Machine Learning Applications E-Book
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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
Machine Learning Applications Practical resource on the importance of Machine Learning and Deep Learning applications in various technologies and real-world situations Machine Learning Applications discusses methodological advancements of machine learning and deep learning, presents applications in image processing, including face and vehicle detection, image classification, object detection, image segmentation, and delivers real-world applications in healthcare to identify diseases and diagnosis, such as creating smart health records and medical imaging diagnosis, and provides real-world examples, case studies, use cases, and techniques to enable the reader's active learning. Composed of 13 chapters, this book also introduces real-world applications of machine and deep learning in blockchain technology, cyber security, and climate change. An explanation of AI and robotic applications in mechanical design is also discussed, including robot-assisted surgeries, security, and space exploration. The book describes the importance of each subject area and detail why they are so important to us from a societal and human perspective. Edited by two highly qualified academics and contributed to by established thought leaders in their respective fields, Machine Learning Applications includes information on: * Content based medical image retrieval (CBMIR), covering face and vehicle detection, multi-resolution and multisource analysis, manifold and image processing, and morphological processing * Smart medicine, including machine learning and artificial intelligence in medicine, risk identification, tailored interventions, and association rules * AI and robotics application for transportation and infrastructure (e.g., autonomous cars and smart cities), along with global warming and climate change * Identifying diseases and diagnosis, drug discovery and manufacturing, medical imaging diagnosis, personalized medicine, and smart health records With its practical approach to the subject, Machine Learning Applications is an ideal resource for professionals working with smart technologies such as machine and deep learning, AI, IoT, and other wireless communications; it is also highly suitable for professionals working in robotics, computer vision, cyber security and more.
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Seitenzahl: 354
Veröffentlichungsjahr: 2023
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Table of Contents
Cover
Table of Contents
Series Page
Title Page
Copyright Page
About the Authors
Preface
1 Statistical Similarity in Machine Learning
1.1 Introduction
1.2 Featureless Machine Learning
1.3 Two‐Sample Homogeneity Measure
1.4 The Klyushin–Petunin Test
1.5 Experiments and Applications
1.6 Summary
References
2 Development of ML‐Based Methodologies for Adaptive Intelligent E‐Learning Systems and Time Series Analysis Techniques
2.1 Introduction
2.2 Methodological Advancement of Machine Learning
2.3 Machine Learning on Time Series Analysis
2.4 Conclusion
Acknowledgment
Conflict of Interest
References
3 Time‐Series Forecasting for Stock Market Using Convolutional Neural Network
3.1 Introduction
3.2 Materials
3.3 Methodology
3.4 Accuracy Measurement
3.5 Result and Discussion
3.6 Conclusion
Acknowledgement
References
4 Comparative Study for Applicability of Color Histograms for CBIR Used for Crop Leaf Disease Detection
4.1 Introduction
4.2 Literature Review
4.3 Methodology
4.4 Results and Discussions
4.5 Conclusion
References
Biographies of Authors
5 Stock Index Forecasting Using RNN‐Long Short‐Term Memory
5.1 Introduction
5.2 Materials
5.3 Methodology
5.4 Result and Discussion
5.5 Conclusion
Acknowledgement
References
6 Study and Analysis of Machine Learning Models for Detection of Phishing URLs
6.1 Introduction
6.2 Literature Review
6.3 Methodology
6.4 Results and Experimentation
6.5 Model‐Metric Analysis
6.6 Conclusion
References
7 Real‐World Applications of BC Technology in Internet of Things
7.1 Introduction
7.2 Review of Existing Study
7.3 Background of Blockchain
7.4 Blockchain Technology in Internet of Things
7.5 Challenges and Concerns in Integrating Blockchain with the IoT
7.6 Blockchain Applications for the Internet of Things (BIoT Applications)
7.7 Application of BIoT in Healthcare
