Phon-Amnuaisuk / Au / Omar Computational Intelligence in Information Systems

Proceedings of the Computational Intelligence in Information Systems Conference (CIIS 2016)
1. Auflage 2017
ISBN: 978-3-319-48517-1
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 532, 314 Seiten, eBook

Reihe: Advances in Intelligent Systems and Computing

This book constitutes the Proceedings of the Computational Intelligence in Information Systems conference (CIIS 2016), held in Brunei, November 18–20, 2016. The CIIS conference provides a platform for researchers to exchange the latest ideas and to present new research advances in general areas related to computational intelligence and its applications. The 26 revised full papers presented in this book have been carefully selected from 62 submissions. They cover a wide range of topics and application areas in computational intelligence and informatics.

Zielgruppe

Research

Autoren/Hrsg.

Phon-Amnuaisuk, Somnuk

Au, Thien-Wan

Omar, Saiful

Inhaltsverzeichnis

1;Preface;6
2;Organization;7
2.1;Honorary Chair/Advisor;7
2.2;Steering Committee;7
2.3;Chairperson;7
2.4;Members;7
2.5;International Advisory Board;7
2.6;Working Committees;8
2.7;Chairman and Co-chairs;8
2.8;Secretariat;8
2.9;Technical;8
2.10;Finance;8
2.11;Ceremony and Logistics;9
2.12;Welfare and Accommodation;9
2.13;Web Master;9
2.14;Sponsorship, Promotion and Publicity;9
2.15;Publishing;9
2.16;Invitation and Protocol;9
2.17;Refreshment;9
2.18;Car and Traffic;10
2.19;Souvenir and Certificate;10
2.20;Special Session Organizers;10
2.21;International Technical Committee;10
2.22;Organizer;12
2.23;Technical Sponsors;12
3;Contents;13
4;Intelligent Systems and their Applications;16
5;On Using Genetic Algorithm for Initialising Semi-supervised Fuzzy c-Means Clustering;17
5.1;1 Introduction;17
5.2;2 Methodology;19
5.2.1;2.1 Semi-supervised Fuzzy C-Means;19
5.2.2;2.2 The Genetic Algorithm;20
5.3;3 Experiments;21
5.4;4 Results and Discussion;22
5.5;5 Conclusion;27
5.6;References;28
6;Estimation of Confidence-Interval for Yearly Electricity Load Consumption Based on Fuzzy Random Auto-Regression Model;29
6.1;Abstract;29
6.2;1 Introduction;29
6.3;2 Fundamental Theories of Fuzzy Random Variable and Fuzzy Random Auto-Regression Model;30
6.3.1;2.1 Fuzzy Random Variables;30
6.3.2;2.2 Fuzzy Random Auto-Regression (FR-AR) Model;32
6.4;3 Proposed LRS of TFN in Estimating Confidence-Interval of FR-AR Model;33
6.5;4 Empirical Analysis;36
6.6;5 Conclusion;39
6.7;Acknowledgment;39
6.8;References;39
7;Improved Discrete Bacterial Memetic Evolutionary Algorithm for the Traveling Salesman Problem;41
7.1;Abstract;41
7.2;1 Introduction;41
7.2.1;1.1 The Traveling Salesman Problem;41
7.2.2;1.2 The TSP as an NP-Hard Task;42
7.2.3;1.3 Our Previous Work;42
7.3;2 The Discrete Bacterial Memetic Evolutionary Algorithm;43
7.3.1;2.1 Bacterial Evolutionary Algorithm;43
7.3.2;2.2 Local Search;48
7.3.3;2.3 Comparison of Optimal Tour Lengths;49
7.3.4;2.4 Comparison of Runtimes;50
7.4;3 Conclusions;51
7.5;References;51
8;Improved Stampede Prediction Model on Context-Awareness Framework Using Machine Learning Techniques;53
8.1;Abstract;53
8.2;1 Introduction;53
8.2.1;1.1 Purpose of the Study;54
8.3;2 Related Works;55
8.4;3 Proposed Work;56
8.4.1;3.1 Decision Tree;56
8.4.2;3.2 K-Means;57
8.4.3;3.3 Participant Nodes Behavior for Group Clustering;59
8.5;4 Experimental Results;59
8.5.1;4.1 Performance Evaluation Criteria;59
8.6;5 Discussion of Results;61
8.7;6 Conclusion;63
8.8;References;63
9;Image Classification for Snake Species Using Machine Learning Techniques;66
9.1;1 Introduction;66
9.2;2 Related Works;67
9.3;3 The Snakes of Perlis Corpus;68
