ICT for Intelligent Systems: Proceedings of ICTIS 2023 (Smart Innovation, Systems and Technologies, 361)

✍ Scribed by Jyoti Choudrie (editor), Parikshit N. Mahalle (editor), Thinagaran Perumal (editor), Amit Joshi (editor)

• Publisher: Springer
• Year: 2023
• Tongue: English
• Leaves: 524
• Category: Library

✦ Synopsis

This book gathers papers addressing state-of-the-art research in all areas of information and communication technologies and their applications in intelligent computing, cloud storage, data mining and software analysis. It presents the outcomes of the Seventh International Conference on Information and Communication Technology for Intelligent Systems (ICTIS 2023), held in Ahmedabad, India. It discusses the fundamentals of various data analysis techniques and algorithms, making it a valuable resource for researchers and practitioners alike.

✦ Table of Contents

Preface ICTIS 2023
Contents
About the Editors
Intrusion Detection Model for IoT Networks Using Graph Convolution Networks(GCN)
1 Introduction
2 Literature Survey
3 System Model
3.1 Dataset
3.2 Feature Extraction
3.3 Graph Construction
3.4 Graph Classification
4 Implementation
5 Conclusions
References
Drowsiness Detection System
1 Introduction
2 Literature Review
3 Project Implementation
3.1 Eye Aspect Ratio (EAR)
3.2 Mouth Aspect Ratio (MAR)
3.3 Mouth Aspect Ratio Over Eye Aspect Ratio (MOE)
4 Parameters
5 Results
6 Conclusion
References
A Deep Learning Technique to Recommend Music Based on Facial and Speech Emotions
1 Introduction
2 Literature Survey
3 Proposed System
3.1 Datasets
3.2 Methodology
4 Implementation
4.1 Facial Emotion Recognition
4.2 Speech Emotion Recognition
4.3 Music Recommendation
5 Results and Discussions
5.1 Datasets and Performance Measures
5.2 Web Application Results
6 Conclusion and Future Work
References
Smart Chair Posture Detection and Correction Using IOT
1 Introduction
2 Prototype Requirements
2.1 Hardware Requirements
2.2 Software Requirements
3 Design
3.1 Flowchart
3.2 Pictorial Block Diagram
4 Implementation
4.1 Interfacing FSR with Arduino UNO
4.2 Interfacing MPU6050 with Arduino UNO
4.3 Connecting Arduino and Raspberry Pi
4.4 Machine Learning on Raspberry Pi
4.5 Raspbian OS
4.6 Python
4.7 Thonny Python IDE
4.8 Thingspeak Cloud Integration with Raspberry Pi
4.9 MIT App Inventor
4.10 Firebase Realtime Database
5 Results
5.1 Final Prototype
5.2 Results
6 Conclusion and Future Scope
6.1 Conclusion
6.2 Future Scope
References
The Opinions Imparted on Singular’s Face
1 Introduction
2 Methodology
3 Data Flow
3.1 DFD Levels and Layers
4 Problem Statement
5 Approach
6 Experimental Studies
6.1 Benchmark Dataset
6.2 Experimental Setup
7 Related Work
8 Result and Discussions
9 Conclusion
References
Abnormal Human Behavior Detection from a Video Sequence Using Deep Learning
1 Introduction
2 Background Study
2.1 Deep Learning
2.2 Convolutional Neural Networks (CNN)
2.3 Artificial Neural Network (ANN)
2.4 Recurrent Neural Network (RNN)
2.5 Long Short-Term Memory (LSTM)
3 Methodology
3.1 Convolution
3.2 Recurrent
4 Results and Discussion
5 Conclusion and Future Work
References
Role of Deep Learning in a Secure Telemedicine System with a Case Study of Heart Disease Prediction
