Machine Learning and Computational Intelligence Techniques for Data Engineering

Name: Machine Learning and Computational Intelligence Techniques for Data Engineering | Proceedings of the 4th International Conference MISP 2022, Volume 2
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Proceedings of the 4th International Conference MISP 2022, Volume 2

Pradeep Singh Deepak Singh Vivek Tiwari Sanjay Misra(Editor)

Springer (Publisher)

1st Edition

Published on 15. May 2023

XIV, 896 pages

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978-981-99-0047-3 (ISBN)

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Dr. Pradeep Singh received a Ph.D. in Computer science and Engineering from the National Institute of Technology, Raipur, and an M.Tech. in Software engineering from the Motilal Nehru National Institute of Technology, Allahabad, India. Dr. Singh is an Assistant Professor in the Computer Science & Engineering Department at the National Institute of Technology. He has over 15 years of experience in various government and reputed engineering institutes. He has published over 80 refereed articles in journals and conference proceedings. His current research interests areas are machine learning and evolutionary computing and empirical studies on software quality, and software fault prediction models.

Dr. Deepak Singh completed his Bachelor of Engineering from Pt. Ravi Shankar University, Raipur, India, in 2007. He earned his Master of Technology with honors from CSVTU Bhilai, India, in 2011. He received a Ph.D. degree from the Department of Computer Science and Engineering at the National Institute of Technology (NIT) in Raipur, India, in 2019. Dr. Singh is currently working as an Assistant Professor at the Department of Computer Science and Engineering, National Institute of Technology Raipur, India. He has over 8 years of teaching and research experience along with several publications in journals and conferences. His research interests include evolutionary computation, machine learning, domain adaptation, protein mining, and data mining.

Dr. Vivek Tiwari is a Professor in Charge of the Department of Data Science and AI and Faculty of Computer Science and Engineering at IIIT Naya Raipur, India. He received B.Eng. from the Rajiv Gandhi Technical University, Bhopal, in 2004 and M.Tech. from SATI, Vidisha (MP), in 2008. He obtained a Ph.D. degree from the National Institute of Technology, Bhopal (MA-NIT), India, in 2015 in data mining and warehousing. Dr. Tiwari has over 65 research papers, 2 edited books, and one international patent published to his credit. His current research interest is in machine/deep learning, data mining, pattern recognition, business analytics, and data warehousing.

Dr. Sanjay Misra Sr. Member of IEEE and ACM Distinguished Lecturer, is Professor at Østfold University College (HIOF), Halden, Norway. Before coming to HIOF, he was Professor at Covenant University (400-500 ranked by THE (2019)) for 9 years. He holds a Ph.D. in Information & Knowledge Engineering (Software Engineering) from the University of Alcala, Spain, and an M.Tech. (Software Engineering) from MLN National Institute of Tech, India. Total around 600 articles (SCOPUS/WoS) with 500 co-authors worldwide (-130 JCR/SCIE) in the core & appl. area of Software Engineering, Web engineering, Health Informatics, Cybersecurity, Intelligent systems, AI, etc.

