Handbook of research on AI and ML for intelligent machines and systems:
"By compiling recent advancements in intelligent machines that rely on machine learning and deep learning technologies, this book serves as a vital resource for researchers, graduate students, PhD scholars, faculty members, scientists, and software developers. It offers valuable insights into t...
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| Schriftenreihe: | A volume in the Advances in Computational Intelligence and Robotics (ACIR) Book Series
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| Online-Zugang: | Inhaltsverzeichnis |
| Zusammenfassung: | "By compiling recent advancements in intelligent machines that rely on machine learning and deep learning technologies, this book serves as a vital resource for researchers, graduate students, PhD scholars, faculty members, scientists, and software developers. It offers valuable insights into the key concepts of AI and ML, covering essential security aspects, current trends, and often overlooked perspectives that are crucial for achieving comprehensive understanding. It not only explores the theoretical foundations of AI and ML but also provides guidance on applying these techniques to solve real-world problems. Unlike traditional texts, it offers flexibility through its distinctive module-based structure, allowing readers to follow their own learning paths"-- |
| Beschreibung: | xxvii, 503 Seiten Illustrationen, Diagramme 29 cm |
| ISBN: | 9781668499993 1668499991 |
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Table of Contents Preface. xxii Acknowledgement. xxvii Chapter 1 Conceptualising the Role of Intellectual Property and Ethical Behaviour in Artificial Intelligence. 1 Princy Pappachan, Asia University, Taiwan Sreerakuvandana, Jain University, India Mosiur Rahaman, Asia University, Taiwan Chapter 2 Measuring Throughput and Latency of Machine Learning Techniques for Intrusion Detection. 27 Winfred Yaokumah, University of Ghana, Ghana Charity Y. Μ. Baidoo, University of Ghana, Ghana Ebenezer Owusu, University of Ghana, Ghana Chapter 3 Securing Digital Ecosystems: Harnessing the Power of Intelligent Machines in a Secure and Sustainable Environment. 50 Mario Casillo, University of Salerno, Italy Francesco Colace, University of Salerno, Italy Brij B. Gupta, Asia University, Taichung, Taiwan Lebanese American University, Beirut, Lebanon Francesco Marongiu, University of Salerno, Italy Domenico Santaniello, University of Salerno, Italy Chapter 4 IoT-Based Economic Flame Detection Device for Safety. 75 Suchismita Satapathy, KITT University, India Chapter 5 Human Face Mask Detection Using
YOLOv7+CBAM in Deep Learning. 94 Xinyi Gao, Auckland University of Technology, New Zealand Minh Nguyen, Auckland University of Technology, New Zealand Wei Qi Yan, Auckland University of Technology, New Zealand
Chapter 6 The Role of AI in Improving Interaction With Cultural Heritage: An Overview. 107 Mario Casillo, University of Salerno, Italy Francesco Colace, University of Salerno, Italy Brij B. Gupta, Asia University, Taichung, Taiwan Lebanese American University, Beirut, Lebanon Angelo Lorusso, University of Salerno, Italy Domenico Santaniello, University of Salerno, Italy Carmine Valentino, University of Salerno, Italy Chapter 7 Machine Learning Approach for Robot Navigation Using Motor Imagery Signals. 137 Pratyay Das, MaulanaAbul KalamAzad University of Technology, West Bengal, India Amit Kumar Shankar, Maulana Abul Kalam Azad University of Technology, West Bengal, India Ahona Ghosh, MaulanaAbul KalamAzad University of Technology, West Bengal, India Sriparna Saha, MaulanaAbul KalamAzad University of Technology, West Bengal, India Chapter 8 Cloud Solutions for Smart Parking and Traffic Control in Smart Cities. 169 Maganti Syamala, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, India J. Malathi, Department of Computer Science and Business Systems, Sri Sai Ram Engineering College, Chennai, India Vikash Singh, Department of Civil Engineering, Institute of Engineering and Technology, Lucknow, India Hari Priya G. S., Department of Computer Science, M.S. Ramaiah College ofArts Science and Commerce, Bengaluru, India B. Uma Maheswari, Department of Computer Science and Engineering, St. Joseph's College of Engineering, Chennai, India
