Details

Subject(s)

• Computational intelligence [Browse]
• Artificial intelligence [Browse]

Series

• Advances in Intelligent Systems and Computing [More in this series]
• Advances in Intelligent Systems and Computing ; v.1381 [More in this series]

Bibliographic references

Includes bibliographical references and index.

Source of description

Description based on print version record.

Contents

• Intro
• Preface
• About SoCTA Series
• About STEM-Research Society
• Contents
• About the Editors
• A Study on the Effect of Optimal Control Strategies: An SIR Model with Delayed Logistic Growth
• 1 Introduction
• 2 Mathematical Model
• 3 Boundedness of System
• 4 Optimal Control for Delayed System
• 4.1 Existence of Optimal Control Pair
• 4.2 Characterization of Optimal Control
• 5 Numerical Section
• 6 Conclusion
• References
• Emperor Penguin Optimized Clustering for Improved Multilevel Hierarchical Routing in Wireless Sensor Networks
• 2 Related Work
• 3 Proposed Methodology
• 3.1 System Model
• 3.2 Energy Model
• 3.3 Clustering by EPO
• 4 Results and Discussion
• 5 Conclusion
• A Collaborative Filtering-Based Recommendation System for Preliminary Detection of COVID-19
• 3 Methodology
• 3.1 Creating User Item Rating Matrix
• 3.2 Similarity Computation
• 3.3 Selecting Nearest Neighbours
• 3.4 Generating Prediction
• 3.5 Providing Recommendations
• 5 Conclusion and Future Work
• Frequencies of Nonuniform Triangular Plate with Two-Dimensional Parabolic Temperature
• 2 Analysis
• 3 Results and Discussions
• 4 Results Comparison
• 5 Conclusions
• Machine Learning in Finance: Towards Online Prediction of Loan Defaults Using Sequential Data with LSTMs
• 2 Preliminaries and Related Work
• 2.1 Loan Default Prediction Problem
• 2.2 Classifiers for Loan Default Prediction
• 2.3 Ensemble Learning
• 2.4 Recurrent Neural Network (RNN)
• 3.1 Data Preparation and Cleaning
• 3.2 Method 1-RNN-Based Ensemble
• 3.3 Method 2-Hybrid DNN-RNN
• 4 Evaluation and Results
• References.
• Use of Marker-Controlled Watershed Segmentation to Classify Cumulonimbus Cloud with Pre-trained CNN
• 2 Different Cloud Types
• 3 Marker-Controlled Watershed Segmentation
• 4 Pre-trained CNN (ALEXNET)
• 5 Proposed Method
• 6 Implementation
• 7 Experimental Results
• 8 Conclusion
• Optimizing Drug Schedule for Cell-Cycle Specific Cancer Chemotherapy
• 2 Mathematical Formulation of Cell Cycle-Specific Chemotherapy
• 3 Problem Formulation
• 4 Non-dominated Sorting Genetic Algorithm-II (NSGA-II)
• 5 Simulations and Results
• A Review of Feature Extraction Techniques for EEG-Based Emotion Recognition System
• 2 Search Process
• 3 EEG-Based Emotion Recognition System
• 4 EEG-Based Feature Extraction Methods
• 4.1 Time Statistical Domain Features
• 4.2 Frequency Domain
• 4.3 Time-Frequency Domain
• 5 Discussions
• Task Scheduling and Load Balancing Techniques Using Genetic Algorithm in Cloud Computing
• 2.1 Task Scheduling Problem
• 2.2 Load Balancing
• 3 Proposed Algorithm
• 4 Performance Evaluation
• 4.1 Completion Time
• 4.2 Execution Cost
• Riemann Problem in Generalized Chaplygin Gas
• 2 Governing Equations
• 3 Numerical Schemes
• 3.1 HLL Scheme
• 4 Results and Discussions
• 4.1 Solution of Riemann Problem by HLL Scheme
• 4.2 Nonlinear Wave Propagation
• Appendix
• Flux Vector Splitting
• Vanleer Scheme
• Teaching-Learning Perception Toward Blended E-learning Portals During Pandemic Lockdown
• 1.1 Overview of Students Enrollment
• 2 Review of Literature
• 3 Objectives of the Study
• 3.1 Research Methodology
• 4 Data Analysis and Interpretation
• 5 Conclusion.
• An Alternative Approach to Compress Text Files Using Fibonacci Sequence and Lucas Series
• 1 Literature Review
• 1.1 Introduction
• 1.2 Lossless Data Compression
• 2 Proposed Concepts
• 2.1 Fibonacci Sequence
• 2.2 Lucas Number
• 2.3 Lucas Representation
• 3 Compression and Decompression
• 3.1 Compression Technique
• 3.2 Decompression Technique
• 3.3 Calculation
• 4 Performance of Proposed Algorithm
• 5 Graphical Representation of Space Complexity
• Smart Bin Management System with IoT-Enabled Technology
• 3 Architecture of Smart Bin Management System
• 3.1 Data Gathering via Nodes
