Proceedings of the 2nd International Conference on Big Data, IoT and Machine Learning

Name: Proceedings of the 2nd International Conference on Big Data, IoT and Machine Learning | BIM 2023
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BIM 2023

Mohammad Shamsul Arefin M. Shamim Kaiser Touhid Bhuiyan Nilanjan Dey Mufti Mahmud(Editor)

Springer (Publisher)

1st Edition

Published on 29. March 2024

XL, 1142 pages

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978-981-99-8937-9 (ISBN)

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Prof. Mohammad Shamsul Arefin received his Doctor of Engineering Degree in Information Engineering from Hiroshima University, Japan, with support of the scholarship of MEXT, Japan. His research includes data privacy and mining, big data management, IoT, machine learning, image information processing, IT for agriculture, education and environment and green computing. Prof. Arefin is the Chair of IEEE Computer Society Bangladesh Chapter. He is a senior member of IEEE, a member of ACM and a fellow of IEB and BCS. Dr. Arefin has more than 150 referred publications in international journals, book series and conference proceedings. He delivered more than 40 keynote speeches/ invited talks. He also received a good number of research grants/funds from home and abroad.

Dr. M Shamim Kaiser is currently working as the director of the Institute of Information Technology of Jahangirnagar University, Savar, Dhaka-1342, Bangladesh. He is also a professor at the IIT. He received his Bachelor's and Master's degrees in Applied Physics Electronics and Communication Engineering from the University of Dhaka, Bangladesh, in 2002 and 2004, respectively, and the Ph. D. degree in Telecommunication Engineering from the Asian Institute of Technology, Thailand, in 2010. His current research interests include data analytics, machine learning, wireless network and signal processing, cognitive radio network, big data and cyber security, and renewable energy. He has authored more than 100 papers in different peer-reviewed journals and conferences. He is an associate editor of the IEEE Access Journal and guest editor of Brain Informatics Journal and Cognitive Computation Journal.

Prof. Dr. Touhid Bhuiyan has received his Ph.D. from Queensland University of Technology, Australia. Currently, he is the head of the Department of Computer Science and Engineering, Daffodil International University, Bangladesh. He is a certified ethical hacker. He has received the Cyber Security: Cyber Risk and Resilience certificate from the University of Oxford. He is the recipient of the Australian Postgraduate Award (APA) and Deputy Vice-Chancellor's Initiative Scholarship from QUT, Australia. He was the director of the Cyber Security Centre, Daffodil International University (DIU), Bangladesh. His research interests are in cyber security, intelligent recommendations, social network, trust management, big data analytics, e-Health, e-Learning, etc. He has more than 114 research publications in renowned national and international journals, books and conference proceedings. His research interest includes cyber security, artificial intelligence, online learning, database management and software engineering.

Dr. Nilanjan Dey is an Associate Professor in the Department of Computer Science and Engineering, Techno International New Town, Kolkata, India. He is a visiting fellow of the University of Reading, UK. He also holds a position as an Adjunct Professor at Ton Duc Thang University, Ho Chi Minh City, Vietnam. Previously, he held an honorary position as a Visiting Scientist at Global Biomedical Technologies Inc., CA, USA (2012-2015). He was awarded his PhD from Jadavpur University in 2015. He is the Editor-in-Chief of the International Journal of Ambient Computing and Intelligence, IGI Global, USA. He is the Series Co-Editor of Springer Tracts in Nature-Inspired Computing (Springer Nature), Data-Intensive Research(Springer Nature),Advances in Ubiquitous Sensing Applications for Healthcare, Elsevier and Hybrid Computational Intelligence for Pattern Analysis and Understanding, Elseviser. He is an associate editor ofIET Image Processing and editorial board member of Complex & Intelligent Systems, Springer Nature, Applied Soft Computing, Elsevier, etc. He has 35 authored books and over 300 publications in the area of medical imaging, machine learning, computer aided diagnosis, data mining, etc. He is a Fellow of IETE and Senior member of IEEE.

Dr. Mufti Mahmud is an associate professor of Cognitive Computing at the Department of Computer Science of Nottingham Trent University (NTU). He is a fellow of the Higher Education Academy, a senior member of the Institute of Electrical and Electronics Engineers (IEEE) and the Association of Computing Machinery (ACM), and a professional member of the British Computer Society (BCS). Dr. Mahmud has been listed among the top 2% cited scientists worldwide in computer science (2020) and has been the winner of the 2021 Vice-Chancellor's Outstanding Research Award for Early Career Researchers. Dr. Mahmud is a section editor (big data analytics) of the Cognitive Computation Journal, regional editor (Europe) of the Brain Informatics Journal and associate editor (neuroprosthetics) of the Frontiers in Neuroscience Journal.

