Recent Trends in Intelligence Enabled Research
Description
This book gathers extended versions of papers presented at DoSIER 2023 (Fifth Doctoral Symposium on Intelligence Enabled Research, held at Cooch Behar Government Engineering College, West Bengal, India, during December 20-21, 2023). The papers address the rapidly expanding research area of computational intelligence, which, no longer limited to specific computational fields, has since made inroads in signal processing, smart manufacturing, predictive control, robot navigation, smart cities, and sensor design, to name but a few. Presenting chapters written by experts active in these areas, the book offers a valuable reference guide for researchers and industrial practitioners alike and inspires future studies.
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Persons
Dr. Siddhartha Bhattacharyya [FRSA, FIET (UK), FIEI, FIETE, LFOSI, SMIEEE, SMACM, SMAAIA, SMIETI] is currently Senior Researcher at VSB Technical University of Ostrava, Czech Republic. He is Acclaimed Author with over 400 publications, including 100 plus authored/edited volumes with reputed publishers. Bhattacharyya's honors include receiving the South East Asian Regional Computing Confederation International Digital Award ICT Educator of the Year (2017), the Distinguished HoD Award, the Distinguished Professor Award conferred by the Computer Society of India, Mumbai Chapter (2017), ACM Distinguished Speaker (2018-2020), and IEEE Computer Society Distinguished Visitor (2021-2024). He was inducted into the ACM Hall of Fame in 2020 as a mark of appreciation for his contributions to hybrid computational intelligence and quantum computing research. His research interests include soft computing, pattern recognition, multimedia data processing, hybrid intelligence, and quantum computing.
Dr. Gautam Das is a professor of the ECE department of Cooch Behar Government Engineering College, West Bengal. He completed B.Tech. and M.Tech. from the Institute of Radio Physics and Electronics, Calcutta University, and subsequently completed Ph.D. from NBU. Dr. Das has more than 19 years of teaching and research experience. He has been the author and co-author of many journals and conference papers and participated in/organized national and international conferences. His area of interest includes system-on-chip testing and design of smart city.
Dr. Sourav De [SMIEEE, MACM, MIEI, LISTE, MCSTA, MIAENG] is currently Associate Professor of Computer Science and Engineering at Cooch Behar Government Engineering College, West Bengal. With over 18 years of academic experience, he has authored one book, edited 12 books, contributed to more than 54 research publications in internationally reputed journals, edited books, and international IEEE conference proceedings, and has five patents to his credit. His research interests include soft computing, pattern recognition, image processing, and data mining.
Leo Mrsic is Vice Rector for science and research at Algebra University and Vice President for Technological Development at National Council for Higher Education, Science and Technological Development, Head of BDV i-Silver Data Center Algebra LAB, Zagreb, Croatia, Permanent Court Expert in the fields of finance, accounting, bookkeeping, and informatics (12+ years) with a large number (150+) of successfully completed complex expertise procedures, and IPMA A Certified Project Director with 100+ successfully completed complex projects.