Murugan S., Sona College of Technology, India Chapter 9 Building Sustainable Smart Cities Through Cloud and Intelligent Parking System. 195 Monika Sharma, Department of Computer Science and Engineering, The Technological Institute of Textile and Sciences, Bhiwani, India Manju Sharma, Department of Computer Science and Engineering, University Institute of Engineering and Technology, India Maharshi Dayanand University, India Neerav Sharma, Department of Computer Science and Engineering, BITS College, Bhiwani, India Sampath Boopathi, Mechanical Engineering, Muthayammal Engineering College, India
Chapter 10 A Study on AI and Blockchain-Powered Smart Parking Models for Urban Mobility. 223 K. Sundaramoorthy, Department of Information Technology, Jerusalem College of Engineering, India Ajeet Singh, School of Computing Science and Engineering, VIT Bhopal University, India G. Sumathy, Department of Computational Intelligence, SRM Institute of Science and Technology, India A. Maheshwari, Department of Computational Intelligence, SRM Institute of Science and Technology, India A. R. Arunarani, Department of Computational Intelligence, SRM Institute of Science and Technology, India Sampath Boopathi, Mechanical Engineering, Muthayammal Engineering College, India Chapter 11 Machine Learning and Deep Learning for Intelligent Systems in Small Aircraft Applications. 251 U. Rahamathunnisa, School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, India Akash Mohanty, School of Mechanical Engineering, Vellore Institute of Technology, India K. Sudhakar, Department of Computer Science and Engineering, Madanapalle Institute of Technology and Science, Madanapalle, India S. Anitha Jebamani, Department of Information Technology, Sri Sai Ram Engineering College, Chennai, India R. Udendhran, Department of Computational Intelligence, SRM Institute of Science and Technology, India Sureshkumar Myilsamy, Mechanical Engineering, Bannari Amman Institute of Technology, India Chapter 12 Machine Learning in E-Health and Digital Healthcare: Practical Strategies for Transformation. 276 T K. Sethuramalingam, Department of
Electronics and Communication Engineering, Karpagam College of Engineering, Coimbatore, India Rajkumar G. Nadakinamani, Badr Al Samaa Hospital, Oman G. Sumathy, Department of Computational Intelligence, SRM Institute of Science and Technology, India Sureshkumar Myilsamy, Mechanical Engineering, Bannari Amman Institute of Technology, India Chapter 13 Unsupervised Learning Techniques for Vibration-Based Structural Health Monitoring Systems Driven by Data: A General Overview. 305 Francesco Colace, University of Salerno, Italy Brij B. Gupta, Asia University, Taichung, Taiwan Lebanese American University, Beirut, Lebanon Angelo Lorusso, University of Salerno, Italy Alfredo Troiano, University of Salerno, Italy Domenico Santaniello, University of Salerno, Italy Carmine Valentino, University of Salerno, Italy
Chapter 14 Convergence of Data Science-AI-Green Chemistry-Affordable Medicine: Transforming Drug Discovery. 348 B. Rebecca, Department of Computer Science and Engineering (Data Science), Marri Laxman Reddy Institute of Technology and Management, Hyderabad, India K. Pradeep Mohan Kumar, Department of Computing Technologies, SRM Institute of Science and Technology, Chennai, India S. Padmini, Department of Computing Technologies, SRM Institute of Science and Technology, Chennai, India Bipin Kumar Srivastava, Department ofApplied Sciences, Galgotias College of Engineering and Technology, India Shubhajit Halder, Department of Chemistry, Hislop College, Nagpur, India Sampath Boopathi, Mechanical Engineering, Muthayammal Engineering College, India Chapter 15 Intelligent Machines, loT, and AI in Revolutionizing Agriculture for Water Processing. 374 Krishnagandhi Pachiappan, Department of Electrical and Electronics Engineering, Nandha Engineering College, India K. Anitha, Department of Computing Technologies, School of Computing, SRM Institute of Science and Technology, India R. Pitchai, Department of Computer Science and Engineering, B.V. Raju Institute of Technology, India S. Sangeetha, Department of Computer Science Engineering, Karpagam College of Engineering, India T.VV Satyanarayana, Department of Electronics and Communication Engineering, Mohan Babu University, India Sampath Boopathi, Mechanical Engineering, Muthayammal
Engineering College, India Chapter 16 A Mixture Model for Fruit Ripeness Identification in Deep Learning. 400 Bingjie Xiao, Auckland University of Technology, New Zealand Minh Nguyen, Auckland University of Technology, New Zealand Wei Qi Yan, Auckland University of Technology, New Zealand Chapter 17 YOLO Models for Fresh Fruit Classification From Digital Videos. 421 Yinzhe Xue, Auckland University of Technology, New Zealand Wei Qi Yan, Auckland University of Technology, New Zealand Compilation of References. 436 About the Contributors.493 Index.500
