Machine intelligence, big data analytics, and IoT in image processing : practical applications / / edited by Ashok Kumar [and four others]

• Pubblicazione: Beverly, Massachusetts ; ; Hoboken, New Jersey : , : Scrivener Publishing : , : Wiley, , [2023]; ©2023
• Descrizione fisica: 1 online resource (500 pages)
• Disciplina: 005.7
• Soggetto topico: Big data; Internet of things; Machine learning
• Persona (resp. second.): KumarAshok
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Part I: Demystifying Smart Healthcare -- Chapter 1 Deep Learning Techniques Using Transfer Learning for Classification of Alzheimer's Disease -- 1.1 Introduction -- 1.2 Transfer Learning Techniques -- 1.3 AD Classification Using Conventional Training Methods -- 1.4 AD Classification Using Transfer Learning -- 1.5 Conclusion -- References -- Chapter 2 Medical Image Analysis of Lung Cancer CT Scans Using Deep Learning with Swarm Optimization Techniques -- 2.1 Introduction -- 2.2 The Major Contributions of the Proposed Model -- 2.3 Related Works -- 2.4 Problem Statement -- 2.5 Proposed Model -- 2.5.1 Swarm Optimization in Lung Cancer Medical Image Analysis -- 2.5.2 Deep Learning with PSO -- 2.5.3 Proposed CNN Architectures -- 2.6 Dataset Description -- 2.7 Results and Discussions -- 2.7.1 Parameters for Performance Evaluation -- 2.8 Conclusion -- References -- Chapter 3 Liver Cancer Classification With Using Gray-Level Co-Occurrence Matrix Using Deep Learning Techniques -- 3.1 Introduction -- 3.1.1 Liver Roles in Human Body -- 3.1.2 Liver Diseases -- 3.1.3 Types of Liver Tumors -- 3.1.3.1 Benign Tumors -- 3.1.3.2 Malignant Tumors -- 3.1.4 Characteristics of a Medical Imaging Procedure -- 3.1.5 Problems Related to Liver Cancer Classification -- 3.1.6 Purpose of the Systematic Study -- 3.2 Related Works -- 3.3 Proposed Methodology -- 3.3.1 Gaussian Mixture Model -- 3.3.2 Dataset Description -- 3.3.3 Performance Metrics -- 3.3.3.1 Accuracy Measures -- 3.3.3.2 Key Findings -- 3.3.3.3 Key Issues Addressed -- 3.4 Conclusion -- References -- Chapter 4 Transforming the Technologies for Resilient and Digital Future During COVID-19 Pandemic -- 4.1 Introduction -- 4.2 Digital Technologies Used -- 4.2.1 Artificial Intelligence -- 4.2.2 Internet of Things -- 4.2.3 Telehealth/Telemedicine. ; 4.2.4 Cloud Computing -- 4.2.5 Blockchain -- 4.2.6 5G -- 4.3 Challenges in Transforming Digital Technology -- 4.3.1 Increasing Digitalization -- 4.3.2 Work From Home Culture -- 4.3.3 Workplace Monitoring and Techno Stress -- 4.3.4 Online Fraud -- 4.3.5 Accessing Internet -- 4.3.6 Internet Shutdowns -- 4.3.7 Digital Payments -- 4.3.8 Privacy and Surveillance -- 4.4 Implications for Research -- 4.5 Conclusion -- References -- Part II: Plant Pathology -- Chapter 5 Plant Pathology Detection Using Deep Learning -- 5.1 Introduction -- 5.2 Plant Leaf Disease -- 5.3 Background Knowledge -- 5.4 Architecture of ResNet 512 V2 -- 5.4.1 Working of Residual Network -- 5.5 Methodology -- 5.5.1 Image Resizing -- 5.5.2 Data Augmentation -- 5.5.2.1 Types of Data Augmentation -- 5.5.3 Data Normalization -- 5.5.4 Data Splitting -- 5.6 Result Analysis -- 5.6.1 Data Collection -- 5.6.2 Feature Extractions -- 5.6.3 Plant Leaf Disease Detection -- 5.7 Conclusion -- References -- Chapter 6 Smart Irrigation and Cultivation Recommendation System for Precision Agriculture