Content
- Intro
- Preface
- Contents
- About the Editors
- Enhanced Methodology for Boosting Employee Retention Through Various ML and Data Engineering Methods
- 1 Introduction
- 2 Related Study
- 3 Solution Architecture for HR Analytics
- 4 Universal Integration
- 5 Results and Discussion
- 6 Conclusion
- References
- Intensity Estimation of Tropical Cyclones from Satellite Imagery Over North Indian Ocean
- 1 Introduction
- 1.1 Research on Tropical Cyclones Forecast
- 1.2 Challenges
- 1.3 The TC Intensity Estimation Problems
- 1.4 Motivation TC Intensity Estimation
- 2 TC Intensity Estimation
- 2.1 Overview
- 2.2 Intensity Estimation Over the NIO
- 3 TC Intensity Estimation Using Various Feature Extraction Techniques from Satellite Images
- 3.1 TC Intensity Estimation Using Feature Vector Analysis [22, 41]
- 3.2 TC Intensity Estimation by Multilayer Perceptron [42]
- 3.3 Feature Fusion and Machine Learning Classifier for TC Intensity Estimation [30, 43, 44]
- 3.4 Multilayer Multi-block Local Binary Pattern for TC Intensity Estimation [45, 46]
- 4 Discussion on Results
- 5 Conclusion
- References
- Real-Time State Estimation of Twin Rotor Multi-Input Multi-Output (MIMO) System Using Variants of Kalman Filter: A Comparative Analysis
- 1 Introduction
- 1.1 Novelty of the Work
- 2 Nonlinear Modeling of TRMS
- 3 Estimation of Nonlinear States
- 3.1 Extended Kalman Filter
- 3.2 Unscented Kalman Filter
- 3.3 Ensemble Kalman Filter
- 4 Comparative Analaysis
- 5 Conclusion
- References
- Movie-LSTM and Lexicon Technique-Based Movie Review Analysis
- 1 Introduction
- 2 Literature Review
- 3 Proposed Methodology
- 3.1 Dataset Preprocessing
- 3.2 Feature Selection
- 3.3 Movie-LSTM Model
- 4 Experimental Results
- 4.1 Dataset Description
- 4.2 Performance Metrics
- 4.3 Result and Discussions
- 5 Conclusion
- References
- A Proposed Grid-Based Elephant Detection Model Using Artificial Intelligence (AI) to Prevent Crop Damage in Farming Fields
- 1 Introduction
- 2 Methodology
- 2.1 Discussing the Details of Proposed Workflow in the Following Steps
- 2.2 Simulation Environment and Scenario
- 3 Result Analysis
- 3.1 Discussion
- 3.2 Future Scope
- 3.3 Conclusion
- References
- Implementation of the Method of the Areas' Ratio on FPGA
- 1 Introduction
- 2 Structure the Method of the Areas' Ratio
- 3 Implementation of the Method Area's Ratio on FPGA
- 3.1 Determination of the Total Area (Stage 1)
- 3.2 Calculation of the Width of the Truncated MFs (Stage 2)
- 3.3 Calculation of the Total Area of the Truncated MFs (Stage 3)
- 3.4 Calculation Areas' Ratio (Stage 4)
- 3.5 Determination of a Crisp Value at the Defuzzifier Output (Stage 5)
- 4 Parallelization of Calculations to Improve Performance Device of Defuzzification
- 5 Experiment Results
- 6 Conclusion
- References
- Analysis of Pathfinding Algorithms for Mobile Robots Movement
- 1 Introduction
- 2 Algorithms' Overview
- 2.1 DFS
- 2.2 BFS
- 2.3 Wave Propagation Algorithm
- 3 Experiment Results
- 3.1 Results of Test 1
- 3.2 Results of Test 2
- 3.3 Results of Test 3
- 4 Conclusion
- References
- Enhancing Avatar Emotion Detection Using Deep Learning with Modified VGG16 Architecture
- 1 Introduction
- 2 Proposed Methodology
- 2.1 Preprocessing Phase
- 2.2 Classification Phase
- 3 Dataset Description
- 4 Experimental Results
- 5 Conclusion and Future Work
- References
- Deep Learning Approach to Satellite Collision Avoidance Using Long Short-Term Memory
- 1 Introduction
- 2 Basics and Background
- 3 The Proposed Approach
- 4 Experimental Results and Analysis
- 4.1 Dataset Description
- 4.2 Evaluation Measure