9.3.1;3.1 Feature Extraction;69
9.4;4 Experiment;70
9.4.1;4.1 Performance Metrics;70
9.4.2;4.2 Results and Discussion;71
9.5;5 Conclusion;72
9.6;References;72
10;Rides for Rewards (R4R): A Mobile Application to Sustain an Incentive Scheme for Public Bus Transport;74
10.1;Abstract;74
10.2;1 Introduction;74
10.3;2 Problem Statement;75
10.4;3 Related Literature;75
10.5;4 Methodology of the Rides for Rewards (R4R) Application;76
10.5.1;4.1 Pre-Initiation Survey;77
10.5.2;4.2 Collect Requirements;77
10.5.3;4.3 Assess Requirements;78
10.5.4;4.4 Develop Prototype: R4R Mobile Application;79
10.5.5;4.5 Conduct Pilot Study;82
10.6;5 Results;83
10.7;6 Conclusions and Future Work;83
10.8;Acknowledgements;84
10.9;References;84
11;Mobile mBus System Using Near Field Communication;86
11.1;Abstract;86
11.2;1 Introduction;86
11.3;2 NFC Characteristics in Related Works;87
11.4;3 Methodology;88
11.5;4 mBus System;89
11.5.1;4.1 System Database;90
11.5.2;4.2 Security–Verification Code;90
11.5.3;4.3 Analysis on bTag;91
11.5.4;4.4 mBus GUI;91
11.6;5 Discussion and Future Works;93
11.7;Acknowledgements;94
11.8;References;94
12;An Agent Model for Analysis of Trust Dynamics in Short-Term Human-Robot Interaction;95
12.1;Abstract;95
12.2;1 Introduction;95
12.3;2 Trust and Human-Robot Interaction;96
12.3.1;2.1 Trust Dynamics;96
12.3.2;2.2 Long-Term and Short-Term Human Robot Interaction;97
12.4;3 Computational Modeling;97
12.4.1;3.1 Instantaneous Relationships;98
12.4.2;3.2 Temporal Relationships;100
12.5;4 Simulation;100
12.6;5 Evaluation;102
12.6.1;5.1 Mathematical Analysis;102
12.6.2;5.2 Logical Verification;103
12.7;6 Conclusion;105
12.8;Acknowledgements;106
13;An Ambient Agent Model for a Reading Companion Robot;108
13.1;Abstract;108
13.2;1 Introduction;108
13.3;2 Companion Robots;109
13.4;3 The Ambient Agent Model;110
13.4.1;3.1 A Dynamical Domain Model of Cognitive Load and Reading Performance;111
13.4.2;3.2 Belief Base;111
13.4.3;3.3 Analysis Model;112
13.4.4;3.4 Support Model;113
13.5;4 Ontology and Specifications;114
13.6;5 Simulation Results;117
13.7;6 Automated Verification;118
13.8;7 Conclusion;119
13.9;Acknowledgement;119
13.10;References;119
14;Student Acceptance and Attitude Towards Using 3D Virtual Learning Spaces;121
14.1;Abstract;121
14.2;1 Introduction;121
14.3;2 Literature Review;122
14.3.1;2.1 Virtual Worlds and Virtual Learning Spaces;122
14.3.2;2.2 The Technology Acceptance Model (TAM);123
14.3.3;2.3 The Technology Acceptance Model (TAM) and Virtual Worlds;123
14.4;3 Research Model and Hypotheses;124
14.5;4 Research Methodology;126
14.5.1;4.1 Data Collection and Procedure;126
14.5.2;4.2 Instrumentation;127
14.6;5 Results;127
14.6.1;5.1 Background Profile;127
14.6.2;5.2 Validity and Reliability;127
14.6.3;5.3 Hypothesis Testing: Regression Analysis;129
14.7;6 Discussion and Conclusion;130
14.8;References;131
15;Data Mining and Its Applications;133
16;Class Noise Detection Using Classification Filtering Algorithms;134
16.1;Abstract;134
16.2;1 Introduction;134
16.3;2 Related Works;135
16.4;3 Methodology;135
16.4.1;3.1 Phase1: Data Preparation;135
16.4.2;3.2 Phase2: Noise Detection;136
16.4.3;3.3 Phase3: Noise Classification;137
16.5;4 Experimental Studies;137
16.5.1;4.1 Datasets;137
16.5.2;4.2 Performance Measure;138
16.6;5 Results and Discussions;138
16.6.1;5.1 Noise Detection Evaluation Results in Terms of Precision;138
16.6.2;5.2 Noise Detection Evaluation Results in Terms of Recall;139
16.6.3;5.3 Noise Detection Evaluation Results in Terms of F-Measure;140
16.6.4;5.4 Noise Classification Results in Terms of Accuracy;140
16.7;6 Conclusion;142
16.8;References;142
17;A Novel Robust R-Squared Measure and Its Applications in Linear Regression;144
