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ISBN # 1-60132-512-6; American Council on Science & Education ISBN # 1-60132-512-6; American Council on Science & Education / CSCE 2020 CSCI 2020 BOOK of ABSTRACTS The 2020 World Congress in Computer Science, Computer Engineering, and Applied Computing CSCE 2020 https://americancse.org/events/csce2020 July 27-30, 2020 Luxor Hotel (MGM Property), 3900 Las Vegas Blvd. South, Las Vegas, 89109, USA Table of Contents CSCE 2020 Keynote Address ..................................................................................................... ... 2 Int'l Conf. on Applied Cognitive Computing (ACC) ....................................................................... ... 2 Int'l Conf. on Bioinformatics & Computational Biology (BIOCOMP) ............................................. ... 6 Int'l Conf. on Biomedical Engineering & Sciences (BIOENG) ...................................................... 11 Int'l Conf. on Scientific Computing (CSC) ....................................................................................... 13 SESSION: Military & Defense Modeling and Simulation ................................................................. 20 Int'l Conf. on e-Learning, e-Business, EIS & e-Government (EEE) ................................................. 22 Int'l Conf. on Embedded Systems, CPS & Applications (ESCS) ..................................................... 27 Int'l Conf. on Foundations of Computer Science (FCS) .................................................................. 30 Int'l Conf. on Frontiers in Education: CS & CE (FECS) ................................................................... 33 Int'l Conf. on Grid, Cloud, & Cluster Computing (GCC) .................................................................. 42 Int'l Conf. on Health Informatics & Medical Systems (HIMS) .......................................................... 45 Int'l Conf. on Artificial Intelligence (ICAI) ......................................................................................... 55 SESSION: Hardware Acceleration in Artificial Intelligence ............................................................. 73 SESSION: Artificial Intelligence for Smart Cities ............................................................................. 75 SESSION: Applications of Advanced AI Techniques to Information Management .......................... 79 WORKSHOP: Intelligent Linguistic Technologies ........................................................................... 80 Int'l Conf. on Data Science (ICDATA) ............................................................................................. 82 Int'l Conf. on Internet Computing & IoT (ICOMP) ............................................................................ 97 Int'l Conf. on Wireless Networks (ICWN) ...................................................................................... 100 Int'l Conf. on Information & Knowledge Engineering (IKE) ............................................................ 103 WORKSHOP: Electronics & Information Technology (IWEIT-2020) ............................................. 111 Int'l Conf. on Image Processing, Computer Vision, & Pattern Recognition (IPCV) ........................ 112 Int'l Conf. on Modeling, Simulation & Visualization Methods (MSV) .............................................. 119 Int'l Conf. on Parallel & Distributed Processing (PDPTA) .............................................................. 125 WORKSHOP: Mathematical Modeling and Problem Solving (MPS) ............................................. 132 Int'l Conf. on Security and Management (SAM) ............................................................................ 136 Int'l Conf. on Software Engineering Research and Practice (SERP) ............................................. 146 Note that the titles of papers, the authors' names and the abstracts that appear in this book ("Book of Abstracts") were extracted from the papers that were submitted to the EVALUATION web portal (i.e., extracted from the first draft submissions). The official published proceedings/book will have any and all changes/revisions that authors may have done. ~ 1 ~ ISBN # 1-60132-512-6; American Council on Science & Education / CSCE 2020 CSCE 2020 KEYNOTE ADDRESS The Psychology of Artificial Intelligence: A Conversation with SANDI Dr. James A. Crowder Systems Fellow at Colorado Engineering, Inc., USA (Formerly at Raytheon Technologies. Recipient of technical achievement awards from Lockheed Martin, General Dynamics, Northrup Grumman, & Raytheon Technologies.) The 4th International Conference on Applied Cognitive Computing (ACC 2020) https://americancse.org/events/csce2020/conferences/acc20 An Adaptive Tribal Topology for Particle Swarm Optimization Kenneth Brezinski, Ken