1 Introduction
1.1 What Is Deep Learning?
1.2 What Is Telemedicine?
1.3 Deep Learning in Telemedicine
2 Lierature Review
3 Work Has Been Done in Industry
4 Architecture of Proposed Scheme
5 Practical Implementation
6 Conclusion and Future Scope
References
Comparative Analysis of Chronic Kidney Disease Prediction Using Supervised Machine Learning Techniques
1 Introduction
2 Related Work
3 Proposed Work
3.1 Logistic Regression (LR)
3.2 Random Forest (RF)
4 Conclusion and Future Discussion
5 Limitations
References
Prediction of PCOS and PCOD in Women Using ML Algorithms
1 Introduction
2 Literature Survey
2.1 Prediction and Symptoms of PCOS and PCOD
2.2 Impact of Stress and Anxiety in Women Suffering from PCOS and PCOD
2.3 Effects on Health in PCOS Patients
2.4 Treatment
2.5 Methods Used
3 Ensemble Learning Algorithms
3.1 Random Forest
3.2 Bagging Classifier
3.3 AdaBoosting
3.4 Gradient Boosting
4 Methodology
5 Results
6 Conclusion
References
Privacy Preserving Early Disease Diagnosis in Human Nails Using Swarm Learning
1 Introduction
2 Literature Review
3 Proposed Methodology
3.1 The Swarm Learning Node
3.2 The Swarm Network Node
3.3 The Machine Learning Node
4 Experiments
4.1 Dataset
4.2 Implementation
5 Results
6 Conclusion
References
Skin Cancer Recognition Using CNN, VGG16 and VGG19
1 Introduction
2 Literature Survey
3 Proposed System
3.1 Dataset
3.2 Pre-processing
3.3 Training Using CNN, Vgg16, and Vgg19 Algorithm
4 Result and Discussion
5 Discussion
6 Future Scope
References
Diagnosis of Cardiovascular Disease Using Machine Learning Algorithms and Feature Selection Method for Class Imbalance Problem
1 Introduction
2 Materials and Methods
2.1 Machine Learning Algorithms Used
2.2 StandardScaler: Standardization
2.3 SVM_Balance: Over-sampling Technique
2.4 CFS
2.5 Performance Metrics
3 Experimental Setup
3.1 Dataset
3.2 Results and Discussions
4 Conclusion
References
Similarity Based Answer Evaluation in Academic Questions Using Natural Language Processing Techniques
1 Introduction
2 Related Work
3 Natural Language Processing Techniques
4 Similarity Matching Techniques
5 Machine Learning Classifiers
5.1 Decision Tree Classification
6 Deep Learning Classifiers
7 Time Complexity
8 Limitation
9 Conclusion
References
Fake News Detection Using Machine Learning and Deep Learning Classifiers
1 Introduction
2 Related Works
3 Machine Learning Classifiers
3.1 Random Forest Algorithm
3.2 Navies Bayes Algorithm
4 Deep Learning Classifier
5 Experimental Results
6 Conclusion
References
Survey on Pre-Owned Car Price Prediction Using Random Forest Algorithm
1 Introduction
2 Related Works
3 Machine Learning Algorithms
4 Algorithms in Machine Learning
4.1 Logistic Regression Algorithm
4.2 Random Forest Algorithm
4.3 Support Vector Machine
5 Experimental Results
6 Conclusion
References
Sentiment Analysis of Youtube Comment Section in Indian News Channels
1 Introduction
2 Literature Survey
3 Methodology
3.1 Data Collection
3.2 Data Preprocessing
3.3 Sentiment Analysis
3.4 Named Entity Recognition
4 Results and Discussion
5 Conclusion
References
Deep Learning Framework for Speaker Verification Under Multi Sensor, Multi Lingual and Multi Session Conditions
1 Introduction
1.1 Related Work
2 Methodology
2.1 Data Set
2.2 Pre-processing
2.3 Sinc-Net
2.4 Standard CNN Block
2.5 Softmax
2.6 Block Diagram
3 Results and Discussions