7.8 Application of BIoT in Voting
7.9 Application of BIoT in Supply Chain
7.10 Summary
References
8 Advanced Persistent Threat
8.1 Introduction
8.2 Background Study
8.3 Literature Review
8.4 Research Questions
8.5 Research Objectives
8.6 Research Hypothesis
8.7 Phases of APT Outbreak
8.8 Research Methodology
8.9 A Deception Exemplary of Counter‐Offensive
8.10 Conclusion
Acknowledgment
Conflict of Interest
References
9 Integration of Blockchain Technology and Internet of Things: Challenges and Solutions
9.1 Introduction
9.2 Overview of Blockchain–IoT Integration
9.3 How Blockchain–IoT Work Together
9.4 Blockchain–IoT Applications
9.5 Related Studies on Integration of IoT and Blockchain Applications
9.6 Challenges of Blockchain–IoT Integration
9.7 Solutions of Blockchain‐IoT Integration
9.8 Future Directions for Blockchain–IoT Integration
9.9 Conclusion
References
10 Machine Learning Techniques for SWOT Analysis of Online Education System
10.1 Introduction
10.2 Motivation
10.3 Objectives
10.4 Methodology
10.5 Dataset Preparation
10.6 Data Visualization and Analysis
10.7 Machine Learning Techniques Implementation
10.8 Conclusion
References
11 Crop Yield and Soil Moisture Prediction Using Machine Learning Algorithms
11.1 Introduction
11.2 Literature Review
11.3 Methodology
11.4 Result and Discussion
11.5 Conclusion
References
12 Multirate Signal Processing in WSN for Channel Capacity and Energy Efficiency Using Machine Learning
12.1 Introduction
12.2 Energy Management in WSN
12.3 Different Strategies to Increase Energy Efficiency
12.4 Algorithm Development
12.5 Results
12.6 Summary
References
13 Introduction to Mechanical Design of AI‐Based Robotic System
13.1 Introduction
13.2 Mechanisms in a Robot
13.3 Kinematics
13.4 Conclusion
Acknowledgment
Conflict of Interest
References
Index
End User License Agreement
List of Tables
Chapter 3
Table 3.1 Forecasted error rate of stock indices over 6 years.
Table 3.2 Prediction error rate over different activation functions.
Table 3.3 RMSE values and average values of different methods.
Table 3.4 Comparison of the RMSE and the Average RMSE of BSE Sensex for Dif...
Table 3.5 Comparison of the RMSE and the average RMSE of Taiex for differen...
Table 3.6 Comparison of the RMSE and the average RMSE of Kospi for differen...
Chapter 4
Table 4.1 Soybean diseases.
Table 4.2 Average precision, recall, and disease detection efficiency of CB...
Chapter 5
Table 5.1 RMSES and average RMSE comparison for various indices.
Table 5.2 RMSE comparison for the proposed method with the earlier methods ...
Table 5.3 RMSE comparison for the proposed method with the earlier methods ...
Table 5.4 RMSE comparison for the proposed method with the earlier methods ...
Chapter 6
Table 6.1 Features table.
Table 6.2 Comparison of models based on different performance metrics.
Chapter 7
Table 7.1 Review of the existing study.
Table 7.2 Comparison of distributive ledger–IoT technologies.
Chapter 10
Table 10.1 Dataset description.
Table 10.2 Comparative studies from year 2020.
Table 10.3 Comparative studies from year 2021 to 2022.
Chapter 11
Table 11.1 Accuracy and standard deviation table for crop yield prediction....
Table 11.2 Accuracy table for soil moisture monitoring.
Table 11.3 Actual vs predicted value in soil moisture prediction.
Chapter 12
Table 12.1 Comparison of network coding.
Table 12.2 Performance table using the proposed algorithm.
List of Illustrations
Chapter 1
Figure 1.1 Behavior of P‐and KS‐statistics in the testing the location and s...
Chapter 3
Figure 3.1 Stock market analysis based on existing research.
Figure 3.2 Architecture of the convolutional neural network.
Figure 3.3 Actual and forecasted value of 2015.
Figure 3.4 Actual and forecasted value of 2016.
Figure 3.5 Actual and forecasted value of BSE 2017.
Figure 3.6 Actual and forecasted value of BSE 2018.
Figure 3.7 Actual and forecasted value of BSE 2019.
Figure 3.8 Actual and forecasted value of BSE 2020.
Figure 3.9 Actual and forecasted value of TAIEX 2015.
Figure 3.10 Actual and forecasted value of TAIEX 2016.
Figure 3.11 Actual and forecasted value of TAIEX 2017.
Figure 3.12 Actual and forecasted value of TAIEX 2018.
Figure 3.13 Actual and forecasted value of TAIEX 2019.
Figure 3.14 Actual and forecasted value of TAIEX 2020.
Figure 3.15 Actual and forecasted value of KOSPI 2015.
Figure 3.16 Actual and forecasted value of KOSPI 2016.
Figure 3.17 Actual and forecasted value of KOSPI 2017.