Content

Intro
Organization
Preface
Contents
Editors and Contributors
Informatics for Emerging Applications
A Deep Learning Approach to Predict Cryptocurrency Price by Evaluating Sentiment and Stock Market Correlations
1 Introduction
2 Related Work
3 Methodology
3.1 Proposed System
3.2 Data Preprocessing
3.3 Model for Cryptocurrency Data
3.4 Model for Sentiment Analysis
4 Evaluation
4.1 Dataset Description
4.2 Experimentation and Result Analysis
5 Conclusion
References
Dominance by Stability: A Framework for Top k Dominating Query on Incomplete Data
1 Introduction
2 Related Works
3 Top-k Dominating Query by Stability (TKDS)
3.1 Bucketing
3.2 Dominating Score Computation
3.3 Bucket Implementation
3.4 Top-k Query Processing
4 Performance Evaluation
4.1 Dataset
4.2 Result Analysis
5 Conclusion
References
Phylogeny Reconstruction Using k-mer Derived Transition Features
1 Introduction
2 Literature Review
3 Methodology
3.1 k-mer Length, Distribution Vector, and Position List Generation
3.2 Standard Deviation, Median, and Transition Spatial Features
3.3 Phylogenetic Distance and Tree Reconstruction
4 Experimental Results
4.1 Datasets and Configurations
4.2 Soundness of k-mer Length l and Scalability of the Method
4.3 Benchmark Test Performance
4.4 Performance with Respect to State-of-the-Art Methods
4.5 Discussion
5 Conclusion
References
Developing an Interpretable Machine Learning Model for Divorce Prediction
1 Introduction
2 Related Works
3 Understandable AI Model for Divorce Prediction
3.1 Proposed Methodology
3.2 Evaluation Metrics
3.3 Dataset Description
4 Result and Discussion
4.1 Performance Analysis of Individual Classifier
4.2 SHAP Value Analysis
5 Conclusion and Future Work
References
Riot Perception and Safety Navigation of Autonomous Vehicles Using Deep Learning
1 Introduction
2 Literature Review
3 Dataset Description and Preprocessing
4 Methodology
4.1 Model Architecture
4.2 Training YOLOv8
5 Result and Discussions
6 Implementation and Future Work
7 Conclusion
References
An Explainable AI Enable Approach to Reveal Feature Influences on Social Media Customer Purchase Decisions
1 Introduction
2 Literature Review
3 Methodology
3.1 Overview of the Proposed Methodology
3.2 Description of the Dataset
3.3 Techniques for Dataset Preprocessing
3.4 ML Algorithms for Analysis
3.5 Performance Measure Metrics
3.6 Details of XAI Tools
4 Result and Analysis
4.1 Performance of the ML Algorithms to Predict Social Media Customer Purchase Decision
4.2 Explainability of RF by the XAI Tools
5 Conclusion and Future Works
References
Field Programmable Gate Array in DNA Computing
1 Introduction
2 Background
2.1 DNA Computing
2.2 DNA Basic Operations
3 FPGA Logic Block
3.1 Architecture of Basic Components
3.2 Working Procedure
4 FPGA Logic Block Algorithm
5 Conclusion
References
XAI-Driven Model Explainability and Prediction of P2P Bank Loan Default Network
1 Introduction
2 Literature Review
3 Methodology
3.1 Overview of Proposed Methodology
3.2 Description of the Dataset
3.3 Feature Selection Procedure
3.4 Data Balancing in Highly Imbalanced Dataset
3.5 Description of the ML Algorithms for Prediction
3.6 Performance Measure Techniques
3.7 Description of the Explainable AI Tools
4 Result and Analysis
5 Conclusion and Future Works
References
Design Implication of a Compact-Sized, Low-Fidelity Rover for Tough Terrain Exploration
1 Introduction
2 Comparative Study
3 Foundational Concepts and Technologies
3.1 Embedded System and Robotics
3.2 Navigation System in Miniature Robots
3.3 Low-Fidelity Robot
3.4 Mini Rover
4 Systematic Approach
4.1 Task Outline
5 Implementation
5.1 System Design
5.2 Mathematical Calculation
6 Discussions and Analysis
7 Conclusion
References
VioNet: An Enhanced Violence Detection Approach for Videos Using a Fusion Model of Vision Transformer with Bi-LSTM and 3D Convolutional Neural Networks
1 Introduction
2 Related Works
3 Proposed Method
4 Result and Discussion
4.1 Dataset
4.2 Implementation Details
4.3 Performance Evaluation