17.1;1 Introduction;144
17.2;2 Existing Measures and Improvement Scope;145
17.2.1;2.1 Outliers and Leverage Points;145
17.2.2;2.2 Contamination vs. Traditional Measures;145
17.2.3;2.3 Robust Regression and Related GoF Measures;146
17.3;3 Proposed Methodology;146
17.3.1;3.1 RoR2 Computation Process;146
17.3.2;3.2 Main Algorithm;147
17.4;4 Empirical Results;149
17.4.1;4.1 Simulation Construct;149
17.4.2;4.2 Model Selection Performance;151
17.4.3;4.3 Contamination Detection Performance;152
17.4.4;4.4 Regression Estimator Performance;152
17.4.5;4.5 Performance Assessment Based on Real Datasets;153
17.5;5 Conclusions and Next Steps;154
17.6;References;155
18;An Improvement to StockProF: Profiling Clustered Stocks with Class Association Rule Mining;156
18.1;Abstract;156
18.2;1 Introduction;156
18.3;2 An Overview of StockProF;157
18.4;3 Methodology;158
18.4.1;3.1 Preparation of the Stock Data Set;158
18.4.2;3.2 Mining Class Association Rules;160
18.5;4 Results and Discussion;160
18.5.1;4.1 Profiling the Clusters;161
18.5.2;4.2 Building Stock Portfolios;162
18.5.3;4.3 Average Capital Performance;162
18.6;5 Conclusion;164
18.7;References;164
19;Empirical Study of Sampling Methods for Classification in Imbalanced Clinical Datasets;165
19.1;Abstract;165
19.2;1 Introduction;165
19.3;2 Decision Trees;167
19.4;3 Data Imbalance;167
19.4.1;3.1 Undersampling;167
19.4.2;3.2 Oversampling;168
19.4.3;3.3 Model Evaluation;169
19.5;4 Experimental Datasets;169
19.6;5 Experiment Design;171
19.7;6 Results and Analysis;172
19.8;7 Conclusion;175
19.9;References;175
20;Internetworking, Security and Internet of Things;176
21;Internet of Things (IoT) with CoAP and HTTP Protocol: A Study on Which Protocol Suits IoT in Terms of Performance;177
21.1;Abstract;177
21.2;1 Introduction;177
21.3;2 HTTP and CoAP;178
21.3.1;2.1 Constrained Devices;178
21.3.2;2.2 Process of Communication Made Between HTTP and CoAP;179
21.3.3;2.3 HTTP and CoAP Message Format;179
21.4;3 Implementation;180
21.4.1;3.1 HTTP and CoAP Implementation;181
21.4.2;3.2 Network Performance Measures;181
21.4.3;3.3 Testing;181
21.5;4 Findings;182
21.5.1;4.1 Large Data;182
21.6;5 Conclusion;184
21.7;References;185
22;NTRU Binary Polynomials Parameters Selection for Reduction of Decryption Failure;187
22.1;1 Introduction;187
22.2;2 NTRU Parameters;188
22.3;3 NTRU Operation;189
22.4;4 Decryption Failure Approximation;189
22.4.1;4.1 Recap of Previous Decryption Failure Approximation;189
22.4.2;4.2 Computational Approximation of Decryption Failure for NTRU Binary Polynomials;190
22.5;5 Studying the Relationship Between the Parameters and Their Effect on the NTRU Key Generation, Encryption and Decryption;191
22.5.1;5.1 Testing Parameters and Environment;191
22.5.2;5.2 Testing for Identification of Influential Parameters: Key Determinants of Decryption Failure;192
22.5.3;5.3 Studying the Private Key Polynomial f;194
22.5.4;5.4 Using Machine Learning to Analyze the Relationship Between the Polynomial f and Large Modulus q;194
22.6;6 Conclusion;197
22.7;References;197
23;Energy Efficient Operational Mechanism for TDM-PON Supporting Broadband Access and Local Customer Internetworking;200
23.1;1 Introduction;200
23.2;2 System Model;203
23.3;3 Proposed Energy Efficient Operational Mechanism;204
23.4;4 Results and Discussion;206
23.5;5 Conclusion;208
23.6;References;208
24;Performance Analysis of MANET Under Black Hole Attack Using AODV, OLSR and TORA;210
24.1;Abstract;210
24.2;1 Introduction;210
24.3;2 Background Study;211
24.4;3 Simulation;212
24.4.1;3.1 Network Layout;212
24.4.2;3.2 Parameter Configurations;213
24.4.3;3.3 Black Hole Attack Configurations;213
24.5;4 Results;215