Detailed Table of Contents Preface. xxii Acknowledgement. xxvii Chapter 1 Conceptualising the Role of Intellectual Property and Ethical Behaviour in Artificial Intelligence.1 Princy Pappachan, Asia University, Taiwan Sreerakuvandana, Jain University, India Mosiur Rahaman, Asia University, Taiwan The development of artificial intelligence has significantly affected all facets of human life, leading to many ethical and legal questions that have positive and negative consequences for individuals, organisations, and society. Amidst this lies the interaction between intellectual property rights and ethical behaviour in the development and use of artificial intelligence. While intellectual property acts as a catalyst for innovation, ethical behaviour ensures responsible and accountable artificial intelligence consistent with social standards. Accordingly, the beneficial effects of artificial intelligence can only be guaranteed by balancing intellectual property rights and ethical behaviour. This chapter thus discusses the notion of intellectual property and ethical behaviour in the context of artificial intelligence by providing a comprehensive historical review and reflecting on the creation and implementation of ethical artificial intelligence while preserving intellectual property rights.
Chapter 2 Measuring Throughput and Latency of Machine Learning Techniques for Intrusion Detection. 27 Winfred Yaokumah, University of Ghana, Ghana Charity Y. Μ. Baidoo, University of Ghana, Ghana Ebenezer Owusu, University of Ghana, Ghana When evaluating the effectiveness of machine learning algorithms for intrusion detection, it is insufficient to only focus on their performance metrics. One must also focus on the overhead metrics of the models. In this study, the performance accuracy, latency, and throughput of seven supervised machine learning algorithms and a proposed ensemble model were measured. The study performs a series of experiments using two recent datasets, and two filter-based feature selection methods were employed. The results show that, on average, the naive bayes achieved the lowest latency, highest throughput, and lowest accuracy on both datasets. The logistics regression had the maximum throughput. The proposed ensemble method recorded the highest latency for both feature selection methods. Overall, the Spearman feature selection technique increased throughput for almost all the models, whereas the Pearson feature selection approach maximized performance accuracies for both datasets.
Chapter 3 Securing Digital Ecosystems: Harnessing the Power of Intelligent Machines in a Secure and Sustainable Environment. 50 Mario Casillo, University of Salerno, Italy Francesco Colace, University of Salerno, Italy Brij B. Gupta, Asia University, Taichung, Taiwan Lebanese American University, Beirut, , Lebanon Francesco Marongiu, University of Salerno, Italy Domenico Santaniello, University of Salerno, Italy Industries are evolving towards an integral digitisation of their processes. In the face of ever-faster market demands and ever-increasing quality, information technology (ΓΤ) progress represents the only solution to these needs. Industry 4.0 was born with this focus, where cybernetic systems interact with each other to achieve, efficiently, a predetermined goal. The whole process takes place with minimal, or in some cases total, absence of human intervention, leaving the systems to interact in full autonomy. This approach commonly falls under the internet of things (loT) paradigm, in which all objects, regardless of size and functionality, are connected in a standard network exchanging information. In this sense, objects acquire intelligence because they can modify their behaviours based on the data they receive and transmit. Chapter 4 IoT-Based Economic Flame Detection Device for Safety. 75 Suchismita Satapathy, KIIT University, India Mainly fires are of three types