Figure 3.18 Actual and forecasted value of KOSPI 2018.
Figure 3.19 Actual and forecasted value of KOSPI 2019.
Figure 3.20 Actual and forecasted value of KOSPI 2020.
Chapter 4
Figure 4.1 CBIR system for disease detection.
Figure 4.2 Color histogram in RGB, HSV, and YCbCr color spaces. (a) RGB hist...
Figure 4.3 Feature extraction and CBIR system using color histogram. (a) Dat...
Figure 4.4 Detection of soybean alfalfa mosaic virus (AMV) disease using RGB...
Figure 4.5 Detection of soybean Septoria Brown Spot (SBS) disease using RGB,...
Figure 4.6 Detection of soybean Healthy leaf using RGB, HSV, and YCbCr histo...
Chapter 5
Figure 5.1 RNN structure.
Figure 5.2 RNN model.
Figure 5.3 LSTM model.
Figure 5.4 Actual vs forecasting for TAIEX from 2015 to 2020. The comparison...
Figure 5.5 Actual vs forecasting for BSE‐SENSEX from 2015 to 2020. The above...
Figure 5.6 Actual vs forecasting for KOSPI from 2015 to 2020. In this articl...
Chapter 6
Figure 6.1 Proposed system architecture.
Figure 6.2 Training and test data accuracy.
Figure 6.3 Training and test data recall.
Figure 6.5 Training and test data F1score.
Chapter 7
Figure 7.1 Types of Blockchain.
Figure 7.2 Blockchain applications for IOT.
Chapter 8
Figure 8.1 Phases of APT outbreak.
Figure 8.2 Stages of deception exemplary of counter offensive.
Chapter 9
Figure 9.1 Centralized network.
Figure 9.2 Decentralized network.
Figure 9.3 Distributed network.
Figure 9.4 Data flow in IoT without blockchain.
Figure 9.5 Data flow in IoT with blockchain.
Figure 9.6 Blockchain–IoT applications.
Chapter 10
Figure 10.1 Diagram of the process of methodology.
Figure 10.2 Understanding level during online classes.
Figure 10.3 Based on recalling teaching contents.
Figure 10.4 Online learning based on bored feelings.
Figure 10.5 Offline learning analysis based on tiredness due to writing.
Figure 10.6 Level of ranking in the offline education system.
Figure 10.7 Level of ranking of the online education system.
Figure 10.8 Challenges faced in offline classes.
Figure 10.9 Understanding during offline classes.
Figure 10.10 Displaying students who got affected on their education due to ...
Chapter 11
Figure 11.1 Linear regression algorithm. https://www.javatpoint.com/linear‐r...
Figure 11.2 KNN algorithm.
Figure 11.3 Support vector machine algorithm. https://www.javatpoint.com/mac...
Figure 11.4 Scatter plot of crop yield prediction obtained using linear regr...
Chapter 12
Figure 12.1 Wireless sensor node.
Figure 12.2 A multi‐rate network example.
Figure 12.3 Data sending without NC.
Figure 12.4 Data sending with change of rate with no network coding.
Figure 12.5 Data sending with network coding.
Figure 12.6 Comparison of time.
Figure 12.7 Comparison of energy minimization.
Chapter 13
Figure 13.1 (a) Serial or open‐chain arm exoskeleton, (b) parallel or closed...
Figure 13.2 An arm exoskeleton to be worn by the operator for teleoperation....
Figure 13.3 A general form of the Stewart platform.
Figure 13.4 Sarrus mechanism having three different positions.
Figure 13.5 Joints present in a robotic system (a) Revolute Joint, (b) Spher...
Guide
Cover Page
Table of Contents
Series Page
Title Page
Copyright Page
About the Authors
Preface
Begin Reading
Index
WILEY END USER LICENSE AGREEMENT
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Ahmet Murat Tekalp
Machine Learning Applications
From Computer Vision to Robotics
Edited by
Indranath Chatterjee
Department of Computer Engineering
Tongmyong University
Busan, South Korea
Sheetal Zalte
Department of Computer Science
Shivaji University
Kolhapur, Maharashtra, India
Copyright © 2024 by The Institute of Electrical and Electronics Engineers, Inc. All rights reserved.
