all-around postgraduate performer in IIT, Kharagpur, India, in 1994. She has also received the Young Scientist Awards of the Indian National Science Academy (INSA), 2000, the Indian Science Congress Association (ISCA), 2000, the Young Engineer Award of the Indian National Academy of Engineers (INAE), 2002, the Swarnajayanti fellowship from the Department of Science and Technology (DST), 2007, and the prestigious Humboldt Fellowship from Germany, 2009. She has authored/co-authored more than 175 technical articles in international journals, book chapters, and conference/workshop proceedings. She has delivered many invited talks and tutorials around the world, and has been the chair and member of several conference committees. She has published four authored and edited books from publishers like Springer, World Scientific and Wiley. She has also edited journals special issues in the area of soft computing, data mining, and bioinformatics. Her research interests include computational biology and bioinformatics, soft and evolutionary computation, pattern recognition and data mining. She is a senior member of the IEEE. Hisashi Bannai is a Senior Researcher of Rural Finance Research Institute in Japan. He got MS in the major of Social Economic Thoughts (1981) in Kokugakuin University, and PhD in the major of Re- source and Environmental Economics (2004) in Tohoku University. His careers as in rural research field were started as a Senior Researcher of Rural Finance Research Institute collaborating with Prof. Masao Miwa in Graduate School of Economics in Kokugakuin University (1994), doctor course researcher collaborating with Prof. Kazuo Morozumi in Graduate School of Agricultural Economics in Tohoku University (2000). He was awarded the JA Prize (2008) to explore for structural problems of Japanese agricultural cooperatives by Central Union of Agricultural Cooperatives (Zenchu).Now, he dedicates his time to explore a new way of Revival for Japanese rural community applying a revival model based on American rural industry.Japan Siddhartha Bhattacharyya did his Bachelors in Physics, Bachelors in Optics and Optoelectronics and Masters in Optics and Optoelectronics from University of Calcutta, India in 1995, 1998 and 2000 respectively. He completed PhD in Computer Science and Engineering from Jadavpur University, India in 2008. He is currently a Lecturer in Computer Science and Information Technology of University Institute of Technology, The University of Burdwan, India. He was a Lecturer in Information Technol- ogy of Kalyani Government Engineering College, India during 2001-2005. He is a co-author of a book and more than 50 research publications. He was the member of the Young Researchers’ Committee of the WSC 2008 Online World Conference on Soft Computing in Industrial Applications. His research interests include soft computing, pattern recognition and quantum computing. Dr. Bhattacharyya is a life member of OSI and ISTE, India. He is a member of IAENG, Hong Kong and IRSS, Poland. Xiaoxi Chen was born in Xiamen, China, in 1989. He is a sophomore student in the School of In- formation Science & Technology, Xiamen University. In 2008, he joined the Artificial Brain Lab (ABL) 396 About the Contributors of Xiamen University, a Fujian Province Key Laboratory, and became the youngest member of ABL. In the lab, he studied with Professor Hugo de GARIS, generally recognized to be the father of the artificial brain. His interests include Artificial Brains, neural networks, GPGPU (General-Purpose computing on Graphics Processing Units) and CUDA parallel computing. Elisabeth Damour has a Master degree in International Marketing and in Russian Literature: she is a specialist of Chekhov’s dramas. She has run an artistic career as a dancer and collaborated with designers in London and in Saõ Paolo. She collaborates as a consultant in Innovation Programs and in the Master Certification in Paris Business Schools. She is a certified Trainer and Coach (member of ICF) in Process Communication, Frederic M. Hudson Institute. As an Expressive Art Therapist and a performer, she runs workshops in Japan and U.K. She is a member of the D. W. Winnicott Association, the Squiggle Foundation in U.K. She is a regular contributor to magazines and a conference speaker in Design International Conferences: in European – Helsinki, Berlin - and Japanese Universities in Sap- poro and Osaka. Madhura Datta is presently a Lecturer in UGC-Academic Staff College of the University of Cal- cutta. She did her MSc in Computer and Information Science from the University of Calcutta in 2002 and her M.Tech degree in Computer Science and Engineering in 2004 from the same University. She is pursuing her PhD work on face recognition. Yaokai Feng received his B.E. and M.E. degrees in Computer Science from Tianjin University, China, in 1986 and 