24.6;5 Findings and Analysis;217
24.7;6 Conclusions and Future Work;219
24.8;References;219
25;Management Information Systems and Education Technology;220
26;Enhancement of Learning Management System by Integrating Learning Styles and Adaptive Courses;221
26.1;Abstract;221
26.2;1 Introduction;221
26.2.1;1.1 Learning Management System in General;222
26.3;2 Problem Statement;223
26.4;3 Literature Review;223
26.4.1;3.1 Learning Styles;223
26.4.2;3.2 Learning Styles Model by Kolb;224
26.4.3;3.3 The Felder-Silverman Learning Style Model (FSLSM);224
26.5;4 Research Methodology;225
26.6;5 Discussion;226
26.7;References;226
27;InterviewME: A Comparative Pilot Study on M-Learning and MAR-Learning Prototypes in Malaysian English Language Teaching;229
27.1;Abstract;229
27.2;1 English as a Second Language in Malaysia;229
27.2.1;1.1 Listening and Speaking;230
27.2.2;1.2 Mobile Learning and ELT in Malaysia;231
27.2.3;1.3 Mobile Augmented Reality in ELT;232
27.3;2 InterviewME MAR-Learning Application Prototype;233
27.4;3 Methodology;236
27.4.1;3.1 Hypotheses;237
27.5;4 Results and Discussion;238
27.6;5 Conclusion;241
27.7;References;241
28;A Preliminary Evaluation of ICT Centers Performance Using COBIT Framework: Evidence from Institutions of Higher Learning in Brunei Darussalam;245
28.1;Abstract;245
28.2;1 Introduction;245
28.2.1;1.1 COBIT Structure;247
28.2.2;1.2 Why COBIT?;247
28.3;2 Review of Literature;247
28.3.1;2.1 Maturity Level Model;248
28.4;3 Research Methodology;249
28.5;4 Results and Discussion;249
28.6;5 Conclusion;253
28.7;References;253
29;A Cognitive Knowledge-based Framework for Adaptive Feedback;255
29.1;Abstract;255
29.2;1 Introduction;255
29.3;2 Background;256
29.3.1;2.1 Approaches to Adaptive Feedback in Learning Environments;258
29.3.2;2.2 Knowledge Modeling in Cognitive Knowledge Base;259
29.4;3 Proposed Framework for Adaptive Feedback;261
29.5;4 Conclusion;263
29.6;Acknowledgment;263
29.7;References;263
30;Creative Computing;266
31;Towards Developing a Therapeutic Serious Game Design Model for Stimulating Cognitive Abilities: A Case for Children with Speech and Language Delay;267
31.1;Abstract;267
31.2;1 Introduction;267
31.3;2 Related Works;269
31.3.1;2.1 Serious Games for Therapeutic and Cognitive Stimulation;269
31.3.2;2.2 User Characteristics of CSLD;270
31.3.3;2.3 The Cognitive Development of a Child;270
31.4;3 Preliminary Study;271
31.5;4 Discussion;273
31.6;5 Conclusion;274
31.7;References;275
32;3D Facial Expressions from Performance Data;278
32.1;1 Introduction;278
32.2;2 Background;279
32.3;3 Performance Driven Facial Animation;280
32.4;4 Creative Process and Results;281
32.5;5 Conclusion and Future Direction;284
32.6;References;285
33;Computational Complexity and Algorithms;287
34;Constrained Generalized Delaunay Graphs are Plane Spanners;288
34.1;1 Introduction;288
34.2;2 Preliminaries;290
34.2.1;2.1 Auxiliary Lemmas;290
34.3;3 The Constrained Generalized Delaunay Graph;292
34.3.1;3.1 Planarity;292
34.3.2;3.2 Spanning Ratio;293
34.4;4 Conclusion;298
34.5;References;299
35;Solving the Longest Oneway-Ticket Problem and Enumerating Letter Graphs by Augmenting the Two Representative Approaches with ZDDs;301
35.1;1 Introduction;301
35.2;2 Preliminaries;303
35.2.1;2.1 Zero-Suppressed Binary Decision Diagrams;303
35.2.2;2.2 Frontier-Based Search;304
35.3;3 FBS for Degree Specified Graphs;305
35.4;4 New ZDD Operations;306
35.5;5 ZDDs over the Set of Edges and Vertices;307
35.6;6 Constructing ZDDs for Letter Graphs;307
35.7;7 Experiments;309
35.7.1;7.1 Finding the Longest Oneway-Ticket;309
35.7.2;7.2 Enumerating Letter and Multi-letter Graphs;311
35.8;References;312
36;Author Index;313