4.4 Comparison with Other Methods
5 Conclusion
References
Rank Your Summaries: Enhancing Bengali Text Summarization Via Ranking-Based Approach
1 Introduction
2 Bengali Summary Ranker
2.1 Proposed Approach
2.2 Models
3 Evaluation
3.1 Datasets
3.2 Hyper-Parameter Settings
3.3 Evaluation Metrics
3.4 Experimental Results
4 Result Analysis
4.1 Quantitative Analysis
4.2 Qualitative Analysis
5 Related Works
6 Conclusion
References
An Efficient Machine Learning Classification Model for Rainfall Prediction in Bangladesh
1 Introduction
2 Related Work
3 Methodology
3.1 Predicted Models
3.2 Models Setting and Analysis Steps
3.3 Flow Diagram of This Study
3.4 Experiment Dataset
3.5 Data Preprocess
4 Results and Discussion
4.1 Actual and Predicted Results
4.2 Models Performance Table
4.3 Graphical Representation
5 Conclusions and Future Work
References
Study on the Analysis and Prediction of Drug Addiction Among University Students of Bangladesh Using Machine Learning
1 Introduction
1.1 Data Collection
1.2 Assuring the Quality
1.3 Choosing an Algorithm
1.4 Limitations
1.5 Ethical Consideration
2 Literature Review
3 Background Study
3.1 K-Nearest Neighbor
3.2 Logistic Regression
3.3 Gaussian Naïve Bayes
3.4 Support Vector Machine
3.5 Random Forest
3.6 Neural Network (Multilayer Perceptron)
4 Methodology
4.1 Data Assemblage and Dataset
4.2 Visualization
4.3 Algorithm Analysis
5 Experiment Results
6 Conclusion and Future Work
References
Artificial Intelligence for Imaging Applications
A Deep CNN-Based Approach for Revolutionizing Bengali Handwritten Numeral Recognition
1 Introduction
2 Literature Review
3 Methodology
3.1 Dataset Acquisition and Description
3.2 Data Augmentation
3.3 Convolutional Neural Network
3.4 Proposed Architecture
4 Experiment and Result Analysis
4.1 Data Preprocessing
4.2 Experimental Settings
4.3 Result Analysis
4.4 Evaluating Different Route Configurations
4.5 Comparison with Prior Works
5 Conclusion
References
Performance Analysis of Multiple Deep Learning Models for Image Retrieval Problems
1 Introduction
2 Related Work
2.1 Literature Review
2.2 Deep Learning Methods
3 Research Methodology
3.1 Image Acquisition
3.2 Model Adaptation
3.3 Implementation and Training
4 Experimental Result and Analysis
5 Conclusion
References
Advancing Lung Cancer Diagnosis Through Deep Learning and Grad-CAM-Based Visualization Techniques
1 Introduction
2 Literature Review
3 Materials and Methodology
3.1 Dataset Description
3.2 Data Preprocessing
3.3 Proposed Workflow
3.4 Model Architecture
3.5 Grad-CAM Visualization
4 Result Analysis
4.1 Method Evaluation Metrics
4.2 Comparison with Pre-Trained Other Models
4.3 Comparison with Related Works
4.4 Obtained Result
5 Discussion
6 Conclusion
References
A Novel Approach to Detect Stroke from 2D Images Using Deep Learning
1 Introduction
2 Related Works
3 Data Sets Characteristics
4 Proposed Methodology
5 Result and Discussion
5.1 Batch Size
5.2 Impact of Learning Rate
5.3 Adam Optimizer
5.4 Kernel Size
5.5 Comparison with Current Studies
6 Conclusion and Future Work
References
Enhancing Pneumonia Diagnosis: An Ensemble of Deep CNN Architectures for Accurate Chest X-Ray Image Analysis
1 Introduction
2 Literature Review
3 Proposed Method
4 Dataset
5 Image Pre-processing
5.1 Resizing
5.2 Augmentation
5.3 Normalization
6 Deep CNN Model Architectures Using Transfer Learning
6.1 Convolutional Neural Network Model Architectures
6.2 Transfer Learning: Fine Tuning
7 Ensemble Learning
8 Results and Discussion
8.1 Output of Single Model
8.2 Output of Ensemble Model
9 Conclusion
References
Dataset for Road Roughness Assessment Using Image Classification Techniques and Deep Learning Models: A Case Study on Bangladeshi National Highways
1 Introduction
2 Related Works
3 Methodology
3.1 Data Collection
3.2 Data Preprocessing
3.3 Dataset Comparison
3.4 Model Training
3.5 Feature Map Extraction
3.6 Analysis
4 Conclusion
References
Noise-Aware-Based Texture Descriptor, Evaluation Adjacent Distance Local Ternary Pattern EAdLTP for Image Classification