Content

Intro
Contents
About the Editors
A Review on Rainfall Prediction Using Neural Networks
1 Introduction
2 Literature Survey
3 Theoretical Analysis of Survey and Discussion
4 Conclusions
References
Identifying the Impact of Crime in Indian Jail Prison Strength with Statical Measures
1 Introduction
2 Related Work
3 Methods and Materials
3.1 Dataset
3.2 Experimental Work
3.3 Correlation Coefficient Between Two Random Variables
4 Result and Discussion
5 Conclusion
References
Visual Question Answering Using Convolutional and Recurrent Neural Networks
1 Introduction
2 Literature Survey
3 Dataset Description
4 Proposed Method
4.1 Experiment 1
4.2 Experiment 2
5 Results and Analysis
5.1 Experiment 1
5.2 Experiment 2
6 Conclusion
References
Brain Tumor Segmentation Using Deep Neural Networks: A Comparative Study
1 Introduction
2 Methodology
2.1 2-Path Convolutional Neural Network
2.2 Cascaded Architecture
2.3 U-Net
3 Empirical Studies
3.1 Dataset
3.2 Experiment Setup
3.3 Data Preprocessing
3.4 Performance Evaluation Metrics
4 Visualization and Result Analysis
4.1 Cascaded CNN
4.2 U-Net
5 Conclusions
References
Predicting Bangladesh Life Expectancy Using Multiple Depend Features and Regression Models
1 Introduction
2 Literature Review
3 Proposed Methodology
3.1 Data Preprocessing
3.2 Regressor Relevant Theory
3.3 Preformation Calculation
4 Results and Discussions
5 Conclusion and Future Work
References
A Data-Driven Approach to Forecasting Bangladesh Next-Generation Economy
1 Introduction
2 Literature Review
3 Methodology
4 Analysis and Results
5 Conclusion and Future Work
References
A Cross Dataset Approach for Noisy Speech Identification
1 Introduction
2 Problem Statement
3 Prior Work
4 Experimental Setup
4.1 Phoneme Detection rate
4.2 Softmax Probability of Clean Speech and Noisy Speech
4.3 Utterance Level Scoring
5 Results
6 Conclusion and Future Work
References
A Robust Distributed Clustered Fault-Tolerant Scheduling for Wireless Sensor Networks (RDCFT)
1 Introduction
2 Literature Review
2.1 Classification of Fault Levels
2.2 Redundancy Based Fault Tolerance in WSNs
3 Proposed Work
3.1 Network Model, Preliminaries, and Assumptions
3.2 Fault Detection and Recovery
3.3 Redundancy Check and Clustering in WSNs
3.4 Selection of Cluster Head
3.5 Algorithm Phase: Distributed Clustered Fault-Tolerant Scheduling
3.6 Simulation Setup and Results
4 Conclusion and Future Remarks
References
Audio Scene Classification Based on Topic Modelling and Audio Events Using LDA and LSA
1 Introduction
2 Related Work
3 LSA and LDA
3.1 Latent Semantic Analysis (LSA)
3.2 Latent Dirichlet Allocation (LDA)
4 Framework of the Proposed Work
4.1 Input Vocabulary Creation
4.2 Event Term Cooccurrence Matrix
4.3 Output Generation
5 Experimental Results
6 Conclusion and Future Enhancement
References
Diagnosis of Brain Tumor Using Light Weight Deep Learning Model with Fine Tuning Approach
1 Introduction
2 Motivation
3 Literature Review
4 Research Gap
5 Our Contribution
6 Characteristics improved using our Brain Tumor Analysis Model
6.1 Light Weight
6.2 Reliability
6.3 Time Efficiency
7 Dataset
8 Deep Learning Based Brain Tumor Diagnosis Using Yolov5
8.1 Yolov5
9 Proposed Model
10 Conclusion
References
Comparative Study of Loss Functions for Imbalanced Dataset of Online Reviews
1 Introduction
2 Literature Review
3 Loss Functions
3.1 Cross-Entropy Loss
3.2 Focal Loss
4 Dataset
5 Methodology
6 Training and Classification
7 Results
8 Conclusion
References
