Building Intelligent Systems Using Machine Learning and Deep Learning: Security, Applications and Its Challenges

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Abhaya Kumar Sahoo, PhD – Associate Professor, School of Computer Engineering of Industrial Technology (Deemed to be University), Bhubaneswar, Odisha, India
Chittaranjan Pradhan, PhD – Associate Professor, School of Computer Engineering, Kalinga Institute of Industrial Technology (Deemed to be University), Bhubaneswar, Odisha, India
Bhabani Shankar Prasad Mishra, PhD – Professor, School of Computer Engineering, Kalinga Institute of Industrial Technology (Deemed to be University), Bhubaneswar, Odisha, India
Brojo Kishore Mishra, PhD – Professor, Department of Computer Science and Engineering, NIST Institute of Science and Technology (Autonomous), Berhampur, Odisha, India

Series: Computer Science, Technology and Applications; Computational Mathematics and Analysis
BISAC: COM094000; COM021030; COM025000
DOI: https://doi.org/10.52305/SRTQ8806

The primary objective of this book is to provide insight into the design and development of the intelligent system. The proposed book volume mainly focuses on a machine learning and deep learning-based intelligent system that would bring out the latest trends in the field of tourism, healthcare, agriculture, etc. This book provides security solutions for the intelligent system in different applications. The technological gaps between the traditional system and intelligent system are mentioned in the book, which will help in better understanding for the implementation of the intelligent system using machine learning (ML) and deep learning (DL) approaches. Although ML and DL have made great achievements in intelligent systems, there are still substantial open challenges that have not been fully studied. The main open challenges of using ML and DL in intelligent systems are: (i) Better performance of the system (ii) Time complexity of the jobs running inside an intelligent system (iii) Managing overload tasks (iv) Providing security towards the system. This book will definitely help academicians, researchers and industry people towards the security, design and development of the intelligent system.

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Table of Contents

Preface

Chapter 1. Intelligent Systems for Future Applications Using Machine Learning
Anil Kumar Meher1, Abhaya Kumar Sahoo2 and Rekhanjali Sahoo3
1Department of Computer Science & Engineering, Centurion University of Technology and Management, Bhubaneswar, India
2School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, India
3Department of Computer Science Engineering, GITA Autonomous College, Bhubaneswar, India

Chapter 2. Fundamental Models in Intelligent Systems Using Machine Learning and Deep Learning
R. Rathi1, E. P. Ephzibah1, V. Mareeswari1, P. Visvanathan1, R. Kanchana2 and E. Deepakraj1
1
School of Information Technology and Engineering, VIT, Vellore, India
2Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, India

Chapter 3. A Comparative Analysis of Machine Learning Algorithms on Intrusion Detection Systems
Vijayan R., PhD, Mareeswari V., PhD, Rathi R., PhD, Ephzibah EP, PhD and Harshitha KSR, MTech (SE)
School of Computer Science Engineering and Information Systems (SCORE), Vellore Institute of Technology (VIT), Vellore, India

Chapter 4. A Novel Approach for Requirement-Based Test Case Prioritization Using Machine Learning Techniques
Aishwaryarani Behera1, MTech, Arup Abhinna Acharya1, PhD, Sanjukta Mohanty2, MTech and Namita Panda1, PhD
1School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, India
2Computer Science and Engineering, Odisha University of Technology and Research, Bhubaneswar, India

Chapter 5. The Detection and Prevention of Phishing Threats in OSN Using Machine Learning Techniques
Smrutisrita Samal1, BTech, Sanjukta Mohanty1, MTech and Arup Abhinna Acharya2, PhD (ABD)
1Department of Computer Science and Engineering, OUTR, Odisha, India
2School of Computer Engineering, KIIT Deemed to be University, Odisha, India

Chapter 6. A Novel Approach to Detecting Apple Disease Using CNN
Chittaranjan Pradha, Mritunjay Kumar, Divyansi Mishra and Biswaroop Nath
School of Computer Engineering, Kalinga Institute of Industrial Technology, Odisha, India

Chapter 7. A Novel Sigmoid Butterfly Optimization Deep Learning Model for Big Data Classification
R. Umanesan1, PhD, R. Kanchana1, PhD, R. Rathi2, PhD and P. Visvanathan2, ME
1Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, India
2School of Information Technology and Engineering, VIT University, Vellore, India

Chapter 8. An Analysis of Optical Character Recognition-Based Machine Translation for Low Resource Languages
P. Mahesha, Trisiladevi C. Nagavi, Harshitha L. P., Swathi Alse and Shifali B. Shetty
Department of Computer Science and Engineering, S. J. College of Engineering, JSS Science and Technology University Mysore, India

Chapter 9. Generative AI for Bio-Signal Analysis and Augmentation
Shiyona Dash1, Mtech, Ashis Kumar Parida1, Btech, Kunal Pal1, PhD and Mirza Khalid Baig1, PhD
Department of Biotechnology and Medical Engineering, National Institute of Technology, Rourkela, Odisha, India

Chapter 10. Deep Learning for the Closed Loop Diabetes Management System
Deepjyoti Kalita1, MTech, Hrishita Sharma1, MTech, Ujjal Naskar1, MS (Pharm), Bikash Kumar Mishra1, BTech, Jayanta Kumar Panda2, MD and Khalid B. Mirza1, PhD
1Department of Biotechnology and Medical Engineering, National Institute of Technology, Rourkela, Odisha, India
2SCB Medical College and Hospital, Cuttack, Odisha, India

Chapter 11. Digital Image Spatial Feature Learning and Mapping Using Geospatial Artificial Intelligence: A Case Study
Ajay Kumar1, Jay Prakash Singh1, Deepjyoti Choudhury1, Vivek Kumar1 and Sunil Kumar Bisoyi2
1Department of Computer Science and Engineering, Manipal University Jaipur, Rajasthan India
2Postdoctoral Research Fellowship, Department of Mining Engineering, IIT (ISM) Dhanbad, Jharkhand, India

Index

Authors’ Biography

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