1 Introduction
2 Background Study
2.1 Local Binary Pattern LBP
2.2 Local Ternary Pattern LTP
3 Noise-Aware-Based Evaluation Window-Based Adjacent Distance Local Ternary Pattern EAdLTP
3.1 Encoding the Value of xp
3.2 Calculating the Value of Adjacent Distance Local Ternary Pattern EAdLTP
4 Experiment Analysis
5 Conclusion
References
Sentiment Analysis from YouTube Video Using Bi-LSTM-GRU Classification
1 Introduction
2 Previous Work
3 Research Methodology
3.1 Data Collection
3.2 Data Processing
3.3 Sentiment Analysis (TextBlob)
3.4 Feature Extraction
3.5 Bi-LSTM
3.6 GRU
4 Experimental Analysis
5 Conclusion and Future Work
References
Brain Tumor Segmentation with Efficient and Low-Complex Architecture Using RCNN and Modified U-Net
1 Introduction
2 Literature Review
3 Methodology
3.1 Two-Channel CNN
3.2 Modified U-Net
3.3 Proposed Architecture
3.4 Dataset Collection and Preprocessing
4 Result and Discussion
4.1 Experimental Setup
4.2 Classification of Tumor Regions
4.3 Mask Generation from Selected Region
4.4 Performance Evaluation of the Complete Architecture
5 Conclusion
References
Machine Learning for Disease Detection
An Expert System to Monitor and Risk Assessment of Chronic Disease Patients Using FTOPSIS
1 Introduction
2 Related Works
3 Methodology
3.1 Determine the Laboratory Criteria
3.2 Selection of Evaluation Criteria
4 Results and Discussions
4.1 Dataset
4.2 Experimental Results
5 Conclusion
References
Attention Mechanism-Enhanced Deep CNN Architecture for Precise Multi-class Leukemia Classification
1 Introduction
2 Literature Review
3 Materials and Methods
3.1 Dataset Collection and Description
3.2 Data Preprocessing
3.3 Convolutional Neural Network
3.4 Attention Mechanism
3.5 Proposed Architecture
4 Results and Performance Analysis
4.1 Model Interpretability: What Our CNN Sees
5 Conclusion
References
An Effective Dimensionality Reduction Workflow for the Enhancement of Automated Date Fruit Recognition Utilizing Several Machine Learning Classifiers
1 Introduction
2 Literature Review
3 Materials and Methods
3.1 Dataset Description
3.2 Proposed Workflow
3.3 Filter-Based Feature Selection
3.4 Principal Component Analysis
3.5 Recursive Feature Elimination Based on Random Forest
3.6 Machine Learning Classifiers
4 Experimental Analysis
4.1 Experimental Setup for Machine Learning Classifiers
4.2 Obtained Results
4.3 Comparison with Previous Works
5 Discussion
6 Conclusion
References
An Ensemble Machine Learning Approach with Hybrid Feature Selection Technique to Detect Thyroid Disease
1 Introduction
2 Literature Review
3 Methodology
3.1 Dataset Description
3.2 Hybrid Feature Selection Framework
3.3 Normalization
3.4 Hyperparameter Optimization
3.5 Description of ML Algorithms
3.6 Performance Metrics
4 Experimental Results and Discussions
4.1 Feature Engineering and Hyperparameter Tuning
4.2 Best Model Selection with Base Dataset
4.3 Cross-Validation
4.4 Stress Testing
5 Conclusion
References
Classify Parkinson Disease from MRI Sample Based on Hybrid Feature Extraction Method
1 Introduction
2 Literature Review
2.1 Methodology
2.2 Dataset of Parkinson Disease
2.3 Min-max Normalization-Based Image Preprocessing
2.4 Feature Extraction
2.5 Stationary Wavelet Transform
2.6 Matrix of Gray-Level Co-occurrence
2.7 Classification of Image Using ANN
3 Results Analysis
4 Conclusion
References
Enhancing Diagnosis: An Ensemble Deep Learning Model for Brain Tumor Detection and Classification
1 Introduction
2 Literature Review
3 Proposed Methodology
3.1 Dataset Selection
3.2 Pre-processing and Normalization
3.3 Convolutional Nueral Networks
3.4 Transfer Learning
3.5 Proposed Ensemble Technique
4 Results and Discussion
5 Conclusion
References
Deep Feature Fusion Based Effective Brain Tumor Detection and Classification Approach Using MRI
1 Introduction
2 Related Work
3 Proposed Method
3.1 Datasets
3.2 Image Preprocessing
3.3 Extraction of Deep Features
3.4 Deep CNN Features Fusion
3.5 Classification
3.6 Experimental Setup and Evaluation Metrics