A Hybrid Approach for Missing Data Imputation in Gene Expression Dataset Using Extra Tree Regressor and a Genetic Algorithm
1 Introduction
2 Literature Survey
3 About Genetic Algorithm, K-Means, and Extra Tree Regression
3.1 Genetic Algorithm
3.2 K-Means Algorithm
3.3 Extra Tree Regression
4 About Dataset
5 Proposed Model
5.1 Experimental Implementation
6 Performance Analysis
7 Experimental Results
8 Conclusion and Future Work
References
A Clustering and TOPSIS-Based Developer Ranking Model for Decision-Making in Software Bug Triaging
1 Introduction
2 Motivation
3 Related Work
4 Methodology
5 Illustrative Example: A Case Study
6 Threats to Validity
7 Conclusion and Future Scope
References
GujAGra: An Acyclic Graph to Unify Semantic Knowledge, Antonyms, and Gujarati-English Translation of Input Text
1 Introduction
2 Gujarati Language
3 Literature Review
4 Software Description
4.1 Software Architecture
5 Proposed Algorithm
6 Experiment and Result
7 Conclusion
References
Attribute-Based Encryption Techniques: A Review Study on Secure Access to Cloud System
1 Introduction
2 Background of the Review Study
3 Review Study
4 Review Summary
5 Conclusion
References
Fall Detection and Elderly Monitoring System Using the CNN
1 Introduction
2 Related Work
3 Proposed Method
3.1 ADLs and Falls Comparison
3.2 The Visualization of the Bitmap Generation
3.3 CNN Model
4 Experimental Results and Analysis
4.1 Fall Detection
4.2 Computation Complexity
5 Conclusion
References
Precise Stratification of Gastritis Associated Risk Factors by Handling Outliers with Feature Selection in Multilayer Perceptron Model
1 Introduction
2 Methods
2.1 Data Source
2.2 Data Pre-processing
2.3 Feature Selection
2.4 Learning Curves
2.5 Data Modeling
2.6 Naive Bayes Bernoulli
2.7 Data Package
3 Results and Discussion
3.1 Original Dataset
3.2 Outliers Removed Using Interquartile Range Method
3.3 Outliers Removed Using One-Class SVM
3.4 Outlier Removed Using Isolation Forest
3.5 Outliers Replaced by Median
3.6 Outliers Replaced by Median Values + Feature Selection
4 Benchmarking Machine Learning Systems
5 Risk Factors for Gastritis-Associated H. Pylori
6 Conclusion
References
Portfolio Selection Using Golden Eagle Optimizer in Bombay Stock Exchange
1 Introduction
2 Related Work
3 The Problem Statement
4 Proposed Strategy
4.1 Attack (Exploitation)
4.2 Cruise (Exploration)
5 Experimental Results
6 Conclusion
References
Hybrid Moth Search and Dragonfly Algorithm for Energy-Efficient 5G Networks
1 Introduction
2 Literature Review
3 Methodology
3.1 Delay-Bounded QoS Provisioning
3.2 EPE Under QoS Provisioning
3.3 Optimal Power Allocation Via MS-DA Model
4 Results and Discussions
5 Conclusions
References
Automatic Cataract Detection Using Ensemble Model
1 Introduction
2 Literature Survey
3 Materials and Methods
3.1 Methodology
3.2 Dataset
3.3 Proposed Designed
4 Experiments and Results
4.1 First model
4.2 Second Model
4.3 Third Model
4.4 Ensemble Model
5 Comparative Study
6 Conclusion and Future Scope
References
Nepali Voice-Based Gender Classification Using MFCC and GMM
1 Introduction
2 Literature Review
3 Methodology
3.1 Data Collection
3.2 Data Processing
3.3 Feature Extraction
3.4 Model Training
4 Experiments and Results
5 Conclusion
References
Analysis of Convolutional Neural Network Architectures for the Classification of Lung and Colon Cancer
1 Introduction
2 Related Works
3 Proposed Work
3.1 Image Acquisition and Preprocessing
3.2 Feature Extraction
