Phon-Amnuaisuk / Au / Omar Computational Intelligence in Information Systems
1. Auflage 2017
ISBN: 978-3-319-48517-1
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark
Proceedings of the Computational Intelligence in Information Systems Conference (CIIS 2016)
E-Book, Englisch, 308 Seiten
Reihe: Engineering (R0)
ISBN: 978-3-319-48517-1
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark
Zielgruppe
Research
Autoren/Hrsg.
Weitere Infos & Material
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