(i.e., ground, surface, and crown fire), which occur as wild land/forest fire, residential fire, building fire, and others. The number and effect of fires are an outcome of global warming, extinction of species, and climate change. To battle against these parameters/disasters, it is important to take on an exhaustive and complex methodology that empowers nonstop situational mindfulness and moment responsiveness. The outcome is unrecoverable and dangerous to the climate, environment, people’s lives, and causes economic losses. The issues/barriers in the detection of fire are discussed here. For that, the authors have identified and ranked those challenges by using the best worst method. They have designed an automatic fire alarm detector at sensitive sites as one of the preventive steps to avoid the hazard. It can detect heat in a specific environment, raise an alert, turn off the building’s mains, and even spray water to minimize the intensity of the fire. Chapter 5 Human Face Mask Detection Using YOLOv7+CBAM in Deep Learning. 94 Xinyi Gao, Auckland University of Technology, New Zealand Minh Nguyen, Auckland University of Technology, New Zealand Wei Qi Yan, Auckland University of Technology, New Zealand COVID-19 and its variants have affected millions of people around the world. Wearing a mask is an effective way to reduce the spread of the epidemic. While wearing masks is a proven strategy to mitigate the spread, monitoring compliance remains a challenge. In this chapter, the authors propose a mask detection method based on deep
learning and convolutional block attention module (CBAM). In this chapter, they extract representative features from input images through supervised learning. In order to improve the recognition accuracy under limited computing resources. They choose YOLOv7
network model and incorporate CBAM into its network structure. Compared with the original version of YOLOv7, the proposed network model improves the mean average precision (mAP) up to 0.3% in face mask detection process. Meanwhile, the method improves the detection speed of each frame 73ms. These advancements have significant implications for real-time, large-scale monitoring systems, thereby contributing to public health and safety. Chapter 6 The Role of AI in Improving Interaction With Cultural Heritage: An Overview. 107 Mario Casillo, University of Salerno, Italy Francesco Colace, University of Salerno, Italy Brij B. Gupta, Asia University, Taichung, Taiwan Lebanese American University, Beirut, Lebanon Angelo Lorusso, University of Salerno, Italy Domenico Santaniello, University of Salerno, Italy Carmine Valentino, University of Salerno, Italy Over the years, artificial intelligence techniques have been applied in several application fields, exploiting data to execute different tasks and achieve disparate objectives. Therefore, the cultural heritage field can utilise AI techniques to improve the interaction between visitors and cultural assets. Then, this work aims to present the background related to the principal AI techniques and provides an overview of the literature aimed at improving the user cultural experience. This overview focuses on AI integration with tools aimed to enhance the interaction among visitors and cultural sites, such as recommender systems, context-aware recommender systems, and chatbots. Finally, the most common
measure used for estimating the accuracy of the AI methodologies will be introduced. Chapter 7 Machine Learning Approach for Robot Navigation Using Motor Imagery Signals. 137 Pratyay Das, Maulana Abul Kalam Azad University of Technology, West Bengal, India Amit Kumar Shankar, Maulana Abul Kalam Azad University of Technology, West Bengal, India Ahona Ghosh, Maulana Abul Kalam Azad University of Technology, West Bengal, India Sriparna Saha, Maulana Abul Kalam Azad University of Technology, West Bengal, India Electroencephalography (EEG) signals have been used for different healthcare applications like motor and cognitive rehabilitation. In this study, motor imagery data of different subjects’ rest vs. movement and different movements is categorized from a publicly available dataset. The authors have first applied a lowpass filter to the EEG signals to reduce noise and a fast fourier transform analysis to extract features from the filtered data. Utilizing principal component analysis, relevant features are selected. With an accuracy of 95.02%, they have classified rest vs. movement using the к-nearest neighbor algorithm. Using the random forest algorithm, they have classified various movement types with an accuracy of 96.45%. The success in differentiating between movement and rest raises the possibility that EEG signals can recognize a user’s intention to move. Accurately classifying different movement types opens the possibility of navigating robots accordingly in the real-time scenario for people with motor disabilities to assist them with
robotic arms and prosthetic limbs.