3.3 Classification
3.4 Inception-ResNet V2
4 Experimental Setup
5 Experimented Results
6 Conclusion
References
Wireless String: Machine Learning-Based Estimation of Distance Between Two Bluetooth Devices
1 Introduction
2 Related Works
3 Distance Estimation Between Bluetooth Devices as a Regression Problem
3.1 Generating the Dataset
3.2 Regression
4 Performance Evaluation
4.1 Comparison Using Separate Datasets
4.2 Comparison Using Combined Dataset
5 Conclusions
References
Function Characterization of Unknown Protein Sequences Using One Hot Encoding and Convolutional Neural Network Based Model
1 Introduction
2 Related Work
3 Methodology
3.1 Protein Dataset
3.2 Preprocessing
3.3 Prediction Using Convolutional Neural Network
3.4 Performance Measures
4 Results and Discussion
4.1 Results
4.2 Discussion
5 Conclusion
References
Prediction of Dementia Using Whale Optimization Algorithm Based Convolutional Neural Network
1 Introduction
2 Related Work
3 Proposed WOA Based CNN
4 Experimental Results
4.1 Comparison of Accuracy for Various Values of Dropout Rate and Mini Batch Size
4.2 Comparison of Accuracy
4.3 Comparison of Loss
5 Conclusion
References
Goodput Improvement with Low-Latency in Data Center Network
1 Introduction
2 Related Work
3 Enhanced Multipath Transmission Control Protocol
3.1 Multipath Transmission Control Protocol (MPTCP)
3.2 Packet Sprinkle
4 Design of Proposed Protocol
4.1 Architecture
5 Implementation
6 Performance Analysis
7 Conclusion and Future Work
References
Empirical Study of Image Captioning Models Using Various Deep Learning Encoders
1 Introduction
2 Related Works
2.1 Past Work
2.2 Datasets
3 Image Captioning
3.1 Encoders
3.2 Gated Recurrent Unit (Decoder)
4 Experiments
4.1 Result Analysis
5 Conclusion
References
SMOTE Variants for Data Balancing in Intrusion Detection System Using Machine Learning
1 Introduction
2 Literature Survey
3 Proposed Methodology
3.1 Dataset Description
3.2 Data Preprocessing
3.3 Feature Extraction
3.4 Data Balancing Techniques
3.5 Machine Learning
4 Experimental Implementation and Evaluation
4.1 Evaluation Metrics
4.2 Performance Evaluation on Data Preprocessing
4.3 Performance Evaluation on Feature Extraction
4.4 Performance Evaluation Without Data Balancing Technique
4.5 Performance Evaluation on Different Data Balancing Techniques
5 Conclusion and Future Work
References
Grey Wolf Based Portfolio Optimization Model Optimizing Sharpe Ratio in Bombay Stock Exchange
1 Introduction
2 Related Work
3 The Problem Formulation
4 Proposed Strategy
5 Experimental Results
6 Conclusion
References
Fission Fusion Behavior-Based Rao Algorithm (FFBBRA): Applications Over Constrained Design Problems in Engineering
1 Introduction
2 Background
3 Proposed Methodology
4 Experimental Setup
4.1 Cantilever Beam Problem
4.2 Three Bar Truss Design Problem
4.3 Pressure Vessel Problem
5 Result Discussion
6 Conclusion and Future Scope
References
A Novel Model for the Identification and Classification of Thyroid Nodules Using Deep Neural Network
1 Introduction
2 Related Work
3 Proposed Work
3.1 Data Collection Phase
3.2 Pre-processing Phase
3.3 Feature Extraction Phase
3.4 Classification Phase
3.5 Proposed Algorithm
4 Experimental Work and Result Analysis
5 Conclusion
References
Food Recipe and Nutritional Information Generator
1 Introduction
2 Related Work
3 Dataset
4 Methodology
4.1 Food Image Classification
4.2 Food Calorie Estimation
5 Evaluation/Results
5.1 Food Image Identification
5.2 Calorie Estimation