4 Result Analysis
5 Conclusion
References
An Ensemble-Based Machine Learning Approach to Identify SARS-CoV-2 Virus Infection by Analyzing S Protein Sequences
1 Introduction
2 Methodology
2.1 Dataset Description
2.2 Data Preprocessing
2.3 Machine Learning Model
3 Result Analysis and Discussion
4 Conclusion
References
EEG Signal-Based Autism Spectrum Disorder Detection Through Normalized Mutual Information and Convolutional Neural Network
1 Introduction
2 ASD Detection Using NMI and CNN
2.1 EEG Data Preprocessing
2.2 Connectivity Feature Map Generation
2.3 Classification Using CNN
3 Experimental Studies
3.1 Experimental Setup
3.2 Result Analysis
4 Conclusion
References
ICDP: An Improved Convolutional Neural Network Model to Detect Pneumonia from Chest X-Ray Images
1 Introduction
2 Literature Review
3 Methodology
3.1 Dataset
3.2 Proposed Methodology
3.3 Pre-processing of Pictures
3.4 Convolutional Neural Network
3.5 Proposed ICDP Model Outline
4 Experimental Results and Discussion
4.1 Training and Validation Results
4.2 Performance Comparative Analysis
5 Conclusion and Future Work
References
Explainable Automated Brain Tumor Detection Using CNN
1 Introduction
2 Literature Review
3 Materials and Methods
3.1 Dataset Description
3.2 Data Preprocessing
3.3 Data Augmentation
3.4 Model Architecture
3.5 Grad-Cam-Based Visual Explaination
3.6 Performance Metrics
4 Result Analysis and Discussion
4.1 Hyperparameter Setting of Proposed Model
4.2 Experimental Results for Merged Dataset
4.3 Comparison of Performance to Other Works
5 Conclusion
References
Pattern Recognition and Classification
Drinking Water Quality Analysis and Prediction Using LSTM: Safe Drinking Water for School Children
1 Introduction
2 Related Works
3 Materials and Methods
3.1 System Architecture
3.2 Working Procedure of the System
3.3 The LSTM Model
3.4 Data Collection
3.5 Environment Setup and Training
4 Result and Analysis
4.1 Dataset Analysis
4.2 Dependencies and Correlation of pH, Turbidity, and Temperature
4.3 Water Quality Prediction by LSTM
4.4 Comparison
5 Conclusion
References
An Ensemble Approach for Bangla Handwritten Character Recognition
1 Introduction
2 Literature Review
3 Methodology
3.1 Dataset Description
3.2 VGG-16
3.3 DenseNet-121
3.4 Nadam Optimizer
3.5 Proposed Model Architecture
4 Experimental Analysis
4.1 Preprocessing
4.2 Experimental Setup
4.3 Result Analysis
4.4 Comparison with Previous Works
5 Conclusion
References
An Open-Source Voice Command-Based Human-Computer Interaction System Using Speech Recognition Platforms
1 Introduction
2 Literature Review
3 CMU PocketSphinx, VOSK, and DeepSpeech
3.1 CMU PocketSphinx
3.2 VOSK
3.3 DeepSpeech
4 Methodology
5 Word Error Rate and Speech Recognition Accuracy
6 Implementations, Experiments, and Results
6.1 Voice Interface Implementations
6.2 Environment Setup
6.3 Evaluations
6.4 Word Error Rate (WER)
6.5 Results
7 Performance Analysis
7.1 User Evaluation
7.2 Hardware Resource Performance
8 Future Work
9 Conclusion
References
A Comparative Analysis of Various Deep Learning Models for Traffic Signs Recognition from the Perspective of Bangladesh
1 Introduction
2 Dataset Description
3 Methodology
4 Experimental Results and Comparative Analysis
5 Conclusion
References
Multi-class Brain Tumor Classification with DenseNet-Based Deep Learning Features and Ensemble of Machine Learning Approaches
1 Introduction
2 Related Works
3 Materials and Methods
3.1 Dataset Description
3.2 Dataset Preprocessing
3.3 DenseNet-121-Based Transfer Learning
3.4 Machine Learning and Ensembling Techniques
4 Experimental Setup and Results Analysis
4.1 Training and Experimental Setup
4.2 Results Analysis
5 Conclusion and Future Works
References
An Ensemble Machine Learning Approach to Classify Parkinson's Disease from Voice Signal
1 Introduction
2 Review of Literature
3 Materials and Methods
3.1 Overview of Proposed Methodology
3.2 Dataset Description
3.3 Validation Dataset Description
3.4 Imbalanced Data Handling Techniques