Ferens Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, Canada Abstract: The success of global optimization rests on the ability for a compatible metaheuristic to approximate global search. Particle Swarm Optimization (PSO) is one of such heuristics, with the ideal PSO application being one which promotes swarm diversity while incorporating the global progress of the swarm in its performance. In this paper, the authors introduce an adaptive tribal topology within PSO to improve global coverage. Diversity of the swarm population was dynamically managed through an evaluation of a swarm fitness parameter, which takes into account the relative performance a swarm member and its assigned tribe has on finding better objective evaluations. The fitness function simultaneously promotes the breeding of exemplars and the elimination of swarm members stuck in local minima. The model was evaluated on a series of benchmark problems with unique and diverse search spaces, and the results demonstrate that performance relied on the distribution and scale of the local minima present. Improving the Efficiency of Genetic-based Incremental Local Outlier Factor Algorithm for Network Intrusion Detection Omar Alghushairy, Raed Alsini, Xiaogang Ma, Terence Soule Department of Computer Science, University of Idaho, Moscow, Idaho, USA; Department of Information System and Technology, University of Jeddah, Saudi Arabia; Department of Information System, King Abdulaziz University, Saudi Arabia Abstract: In the era of big data, outlier detection has become an important task for many applications, such as the network intrusion detection system. Data streams are a unique type of big data, which recently has gained a lot of attention from researchers. Nevertheless, there are challenges in applying traditional outlier detection algorithms for data streams. One of the well-known algorithms of outlier detection is Local Outlier Factor (LOF). The issue with LOF is that it needs to store the whole dataset with its distances’ results in memory. In addition, it needs to start from the beginning and recalculate all processes if any change happens in the dataset, such as inserting a new data point. The Genetic-based Incremental Local Outlier Factor (GILOF) ~ 2 ~ ISBN # 1-60132-512-6; American Council on Science & Education / CSCE 2020 has addressed these issues and shown significant results. In this paper, we further improved the GILOF performance in data streams by proposing a new calculation method for LOF. The experiment’s results showed that our new method of calculation has led to better accuracy performance for various real-world datasets. A Grid Partition-based Local Outlier Factor for Data Stream Processing Raed Alsini, Omar Alghushairy, Xiaogang Ma, Terrance Soule Department of Computer Science, University of Idaho, Moscow, Idaho, USA; Department of Information System, King Abdulaziz University, Jeddah, Saudi Arabia; Department of Information System and Technology, University of Jeddah, Jeddah, Saudi Arabia Abstract: Outlier detection is getting significant attention in the research field of big data. Detecting the outlier is important in various applications such as communication, finance, fraud detection, and network intrusion detection. Data streams posed new challenges to the existing algorithms of outlier detection. Local Outlier Factor (LOF) is one of the most appropriate techniques used in the density-based method to determine the outlier. However, it faces some difficulties regarding data streams. First, LOF processes the data all at once, which is not suitable for data streams. Another issue appears when a new data point arrives. All the data points need to be recalculated again significantly. Therefore, it affects the execution time. A new algorithm is proposed in this research paper called Grid Partition-based Local Outlier Factor (GP-LOF). GP-LOF uses a grid for the LOF with a sliding window to detect outliers. The outcome of experiments with the proposed algorithm demonstrates the effectiveness in both performance accuracy and execution time in several real-world datasets compared to the state-of-the-art DILOF algorithm. Cognitive Discovery Pipeline Applied to Informal Knowledge Nicola Severini, Pietro Leo, Paolo Bellavista Alma Mater Studiorum, Bologna, Italy; IBM Active Intelligence Center, IBM Italy, Bologna, Italy Abstract: This paper introduces an Information Extraction (IE) architecture to process unstructured data that are typically generated within non-formal contexts such as the ones that are exchanged during multiple forms of technical meetings. Information Extraction and Knowledge Management are providing practical value in all kind of industries and boosting R&D activities specically. Our project sees a clear opportunity to leverage
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