5.3 Final Output
6 Conclusion
References
Can Machine Learning Algorithms Improve Dairy Management?
1 Introduction
2 Literature Review
3 Methodologies
3.1 General Outlooks and Findings
3.2 Prediction Models for Water and Electricity Consumption
3.3 Body Condition Scoring
3.4 Behavior Classification Based on Sensor
3.5 Grouping the Feeding of Cows
3.6 Grazing
4 Results and Discussion
5 Conclusion
References
Flood Severity Assessment Using DistilBERT and NER
1 Introduction
2 Related Works
3 Proposed Methodology
3.1 Extraction of Tweets
3.2 Preprocessing for DistilBERT
3.3 Classification of Texts
3.4 Preprocessing and Implementation of NER
3.5 Spatiotemporal Modelling
4 Results and Discussions
4.1 Performance Metrics
4.2 Text Classification
4.3 Spatiotemporal Analysis
4.4 Discussions
5 Conclusion and Future Works
References
Heart Disease Detection and Classification using Machine Learning Models
1 Introduction
2 Proposed Methodology and Algorithm Design
3 Results and Discussion
4 Conclusion
References
Recognizing Indian Classical Dance Forms Using Transfer Learning
1 Introduction
2 Methodology
2.1 Dataset
2.2 Feature Extraction
2.3 Classification
3 Implementation
4 Results and Analysis
5 Conclusion
References
Improved Robust Droop Control Design Using Artificial Neural Network for Islanded Mode Microgrid
1 Introduction
2 Droop Control Approach
3 Robust Droop Controller
4 Proposed Control Algorithm
5 Results and Discussion
6 Conclusion
References
AI-Driven Prediction and Data Analytics for Neurological Disorders-A Case Study of Multiple Sclerosis
1 Introduction
2 Algorithm
2.1 Computer-Aided Diagnosis System
2.2 Convolutional Neural Network
3 Preprocessing
3.1 Data augmentation
4 Dataset
5 CNN Model
5.1 Architecture Explanation
6 Results and Discussion
7 Conclusion
References
Rice Leaf Disease Identification Using Transfer Learning
1 Introduction
2 Literature Review
3 Proposed Methodology
3.1 Dataset Description
3.2 Deep Learning Technique
4 Experimental Results
5 Conclusion
References
Surface Electromyographic Hand Gesture Signal Classification Using a Set of Time-Domain Features
1 Introduction
2 Related Works
3 Methodology
3.1 Data Acquisition and Pre-Processing
3.2 Proposed SoTF
3.3 Classification
4 Experimental Evaluation and Results
4.1 Experimental Setup
4.2 NinaPro DB1 Dataset
4.3 Experimental Method
4.4 Results and Discussions
5 Conclusions
References
Supervision Meets Self-supervision: A Deep Multitask Network for Colorectal Cancer Histopathological Analysis
1 Introduction
2 Related Works
2.1 Colorectal Cancer Histopathology
2.2 Deep Metric Learning
2.3 Self-supervised Learning
3 Methodology
3.1 Overview
3.2 Deep Metric Learning
3.3 Image Reconstruction Network
3.4 Final Classification
4 Results and Discussion
4.1 Dataset Description
4.2 Implementation Details
4.3 Evaluation Metrics
4.4 Qualitative Analysis
4.5 Comparison with State of the Art
4.6 Ablation Study
5 Conclusion and Future Work
References
Study of Language Models for Fine-Grained Socio-Political Event Classification
1 Introduction
2 Background Related Works
3 Corpus Acquisition and Annotations
4 Experiments
4.1 BERT
4.2 ELMo
4.3 RoBERTa
4.4 XLNet
5 Results Analysis
6 Error Analysis
6.1 Error(s) Due to Redundancy in Corpus
6.2 Error(s) Due to Model Architecture
7 Conclusion
References
Fruit Recognition and Freshness Detection Using Convolutional Neural Networks
1 Introduction
2 Materials and Methods
2.1 Image Acquisition
2.2 Image Pre-Processing