1992, respectively. And, he received his PhD degree in Information Science from Kyushu University, Japan, in 2004. From 1986 to 1998, he was with the Department of Computer Engineering and Science, Tianjin University, China, where he was an assistant professor/lecturer and he was studying on fault-tolerant systems and database applications. During his doctoral course in Kyushu University, Japan, he was focusing his attention on the study of database. Now, he is with the Graduate School of Information Science and Electrical Engineering, Kyushu University, Japan, as an assistant professor and faculty of Information Science and Electrical Engineering. Now, his study interests include data- base (multidimensional indexing and searching, Kansei database, etc.) and pattern recognition (object recognition, localization, etc.). Hugo de Garis is a Full Professor of Computer Science and Director of the Artificial Brain Lab (ABL) in the Cognitive Science Department of the School of Information Science & Technology (SIST), Xiamen University,
Recommended publications
  • Genetic Operators for Combinatorial Optimization in TSP and Microarray Gene Ordering
    Appl Intell (2007) 26:183–195 DOI 10.1007/s10489-006-0018-y Genetic operators for combinatorial optimization in TSP and microarray gene ordering Shubhra Sankar Ray · Sanghamitra Bandyopadhyay · Sankar K. Pal Published online: 9 November 2006 C Springer Science + Business Media, LLC 2007 Abstract This paper deals with some new operators of ge- 1 Introduction netic algorithms and[-27pc] demonstrates their effectiveness to the traveling salesman problem (TSP) and microarray The Traveling Salesman Problem (TSP) is one of the top ten gene ordering. The new operators developed are nearest problems, which has been addressed extensively by mathe- fragment operator based on the concept of nearest neigh- maticians and computer scientists. It has been used as one of bor heuristic, and a modified version of order crossover op- the most important test-beds for new combinatorial optimiza- erator. While these result in faster convergence of Genetic tion methods [1]. Its importance stems from the fact there is Algorithm (GAs) in finding the optimal order of genes in mi- a plethora of fields in which it finds applications e.g., shop croarray and cities in TSP, the nearest fragment operator can floor control (scheduling), distribution of goods and services augment the search space quickly and thus obtain much bet- (vehicle routing), product design (VLSI layout), microarray ter results compared to other heuristics. Appropriate number gene ordering and DNA fragment assembly. Since the TSP of fragments for the nearest fragment operator and appropri- has proved to belong to the class of NP-hard problems [2], ate substring length in terms of the number of cities/genes heuristics and metaheuristics occupy an important place in for the modified order crossover operator are determined sys- the methods so far developed to provide practical solutions tematically.
    [Show full text]
  • Neural Networks Technique for the Control of Artificial Mobile Agents
    NEURAL NETWORKS TECHNIQUE FOR THE CONTROL OF ARTIFICIAL MOBILE AGENTS A PROJECT THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF Bachelor of Technology In Mechanical Engineering By Pratik Ranjan Bhanjdeo (111ME0284) Under Supervision of Prof. Dayal Ramakrushna Parhi Department of Mechanical Engineering National Institute of Technology, Rourkela 1 CERTIFICATE National Institute of Technology, Rourkela This is to certify that the work contained in this thesis, titled “NEURAL NETWORKS TECHNIQUE FOR THE CONTROL OF ARTIFICIAL MOBILE AGENTS” submitted by Pratik Ranjan Bhanjdeo (111ME0284) is an authentic work that has been carried out by him under my supervision and guidance in partial fulfillment for the requirement for the award of Bachelor of Technology Degree in Mechanical Engineering at National Institute of Technology, Rourkela. To the best of my knowledge, the matter embodied in the thesis has not been submitted to any other University/ Institute for the award of any Degree or Diploma. Place: Rourkela Date: 11TH MAY, 2015 Dr. Dayal Ramakrushna Parhi Professor Department of Mechanical Engineering National Institute of Technology Rourkela – 769008 2 Acknowledgment I am grateful to The Department of Mechanical Engineering for giving me the opportunity to carry out this project, which is an integral fragment of the curriculum in B. Tech at the National Institute of Technology, Rourkela. I would like to express my heartfelt gratitude and regards to my project guide, Prof. Dayal Ramakrushna Parhi, Department of Mechanical Engineering, for being the corner stone of the project. It was his incessant motivation and guidance during periods of doubts and uncertainties that has helped me to carry on with this project.
