Details

Subject(s)

• Natural computation [Browse]

Editor

• Smys, S. [Browse]
• Tavares, João Manuel R. S. [Browse]
• Balas, Valentina Emilia [Browse]

Series

• Advances in Intelligent Systems and Computing [More in this series]
• Advances in Intelligent Systems and Computing ; v.1420 [More in this series]

Bibliographic references

Includes bibliographical references and index.

Source of description

Description based on print version record.

Contents

• Intro
• Preface
• Acknowledgements
• Contents
• About the Editors
• Molecular Docking Analysis of Selected Phytochemicals for the Treatment of Proteus Syndrome
• 1 Introduction
• 2 Methods and Materials
• 3 Results and Discussion
• 4 Conclusion and Future Prospects
• References
• A Deep Learning-Based Detection of Wrinkles on Skin
• 2 Literature Review
• 3 Proposed Methodology
• 3.1 Computer Vision
• 3.2 Convolution Neural Network (CNN)
• 3.3 Hough Trnaformer
• 4 Experimental Results
• 5 Conclusion
• Image Transmission Using Leach and Security Using RSA in Wireless Sensor Networks
• 2 Literature Survey
• 3 Leach with RSA: Proposed Scheme
• 4 Simulation Results
• Code Injection Prevention in Content Management Systems Using Machine Learning
• 2 Background and Related Work
• 3 Our Work
• 3.1 Data Gathering
• 3.2 Data Analysis and Feature Extraction
• 3.3 Dataset
• 3.4 Feature Selection
• 3.5 Machine Learning Using Logistic Regression Algorithm
• 4 Results
• 4.1 Model Interpretation
• A Review of Hyperspectral Image Classification with Various Segmentation Approaches Based on Labelled Samples
• 1.1 Organization of the Paper
• 2 Datasets and Metrics Used for Hyperspectral Image Classification
• 3 Literature Survey
• 3.1 Machine Learning Techniques
• 3.2 Neural Network Techniques
• 3.3 Analysis Dependent on Various Parameters
• 4 Discussion
• 5 Future Technologies
• 6 Conclusion
• Improvements in User Targeted Offline Advertising Using CNN and Deviation-Based Queue Scheduling
• 2 Related Work
• 3 Proposed Work
• 3.1 Overview
• 3.2 Convolutional Neural Network
• 3.3 Deviation-Based Queue Generation
• 4 Results and Discussion.
• 5 Performance Comparison
• 5.1 Overlapping Target Audience Problem
• 5.2 Comparison of TARP 2.0 with Random Display and Display Using TARP
• 5.3 Delay Time Comparison of TARP 2.0 with TARP
• Movie Recommendation System Using Hybrid Collaborative Filtering Model
• 1.1 Literature Survey
• 2 Need and Applicability
• 2.1 Need
• 2.2 Applicability
• 3 Proposed Approach
• 3.1 Dataset Preparation
• 3.2 Dataset Description
• 3.3 Modeling
• Hybrid Pipeline Infinity Laplacian Plus Convolutional Stage Applied to Depth Completion
• 1.1 Related Works
• 2 Method
• 2.1 Used Metric
• 2.2 Proposed Metric
• 3 Practical Model Implementation
• 3.1 Convolutional Stage
• 3.2 Convolutional Stage for the Completed Depth Map
• 4 Parameters Estimation
• 4.1 Learning Curve for the Complete Pipeline
• 4.2 Learning Curve for Median-Pipeline
• 4.3 Learning Curve Convolutional Stage-Median-Pipeline Simplified Metric
• 4.4 Learning Curve SC1-Median-Pipeline with Simple Metric
• 5 Experiments and Dataset
• 5.1 Dataset
• 5.2 Experiments
• 6 Results
• 7 Discussion
• 8 Conclusions
• A Novel Approach of DEMOO with SLA Algorithm to Predict Protein Interactions
• 2 Methodology
• 2.1 Feature Extraction
• 2.2 Label Propagation Algorithm
• 2.3 Incremental Depth Extension (INDEX) Approach
• 2.4 Formation of a PPI Network
• 2.5 Neighborhood Topology with Protein Interaction Prediction
• 2.6 Functional Characteristics of Protein Interaction Prediction
• 2.7 Predicting PPIs Using ASA
• 2.8 Differential Evolution Algorithm for Multi-objective Optimization
• 2.9 Stochastic Learning Automata (SLA)
• 3.1 Performance Measures.
• 3.2 Performance Comparison of Existing and Proposed Methods for DIP and SCOP Datasets
• 4 Conclusion
• Economic Load Dispatch Problem with Valve-Point Loading Effect Using DNLP Optimization Using GAMS
• 2 Economic Load Dispatch Mathematical Model
• 2.1 Constraints
• 2.2 Procedure
• 2.3 Contributions of Proposed Method
• 3 Results
• 4 Conclusions
• Solar Radio Spectrum Classification Based on ConvLSTM
• 2 Solar Radio Spectrum Dataset
• 2.1 The Introduction of SBRS Dataset
• 2.2 Dataset Extension
• 3 Pre-processing of Solar Radio Spectrums
• 3.1 Channel Normalization
• 3.2 Down-Sampling
• 4 Solar Radio Spectrum Classification Model Based on ConvLSTM