2.3 Image Segmentation
2.4 Feature Extraction
2.5 Classification
3 Proposed Methodology
4 Hardware Setup
4.1 Hardware Specifications
5 Results and Discussion
6 Conclusion
References
Modernizing Patch Antenna Wearables for 5G Applications
1 Introduction
2 Antenna Design
3 Antenna Performance Analysis
3.1 Simulation Results
3.2 Bending Performance
3.3 On-Body Performance
4 Conclusion
References
Veridical Discrimination of Expurgated Hyperspectral Image Utilizing Multi-verse Optimization
1 Introduction
2 Related Work
3 Proposed Methodology
3.1 Filter Wrapper Semi-Supervised Band Selection Technique
3.2 Stabilized Smile Frown Technique
3.3 Volume Shrunk Pure Pixel Actualize Method
3.4 Multi-verse Optimization Algorithms
4 Results and Discussions
4.1 Experimental Results and Analysis
4.2 Performance Metrics of Proposed Method
4.3 Comparison Results of the Proposed Method
5 Conclusions
References
Self-supervised Learning for Medical Image Restoration: Investigation and Finding
1 Introduction
2 Methodology
2.1 Combinations of Different Loss Functions
3 Experiments and Result Analysis
3.1 Datasets, Specifications, and Parameter Settings
3.2 Restoration of Brain MRI Dataset
3.3 Restoration of Lung CT Dataset
3.4 Ablation Experiments and Quantitative Analysis
4 Conclusion and Discussion
References
An Analogy of CNN and LSTM Model for Depression Detection with Multiple Epoch
1 Introduction
2 Related Work
3 Experimental Work
3.1 Dataset
3.2 Preprocessing
3.3 Experiment
4 Result
5 Conclusion and Future Work
References
Delaunay Tetrahedron-Based Connectivity Approach for 3D Wireless Sensor Networks
1 Introduction
2 Related Work
3 Proposed Method
4 Performance Evaluation
5 Conclusion
References
CNN Based Apple Leaf Disease Detection Using Pre-trained GoogleNet Model
1 Introduction
2 Related Work
3 Proposed Approach
3.1 Data Acquisition and Preprocessing
3.2 Retrain GoogleNet CNN
3.3 Disease Detection and Classification Process
4 Experimental Results and Discussion
4.1 Comparative Analysis
5 Conclusion
References
Adaptive Total Variation Based Image Regularization Using Structure Tensor for Rician Noise Removal in Brain Magnetic Resonance Images
1 Introduction and Related Work
2 Materials and Methods
2.1 Structure Tensor Matrix
2.2 Proposed Adaptive Total Variation Based Image Regularization Using Structure Tensor
3 Experimental Results and Discussion
4 Conclusion
References
Survey on 6G Communications
1 Introduction
2 Use Case Scenario for 6G Communication
2.1 New Media
2.2 New Services
2.3 New Infrastructure
3 Requirements and Infrastructure for 6G Communication
3.1 High Performance Networking
3.2 Higher Energy Efficiency
3.3 High Security and Privacy
3.4 High Intelligence
3.5 Increased Device Density
3.6 Green Communication
4 5G to 6G Comparison
5 Challenges for 6G Communication
5.1 THz Sources
5.2 Path Loss
5.3 Channel Capacity
6 Conclusion
References
Human Cognition Based Models for Natural and Remote Sensing Image Analysis
1 Introduction
2 Cognition Based Model for Natural Images
2.1 Attention-Based Model
2.2 Understanding Based Model
2.3 Koch and Ullman Model
2.4 Bayesian Model
3 Cognition Based Model for Satellite Images
3.1 Damage Assessment from High-Resolution Satellite Image
3.2 Polsar Image Interpretation Model
4 Comparison of Models
5 Conclusion
References
Comparison of Attention Mechanisms in Machine Learning Models for Vehicle Routing Problems
1 Introduction
2 Problem Definition
3 Sequence-to-Sequence Model for Solving VRPs