3.1 Result Analysis for Multi Lingual Scenario
3.2 Result Analysis for Multi Sensor Scenarios
3.3 Result Analysis for Multi Lingual and Multi Session Scenario
3.4 Result Analysis for Multi Lingual and Multi Sensor Scenarios
3.5 Model Properties
4 Conclusion
References
DLLACC: Design of an Efficient Deep Learning Model for Identification of Lung Air Capacity in COPD Affected Patients
1 Introduction
2 Literature Review
3 Design of the Model
4 Results and Statistical Comparison
5 Conclusion and Futurework
References
Content Based Document Image Retrieval Using Computer Vision and AI Techniques
1 Introduction
2 Literature Survey
3 Methodology
3.1 Image Processing
3.2 Feature Extraction
3.3 Euclidean Distance Calculation
3.4 Comparison Level Cutoff
3.5 Results
4 Conclusion
References
Monitor the Effectiveness of Cardiovascular Disease Illness Diagnostics Utilizing AI and Supervised Machine Learning Classifiers
1 Introduction
2 Supervised Machine Learning Algorithms
2.1 Naive Bayes
2.2 kNN
2.3 LOR
3 Proposed Architecture
3.1 How Does the Model Work?
4 Dataset and Model
4.1 Hospital Data
4.2 Data Preprocessing
5 Conclusion and Future Work
References
Architecture Based Classification for Intrusion Detection System Using Artificial Intelligence and Machine Learning Classifiers
1 Introduction
2 Related Work
2.1 Literature Review
3 Existing Architecture
3.1 Cons (Fig. 1)
3.2 Cons (Fig 2)
3.3 Cons (Fig. 3)
4 Proposed Solution
5 Conclusion
6 Future Work
References
A Novel Privacy-Centric Training Routine for Maintaining Accuracy in Traditional Machine Learning Systems
1 Introduction
1.1 Traditional Training Approach
1.2 On-Device AI Approach
2 Best-Of-Both Worlds Approach
2.1 Novel Training Routine
2.2 Implementation
2.3 Pseudocode
3 Some Identified Applications
4 Future Work
References
Outside the Closed World: On Using Machine Learning for Network Intrusion Detection
1 Introduction
2 Related Work
3 Methodology
4 Results and Discussion
5 Conclusion
References
Data Collection for a Machine Learning Model to Suggest Gujarati Recipes to Cardiac Patients Using Gujarati Food and Fruit with Nutritive Values
1 Introduction
2 Literature Review
3 Dataset
3.1 Type’s of Dataset
4 Conclusion
References
Plant and Weed Seedlings Classification Using Deep Learning Techniques
1 Introduction
2 Literature Review
3 Methodology
3.1 Dataset
3.2 Image Pre-processing
3.3 Data Augmentation
3.4 Data Split
3.5 Model Building
3.6 Compiling and Training the Model
4 Experimental Results and Discussion
5 Conclusion
References
A Comprehensive Review on Various Artificial Intelligence Based Techniques and Approaches for Cyber Security
1 Introduction
2 Impact of Machine Learning in AI
2.1 Impact of Deep Learning Methods Used in AI
2.2 Impact of Expert Systems (ES)
2.3 Exploration of Neural Networks
2.4 AI Security: An Overview
3 Role of Cyber Security
3.1 Different Categories of CS Threats
3.2 Impact and Benefits of Cyber Security
4 Applying of AI on CS
4.1 Intelligent Agents
4.2 Assorted Threat Exposure
5 AI on CS: Transformation Techniques
5.1 Machine Learning Implemented in Cyber-Threat Detection
5.2 Discussion on Deep Learning Methods in Cyber-Threat Detection
5.3 AI Applications and Advantages over Cyber Security
6 AI on CS: Five Major Tools
6.1 Symantec’s Targeted Attack Analytics—TAA Tool
6.2 Sophos’s X-Intercept Tool and Darktrace Antigena Tool (DAT)