Chapter 8 Cloud Solutions for Smart Parking and Traffic Control in Smart Cities. 169 Maganti Syamala, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, India J. Malathi, Department of Computer Science and Business Systems, Sri Sai Ram Engineering College, Chennai, India Vikash Singh, Department of Civil Engineering, Institute of Engineering and Technology, Lucknow, India Hari Priya G. S., Department of Computer Science, M.S. Ramaiah College ofArts Science and Commerce, Bengaluru, India B. Uma Maheswari, Department of Computer Science and Engineering, St. Joseph’s College of Engineering, Chennai, India Murugan S., Sona College of Technology, India Urban mobility trends include 5G connectivity, autonomous vehicles, electric and sustainable modes, AI and machine learning, drones, and air mobility. These technologies enable real-time data exchange, reduce congestion, enhance safety, optimize road capacity, and optimize infrastructure planning. AI and machine learning algorithms provide accurate predictive analytics, adaptive traffic control, and personalized services. Cloud computing, loT, and data analytics enable predictive modeling for mobility planning, traffic flow forecasting, demand forecasting, and behavioral analysis. MaaS platforms facilitate seamless integration of modes, while shared mobility services like car-sharing and ride-hailing grow, reducing private vehicle ownership and promoting efficient resource use. Mobility data transforms urban planning, infrastructure
optimization, mixed-use development, and smart city integration, guiding transportation layouts, traffic signal placements, parking facilities, and neighborhood design. Chapter 9 Building Sustainable Smart Cities Through Cloud and Intelligent Parking System. 195 Monika Sharma, Department of Computer Science and Engineering, The Technological Institute of Textile and Sciences, Bhiwani, India Manju Sharma, Department of Computer Science and Engineering, University Institute of Engineering and Technology, India Maharshi Dayanand University, India Neerav Sharma, Department of Computer Science and Engineering, BITS College, Bhiwani, India Sampath Boopathi, Mechanical Engineering, Muthayammal Engineering College, India This chapter discusses the role of cloud computing and intelligent parking systems in sustainable smart cities, addressing challenges like traffic congestion, pollution, andresource inefficiency. These technologies enhance urban mobility, reduce environmental impact, and improve quality of life in cities facing rapid urbanization worldwide. This chapter offers a thorough analysis of the integration of cloud computing and intelligent parking systems in sustainable urban development, highlighting successful implementations and lessons learned. It also explores potential future developments and policy considerations to facilitate widespread adoption of these technologies, highlighting the importance of global best practices.
Chapter 10 A Study on AI and Blockchain-Powered Smart Parking Models for Urban Mobility. 223 K. Sundaramoorthy, Department of Information Technology, Jerusalem College of Engineering, India Ajeet Singh, School of Computing Science and Engineering, VIT Bhopal University, India G. Sumathy, Department of Computational Intelligence, SRM Institute of Science and Technology, India A. Maheshwari, Department of Computational Intelligence, SRM Institute of Science and Technology, India A. R. Arunarani, Department of Computational Intelligence, SRM Institute of Science and Technology, India Sampath Boopathi, Mechanical Engineering, Muthayammal Engineering College, India Urban problems like traffic jams and a lack of parking spaces can be solved in an innovative way with the help of smart parking models powered by AI and blockchain technology. These models enhance user experience, optimise space allocation, and shorten search times. Predictive analytics and real-time data from loT sensors direct drivers to available parking spaces, minimising traffic and environmental impact. By protecting user privacy, controlling access, and securing transactions, blockchain technology improves AI. Users are empowered by blockchain-based decentralised digital identities, which also guarantee data privacy and transparent business dealings. With less traffic, more user happiness, and significant cost savings, this combination produces user-centric, environmentally friendly, and cost-effective smart parking solutions. The cost-benefit analysis for AI and blockchain-powered
smart parking demonstrates a favourable return on investment, paving the way for smarter, greener cities and more interconnected urban settings. Chapter 11 Machine Learning and Deep Learning for Intelligent Systems in Small Aircraft Applications. 251 U. Rahamathunnisa, School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, India Akash Mohanty, School ofMechanical Engineering, Vellore Institute of Technology, India K. Sudhakar, Department of Computer Science and Engineering, Madanapalle Institute of Technology and Science, Madanapalle, India S. Anitha Jebamani, Department of Information Technology, Sri Sai Ram Engineering College, Chennai, India R. Udendhran, Department of Computational Intelligence, SRM Institute of Science and Technology, India Sureshkumar Myilsamy, Mechanical Engineering, Bannari Amman Institute of Technology, India This chapter explores the integration of machine learning and deep learning techniques in small aircraft applications. The aviation industry is exploring innovative solutions to improve safety, efficiency, and performance in these operations. The chapter explores the advantages, challenges, and future prospects of implementing intelligent systems in small aircraft, including autopilot systems, navigation assistance, fault detection, and pilot support systems. Real-world case studies and applications demonstrate the transformative impact of these technologies on small aircraft operations. The chapter provides a comprehensive overview of the latest advancements in machine learning and deep
learning, highlighting their pivotal role in improving small aircraft intelligence, safety, and efficiency.