3.5 Dimensionality Reduction Techniques
3.6 Algorithms Description
3.7 Performance Measure Techniques
4 Result and Discussion
5 Conclusion and Future Work
References
Classification of Aloe Vera Leaf Diseases Using Deep Learning
1 Introduction
2 Related Works
3 Methodology
3.1 Dataset
3.2 Data Augmentation
3.3 Contrast Enhancement Using HE
3.4 Segmentation Using K-means Clustering
3.5 CNN
3.6 CNN Architectures
3.7 Experimental Setup
3.8 Model Training
3.9 Performance Metrics
4 Experimental Results
5 Comparative Analysis
6 Conclusion
References
A Romanization Method for the Bengali Language with Efficient Encoding Scheme
1 Introduction
2 Background of the Study
2.1 Bengali Script
2.2 Transliteration Unit (TU)
3 Literature Review
4 Proposed Approach
4.1 Dataset
4.2 TU Decomposition
4.3 Feature Representation
4.4 Machine Learning Models Preparation
4.5 Performance Measures
5 Results and Discussion
6 Conclusion
References
Building an Affective Database for Emotion Detection from Natural Bangla Text
1 Introduction
2 Related Works
3 Challenges in Bangla Emotion Detection
4 Methodology
4.1 Building an Affective Database
4.2 Emotion Detection
5 Experimental Analysis
5.1 Evaluation Metrics
5.2 Emotion Dataset
5.3 Results of Emotion Recognition
6 Conclusions
References
An Improved Skew Detection and Correction Method for Bangla Handwritten Document Using Orthogonal Regression and Connected Component Analysis
1 Introduction
2 Related Work
3 Methodology
4 Experimental Results and Analysis
5 Conclusion
References
Data Science for Wellbeing
Computing Skyline Query on Incomplete Data
1 Introduction
2 Related Works
2.1 Replace-Based Algorithms
2.2 Sort-Based Algorithms
2.3 Table-Scan-Based Algorithms
2.4 Decision Tree-Based Algorithms
3 Methodology
4 Proposed Algorithm
4.1 Phase 1: Counting Incomplete Dimensions
4.2 Phase 2: Pruning
4.3 Phase 3: Finding Weighting Factor
4.4 Phase 4: Creating Weighted Matrix
4.5 Phase 5: Grouping
4.6 Phase 6: Finding Local skylines
4.7 Phase 7: Finding Candidate Skylines
4.8 Phase 8: Retrieving Final Skylines
5 Experimental Results
5.1 Dataset
6 Conclusion
References
Improving Solar Panel Efficiency: A CNN-Based System for Dust Detection and Maintenance
1 Introduction
2 Literature Review
3 System Architecture and Design
3.1 Dataset Interpretation
3.2 Dataset Preprocessing
4 Implementation and Experimental Result
4.1 Implementation
4.2 Experimental Results
5 Conclusion
References
An Improved Framework for Power Efficiency and Resource Distribution in Cloud Computing Using Machine Learning Algorithm
1 Introduction
2 Literature Review
3 Materials and Methods
3.1 Methodology
3.2 Power Consumption Above the System of Clouds
3.3 Algorithm for Load Stabilizing, Scheduling, and Power Efficiency
3.4 Tools for Simulation
3.5 Simulation Output
4 Results and Discussion
4.1 Comparison Between the Proposed Algorithm and Other Active Algorithms
4.2 Power Expenditure
4.3 Execution Time
5 Conclusion
References
Brain Ischemic Stroke Segmentation Using Ensemble Deep Learning
1 Introduction
2 Literature Review
3 Methodology
3.1 Deep Learning Architecture
3.2 InceptionV3
3.3 3D-U-Net
4 Experimental Setup
4.1 Dataset
4.2 Pre-processing
4.3 Evaluation Matrix
4.4 Hyper-parameters
5 Results and Discussion
5.1 Quantitative Analysis
5.2 Qualitative Analysis
5.3 Comparative Analysis Utilizing Advanced Methods
5.4 3D versus 2D
6 Conclusions
References
A Hypergraph-Based Approach to Recommend Online Resources in a Library
1 Introduction
2 Related Work
3 Experiment Design
3.1 Dataset Preparation
3.2 User and Session Identification with URL Extraction
3.3 Recommender System using Online Resource Items
3.4 Tools Used
4 Results Analysis
5 Conclusion and Future Work
References
A Data-Driven Approach to Predict Scores in T20 Cricket Match Using Machine Learning Classifier
1 Introduction
2 Related Works
3 Methodology
3.1 Data Analysis
3.2 XGBoost Regression
3.3 Lasso Regression
3.4 Ridge Regression
3.5 Data Flow
3.6 User Interface
4 Results and Discussion