Driven by IoT -- 6.1 Introduction -- 6.1.1 Background of the Problem -- 6.1.1.1 Need of Water Management -- 6.1.1.2 Importance of Precision Agriculture -- 6.1.1.3 Internet of Things -- 6.1.1.4 Application of IoT in Machine Learning and Deep Learning -- 6.2 Related Works -- 6.3 Challenges of IoT in Smart Irrigation -- 6.4 Farmers' Challenges in the Current Situation -- 6.5 Data Collection in Precision Agriculture -- 6.5.1 Algorithm -- 6.5.1.1 Environmental Consideration on Stage Production of Crop -- 6.5.2 Implementation Measures -- 6.5.2.1 Analysis of Relevant Vectors -- 6.5.2.2 Mean Square Error -- 6.5.2.3 Potential of IoT in Precision Agriculture -- 6.5.3 Architecture of the Proposed Model -- 6.6 Conclusion -- References -- Chapter 7 Machine Learning-Based Hybrid Model for Wheat Yield Prediction. ; 7.1 Introduction -- 7.2 Related Work -- 7.3 Materials and Methods -- 7.3.1 Methodology for the Current Work -- 7.3.1.1 Data Collection for Wheat Crop -- 7.3.1.2 Data Pre-Processing -- 7.3.1.3 Implementation of the Proposed Hybrid Model -- 7.3.2 Techniques Used for Feature Selection -- 7.3.2.1 ReliefF Algorithm -- 7.3.2.2 Genetic Algorithm -- 7.3.3 Implementation of Machine Learning Techniques for Wheat Yield Prediction -- 7.3.3.1 K-Nearest Neighbor -- 7.3.3.2 Artificial Neural Network -- 7.3.3.3 Logistic Regression -- 7.3.3.4 Naïve Bayes -- 7.3.3.5 Support Vector Machine -- 7.3.3.6 Linear Discriminant Analysis -- 7.4 Experimental Result and Analysis -- 7.5 Conclusion -- Acknowledgment -- References -- Chapter 8 A Status Quo of Machine Learning Algorithms in Smart Agricultural Systems Employing IoT-Based WSN: Trends, Challenges and Futuristic Competences -- 8.1 Introduction -- 8.2 Types of Wireless Sensor for Smart Agriculture -- 8.3 Application of Machine Learning Algorithms for Smart Decision Making in Smart Agriculture -- 8.4 ML and WSN-Based Techniques for Smart Agriculture -- 8.5 Future Scope in Smart Agriculture -- 8.6 Conclusion -- References -- Part III: Smart City and Villages -- Chapter 9 Impact of Data Pre-Processing in Information Retrieval for Data Analytics -- 9.1 Introduction -- 9.1.1 Tasks Involved in Data Pre-Processing -- 9.2 Related Work -- 9.3 Experimental Setup and Methodology -- 9.3.1 Methodology -- 9.3.2 Application of Various Data Pre-Processing Tasks on Datasets -- 9.3.3 Applied Techniques -- 9.3.3.1 Decision Tree -- 9.3.3.2 Naive Bayes -- 9.3.3.3 Artificial Neural Network -- 9.3.4 Proposed Work -- 9.3.4.1 PIMA Diabetes Dataset (PID) -- 9.3.5 Cleveland Heart Disease Dataset -- 9.3.6 Framingham Heart Study -- 9.3.7 Diabetic Dataset -- 9.4 Experimental Result and Discussion -- 9.5 Conclusion and Future Work -- References. ; Chapter 10 Cloud Computing Security, Risk, and Challenges: A Detailed Analysis of Preventive Measures and Applications -- 10.1 Introduction -- 10.2 Background -- 10.2.1 History of Cloud Computing -- 10.2.1.1 Software-as-a-Service Model -- 10.2.1.2 Infrastructure-as-a-Service Model -- 10.2.1.3 Platform-as-a-Service Model -- 10.2.2 Types of Cloud Computing -- 10.2.3 Cloud Service Model -- 10.2.4 Characteristics of Cloud Computing -- 10.2.5 Advantages of Cloud Computing -- 10.2.6 Challenges in Cloud Computing -- 10.2.7 Cloud Security -- 10.2.7.1 Foundation Security -- 10.2.7.2 SaaS and PaaS Host Security -- 10.2.7.3 Virtual Server Security -- 10.2.7.4 