Chapter 12 Machine Learning in E-Health and Digital Healthcare: Practical Strategies for Transformation.276 T. K. Sethuramalingam, Department of Electronics and Communication Engineering, Karpagam College of Engineering, Coimbatore, India Rajkumar G. Nadakinamani, Badr Al Samoa Hospital, Oman G. Sumathy, Department of Computational Intelligence, SRM Institute of Science and Technology, India Sureshkumar Myilsamy, Mechanical Engineering, Bannari Amman Institute of Technology, India Machine learning is revolutionizing healthcare by offering innovative solutions to complex challenges. This chapter explores the practical strategies, ethical considerations, and real-world applications of machine learning in the healthcare domain. It delves into data collection and management, model development, integration with existing systems, and the importance of interdisciplinary collaboration. The chapter also discusses the ethical dimensions of healthcare AI, such as data privacy, bias mitigation, and regulatory compliance. Real-world case studies highlight the impact of machine learning on early disease detection, drug discovery, and precision medicine. The chapter concludes by examining future trends, including emerging technologies like quantum computing, nanomedicine, and the growing role of AI in drug discovery and genomic medicine. As machine learning continues to reshape healthcare, understanding these practical strategies and ethical considerations is essential for optimizing patient care and advancing the healthcare industry. Chapter 13 Unsupervised Learning Techniques for
Vibration-Based Structural Health Monitoring Systems Driven by Data: A General Overview. 305 Francesco Colace, University of Salerno, Italy Brij B. Gupta, Asia University, Taichung, Taiwan Lebanese American University, Beirut, Lebanon Angelo Lorusso, University of Salerno, Italy Alfredo Troiano, University of Salerno, Italy Domenico Santaniello, University of Salerno, Italy Carmine Valentino, University of Salerno, Italy Structural damage detection is a crucial issue for the safety of civil buildings, which are subject to gradual deterioration over time and at risk from sudden seismic events. To prevent irreparable damage, the scientific community has directed its attention toward developing innovative methods for structural health monitoring (SHM), which can provide a timely and reliable assessment of structural conditions. In this domain, the significance of unsupervised learning approaches has grown considerably, as they enable the identification of structural irregularities solely based on data obtained from intact structures to train statistical models. Despite the importance of studies on unsupervised learning methods for structural health monitoring, no reviews are specifically dedicated to this topic, considering the application part. The review of studies, therefore, made it possible to highlight the progress achieved in this field and identify areas where improvements could still be made to develop increasingly accurate and effective methods for structural
damage detection.
Chapter 14 Convergence of Data Science-AI-Green Chemistry-Affordable Medicine: Transforming Drug Discovery. 348 B. Rebecca, Department of Computer Science and Engineering (Data Science), Marri Laxman Reddy Institute of Technology and Management, Hyderabad, India K. Pradeep Mohan Kumar, Department of Computing Technologies, SRM Institute of Science and Technology, Chennai, India S. Padmini, Department of Computing Technologies, SRM Institute of Science and Technology, Chennai, India Bipin Kumar Srivastava, Department ofApplied Sciences, Galgotias College of Engineering and Technology, India Shubhajit Halder, Department of Chemistry, Hislop College, Nagpur, India Sampath Boopathi, Mechanical Engineering, Muthayammal Engineering College, India The drug discovery and design process has been significantly transformed by the integration of data science, artificial intelligence (AI), green chemistry principles, and affordable medicine. AI techniques enable rapid analysis of vast datasets, predicting molecular interactions, optimizing drug candidates, and identifying potential therapeutics. Green chemistry practices promote sustainability and efficiency, resulting in environmentally friendly and cost-effective production processes. The goal is to develop affordable medicines that are not only efficacious but also accessible to a wider population. This chapter explores case studies and emerging trends to highlight the transformation
of the pharmaceutical industry and innovation in drug discovery. Chapter 15 Intelligent Machines, loT, and AI in Revolutionizing Agriculture for Water Processing. 374 Krishnagandhi Pachiappan, Department of Electrical and Electronics Engineering, Nandha Engineering College, India K. Anitha, Department of Computing Technologies, School of Computing, SRM Institute of Science and Technology, India R. Pitchai, Department of Computer Science and Engineering, B.V. Raju Institute of Technology, India S. Sangeetha, Department of Computer Science Engineering, Karpagam College of Engineering, India T V.V. Satyanarayana, Department of Electronics and Communication Engineering, Mohan Babu University, India Sampath Boopathi, Mechanical Engineering, Muthayammal Engineering College, India Modern agriculture faces numerous challenges, ranging fromrising global food demand to water scarcity. To address these issues, the incorporation of intelligent machines, the Internet of Things (loT), and artificial intelligence (AI) in agricultural water processing has become critical. This chapter investigates these technologies’ transformative potential for optimizing water usage, increasing crop yields, and ensuring sustainable agricultural practices. It delves into the key concepts and applications, emphasizing the advantages and disadvantages of this novel approach. Farmers can make data-driven decisions, automate irrigation processes, and adapt to changing environmental conditions by leveraging AI and IoT-enabled systems, ultimately contributing to a more efficient and
environmentally friendly agricultural sector.