• 3.2 Processing and Analytics
• 3.3 Projection and Action of Event
• 4 Architecture of Smart Bin Management System
• 4.1 Hardware Implementation
• 4.2 Software and Cloud
• 4.3 Real-Time Data Monitoring Console
• 5 Efficiency over Other Bin Management System
• Non-living Caretaker as a Medicine Box Based on Medical Equipment Using Internet of Things
• 1.1 Objective
• 1.2 Scope
• 2.1 Motivation
• 2.2 Related Work
• 3 Proposed Model
• 3.1 Modules Description
• 4 Simulation and Result
• 5 Conclusion and Future Direction
• Trends and Advancements in Genome Data Compression and Processing Algorithms
• 2 Genome Data Compression: A Literature Survey
• 3 Genome Data Processing: A Literature Survey
• 3.1 Repeat Information-Based Approaches for Genomic Data Processing
• 3.2 Index-Based Approaches for Genomic Data Processing
• 4 Research Gaps in Genome Data Compression and Processing
• Identification of Skin Diseases Using Convolutional Neural Network
• 2 Methodology
• 2.1 Data Collection.
• 2.2 Selection of Deep Learning Models
• 2.3 Performance Metrics for Classifiers
• 3 Implementation of CNN
• 4 Result
• Gene Sequence Classification Using K-mer Decomposition and Soft-Computing-Based Approach
• 2 Proposed Method
• 2.1 Collection of Data and Preparation of Datasets
• 2.2 K-mer Decompositions of Gene Sequence
• 2.3 Training
• 2.4 Testing
• 3 Result Discussions
• 4 Conclusion
• Deep CNN Model for Identification of Fruit Disease
• 3 The Proposed Method
• 3.1 Convolutional Neural Network
• 4 Experimental Analysis
• 4.1 Changing of the Dataset
• 4.2 Analysis of the Three-Layer Implementations
• A Survey on Potential Techniques and Issues in 6G Communication
• 2 Evolution of Mobile Communication Network
• 2.1 1G: First Generation
• 2.2 2G: Global System for Mobile Communication
• 2.3 3G: Universal Mobile Telecommunication System
• 2.4 4G: Long-Term Evolution
• 2.5 5G: Green Communication
• 3 6G Green Communication: Vision
• 4 Promising Techniques for 6G Communication
• 4.1 Spectrum Communication Technique
• 4.2 New Communication Paradigm
• 4.3 Fundamental Techniques
• 5 Problems to Be Resolve in 6G Communication and Their Promising Solutions
• 5.1 Power Supply Issue
• 5.2 Network Security
• 5.3 Hardware Design Issue
• Impact of COVID-19 in Architecture: A Survey-Based Method for Measuring and Understanding
• 2 Literature Review
• 3 Materials and Methods
• 4 Results
• 4.1 Practicing Architects Opinion
• 4.2 Bachelor of Architecture Students' Feedback
• 4.3 Architectural Academicians View Point
• Classification of Human Activities Using Support Vector Machine-A Review.
• 3 Methods
• 3.1 Smartphone Sensors
• 3.2 Data Set
• 3.3 Feature Extraction
• 3.4 Classification Algorithms
• 3.5 Applications of Human Activity Recognition
• 4 Discussion
• Smartphone Inertial Sensors-Based Human Activity Detection Using Support Vector Machine
• 3 Proposed Approach
• 3.2 Dataset
• 3.3 Preprocessing of Dataset
• 3.4 Classification Algorithm-Support Vector Machine
• 475K Dataset
• 3 Understanding Dataset
• 3.1 Dataset Details
• 4 Validation
• 5 Future Scope
• 6 Conclusions
• Docker Security: Architecture, Threat Model, and Best Practices
• 2 Docker Architecture
• 2.1 Docker Daemon
• 2.2 Docker Client
• 2.3 Docker Registry
• 3 Threat Model
• 4 Docker Security Issues and Solution
• 4.1 Issues Arising Out of Images
• 4.2 Issues Arising Out of Docker Misconfiguration
• 4.3 Issues Arising Out of Network
• 5 Case Study
• 5.1 System Configuration
• 5.2 Attack Scenario: 1
• 5.3 Attack Scenario: 2
• Breast DCE-MRI Segmentation for Lesion Detection Using Clustering with Multi-verse Optimization Algorithm
• 1.1 Background
• 1.2 Related Works
• 1.3 Objectives
• 2 Materials and Methods
• 2.1 DCE-MRI Dataset
• 2.2 Proposed Method
• 3 Results and Discussion
• Using Linguistically Non-local Punjabi Queries to Search the Global Web
• 3.1 Keyword Extraction Module
• 3.2 Query Reformulation Module
• 4 Test and Results
• A Supervised Learning Approach by Machine Learning Algorithms to Predict Diabetes Mellitus (DM) Risk Score.

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ISBN

981-16-1696-5

OCLC

1259588727

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