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The Handbook of Computational Sciences is a comprehensive collection of research chapters that brings together the latest advances and trends in computational sciences and addresses the interdisciplinary nature of computational sciences, which
require expertise from multiple disciplines to solve complex problems.
This edited volume covers a broad range of topics, including computational physics, chemistry, biology, engineering, finance, and social sciences. Each chapter provides an in-depth discussion of the state-of-the-art techniques and methodologies used in the respective field. The book also highlights the challenges and opportunities for future research in these areas.
The volume pertains to applications in the areas of imaging, medical imaging, wireless and WS networks, IoT with applied areas, big data for various applicable solutions, etc. This text delves deeply into the core subject and then broadens to encompass the interlinking, interdisciplinary, and cross-disciplinary sections of other relevant areas. Those areas include applied, simulation, modeling, real-time, research applications, and more.
Audience
Because of the book's multidisciplinary approach, it will be of value to many researchers and engineers in different fields including computational biologists, computational chemists, and physicists, as well as those in life sciences, neuroscience, mathematics, and software engineering.
List of contents
Preface xv
1 A Sensor-Based Automated Irrigation System for Indian Agricultural Fields 1
I.S. Akila and Ahmed A. Elngar
1.1 Introduction 1
1.2 Literary Survey 2
1.3 Proposed System 4
1.3.1 System Architecture 4
1.3.2 Flow of Automated Irrigation 5
1.3.3 Interfacing Sensors 5
1.3.4 Physical Characteristics Determination Using Image Processing 8
1.3.4.1 Fractal Dimension Estimation 8
1.3.4.2 Box-Counting Method 9
1.3.4.3 Physical Parameters 9
1.4 Performance Studies 11
1.4.1 Experimental Environment 11
1.4.2 Accessing Shared Variables from NI Data Dashboard 12
1.4.2.1 Scenario 1 14
1.4.2.2 Scenario 2 14
1.4.2.3 Scenario 3 14
1.4.2.4 Scenario 4 14
1.5 Image Processing to Determine Physical Characteristics 15
1.6 Conclusion and Future Enhancements 20
1.6.1 Conclusion 20
1.6.2 Future Scope 20
References 20
2 An Enhanced Integrated Image Mining Approach to Address Macro Nutritional Deficiency Problems Limiting Maize Yield 23
Sridevy S., Anna Saro Vijendran and Ahmed A. Elngar
2.1 Introduction 24
2.2 Related Work 24
2.3 Motivation 26
2.4 Framework of Enhanced Integrated Image Mining Approaches to Address Macro Nutritional Deficiency Problems Limiting Maize Yield 26
2.5 Algorithm - Enhanced Integrated Image Mining Approaches to Address Macro Nutritional Deficiency Problems Limiting Maize Yield 28
2.6 Conclusion 34
References 35
3 Collaborative Filtering Skyline (CFS) for Enhanced Recommender Systems 37
Shobana G. and Ahmed A. Elngar
3.1 Introduction and Objective 38
3.1.1 Objective 39
3.2 Motivation 39
3.3 Literature Survey 40
3.4 System Analysis and Existing Systems 43
3.4.1 Drawbacks 45
3.5 Proposed System 46
3.5.1 Feasibility Study 46
3.5.2 Economic Feasibility 46
3.5.3 Operational Feasibility 47
3.5.4 Technical Feasibility 47
3.5.5 Problem Definition and Project Overview 48
3.5.6 Overview of the Project 48
3.5.7 Exact Skyline Computation 51
3.5.8 Approximate Skyline Computation 52
3.5.9 Module Description 53
3.5.9.1 Modules 53
3.5.10 Data Flow Diagram 54
3.5.10.1 External Entity (Source/Sink) 57
3.5.10.2 Process 57
3.5.10.3 Data Flow 57
3.5.10.4 Data Store 58
3.5.11 Rules Used For Constructing a DFD 58
3.5.12 Basic DFD Notation 59
3.5.13 Profiles 59
3.5.14 Uploaded My Videos 60
3.5.15 Rating My Videos 60
3.6 System Implementation 63
3.6.1 Implementation Procedures 63
3.6.1.1 User Training 64
3.6.1.2 User Manual 64
3.6.1.3 System Maintenance 64
3.6.1.4 Corrective Maintenance 65
3.6.1.5 Adaptive Maintenance 65
3.6.1.6 Perceptive Maintenance 65
3.6.1.7 Preventive Maintenance 65
3.7 Conclusion and Future Enhancements 65
3.7.1 Conclusion 65
3.7.2 Enhancements 66
References 66
4 Automatic Retinopathic Diabetic Detection: Data Analyses, Approaches and Assessment Measures Using Deep Learning 69
Rinesh S., Mahdi Ismael Omar, Thamaraiselvi K., V. Karthick and Vigneshwar Manoharan
4.1 Introduction 70
4.2 Related Work 72
4.3 Initial Steps and Experimental Environment 74
4.3.1 Analyzing the Principle Components 74
4.3.2 Firefly Algorithm
About the author
Ahmed A. Elngar, PhD, is the founder and head of Scientific Innovation Research Group (SIRG) and assistant professor of Computer Science at the Faculty of Computers and Information, Beni-Suef University, Egypt.
Vigneshwar. M., is head of R & D and Academic Initiatives, Cybase Technologies, Coimbatore, Tamil Nadu, India. He has around 9 years in industry & research and 8 years in academia. He has an M.E., degree in computer science and engineering, and has published more than 110 international/national conference and journal publications as well as numerous awards.
Krishna Kant Singh, PhD, is an associate professor in the Faculty of Engineering & Technology, Jain (Deemed-to-be University), Bengaluru, India. Dr. Singh has acquired BTech, MTech, and PhD (IIT Roorkee) in the area of machine learning and remote sensing. He has authored more than 50 technical books and research papers in international conferences and SCIE journals.
Zdzislaw Polkowski, PhD, is a professor in the Faculty of Technical Sciences, Jan Wyzykowski University, Polkowice, Poland. He has published more than 75 research articles in peer-reviewed journals.