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Library of Congress Cataloging‐in‐Publication Data
Names: Chatterjee, Indranath, editor. | Zalte, Sheetal S., editor. | John Wiley & Sons, publisher.Title: Machine learning applications : from computer vision to robotics / edited by Indranath Chatterjee, Sheetal Zalte.Description: Hoboken, New Jersey : Wiley‐IEEE Press, [2024] | Includes bibliographical references and index.Identifiers: LCCN 2023043411 (print) | LCCN 2023043412 (ebook) | ISBN 9781394173327 (cloth) | ISBN 9781394173334 (adobe pdf) | ISBN 9781394173341 (epub)Subjects: LCSH: Machine learning–Industrial applications. | Machine learning–Scientific applications. | Deep learning (Machine learning)–Industrial applications. | Deep learning (Machine learning)–Scientific applications.Classification: LCC Q325.5 .M321323 2024 (print) | LCC Q325.5 (ebook) | DDC 006.3/1–dc23/eng/20231023LC record available at https://lccn.loc.gov/2023043411LC ebook record available at https://lccn.loc.gov/2023043412
Cover Design: WileyCover Image: © Yuichiro Chino/Getty Images
About the Authors
Dr. Indranath Chatterjee is working as a professor in the Department of Computer Engineering at Tongmyong University, Busan, South Korea. He received his PhD in Computational Neuroscience from the Department of Computer Science, University of Delhi, Delhi, India. His research areas include computational neuroscience, schizophrenia, medical imaging, fMRI, and machine learning. He has authored and edited nine books on computer science and neuroscience published by renowned international publishers. To date, he has published more than 70 research papers in international journals and conferences. He is the recipient of various global awards in neuroscience. He is serving as the Chief Section Editor of a few renowned international journals and as a member of the advisory board and editorial board of various international journals and open‐science organizations worldwide. He is working on several projects of government and non‐government organizations as PI/co‐PI, related to medical imaging and machine learning for a broader societal impact, in collaboration with several universities globally. He is an active professional member of the Association of Computing Machinery (ACM, USA), the Organization of Human Brain Mapping (OHBM, USA), the Federation of European Neuroscience Society (FENS, Belgium), the Association for Clinical Neurology and Mental Health (ACNM, India), and the International Neuroinformatics Coordinating Facility (INCF, Sweden).
Dr. Sheetal S. Zalte‐Gaikwad is an assistant professor in the Computer Science Department at Shivaji University, Kolhapur, India. She pursued BSc and MSc from Pune University, India. She earned her PhD in mobile ad‐hoc network at Shivaji University, India. She has 14 years of teaching experience in computer science. She has published more than 40 research papers in reputed international journals and conferences. She has also published book chapters with Springer, Bentham, CRC Taylor, Wiley, and Francis. Her research areas are MANET, VANET, blockchain security. She has also authored a book, Computational Theory, Problems and Solutions. She worked as the lead editor for the book, Synergistic Interaction of Big Data with Cloud Computing for Industry 4.0, CRC Press, Taylor and Francis Publisher, USA.
Preface
In our rapidly evolving world, the transformative power of machine learning (ML) and deep learning (DL) technologies is undeniable. From robotics and vehicle automation to financial services, retail, manufacturing, healthcare, and beyond, ML and DL are revolutionizing industries and driving improvements in business operations. The potential of these advanced technologies to enhance our lives and reshape our future is immense.
In this book, we delve into the remarkable advancements made possible by ML and DL, showcasing case studies that demonstrate how these technologies have facilitated breakthroughs in business intelligence, enabling faster and more efficient decision‐making processes. We explore a wide range of applications, from facial recognition to natural language processing, and illustrate how ML and DL play a central role in the continuous learning and data simulation capabilities of cars in real‐time.
While it is crucial to acknowledge the potential challenges and implications associated with ML and DL, it is equally important to recognize the positive impact they can have on our society. This book aims to shed light on real‐world examples that highlight how ML and DL can create better technology to support modern thinking. Whether you are a novice or a specialist in the field, these captivating case studies will offer valuable insights into various applications where ML and DL techniques play a significant role.
Within these pages, we uncover the inner workings of ML algorithms, revealing how they transform digital images, which are mere series of numbers, into meaningful patterns through image processing techniques. We also explore the complex landscapes of risk modeling, genomic sequencing, and modeling, where ML and DL implementations require extensive cloud environments with high‐performance data processing and management capabilities.
Moreover, we examine the competitive landscape of ML‐ and DL‐based platforms, where major vendors such as Amazon, Google, Microsoft, IBM, and others vie for customers by offering comprehensive services encompassing data collection, classification, modeling, training, and application deployment.