    [Show full text]
  • Finalabc.Pdf
    A. Department goals and objectives are linked to the university and college mission and strategic priorities, and to their strategy for improving their position within the discipline. (1 page maximum) 1. What is the department’s mission and is it clearly aligned with the university and college mission and direction? The department strives to offer quality undergraduate and graduate courses / programs; places a high priority on quality research and effective teaching; seeks to attract highly- qualified students; and strives to attract and nurture high-caliber faculty. This mission is consistent with university goals of ensuring student success, enhancing academic excellence, expanding breakthrough research, and developing / recognizing our people. 2. How does the department’s mission relate to curriculum; enrollments; faculty teaching; research/professional/creative activity and outreach? Is it aligned with the college’s strategic priorities? The departmental mission aligns with university and college strategic priorities, although the department is now in financial jeopardy under KSU’s new RCM budget model due to developmental growth in faculty numbers under state direction to grow a doctoral program. 3. How does the department contribute to university-wide curricular needs through general education and service instruction? The department was limited to a single Liberal Education Course (CS 10051 Introduction to Computer Science), but may have an opportunity to offer more courses under the new Kent Core curriculum. However, unlike many universities, KSU does not have a university-wide computing requirement, and does not have a large number of engineering majors taking CS courses. 4. How does the department promote diversity? The department has an active NSF S-STEM grant for broadening participation, and a diverse set of faculty, two of whom are women, and outreach activities to women and other minority students in computing.
    [Show full text]
  • Information Processing Beyond Quantum Computation
    Information Processing beyond Quantum Computation∗ Apoorva Patel Centre for High Energy Physics and Supercomputer Education and Research Centre Indian Institute of Science, Bangalore-560012, India E-mail: [email protected] Abstract– Recent developments in quantum computation have Instructions made it clear that there is a lot more to computation than the con- ventional Boolean algebra. Is quantum computation the most ❄ general framework for processing information? Having gath- Physical Input ✲ ✲ Output ered the courage to go beyond the traditional definitions, we are Device now in a position to answer: Certainly not. The meaning of a mes- sage being “a collection of building blocks” can be explored in a ✻ variety of situations. A generalised computational framework is proposed based on group theory, and it is illustrated with well- Oracles/Look-up Tables known physical examples. A systematic information theoretical Fig. 1. Schematic representation of a computer. approach is yet to be developed in many of these situations. Some Every computation may not use oracles or look-up tables. directions for future development are pointed out. drawn parallel to a given line and passing through a point out- side the given line. Euclid considered one parallel line to be I. MOTIVATION the self-evident answer, but could not prove that. So he in- cluded this property as a postulate in his theory, even though The silicon transistor was invented about half a century ago. it was quite different from the first four which defined basic Since then the semiconductor technology has grown at a rapid components of geometry. This state of affairs no doubt trou- pace to pervade almost all aspects of our lives.
    [Show full text]
  • Revisited: Machine Intelligence in Heterogeneous Multi Agent Systems
    Revisited: Machine Intelligence in Heterogeneous Multi Agent Systems Kaustav Jyoti Borah1, Department of Aerospace Engineering, Ryerson University, Canada email: [email protected] Rajashree Talukdar2, B. Tech, Department of Computer Science and Engineering, SMIT, India. Abstract: Machine learning techniques have been widely applied for solving decision making problems. Machine learning algorithms perform better as compared to other algorithms while dealing with complex environments. The recent development in the area of neural network has enabled reinforcement learning techniques to provide the optimal policies for sophisticated and capable agents. In this paper we would like to explore some algorithms people have applied recently based on interaction of multiple agents and their components. We would like to provide a survey of reinforcement learning techniques to solve complex and real-world scenarios. 1. Introduction An entity that recognizes its ambience with the help of sensors and uses its effectors to act upon that environment is called an agent [1]. Multi agent systems is a subfield of machine learning is used in many intelligent autonomous systems to make it smarter. To coordinate with other independent agents’ behaviour in multiple agent systems environment, ML techniques aims to provide principles for construction of complex systems. When the agents are not dependent of one another in such a way that they can have approach to the environment independently. Hence, they need to embrace new circumstances. Therefore, learning and exploring about the environment demands the inclusion of a learning algorithm for each agent. Some amount of interaction becomes mandatory amongst the different agents in a multi agent system for them to act like a group.