4.1 Output of Lasso Regression
4.2 Output of Ridge Regression
4.3 Output of XGBoost Regression
5 Future Work
6 Conclusion
References
The Comparison of Machine Learning Algorithms to Find the Career Path by Bloom's Taxonomy Evaluation
1 Introduction
1.1 Problem Statement
2 Literature Review
3 Proposed Methodology
3.1 Data Collection and Analysis
3.2 Data Preprocessing
3.3 Data Split
3.4 Classifier Description
3.5 Performance Evaluation
4 Results and Discussions
5 Conclusion and Future Scope
References
Privacy Preservation of Multivariate Sensitive Data Using Hybrid Perturbation Technique
1 Introduction
2 Related Work
3 Proposed Approach
4 Evaluation Metrics
4.1 Privacy of Data
4.2 Utility Analysis
5 Performance Analysis
5.1 Dataset
5.2 Classification and Experimental Set-Up
5.3 Analysis of Privacy
5.4 Analysis of Utility
6 Conclusion
References
Multi-label Sentiment Analysis of Product Reviews of Online Shop
1 Introduction
2 Related Works
3 Methodology
3.1 Overview of Framework
3.2 Data Preprocessing
3.3 Implementation of Multi-label Classifier
3.4 Implementation of Sarcasm Detection Classifier
4 Result and Discussion
4.1 Dataset Description
4.2 Problem Transformation Methods
4.3 Sarcasm Detection
4.4 Testing Real Data
5 Evaluation of Performance
5.1 Multi-label Classifier
5.2 Sarcasm Detector
6 Conclusion
References
Road Accidents Severity Prediction Using a Voting-Based Ensemble ML Model
1 Introduction
2 Related Works
3 Methodology
3.1 Data Collection
3.2 Data Pre-processing
3.3 Feature Selection
3.4 Imbalance Data
3.5 Voting-Based Ensemble Model
4 Result and Discussion
5 Conclusion
References
Forecasting Crucial Biogeochemical Indicators of the Southern Ocean for Climate Monitoring Using Modified Kernel-Based Support Vector Regression
1 Introduction
2 Related Works
3 Methodology
3.1 Study Design
3.2 Data Pre-processing and Analysis
3.3 Support Vector Regression (SVR)
3.4 Kernel Design
3.5 Regression Evaluation Metrics
4 Result Analysis
4.1 Kernel Performance Evaluation
4.2 Validity and Reliability
4.3 Discussion
5 Conclusion
References
Identifying Hidden Factors for Verbal Harassment Comments on Social Media
1 Introduction
2 Literature Review
2.1 Systematic Review
2.2 Research Review
3 Research Methodology
3.1 Research Objective
3.2 Article Session
4 Result and Analysis
4.1 Judgmental Analysis
5 Discussion and Future Work
5.1 Text Analysis
5.2 Develop Algorithms for a Generalized Solution
5.3 LSTM, SVM, and Naive Bayes for Classifying Sexual Harassment Comments on Social Media
6 Conclusion
References
Security Detection and Counter Measures
Bengali Hate Speech Detection with BERT and Deep Learning Models
1 Introduction
2 Related Research
2.1 Related Research on Other Languages
2.2 Related Research on Bangla Language
3 Materials and Proposed Methodology
3.1 Architecture of the Models
3.2 Performance Evaluation
4 Result and Observation
4.1 Dataset
4.2 Data Prepossessing
4.3 Performance Analysis
5 Conclusion and Future Work
References
Gender-Abusive Language Detection in Bengali Using Machine Learning Algorithms
1 Introduction
2 Related Works
3 Materials and Methods
3.1 Dataset Construction and Description
3.2 Proposed Preprocessing Approach
4 Machine Learning Approaches
4.1 Logistic Regression (LR)
4.2 Decision Tree (DT)
4.3 Random Forest (RF)
4.4 K-Nearest Neighbor (KNN)
4.5 Support Vector Machine (SVM)
4.6 Naïve Bayes (NB)
5 Experimental Analysis
5.1 Implementation of Baseline Methods
5.2 Evaluation Metric
5.3 Results and Discussion
6 Conclusion
References
DPoS-Based Blockchain Payments for Electrified Roads: Ensuring Security, Efficiency and Transparency
1 Introduction
2 Literature Review
3 Methodology
3.1 Election of the Delegates
3.2 Transactions Initiated by Vehicles
3.3 Delegates Validating the Transactions
3.4 Production of the Blocks
3.5 Block Verification and Confirmation
3.6 Rewards for the Delegates
3.7 Finalization of Transactions
4 Result Analysis
5 Conclusion
References
A Digital Certificate Forgery Prevention Using Blockchain Technology