6.3 IBM Q-Radar Advisor Tool (IQAT) and Vectra’s Cognito Tool (VCT)
7 Conclusion and Future Work
References
Applicability of Machine Learning for Personalized Medicine
1 Introduction
2 Literature Review
3 Methods
3.1 Supervised Learning
3.2 Unsupervised Learning
3.3 Reinforcement Learning
4 Discussion and Conclusion
References
I-LAA: An Education Chabot
1 Introduction
2 Literature Review
3 Methodology
4 Results and Analysis
5 Results
6 Future Scope
7 Conclusion
References
A Comparison of Machine Learning Approaches for Forecasting Heart Disease with PCA Dimensionality Reduction
1 Introduction
2 Related Work
3 Algorithms Used
3.1 Naïve Bayes
3.2 Random Forest
3.3 Logistic Regression
3.4 Neural Network
3.5 Decision Tree
3.6 XGBoost
3.7 Support Vector Machine
4 Proposed Methodology
4.1 Data Collections
4.2 Pre-processing and Feature Scaling
4.3 Feature Reduction Technique
4.4 Performance Evaluation Criteria
5 Results and Discussion
6 Conclusion
References
Comparative Study of a Computer Vision Technique for Locating Instances of Objects in Images Using YOLO Versions: A Review
1 Introduction
1.1 Object Detection
1.2 YOLO Algorithm
1.3 Convolutional Neural Network
2 Working of YOLO Algorithm
3 Advantages of One Version Over Another from V1 to V8
4 Literature Review
5 Conclusion
References
Remotely Accessed Smart CCTV System Using Machine Learning
1 Introduction
2 Need for CCTV Camera
3 Concept Development
3.1 Monitor
3.2 Identify the Family Member
3.3 In and Out
3.4 Detect Motion in a Rectangular Frame
3.5 Recording
3.6 Motion
4 Discussion and Result
4.1 Graphical Interface
4.2 Monitor
4.3 Identify
4.4 In and Out
4.5 Rectangle
4.6 Record
4.7 Motion
5 Conclusion
References
Enhancing Surveillance and Face Recognition with YOLO-Based Object Detection
1 Introduction
2 Literature Survey
3 Methodology
3.1 YOLO for Object Detection
3.2 Face-Recognition Methodology
4 Result
5 Future Scope
6 Conclusion
References
Heart Disease Prediction Using Supervised Learning
1 Introduction
2 Literature Review
3 Methodology
3.1 ECG Signal
3.2 Dataset Preparation
3.3 Feature Selection
3.4 Parameters Used for Prediction
4 Results
4.1 Logistic Regression
4.2 Support Vector Machine
4.3 Decision Trees
4.4 Gaussian Naive Bayes
4.5 Multinomial Naive Bayes
4.6 Gradient Boosting Classifier
4.7 K-Nearest Neighbors (KNN) Classifier
4.8 Random Forest Algorithm
5 Limitations
6 Future Scope
7 Conclusion
References
A Review of Machine Learning Tools and Techniques for Anomaly Detection
1 Introduction
2 Related Work
3 Machine Learning Tools
4 Anomaly Detection Techniques
5 Types of Anomalies
6 Windowing
7 Data Sets
8 Machine Learning Techniques for Anomaly Detection
9 Obstacles While Pursuing the Study/Result
10 Conclusions
References
Model for Effective Project Implementation for Undergraduate Students
1 Introduction
2 Related Work
3 Existing Systems
4 Disadvantages of Existing Systems and Loopholes
5 Advantages of Proposed System
6 Effective Project Implementation Model
6.1 Project Phase-I
6.2 Project Phase-II
7 Project Evaluation and Assessment Indicators
8 Feedback Analysis
9 Impact of Study in Long Run, Limitations and Practical Advantages
10 Conclusions
References
Navigating the Aisles: An Augmented Reality Solution for Gamified Indoor Grocery Store Navigation
1 Problem Statement
2 Survey of Existing Systems
3 Proposed Solutions