4 Attention Mechanisms
5 Simulation Results
6 Discussion and Conclusion
References
Performance Analysis of ResNet in Facial Emotion Recognition
1 Introduction
2 Related Works
3 Methodology
4 Experiment
5 Results
References
Combined Heat and Power Dispatch by a Boost Particle Swarm Optimization
1 Introduction
2 Classical PSO
3 Proposed Methodology
4 Simulation Results and Analysis
5 Conclusion and Prospect Advice
References
A QoE Framework for Video Services in 5G Networks with Supervised Machine Learning Approach
1 Introduction
2 Background Work
3 Design and Analysis
4 Conclusion
References
A Survey of Green Communication and Resource Allocation in 5G Ultra Dense Networks
1 Introduction
2 Review of Recent Literature
2.1 Green Communication: The Advancement
3 Resource Allocation
References
A Survey on Attention-Based Image Captioning: Taxonomy, Challenges, and Future Perspectives
1 Introduction
2 Attention-Based Image Captioning
2.1 Region-Based Attention
2.2 Semantic Attention
2.3 Spatial Attention
2.4 Emotion-Based Attention
2.5 Hybrid Attention
3 Literature Survey
4 Benchmark Datasets
5 Open Research Challenges
6 Conclusions
References
QKPICA: A Socio-Inspired Algorithm for Solution of Large-Scale Quadratic Knapsack Problems
1 Introduction
2 Quadratic Knapsack Problems (QKPs)
3 ICA and BICA
4 The Proposed QKPICA
5 Computational Experiments
6 Results and Discussion
7 Conclusion
References
Balanced Cluster-Based Spatio-Temporal Approach for Traffic Prediction
1 Introduction
2 Related Work
3 Problem Definition
4 Methodology
4.1 Balanced Clustering-Based Traffic Prediction
4.2 Spatio-Temporal Approach with GCN and GRU
5 Experiments
5.1 Data Description
5.2 Evaluation Metrics
5.3 Results
6 Conclusion
References
HDD Failure Detection using Machine Learning
1 Introduction
2 Literature Survey
3 Proposed Methodology
3.1 Dataset
3.2 Data Preprocessing
3.3 Data Balancing
3.4 Feature Selection
3.5 Fault Detection Without Cloud Computing Resources
3.6 Fault Detection with Cloud Computing Resources
4 Experimental Implementation and Evaluation
4.1 Experimental Setup
4.2 Evaluation Matrix
4.3 Performance Evaluation on Data Balancing
4.4 Performance Evaluation on Feature Selection
4.5 Performance Evaluation Using Apache Spark
5 Conclusion and Future Work
References
Energy Efficient Cluster Head Selection Mechanism Using Fuzzy Based System in WSN (ECHF)
1 Introduction
2 Related Work
3 Proposed Work
3.1 Assumption
3.2 Proposed Model
4 Result Analysis
5 Conclusions
References
Choosing Data Splitting Strategy for Evaluation of Latent Factor Models
1 Introduction
2 Related Work
3 Methodology
3.1 Datasets and Matrix Factorization Algorithms in Use
3.2 Experiment Scheme
3.3 Results Interpretation Methodology
4 Results and Discussion
5 Conclusion
References
DBN_VGG19: Construction of Deep Belief Networks with VGG19 for Detecting the Risk of Cardiac Arrest in Internet of Things (IoT) Healthcare Application
1 Introduction
2 Related Works
3 System Model
4 Data Forwarding from Sensing Network
5 Pre-processing of Data
6 Feature Extraction Using Metaheuristic Based Gravitational Search Optimization Algorithm
7 Construction of Deep Belief Networks (DBN) with VGG19
8 Performance Analysis
9 Conclusion
References
Detection of Malignant Melanoma Using Hybrid Algorithm
1 Introduction
2 Literature Review
3 System Architecture
4 Result and Discussion
4.1 Dataset Description
4.2 Performance Parameters
4.3 Experimental Setup
4.4 Tuning Parameter