1 Introduction
2 Related Works
3 Proposed Blockchain Architecture
4 Proposed Blockchain Technology Features
5 Prototype Implementation
5.1 Implementation
5.2 Transaction Fee Comparison
6 Discussion
7 Conclusion and Future Work
References
Risk Evaluation of Explosive and Flammable Chemicals Using Fuzzy Inference System
1 Introduction
1.1 Major Industrial Risk
1.2 Overview Some Accidental Incidents
2 Literature Review
3 Proposed Evaluation Methodology
3.1 System Architecture
3.2 Membership Function
3.3 Fuzzification
3.4 Fuzzy Rule Base
3.5 Defuzzification
4 Results and Discussions
5 Conclusion
References
Internet of Things for Smart Applications
Empowering Women's Safety Through IoT-Based Wearable Devices: A Framework for Real-Time Monitoring and Alerting
1 Introduction
2 Literature Review
3 Proposed System
4 Details Regarding the Proposed System's Specifications
4.1 Implementation
4.2 Hardware Description
4.3 Control Mechanism
4.4 Mobile App Interface
5 Experimental Analysis
5.1 Experimental Setup for Women Safety Devices
5.2 Obtained Results
6 Discussion
7 Conclusion
References
Leveraging Attention Mechanisms to Enhance EfficientNet for Precise Analysis of Chest CT Images
1 Introduction
2 Methodology
2.1 Dataset Description
2.2 Soft and Channel Attention
2.3 EfficientNetB0 V2
3 Proposed Approach
3.1 Dataset Preprocessing and Augmentation
3.2 Proposed Attention-Enhanced EfficientNetB0 V2
4 Result
4.1 Experimental Setup
4.2 Result Analysis
4.3 Competitive Analysis
5 Conclusion
References
Air Pollution or Gases Behind Toxicity for People Awareness
1 Introduction
2 Related Work
3 Research Methodology
3.1 Proposed Model
3.2 Methods
3.3 Data Collection Procedure
3.4 Implementation Requirements
4 Experimental Result and Discussion
5 Conclusion
References
Federated Transfer Learning for Vision-Based Fall Detection
1 Introduction
2 Related Works
2.1 Auxiliary Equipment-Based Methods
2.2 Computer Vision-Based Methods
3 Methodology
3.1 Proposed Framework
3.2 Privacy Preservation with Federated Learning
4 Result Analysis
4.1 Dataset
4.2 Experimental Setup
4.3 Evaluation Metric
4.4 Performance Analysis
5 Limitation and Future Work
6 Conclusion
References
Monitoring Plant Growth in Plant Factories: A Smart IoT Solution
1 Introduction
2 Related Works
3 System Model and Architecture
4 Germination and Planting Process
5 Result
6 Conclusion
References
Revolutionizing Smart Town Surveillance Systems: A Framework for Implementing Drone-Based IoT and AI Technologies
1 Introduction
2 Literature Review
3 Proposed System
4 Details Regarding the Proposed System's Specifications
4.1 Scalability and Performance Algorithm
4.2 Hardware Description
4.3 Control Mechanism
4.4 Web Interface
5 Result and Discussion
5.1 Comparison and Performance Analysis
5.2 Limitations
6 Conclusion
References
Peripheral Blood Smear Image-Based Blood Cancer Detection Using Transfer Learning
1 Introduction
2 Literature Review
3 Methodology
4 Data Description
4.1 VGG-16
4.2 VGG-19
4.3 InceptionV3
4.4 MobileNet
4.5 ResNet50
5 Result and Discussion
5.1 Error Analysis
6 Conclusion
References
Breast Cancer Prediction Using Chemical Reaction Optimization and Classifier
1 Introduction
2 Related Work
3 Methodology
3.1 Data Representation and Description
3.2 Solution Representation
3.3 Classifiers
3.4 Parameters and Operator Selection in CRO
3.5 Working Process of CRO Operators
4 Experimental Result
4.1 Metrics of Evaluation
4.2 Comparison Between Our Proposed and Other Related Methods
5 Conclusion
References
Permutation Feature Importance-Based Cardiovascular Disease (CVD) Prediction Using ANN
1 Introduction
2 Literature Review
3 Materials and Methods
3.1 Dataset
3.2 Data Preprocessing
3.3 Train Test Split of the Dataset
3.4 Prediction with ANN
4 Experimental Result and Analysis
5 Conclusion
References
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Proceedings of the 2nd International Conference on Big Data, IoT and Machine Learning

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