    [Show full text]
  • Dr. Shobha G NATIONAL/INTERNATIONAL
    Dr. Shobha G NATIONAL/INTERNATIONAL CONFERENCES 1. Shobha.G, M. Krishna, S C Sharma, Neural Network Approach Towards Analysis and Prediction of Behavioral Patterns of Stock Market, Proceedings of International Conference on Systemics, Cybernetics and Informatics , Hyderabad, India, Vol 2 pg 400- 403 2004. 2. Shobha.G, M. Krishna, S C Sharma, e-Market Integrator , Proceedings of 7th International Conference on Signal Processing , Beijing China,Vol 3 pg 2612-2615 2004. 3. Shobha.G, M. Krishna, S C Sharma, Java code generator for a PL/SQLwarehouse builder , Proceedings of International Conference on Computers, Controls and Communications, Chennai, India,Vol 1 pg 96- 104 2004. 4. Shobha.G, M. Krishna, S C Sharma,Self organizing map neural network based data mining with cluster computing in inventory applications, Proceedings of Asia Pacific Conference on parallel and Distributed Computing Technologies ,Vol 1 pg 455-459 2004. 5. Shobha.G, M. Krishna, S C Sharma, Neural Network Approach For Cluster Computing Database Applications , Proceedings of First World Congress on Lateral Computing, WCLC 2004, Catalog No. 18122004, 2004. 6. Shobha.G, M. Krishna, S C Sharma, Signature Verification and Forgery Detection , Proceedings of of International Conference on Systemics, Cybernetics and Informatics , Hyderabad, India, Vol 1 pg 637 – 639, 2005. 7. Shobha.G, M. Krishna, S C Sharma, Mining Association Rules For Large DataSets , Proceedings of of International Conference on Data Mining , Las Vegas, Nevada, USA-2006. 8. Shobha.G, Predictor System for industrial data publications in the proceedings of the Second International conference on innovative computing, information and control,0-7695- 1-2882-1/07© 2007 IEEE, Japan, 2007.
    [Show full text]
  • Skruzdžių Kolonijų Technologijos Vaizdams Apdoroti
    VILNIAUS GEDIMINO TECHNIKOS UNIVERSITETAS Raimond LAPTIK SKRUZDŽIŲ KOLONIJŲ TECHNOLOGIJOS VAIZDAMS APDOROTI DAKTARO DISERTACIJA TECHNOLOGIJOS MOKSLAI, ELEKTROS IR ELEKTRONIKOS INŽINERIJA (01T) Vilnius 2009 Disertacija rengta 2005–2009 metais Vilniaus Gedimino technikos universitete. Mokslinis vadovas prof. dr. Dalius NAVAKAUSKAS (Vilniaus Gedimino technikos universitetas, technologijos mokslai, elektros ir elektronikos inžinerija – 01T). VGTU leidyklos TECHNIKA 1675-M mokslo literatūros knyga http://leidykla.vgtu.lt ISBN 978-9955-28-501-4 © VGTU leidykla TECHNIKA, 2009 © Laptik, R., 2009 [email protected] VILNIUS GEDIMINAS TECHNICAL UNIVERSITY Raimond LAPTIK ANT COLONY TECHNOLOGIES FOR IMAGE PROCESSING DOCTORAL DISSERTATION TECHNOLOGICAL SCIENCES, ELECTRICAL AND ELECTRONIC ENGINEERING (01T) Vilnius 2009 Doctoral dissertation was prepared at Vilnius Gediminas Technical University in 2005–2009. Scientific supervisor Prof Dr Dalius NAVAKAUSKAS (Vilnius Gediminas Technical University, Technological Sciences, Electrical and Electronic Engineering – 01T). Santrauka Disertacijoje nagrinėjamos vaizdų apdorojimo skruzdžių kolonijomis tech- nologijos ir susieti dalykai: optimizavimo skruzdžių kolonijomis algoritmai, vaizdų apdorojimo metodai ir jų įgyvendinimas lauku programuojamomis lo- ginėmis matricomis. Tikslas yra pasiūlyti ir ištirti optimizavimu skruzdžių ko- lonijomis grįstus vaizdų apdorojimo būdus ir priemones. Atliekama analitinė optimizavimo skruzdžių kolonijomis technologijų literatūros apžvalga, pagrin- džiant konkrečių vaizdų
    [Show full text]
  • Neural Modeling of Flow Rendering Effectiveness
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by University of Huddersfield Repository Examining Applying High Performance Genetic Data Feature Selection and Classification Algorithms for Colon Cancer Diagnosis MURAD AL-RAJAB, JOAN LU AND QIANG XU, University of Huddersfield, United Kingdom [email protected], [email protected], [email protected] Abstract Background and Objectives: This paper examines the accuracy and efficiency (time complexity) of high performance genetic data feature selection and classification algorithms for colon cancer diagnosis. The need for this research derives from the urgent and increasing need for accurate and efficient algorithms. Colon cancer is a leading cause of death worldwide, hence it is vitally important for the cancer tissues to be expertly identified and classified in a rapid and timely manner, to assure both a fast detection