4.5 Result Analysis
5 Conclusion
References
Shallow CNN Model for Recognition of Infant's Facial Expression
1 Introduction
2 Methodology
2.1 Dataset
2.2 Proposed Shallow Network Architecture
2.3 Training
3 Results and Discussion
4 Conclusion
References
Local and Global Thresholding-Based Breast Cancer Detection Using Thermograms
1 Introduction
2 Literature Survey
3 Dataset
4 Breast Thermogram Analysis
4.1 Pre-processing
4.2 Feature Extraction
4.3 Feature Selection
4.4 Classification
5 Results Analysis
6 Conclusion
References
Multilevel Crop Image Segmentation Using Firefly Algorithm and Recursive Minimum Cross Entropy
1 Introduction
2 Proposed Methodology
2.1 Cross Entropy
2.2 Recursive Minimum Cross Entropy
2.3 Multilevel Thresholding Using Firefly Algorithm
3 Results and Discussion
4 Conclusion
References
Deep Learning-Based Pipeline for the Detection of Multiple Ocular Diseases
1 Introduction
2 Exploratory Data Analysis
3 Proposed Methodology
3.1 Preprocessing
3.2 Detection of Presence of a Disease
3.3 Training to Detect the Type of Disease
3.4 Evaluation
4 Experimental Results
5 Discussion
6 Reproducible Research
7 Conclusion
References
Development of a Short Term Solar Power Forecaster Using Artificial Neural Network and Particle Swarm Optimization Techniques (ANN-PSO)
1 Introduction
2 Methodology
2.1 Data Collection
2.2 Nigerian Solar Data
2.3 Solar Forecasting Using Artificial Neural Networks and Particle Swarm Optimization
3 Results and Discussion
3.1 Results Showing Average GHI Across the Year
3.2 Results Showing GHI Change Across Seasons
3.3 Effect of Climate Change on Global Horizontal Irradiance (GHI)
4 Conclusion
Appendix 1: Average Monthly Solar Irradiance
References
A Rule-Based Deep Learning Method for Predicting Price of Used Cars
1 Introduction
2 Literature Review
3 Material and Method
3.1 Data
3.2 Proposed Methodology
3.3 Evaluation Metrics
4 Implementation and Results
4.1 Implementation
4.2 Results
5 Conclusions and Future Directions
References
Classification of Fundus Images Based on Severity Utilizing SURF Features from the Enhanced Green and Value Planes
1 Introduction
2 Literature Review
3 Methodology
3.1 The Average Gray Value Extraction (AGVE) Algorithm
3.2 Red Score Calculation
3.3 Severity Level Generation
4 Results
5 Discussions
6 Conclusion
References
Hybrid Error Detection Based Spectrum Sharing Protocol for Cognitive Radio Networks with BER Analysis
1 Introduction
2 Related Work
3 Proposed Methodology
3.1 Spectrum Sharing System Model
3.2 Error Detection Based Spectrum Sharing Protocol
4 Result and Discussion
4.1 Performance Analysis
5 Conclusion
References
Lie Detection with the SMOTE Technique and Supervised Machine Learning Algorithms
1 Introduction
2 Methodology
2.1 Supervised Machine Learning Algorithms
2.2 K-Nearest Neighbor (KNN)
2.3 Decision Tree (DT)
2.4 Logistic Regression (LR)
2.5 Random Forest
2.6 Support Vector Machine (SVM)
2.7 Synthetic Minority Oversampling Technique (SMOTE)
2.8 Performance Metrics
3 Experimental and Analysis
3.1 Data Acquisition
3.2 Feature Extraction
3.3 EEG Data Set
3.4 Experimental Environment
3.5 Experimental Results Without Smote
3.6 Experimental Results with Smote
3.7 Comparison Between the SMOTE and Without SMOTE
4 Conclusion
5 Feature Work
References

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Machine Learning and Computational Intelligence Techniques for Data Engineering

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