Chapter 16 A Mixture Model for Fruit Ripeness Identification in Deep Learning. 400 Bingjie Xiao, Auckland University of Technology, New Zealand Minh Nguyen, Auckland University of Technology, New Zealand Wei Qi Yan, Auckland University of Technology, New Zealand Visual object detection is a foundation in the field of computer vision. Since the size of visual objects in an images is various, the speed and accuracy of object detection are the focus of current research projects in computer vision. In this book chapter, the datasets consist of fruit images with various maturity. Different types of fruit are divided into the classes “ripe” and “overripe” according to the degree of skin folds. Then the object detection model is employed to automatically classify different ripeness of fruits. A family of YOLO models are representative algorithms for visual object detection. The authors make use of ConvNeXt and YOLOv7, which belong to the CNN network, to locate and detect fruits, respectively. Y0L0v7 employs the bag-of-freebies training method to achieve its objectives, which reduces training costs and enhances detection accuracy. An extended E-ELAN module, based on the original ELAN, is proposed within YOLOv7 to increase group convolution and improve visual feature extraction. In contrast, ConvNeXt makes use of a standard neural network architecture, with ResNet-50 serving as the baseline. The authors compare the proposed models, which result in an optimal classification model with best precision of 98.9%. Chapter 17 YOLO Models
for Fresh Fruit Classification From Digital Videos.421 Yinzhe Xue, Auckland University of Technology, New Zealand Wei Qi Yan, Auckland University of Technology, New Zealand Identifying food freshness is a very important; it is a part of a long historical actions by humans, because fruit freshness can tell us the information about the quality of foods. With the advancement of machine learning and computer science, which will be broadly employed in factories and markets, instead of manual classification. Recognition of the freshness of food is rapidly being replaced by computers or robots. In this book chapter, the authors conduct the research work on fruit freshness detection, we make use of YOLOv6, YOLOv7, and YOLOv8 in this project to implement fruit classifications based on a variety of digital images, which can improve the efficiency and accuracy of the classification incredibly; after the classification, the output will showcase the result of fruit fresheness classification, namely, fresh, or rotten, etc. They also compare the results of different deep learning models to discover which architecture is the best one in terms of speed and accuracy. At the end of this book chapter, the authors made use of the majority vote method to combine the results of different models to get better accuracy and recall scores. To generate the final result, the authors trained the three models individually, and also propose a majority vote to get a better performance for fresh fruit detection. Compared with the previous work, this
method has higher accuracy and a much faster speed. Because this one uses the clustering method to generate the final result, it will be easy for researchers to change the backbone and get a better result in the future. Compilation of References. 436 About the Contributors.493 Index. 500 |
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| author2_role | edt |
| author2_variant | b g bg |
| author_GND | (DE-588)1103817469 (DE-588)1098191765 |
| author_facet | Gupta, Brij 1982- |
| building | Verbundindex |
| bvnumber | BV049661984 |
| classification_rvk | ST 308 |
| ctrlnum | (OCoLC)1437852519 (DE-599)BVBBV049661984 |
| discipline | Informatik |
| format | Book |
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| genre | (DE-588)4143413-4 Aufsatzsammlung gnd-content |
| genre_facet | Aufsatzsammlung |
| id | DE-604.BV049661984 |
| illustrated | Illustrated |
| indexdate | 2025-01-10T19:03:26Z |
| institution | BVB |
| isbn | 9781668499993 1668499991 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-035005199 |
| oclc_num | 1437852519 |
| open_access_boolean | |
| owner | DE-739 |
| owner_facet | DE-739 |
| physical | xxvii, 503 Seiten Illustrationen, Diagramme 29 cm |
| publishDate | 2024 |