The revolutionizing influence of ML and DL technologies transcends boundaries, revolutionizing nearly every industry worldwide. This book is dedicated to providing extensive coverage of these groundbreaking technologies and illustrating how they are reshaping industries and our lives.
We explore the vast domain of computer vision and its wide‐ranging applications, from everyday life scenarios to the Internet of Things and brain–computer interfaces. With the ability to detect and track humans across multiple streams of data, ML and computer vision represent significant leaps forward, offering tremendous potential in terms of efficiency, productivity, revenue generation, and profitability.
We also examine the critical role played by ML and computer vision in our digital society. They empower individuals with great ideas and limited resources to succeed in business while also enabling established enterprises to harness and analyze the data they collect. Moreover, we highlight how ML contributes to cybersecurity by effectively tracking and preventing monetary frauds online, using examples like PayPal's ML‐powered tools for detecting money laundering.
Throughout this book, we aim to cultivate an understanding of the vital importance of ML and computer vision in our AI‐driven era. By exploring real‐world applications across diverse disciplines and daily‐life scenarios, we hope to provide readers with state‐of‐the‐art algorithms and practical insights that underscore the value of AI in future applications.
Embark on this journey with us as we uncover the exciting world of ML and DL, where cutting‐edge technology meets real‐world impact. May this book empower you to grasp the immense potential of these technologies and inspire you to explore and contribute to their further advancement.
Enjoy the exploration!
November 2023
Indranath ChatterjeeBusan, South KoreaSheetal ZalteKolhapur, Maharashtra, India
2Development of ML‐Based Methodologies for Adaptive Intelligent E‐Learning Systems and Time Series Analysis Techniques
Indra Kumari1, Indranath Chatterjee2, and Minho Lee1
1Korea Institute of Science and Technology Information (KISTI), University of Science and Technology (UST), Daejeon, South Korea
2Department of Computer Engineering, Tongmyong University, Busan, South Korea
2.1 Introduction
Artificial intelligence (AI)'s machine learning (ML) subfield focuses on creating and studying AI software that can teach itself new skills. Definition: ML is the study of how to program computers to learn and make decisions in ways that are indistinguishable from human intelligence (Sarker 2021). The term “machine learning” refers to a technique whereby a computer is taught to optimize a performance metric by analyzing and learning from examples. Generalization and representation are at the heart of ML. The system's ability to generalize to novel data samples is a key feature. According to Herbert Simon, “learning” is the process through which a system experiences adaptive alterations that improve its performance on a given task or collection of activities the next time it is used. If the program's performance on tasks in class T improves with experience E, as measured by the performance measure P, then we say that the program has learned from its past performance and can apply that knowledge to future performance. Tom Mitchell explains that “a computer program is said to learn from experience E concerning some class of tasks T and performance measure P.” Robots with AI can learn from their experiences, identify patterns, and infer their meaning (Patel and Patel 2016).
ML and AI have become so pervasive in our daily lives that they are no longer the purview of specialized researchers trying to crack a difficult issue. Instead of being a fluke, this development has a very natural feel to it. Organizations are now able to harness a massive amount of data in developing solutions with far‐reaching commercial benefits, thanks to the exponential development in processing speed and the introduction of better algorithms for tackling complicated and tough issues. The availability of rich data, new algorithms, and unique methodologies in its numerous applications make financial services, banking, and insurance one of the most important industries with a very high potential in reaping the advantages of ML and AI. Because companies have only scratched the surface of quickly developing fields like deep neural networks and reinforcement learning, the potential of employing these approaches in many applications remains significantly untapped.
Organizations are reaping the benefits of cutting‐edge ML applications in areas such as customer segmentation for targeted marketing of newly released products, the development of optimal portfolio strategies, the identification and prevention of money laundering and other illegal activities in the financial markets, the implementation of more intelligent and effective credit risk management practices, and the maintenance of compliance with regulatory frameworks in financial, accounting, and other operational areas. However, the full potential of ML and AI has yet to be discovered or used. Businesses need to take use of these features if they want to gain and keep a competitive edge over the long run. One of the main reasons for the slow adoption of AI/ML models and methods in financial applications is the lack of familiarity and trust in deploying them in critical and privacy‐sensitive applications. However, the “black‐box” nature of such models and frameworks that analyzes their internal operations in producing outputs and their validations also impedes faster acceptance and deployment of such models in real‐world settings.