Foundation Security: The Application Level -- 10.2.7.5 Supplier Data and Its Security -- 10.2.7.6 Need of Security in Cloud -- 10.2.8 Cloud Computing Applications -- 10.3 Literature Review -- 10.4 Cloud Computing Challenges and Its Solution -- 10.4.1 Solution and Practices for Cloud Challenges -- 10.5 Cloud Computing Security Issues and Its Preventive Measures -- 10.5.1 General Security Threats in Cloud -- 10.5.2 Preventive Measures -- 10.6 Cloud Data Protection and Security Using Steganography -- 10.6.1 Types of Steganography -- 10.6.2 Data Steganography in Cloud Environment -- 10.6.3 Pixel Value Differencing Method -- 10.7 Related Study -- 10.8 Conclusion -- References -- Chapter 11 Internet of Drone Things: A New Age Invention -- 11.1 Introduction -- 11.2 Unmanned Aerial Vehicles -- 11.2.1 UAV Features and Working -- 11.2.2 IoDT Architecture -- 11.3 Application Areas -- 11.3.1 Other Application Areas -- 11.4 IoDT Attacks -- 11.4.1 Counter Measures -- 11.5 Fusion of IoDT With Other Technologies -- 11.6 Recent Advancements in IoDT -- 11.7 Conclusion -- References -- Chapter 12 Computer Vision-Oriented Gesture Recognition System for Real-Time ISL Prediction -- 12.1 Introduction -- 12.2 Literature Review. ; 12.3 System Architecture -- 12.3.1 Model Development Phase -- 12.3.2 Development Environment Phase -- 12.4 Methodology -- 12.4.1 Image Pre-Processing Phase -- 12.4.2 Model Building Phase -- 12.5 Implementation and Results -- 12.5.1 Performance -- 12.5.2 Confusion Matrix -- 12.6 Conclusion and Future Scope -- References -- Chapter 13 Recent Advances in Intelligent Transportation Systems in India: Analysis, Applications, Challenges, and Future Work -- 13.1 Introduction -- 13.2 A Primer on ITS -- 13.3 The ITS Stages -- 13.4 Functions of ITS -- 13.5 ITS Advantages -- 13.6 ITS Applications -- 13.7 ITS Across the World -- 13.8 India's Status of ITS -- 13.9 Suggestions for Improving India's ITS Position -- 13.10 Conclusion -- References -- Chapter 14 Evolutionary Approaches in Navigation Systems for Road Transportation System -- 14.1 Introduction -- 14.1.1 Navigation System -- 14.1.2 Genetic Algorithm -- 14.1.3 Differential Evolution -- 14.2 Related Studies -- 14.2.1 Related Studies of Evolutionary Algorithms -- 14.3 Navigation Based on Evolutionary Algorithm -- 14.3.1 Operators and Terms Used in Evolutionary Algorithms -- 14.3.2 Operator and Terms Used in Evolutionary Algorithm -- 14.4 Meta-Heuristic Algorithms for Navigation -- 14.4.1 Drawbacks of DE -- 14.5 Conclusion -- References -- Chapter 15 IoT-Based Smart Parking System for Indian Smart Cities -- 15.1 Introduction -- 15.2 Indian Smart Cities Mission -- 15.3 Vehicle Parking and Its Requirements in a Smart City Configuration -- 15.4 Technologies Incorporated in a Vehicle Parking System in Smart Cities -- 15.5 Sensors for Vehicle Parking System -- 15.5.1 Active Sensors -- 15.5.2 Passive Sensors -- 15.6 IoT-Based Vehicle Parking System for Indian Smart Cities -- 15.6.1 Guidance to the Customers Through Smart Devices -- 15.6.2 Smart Parking Reservation System. ; 15.7 Advantages of IoT-Based Vehicle Parking System.
• Titolo autorizzato: Machine intelligence, big data analytics, and IoT in image processing
• ISBN: 1-119-86551-4; 1-119-86550-6
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910677645203321
• Lo trovi qui: Univ. Federico II