Multi-Strategy Learning Environment : Proceedings of ICMSLE 2024 / edited by Vrince Vimal, Isidoros Perikos, Amrit Mukherjee, Vincenzo Piuri.

Format:

Book

Contributor:

Vimal, Vrince, editor.

Series:

Algorithms for Intelligent Systems, 2524-7573

Language:

English

Subjects (All):

Computational intelligence.

Artificial intelligence.

Computer vision.

Natural language processing (Computer science).

Game theory.

Computational Intelligence.

Artificial Intelligence.

Computer Vision.

Natural Language Processing (NLP).

Game Theory.

Local Subjects:

Computational Intelligence.

Artificial Intelligence.

Computer Vision.

Natural Language Processing (NLP).

Game Theory.

Physical Description:

1 online resource (710 pages)

Edition:

1st ed. 2024.

Place of Publication:

Singapore : Springer Nature Singapore : Imprint: Springer, 2024.

Summary:

The book presents selected papers from International Conference on Multi-Strategy Learning Environment (ICMSLE 2024), held at Graphic Era Hill University, Dehradun, India, during 12–13 January 2024. This book presents current research in machine learning techniques, deep learning theories and practices, interpretability and explainability of AI algorithms, game theory and learning, multi-strategy learning (MSL) in distributed and streaming environments, and adaptive data analysis and selective inference.

Contents:

Intro

Preface

Contents

About the Editors

1 AI Powered Chat Assistant for Trauma Detection from Text and Voice Conversations with a Direct Doctor Connection

1 Introduction

2 Related Works

2.1 Emotional Intelligence in Communication: Research Challenges, Literature and Recent Achievements

2.2 Chatbot Mobile Isolation App for Depression

2.3 Mental Health Chatbot that Uses NLP and AI to Deliver Behavioral Insights and Remote Healthcare

2.4 Mental Health Support Chatbot Using NLP

2.5 Dost-Chatbot as Mental Health Assistant

2.6 Application of Cognitive Behavioral Therapy in Psychiatry: A Review

2.7 Revivify: Depression Research and Management Using Automated Tweets and Chatbots

2.8 Proposed Chatbot: Thinking and Problem-Solving Experience

2.9 Psykh, the Chatbot Using the Rasa Open Source Framework, to be Your Therapist and Stress Reliever

2.10 Identify Depression in a Person Using Speech Signals by Extracting Energy and Situations

3 Methodology

3.1 Module Description

4 Implementation

4.1 Implementation of Machine Learning Models

4.2 NLP Model Development

5 Result and Discussion

6 Conclusion

References

2 An Intelligent Car Locating System Based on Arduino for a Massive Parking Place

2 Motivation

3 Problem Statement

4 Project Scope

5 Objectives

6 Related Study

7 Comparison with Other System

8 Background Tools and Technology

8.1 Software Tools

8.2 Arduino Simulator

8.3 Hardware Tools

9 PCB Design of Intelligent Car Locating System for a Massive Parking Place

10 Proposed Model of Smart Car Parking System

11 Work Flow Diagram of Smart Car Locating System

12 Final Circuit of Intelligent Car Locating System for a Massive Parking Place

13 Implementation.

13.1 Final Output of Intelligent Car Locating System for a Massive Parking Place

14 Result and Discussion

15 Testing and Evaluation

15.1 Performance Analysis of IoT-Based Intelligent Car Parking System for Large Parking Lot Using Arduino

16 Contribution

17 Conclusion

18 Future Recommendation

3 Electricity Load Forecasting Using LSTM for Household Usage

2 Literature Review

3 LSTM for Electricity Load Forecasting Methodology

3.1 Building Machine-Learning Model

3.2 Training ML Model

4 Results and Discussions

4.1 Electricity Load Prediction Over Different Periods

4.2 Error Analysis for Electricity Load Prediction

4.3 Comparison with Conventional Methods

5 Conclusion

4 A Cybersecurity Classification Model for Detecting Cyberattacks

2 Literature Survey

2.1 Training with Support Vector Machine (SVM) for Cyberattack Detection

2.2 Normalization for Removing Noise from Given Datasets

2.3 Autoencoders with Cybersecurity

3 Dataset Description

4 Experimental Results

5 Implementation of Baumann Skin Type Indicator Using Machine Learning

2 Related Work

3 Brief Overview of the Fundamental Dichotomies of BSTI

4 Proposed Architecture

4.1 Machine Learning Model Architecture

4.2 Dataset

5 Experiments and Results

5.1 Results of the InceptionV3 Model

5.2 Comparison with Other Deep Learning Techniques

6 Conclusion and Future Work

6 An On-demand Data Delivery and Secured Platform in Cloud Computing

1.1 Methodology

3 On-demand Data Delivery and Secured Platform (ODDSP)

3.1 Quality of Service (QoS)

4 Advanced Encryption Standard (AES) for Cloud Data

4.1 Performance Metrics.

4.2 Evaluation Results

7 Real-Time Sign Language Interpreter Using MediaPipe, Dynamic Time Warping, and NLP

3 Proposed Methodology

3.1 Problem Domain

3.2 Problem Definition

3.3 Problem Statement

3.4 Dataset

4.1 MediaPipe Detection/Holistic Model

4.2 Extract Landmarks

4.3 Draw Landmarks

4.4 Models

4.5 Dynamic Time Warping (DTW)

4.6 Sign Prediction

4.7 Phrase Generation

5 Result

6 Discussion

7 Conclusion

8 A Support Vector Machine Classifier Approach for Predicting Preeclampsia and Gestational Hypertension

3 Materials and Methods

3.1 Description of the Dataset

3.2 Data Preprocessing

3.3 Importance of Using SVM in ML Experiments

3.4 Training and Testing Data

3.5 Methodology

3.6 Selection of Algorithm

3.7 Evaluation of the Schemes' Performance

4 Results and Discussion

9 Multilingual Communication: NMT-Based On-Call Speech Translation for Indian Languages

3.1 Dataset

3.2 Algorithms

3.3 Architecture

3.4 Model Training

3.5 Evaluation

4 Result

6 Future Work

10 Brain Tumor Segmentation and Classification Using Deep Learning

2.1 Problem Formulation

2.2 Research Gap

3 Data and Variables

3.1 About Dataset

3.2 Variables for Segmentation Model:

3.3 Variables for Classification Model:

4 Methodology and Model Specifications

4.1 Segmentation Model

4.2 Classification Model

5 Empirical Results

5.1 Segmentation Model

5.2 Classification Model

7 Future Scope

References.

11 ACO-Optimized DRL Model for Energy-Efficient Resource Allocation in High-Performance Computing

3 Problem Definition

4 Methods

4.1 Ant Colony Optimization

4.2 DRL for Resource Allocation in HPC

5 Proposed Model

5.1 ACO-Optimized DRL for Resource Allocation in HPC

6 Experimental Analysis

6.1 Response Time Analysis

6.2 Makespan Analysis

6.3 Energy Consumption Analysis

7 Conclusion and Future Work

12 Business Decision-Making Using Hybrid LSTM for Enhanced Operational Efficiency

4 Data Collection

4.1 Data Preprocessing

5 Hybrid Optimized LSTM Model for Sales Prediction

5.1 Loss Function

6.1 Model Training

6.2 Forecast Analysis

6.3 Performance Analysis

13 Unveiling New Horizons in Machine Learning, NLP-Driven Framework for Student Learning Behavior

3 Modeling and Analysis of Student Learning Behavior

3.1 Role of Machine Learning in Analyzing the Student Behavior

4 Techniques for Modeling Student Learning Behavior

4.1 Employing Natural Language Processing

4.2 Employing Natural Language Processing

5 Conclusion and Scope for Future Work

14 Mirror Text Classification from Image Using Machine Learning Techniques

3.1 Input and Preprocessing

3.2 Clustering and Sub-clustering

3.3 Feature Extraction

3.4 Training

3.5 Mirror Text Identification

15 Using Deep Learning to Identify Types of Lung Diseases from X-Ray Images

2 Objective

3 Literature Survey

4 Proposed System

5 Conclusion.

References

16 A Light-Weight Data Storage and Delivery Platform in Cloud Computing

3 Lempel-Ziv-Markov (LZMA) Compression

3.1 RSA-KEM (Key Encapsulation Mechanism)

3.2 Symmetric-Key Decryption

3.3 Dataset Description

3.4 Performance Metrics

17 Big Data Analytics Security Issues and Solutions in Healthcare

2 The Need for Medical Care Analytics for Big Data

3 Different Big Data Analytics Phases

3.1 Extracting, Data Cleaning, and Data Collection

3.2 Integration and Aggregation of Data

3.3 Data Model

3.4 Data Delivery, Interpretation, and Feedback

4 Role-Based Application Security Principles

5 Big Data Lifecycle

6 Technologies in Use

7 Big Data Security Challenge and Solution

8 Conclusion

18 Framework for Early-Stage Diabetes Mellitus Risk Prediction Using Hybrid Supervised Learning

3 Proposed Framework

4 Experimental Setup

4.1 Evaluation Metrics

5 Results and Discussion

5.1 Prediction Performance of ML Techniques

5.2 Optimization of Prediction Accuracy

19 Brain Tumor Classification in MRI Images: A CNN and U-Net Approach

2 Proposed Method

2.1 Data Collection

2.2 Image Preprocessing

2.3 Image Segmentation

2.4 Feature Extraction

2.5 Convolutional Neural Network (CNN)

2.6 U-Net Architecture

2.7 Callback Functions

3 Result and Discussion

3.1 Evaluation Metrics

3.2 Experimental Analysis and Result

3.3 Prediction of Tumor

4 Conclusion

20 Cross-Modal Text-to-Video Retrieval Using Deep Learning

3 System Architecture

4 Methodology.

5 Model Implementation and Output.

Notes:

Includes bibliographical references and index.

ISBN:

981-9714-88-5