3.1 Augmented Reality: Indoor Navigation
3.2 Gamification
3.3 Project Contribution
4 Results and Analysis
5 Conclusion
References
Design of Sustainable Water Resource Management System for Agriculture Using IOT
1 Introduction
1.1 Water Usage in Agriculture
1.2 IoT-Based System for Water Management
2 Literature Survey
2.1 Analysis of Literature Survey
2.2 Methodologies
3 Conclusion
4 Future Scope
References
IoT Cloud Convergence Use Cases: Opportunities, Challenges—Comprehensive Survey
1 Introduction
2 Background
3 IoT and Cloud Convergence
4 Literature Survey
5 Gap Analysis
6 Roadmap Ahead
7 Conclusions
References
Analysis of Genomic Selection Methodology in Wheat Using Machine Learning and Deep Learning
1 Introduction
1.1 Genomic Selection
1.2 Machine Learning and Deep Learning
2 Multitrait and Unitrait GS
3 GS Versus MAS Versus PS
4 GS for Wheat Breeding Programs
5 Factors Affecting GS for Wheat Breeding Programs
6 GS Models
6.1 GBLUP
6.2 Random Forest
6.3 Penalized Regression Model
6.4 Bayesian Models
6.5 Support Vector Machine
6.6 Convolutional Neural Network
6.7 Multilayer Perceptron
7 Potential of ML and DL for GS in Wheat
8 Conclusion
References
Exploring Machine Learning and Deep Learning Techniques for Potato Disease Detection
1 Introduction
2 Related Works
3 Objective
4 Data Available for the Plant Disease Detection
5 Plant Disease Detection Using Machine Learning
5.1 What is a Plant Disease?
5.2 List of some Algorithms used in machine learning
6 Plant Disease Detection Using Deep Learning
6.1 Deep Learning Methods on Plant Disease Detection
7 Methodology Involved
8 Results and Discussion
9 Conclusion
References
Intelligent Process Automation for Indian Car Sales Forecasting Using Machine Learning Time Series Algorithms
1 Introduction
2 Literature Survey
3 Dataset
4 Methodology
4.1 Types of Time Series Model
5 Proposed System
6 Conclusion
References
Generation of Historical Artwork Using GAN
1 Introduction
2 Literature Review
3 Objectives
4 Methodology
4.1 Building a Generative Adversarial Network (GAN)
4.2 Generative Adversarial Network (GAN) Inversion with Semantic Loss
5 Experimental Setup
6 Results and Discussion
7 Conclusion and Future Work
References
Wheat, Rice and Corn Yield Prediction for Jammu District Using Machine Learning Techniques
1 Introduction and Related Work
2 Materials and Methodologies
2.1 Site
2.2 Data Sources
2.3 Methods
2.4 Performance Metrics
3 Experimental Design and Result
3.1 Exploratory Data Analysis (Statistical Modelling)
3.2 Crop-Wise Development of Machine Learning Prediction Models
3.3 Results and Discussion
4 Conclusion
References
Detection of UDP SYN Flood DDoS Attack Using Random Forest Machine Learning Algorithm in a Simulated Software Defined Network
1 Introduction
2 Background Study
2.1 Distributed Denial -Of-Service Attacks
2.2 Using Random Forest Machine Learning Algorithm in detecting DDoS Attacks
2.3 User Datagram Protocol (UDP) SYN Flood Attack
3 Experimental Setup
3.1 Simulating the SDN Using MININET and RYU Controller
3.2 Generating Normal Traffic
3.3 Generating UDP SYN Flood DDoS Packet Traffic
4 Performance Evaluation of the Model
5 Conclusion and Future Work
References
Capability Based Access Control Mechanism in IoT: a Survey of State of the Art
1 Introduction
2 Literature Survey
3 Propose Work
4 Conclusion
5 Limitations and Future Scope
References