• 5 Experiment Results and Analysis
• Particle Swarm Optimization-Based Neural Network for Wireless Heterogeneous Networks
• Impact Analysis of COVID-19 on Various Indian Sectors
• 4 Dataset
• 5 Screen time Analysis
• 5.1 Hypothesis Testing
• 6 Industrial Analysis
• 6.1 Information Technology
• 6.2 Fast-moving Consumer Goods (FMCG)
• 6.3 Hospitality
• 6.4 Aviation
• 7 Conclusion
• Emotion Recognition in Speech Using MFCC and Classifiers
• 2 Lıterature Revıew
• 3 Methodology
• 3.1 Emotions
• 3.2 Data
• 3.4 Feature Extraction
• 3.5 Mel-frequency Cepstral Coefficient
• A Comparative Analysis on Image Caption Generator Using Deep Learning Architecture-ResNet and VGG16
• 2 Related Works
• 3 Comparison of Image Generator Using ResNet-50 and VGG16
• 3.1 Image Preprocessing
• 3.2 Image Feature Extraction.
• 4 Text Feature Understanding Using LSTM
• 5 Results and Discussion
• Corona Warrior Smart Band
• 3 Block Diagram and Working Principle
• 4 Implementation Platform
• 5 Results
• Cellular Learning Automata: Review and Future Trend
• 2 Cellular Automata (CA)
• 3 Learning Automata (LA) and Reinforcement Learning (RL)
• 4 Cellular Learning Automata (CLA)
• 5 Wireless Sensor and Actuator Networks (WSAN)
• 6 Future Trends
• Computer Vision and Machine Learning-Based Techniques for Detecting the Safety Violations of COVID-19 Scenarios: A Review
• 2.1 Survey on Face Mask Detection
• 2.2 Survey on Social Distancing Detection
• 2.3 Survey on People Count Detection
• 2.4 Comparison Table of Literature Review
• 3 Outcome of Literature Review
• 4 Datasets
• 5 Challenges and Future Perspectives
• Stigmergy-Based Collision-Avoidance Algorithm for Self-Organising Swarms
• 2 Multi-agent Collision Avoidance Based on Stigmergy
• 3 Numerical Experiments
• Handling Security Issues in Software-defined Networks (SDNs) Using Machine Learning
• 2 Need of SDN
• 3 SDN and NFV
• 4 Security Issues in SDN
• 4.1 Hardware Security Issues
• 4.2 Security Threats at Software Level
• 5 Early Approaches for Securing SDN
• 5.1 Intrusion Detection and Prevention System (IDPS) for SDN
• 5.2 Intrusion Tolerant System (ITS) for SDN
• 5.3 Stateful Firewall for SDN
• 5.4 Framework for DDOS Detection and Prevention
• 6 Securing SDN with Machine Learning
• 6.1 Supervised ML for SDN
• 6.2 Unsupervised ML Algorithm for SDN
• 6.3 Semi-supervised Learning Approaches for SDN.
• 6.4 Machine Learning-based NIDS for SDN
• 7 Issues in Existing ML-based SDN
• 8 Conclusion
• 9 Future Scope
• Multi-purpose Web Application Honeypot to Detect Multiple Types of Attacks and Expose the Attacker's Identity
• 3 Methods Used
• 3.1 Honeypot
• 3.2 GUI
• 3.3 Controller
• 3.4 Models
• 3.5 Sequence of Events
• 4 Proposed System
• 4.1 System Working
• 4.2 Overcoming Brute Force Script Attack
• 4.3 Overcoming Static Resource Attack
• 4.4 Overcoming Web Scraping Script (WSS) Attack
• 4.5 Overcoming GET/POST Request
• 4.6 Advantages of the Proposed Method
• 5 Experimental Results
• 6 Conclusion and Future Scope
• An Empirical Approach for Tuning an Autonomous Mobile Robot in Gazebo
• 3 Related Theory
• 3.1 Sensors
• 3.2 Light Detection and Ranging (LIDAR)
• 3.3 Inertial Measurement Unit (IMU)
• 3.4 Adaptive Monte Carlo Localization (AMCL)
• 3.5 Navigation
• 4 Methodology
• 4.1 Costmap Parameters
• 4.2 Planner Parameters
• 5 Experimentation Result
• 6 Result Analysis
• An Investigation on Computational Intelligent Solutions for Highly Dynamic Wireless Sensor Networks
• 2 Computational Intelligent Paradigms
• 2.1 Fuzzy Logic (FL)
• 2.2 Evolutionary Algorithms (EA)
• 2.3 Artificial Neural Networks (ANN)
• 2.4 Swarm Intelligence (SI)
• 3 WSN Challenges
• 4 Related Work
• 4.1 CI Approaches for Energy Management
• 4.2 Heuristic Solutions for Packet Routing
• 4.3 Traffic Management with Channel Dynamics Using CI Methods
• A Study of Underwater Image Pre-processing and Techniques
• 3 Types of Image Pre-processing
• 3.1 Color Conversion
• 4 Binarization
• 5 Filtering
• 6 Resizing.
• 7 Techniques of Underwater Image Processing.

Show 220 more Contents items

ISBN

• 981-16-9572-5
• 981-16-9573-3

Statement on language in description

Princeton University Library aims to describe library materials in a manner that is respectful to the individuals and communities who create, use, and are represented in the collections we manage. Read more...