| publishDateSearch | 2024 |
| publishDateSort | 2024 |
| publisher | IGI Global |
| record_format | marc |
| series2 | A volume in the Advances in Computational Intelligence and Robotics (ACIR) Book Series |
| spelling | Handbook of research on AI and ML for intelligent machines and systems Brij B. Gupta, Francesco Colace Hershey PA, USA IGI Global [2024] © 2024 xxvii, 503 Seiten Illustrationen, Diagramme 29 cm txt rdacontent n rdamedia nc rdacarrier A volume in the Advances in Computational Intelligence and Robotics (ACIR) Book Series "By compiling recent advancements in intelligent machines that rely on machine learning and deep learning technologies, this book serves as a vital resource for researchers, graduate students, PhD scholars, faculty members, scientists, and software developers. It offers valuable insights into the key concepts of AI and ML, covering essential security aspects, current trends, and often overlooked perspectives that are crucial for achieving comprehensive understanding. It not only explores the theoretical foundations of AI and ML but also provides guidance on applying these techniques to solve real-world problems. Unlike traditional texts, it offers flexibility through its distinctive module-based structure, allowing readers to follow their own learning paths"-- Artificial intelligence / Industrial applications Machine learning / Industrial applications Intelligence artificielle / Applications industrielles Apprentissage automatique / Applications industrielles Artificial intelligence / Industrial applications fast Machine learning / Industrial applications fast Robotik (DE-588)4261462-4 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf (DE-588)4143413-4 Aufsatzsammlung gnd-content Robotik (DE-588)4261462-4 s Maschinelles Lernen (DE-588)4193754-5 s DE-604 Gupta, Brij 1982- (DE-588)1103817469 edt Colace, Francesco Sonstige (DE-588)1098191765 oth Online version Handbook of research on AI and ML for intelligent machines and systems Hershey PA : Engineering Science Reference, [2024] Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=035005199&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Handbook of research on AI and ML for intelligent machines and systems Artificial intelligence / Industrial applications Machine learning / Industrial applications Intelligence artificielle / Applications industrielles Apprentissage automatique / Applications industrielles Artificial intelligence / Industrial applications fast Machine learning / Industrial applications fast Robotik (DE-588)4261462-4 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4261462-4 (DE-588)4193754-5 (DE-588)4143413-4 |
| title | Handbook of research on AI and ML for intelligent machines and systems |
| title_auth | Handbook of research on AI and ML for intelligent machines and systems |
| title_exact_search | Handbook of research on AI and ML for intelligent machines and systems |
| title_full | Handbook of research on AI and ML for intelligent machines and systems Brij B. Gupta, Francesco Colace |
| title_fullStr | Handbook of research on AI and ML for intelligent machines and systems Brij B. Gupta, Francesco Colace |
| title_full_unstemmed | Handbook of research on AI and ML for intelligent machines and systems Brij B. Gupta, Francesco Colace |
| title_short | Handbook of research on AI and ML for intelligent machines and systems |
| title_sort | handbook of research on ai and ml for intelligent machines and systems |
| topic | Artificial intelligence / Industrial applications Machine learning / Industrial applications Intelligence artificielle / Applications industrielles Apprentissage automatique / Applications industrielles Artificial intelligence / Industrial applications fast Machine learning / Industrial applications fast Robotik (DE-588)4261462-4 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Artificial intelligence / Industrial applications Machine learning / Industrial applications Intelligence artificielle / Applications industrielles Apprentissage automatique / Applications industrielles Robotik Maschinelles Lernen Aufsatzsammlung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=035005199&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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