of the disease and to expedite the drug discovery process. Methods: In this research, a three-phase approach was proposed and implemented: Phases One and Two examined the feature selection algorithms and classification algorithms employed separately, and Phase Three examined the performance of the combination of these. Results: It was found from Phase One that the Particle Swarm Optimization (PSO) algorithm performed best with the colon dataset as a feature selection (29 genes selected) and from Phase Two that the Support Vector Machine (SVM) algorithm outperformed other classifications, with an accuracy of almost 86%. It was also found from Phase Three that the combined use of PSO and SVM surpassed other algorithms in accuracy and performance, and was faster in terms of time analysis (94%).
    [Show full text]
  • Revisited: Machine Intelligence in Heterogeneous Multi Agent Systems
    Revisited: Machine Intelligence in Heterogeneous Multi Agent Systems Kaustav Jyoti Borah1, Department of Aerospace Engineering, Ryerson University, Canada email: [email protected] Rajashree Talukdar2, B. Tech, Department of Computer Science and Engineering, SMIT, India. Abstract: Machine learning algorithms has been around for decades and employed to solve various sequential decision-making problems. While dealing with complex environments these algorithms faced many key challenges. The recent development of deep learning has enabled reinforcement learning algorithms to calculate the optimal policies for sophisticated and capable agents. In this paper we would like to explore some algorithms people have applied recently based on interaction of multiple agents and their components. This survey provides insights about the deep reinforcement learning approaches which can lead to future developments of more robust methods for solving many real world scenarios. 1. Introduction An agent is an entity that perceives its environment through sensors and acting upon that environment through effectors [1]. Multi agent systems is a new technology that has been widely used in many autonomous and intelligent system simulators to perform smart modelling task. It aims to provide both principles for construction of complex systems involving multiple agents and mechanisms for coordination of independent agents’ behaviors. While there is no generally accepted definition of “agent” in AI [2], In case of multi agent systems all agents are independent of each other such a way that they have access independently to the environment. Hence, they need to adopt new circutance, Therefore, each agent should incorporate a learning algorithm to learn and explore the environment. In addition, and due to interaction among the agents in multi-agent systems, they should have some sort of communication between them to behave as a group.
    [Show full text]
  • Department of Computer Science East Carolina University Self Study October 1, 2011
    Department of Computer Science East Carolina University Self Study October 1, 2011 1 Contents 1 Program Description 4 1.1 Exact title of the unit . .4 1.2 Department authorized to offer degree programs . .4 1.3 Exact titles of degrees granted . .4 1.4 College or school . .4 1.5 Brief history and mission . .4 1.6 Relationship of the program to UNC’s strategic goals, to the ECU mission and to ECU’s strategic directions . .9 1.7 Degree program objectives . 14 1.8 Program enrichment opportunities . 20 1.9 Responsiveness to local and national needs . 20 1.10 Program quality . 21 1.11 Administration . 21 2 Curriculum and Instruction 22 2.1 Foundation curriculum . 22 2.2 Instructional relationship to other programs . 23 2.3 Curriculum assessment and curricular changes . 24 2.4 Bachelor’s degrees . 27 2.5 Certificate programs . 31 2.6 Master’s degrees . 32 2.7 Doctoral degree . 35 3 Students 36 3.1 Enrollment . 36 3.2 Quality of incoming students . 37 3.3 Quality of current and outgoing students . 38 3.4 Degrees granted . 40 3.5 Diversity of student population . 41 3.6 Needs for graduates and student placement . 43 3.7 Funding . 44 3.8 Student involvement in the instructional process . 45 4 Faculty 46 4.1 Faculty list and curriculum vitae . 46 4.2 Faculty profile summary . 47 2 4.3 Visiting, part-time and other faculty . 48 4.4 Advising . 48 4.5 Faculty quality . 49 4.6 Faculty workload distribution . 50 5 Resources 52 5.1 Budget .
