Machine intelligence, big data analytics, and IoT in image processing : practical applications

Machine intelligence, big data analytics, and IoT in image processing practical applications

Detalles Bibliográficos
Otros Autores: Kumar, Ashok, editor (editor)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Beverly, Massachusetts ; Hoboken, New Jersey : Scrivener Publishing [2023]
Colección:Advances in intelligent and scientific computing
Materias:
Big data.
Internet of things.
Machine learning.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009752728106719
Tabla de Contenidos:
  • 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.

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