- 4.3 Experimental Result
- 5 Conclusion and Future Work
- References
- Portfolio Optimization Using Quantum-Inspired Dynamic Flower Pollination Optimizer
- 1 Introduction
- 2 Motivation and Contributions
- 3 Portfolio Management Overview
- 4 Dynamic Flower Pollination Optimizer
- 5 Quantum-inspired Dynamic Flower Pollination Optimizer
- 6 Experimental Results and Findings
- 7 Discussions and Conclusion
- References
- Error-Induced Brain-Actuated Motor Command Generation for Speed-Profile Setting in a 2-Loop Position Control of a Robot Arm
- 1 Introduction
- 2 System Overview
- 3 Modeling of the Proposed Brain-Actuated Speed Controller
- 3.1 Brain-Actuated Proportional-Type Speed Modulation
- 3.2 Zero-Crossing Sensitive Brain-Actuated Speed Modulation
- 3.3 Takagi-Sugeno Fuzzy Model for Speed Adaptation
- 4 Analysis of Stability Margin of the Proposed Controllers
- 5 Experiments
- 5.1 Preamble
- 5.2 Signal Processing
- 5.3 Classification
- 6 Performance Analysis
- 7 Conclusions
- References
- Effect of High-Low Doping Profile on Threshold Voltage Shift of Submicron Double-Gate MOSFET
- 1 Introduction
- 2 Structural Description
- 3 Mathematical Formulation
- 4 Result and Analysis
- 5 Conclusion
- References
- Explainable AI and Machine Learning in Prediction of Housing Prices
- 1 Introduction
- 1.1 Standard Models in Studies
- 2 Literature Review
- 3 Methodology
- 3.1 Data Preprocessing
- 3.2 Removal of Irrelevant Attributes
- 3.3 Conversion of Categorical Data into Binary
- 4 Exploratory Data Analysis
- 5 Results Analysis
- 6 Conclusions
- 7 Future Scope
- References
- A Comprehensive Approach to Classify the Skin Cancer Disease Using Latest CNN Model (YOLOv8)
- 1 Introduction
- 2 Proposed Methodology
- 3 Experiment and Result
- 4 Conclusion
- References
- Uncertain Zone-Based Color Image Enhancement
- 1 Introduction
- 2 Literature Survey
- 2.1 Uncertain Zone
- 3 Enhancement of Image
- 3.1 Image Fuzzification
- 3.2 Image Intensification
- 3.3 Fuzzy Image Entropy
- 3.4 Image Defuzzification
- 4 Proposed Method
- 5 Experiments and Outputs
- 6 Conclusion
- References
- Efficient Partitioning of a Multi-dimensional Axis-Aligned Space into Uniform Non-overlapping Sub-spaces for Diverse Applications
- 1 Introduction
- 1.1 N-Dimensional Axis-Aligned Space
- 1.2 The Partitions
- 1.3 Algorithm in Brief
- 2 Related Works
- 3 Proposed Methodology
- 3.1 Calculating All Vertices of First Partition
- 3.2 Calculating All the Partitions
- 4 Pseudo-Code
- 5 Complexity Analysis
- 5.1 Time Complexity
- 5.2 Space Complexity
- 6 Performance Analysis
- 7 Applications
- 7.1 Optimizing K-Means Clustering Algorithm
- 7.2 Enhancing Kd-Trees for Efficient Spatial Search
- 7.3 Streamlining Vector Embedding Search
- 7.4 Summary
- 8 Conclusion
- References
- Electronic Copyright and Legal Application Regarding Non-fungible Tokens (NFTs)
- 1 Introduction
- 2 Understanding NFTs
- 3 Understanding Blockchain Technology for NFTs
- 3.1 Minting Algorithm
- 3.2 Token Structure (Simplified)
- 3.3 Transaction Flow
- 4 Legal Landscape in USA
- 5 Legal Landscape in India
- 6 Legal Ambiguities in NFT Projects
- 7 Challenges
- 8 Future Proceedings
- 9 How Proposed Work Addresses the Challenges
- 10 Conclusion
- References
- Forecasting of Rainfall in Subdivisions of India Using Machine Learning
- 1 Introduction
- 2 Review of Related Work
- 3 Proposed Methodology
- 3.1 Data Exploration and Analysis
- 3.2 Approach
- 3.3 Dataset Description
- 3.4 Data Preprocessing
- 3.5 Performance Metrics
- 4 Results Analysis
- 4.1 Prediction
- 5 Conclusions
- References
- Author Index