    [Show full text]
  • A Study of Journal Publication Diversity Within the Australian
    Australasian Journal of Information Systems Volume 14 Number 2 June 2007 A STUDY OF JOURNAL PUBLICATION DIVERSITY WITHIN THE AUSTRALIAN INFORMATION SYSTEMS SPHERE CARMINE SELLITTO Centre for International Corporate Governance Research Victoria University Melbourne, Victoria, Australia. Email: [email protected] ABSTRACT This study reports on research that examined DEST data from 14 Australian universities to identify the diversity of journal outlets in the information systems (IS) area. Across a total of 60 years of academic publishing output, 1449 journal articles were evaluated to identify 649 different journals in which IS-related articles were published. The most popular journals used by Australian academics to publish IS- related articles were the Lecture Notes in Computer Science (N=94) in the computer science area, with the Australasian Journal of Information Systems (N=25) being the most popular journal in the pure and business IS sphere. The study also examined publishing output against a set of 50 previously highly rated IS journals and concluded that the average annual publication of articles in these highly rated journals occurred at a very low rate. The research appears to be one of the first studies to use historical DEST data to report journal diversity in the Australian IS-sphere. INTRODUCTION Journal publication has evolved as the accepted manner in which the academic community disseminates knowledge, an important emphasis being placed on appropriate peer-reviewed journals (Sharplin and Mabry 1985; Baker 2000; Tenopir and King 2001). Academic publication tends to not only set the foundations for furthering a discipline, but is also used as a gauge of scholarly success— where an important aspect of researcher productivity can be reflected by their scholarly publication record (Ramsden 1994).
    [Show full text]
  • Dr. Debjani Chakraborty Professor Department of Mathematics Indian Institute of Technology, Kharagpur-721 302, India
    Dr. Debjani Chakraborty Professor Department of Mathematics Indian Institute of Technology, Kharagpur-721 302, India Phone: +91 3222 283638(O)283639(R ) Mobile: (+91)9434610298 email : [email protected] [email protected] Date of Birth:17th June 1966 Academic Qualification: B.Sc. (Hons) in Mathematics, University of Calcutta, 1986. M.Sc. in Mathematics, I.I.T., Kharagpur,1989 Ph.D. in Mathematics, I.I.T., Kharagpur, 1995 Award: o Young Scientist Award 1997 in Mathematics by Indian Science Congress Association. o Young scientist Scheme from DST, India in 1997 o Nominated Member of Indian National Academy of Sciences, Allahabad, India o Research Associateship from CSIR, New Delhi in 1996 o Research Fellowship from CSIR, New Delhi in 1989 o Best paper awardin International conference World Congress in Lateral Computing (WCLC 2004), Indian Institute of Science, Bangalore, India o Best paper award in International Conference on Systems in Medicine and Biology (ICSMB 2010), IIT Kharagpur, India o Best paper in Presidency and state level in 24th West Bengal State Science and Technology Congress, 2016 Research Guidance Ph.D.: 8 (completed) 8 (ongoing) Master’s project: 72 Research interests : Multi-criteria decision analysis Optimization in imprecise and uncertain environment Fuzzy logic & approximate reasoning in medical imaging Video course developed ° 20 lectures on Optimization in NPTEL Phase II in 2015. ° 30 Lectures on Constrained and Unconstrained Optimization in MOOC in 2017. Research projects & consultancy: 1. Individual Scientist in project entitled, Development of an integrated technology to promote decision in fuzzy and/or stochastic environment sponsored by DST, Govt. of India (Year 1996-1999).
    [Show full text]