Signal Processing, Telecommunication and Embedded Systems with AI and ML Applications

Name: Signal Processing, Telecommunication and Embedded Systems with AI and ML Applications | Proceedings of 8th International Conference on Microelectronics Electromagnetics and Telecommunications (ICMEET 2023)
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Proceedings of 8th International Conference on Microelectronics Electromagnetics and Telecommunications (ICMEET 2023)

Vikrant Bhateja V. V. S. S. S. Chakravarthy Jaume Anguera Anumoy Ghosh Wendy Flores Fuentes(Herausgeber*in)

Springer (Verlag)

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Erschienen am 10. Dezember 2024

XIII, 523 Seiten

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Vikrant Bhateja is associate professor in Department of Electronics Engineering Faculty of Engineering and Technology, Veer Bahadur Singh Purvanchal University, Jaunpur, Uttar Pradesh, India. He holds a doctorate in ECE (Bio-Medical Imaging) with a total academic teaching experience of 19+ years with around 190 publications in reputed international conferences, journals and online book chapter contributions; out of which 37 papers are published in SCIE indexed high impact factored journals. One of his paper published in Review of Scientific Instruments (RSI) Journal (under American International Publishers) has been selected as "Editor Choice Paper of the Issue '' in 2016. Among the international conference publications, four papers have received "Best Paper Award ''. He has been instrumental in chairing/co-chairing around 30 international conferences in India and abroad as Publication/TPC chair and edited 52 book volumes from Springer-Nature as a corresponding/co-editor/author on date. He has delivered nearly 22 keynotes, invited talks in international conferences, ATAL, TEQIP and other AICTE sponsored FDPs and STTPs. He has been Editor-in-Chief of IGI Global--International Journal of Natural Computing and Research (IJNCR) an ACM & DBLP indexed journal from 2017-22. He has guest edited Special Issues in reputed SCIE indexed journals under Springer-Nature and Elsevier. He is Senior Member of IEEE and Life Member of CSI.

V. V. S. S. S. Chakravarthy is a Professor, Dean R&D in the Department of Electronics & Communication Engineering at Raghu Institute of Technology, Visakhapatnam. He is senior member of Communication, Signal Processing and Antenna and Propagation Societies of IEEE. He is serving as Vice Chair of IEEE COMSOC/SPS Joint Chapters of IEEE Vizag Bay Section. His research interests include Computational Intelligence, Smart Antenna, Data Modelling, Machine Learning and Evolutionary Computing Tools. He has 18 years of teaching. He served as a co-editor to proceedings of third and fifth International Conference on Microelectronics, Electromagnetics and Telecommunications published in Lecture Notes in Electrical Engineering. He is also a life member of professional bodies like Instrumentation Society of India, International Computer Science and Engineering Society (ICSES) and Soft Computing Research Society. He published more than 40 journal and conference papers along with one Book Chapter which are indexed in SCOPUS and SCI.

Jaume Anguera is the founder of and CTO at Ignion. Prior to this he was the Partner and R&D Manager at Fractus, Barcelona, Spain. He is also serving as Associate Professor at Universitat Ramon LLull, Barcelona, Spain. He is an IEEE Antennas and Propagation Distinguished Lecturer. He holds more than 150 patents. His biography is listed in Who´sWho in the World, Who´sWho in Engineering. Author of more than 250 scientific widely cited papers and international conferences with citationsabove 7500, h-index 50, and i10 index of 150. Author of 6 books. He has participated in more than 21 competitive research projects financed by the Spanish Ministry . He is the author of 6 books, directed more than 100 bachelor and master thesis and 3 Ph.Ds. He is inventor of Virtual AntennaT technology, which enables full functional multi-band wireless connectivity to wireless devices through miniature and off-the-shelf antenna boosters. He has taught more than 20 antenna courses around the world (USA, China, Korea, India, UK, France, Poland, Czech Republic, Tunisia, Spain). With over 21 years of R&D experience, he has developed part of his professional experience with Fractus in South Korea in the design of miniature antennas for large Korean companies such as Samsung and LG. He has received several national and international awards. He is associate editor of the IEEE Open Journal on Antennas and Propagation, Electronics Letters, International Journal of Electronics and Communications, and reviewer in several IEEE and other scientific journals. He is vice-chair of the working group "Software and Modeling" at EurAAP.

Dr. Anumoy Ghosh has obtained his PhD degree from the Dept. of Electronics and Telecommunication Engineering in IIEST Shibpur in 2018. Since 2014, he is working in the Dept. of Electronics and Communication Engineering in NIT Mizoram and is currently holding the position of Assistant Professor Gr-I. He has 46 publications in reputed SCI journals and international conferences. His research group has 10 PhD scholars working in various domains of microwave and millimeter wave engineering. He has served as a reviewer for various SCI journals under the publications house of IEEE, Wiley, Radioengineering and Taylor & Francis. He has given several expert lectures in various institutes on microwave engineering and awareness about microwave technologies. His research interests include Antennas, MIMO technology, RFID, Microwave Energy Harvesting, Frequency Selective Surfaces and THz technology.

Wendy Flores-Fuentes received the master's degree in engineering from Technological Institute of Mexicali in 2006, and the Ph.D. degree in science, applied physics, with emphasis on Optoelectronic Scanning Systems for SHM, from Autonomous University of Baja California in June 2014. She has more than 115 publications which includes journal articles in Elsevier, IEEE Emerald and Springer, book chapters and books in Intech, IGI global and Springer, proceedings articles in IEEE. She has been a panel reviewer of Taylor and Francis, IEEE, Elsevier, and EEMJ (Gh. Asachi Technical University of Iasi. Currently, she is a full-time professor-researcher at Universidad Autónoma de Baja California, at the Faculty of Engineering

Inhalt

Intro
Contents
About the Editors
Learning-Based Traffic Classification for Software-Defined Networks
1 Introduction
2 Literature Survey
2.1 Payload-Based IP Traffic Classification
2.2 ML Classification
3 Proposed Solution
3.1 ML Model Training
3.2 Data Preparation
3.3 Data Clustering
3.4 Classification
3.5 Network Application Development
4 Result and Discussion
4.1 Network Traffic Using K-Means Clustering
4.2 Network Traffic Classification
4.3 Network Performance
5 Conclusion
References
AI-Based Wireless Display Data Extraction Using YOLO v5 Model
1 Introduction
2 Literature Survey
3 Proposed Idea
3.1 Dataset Collection
3.2 ESP 32 Module
3.3 Block Diagram
3.4 Modular Diagram
4 Implementation and Results
4.1 Experimental Setup
4.2 Results and Discussions
5 Conclusion and Future Work
References
Malicious Attack Detection Using Deep Learning in IoT Network
1 Introduction
2 Related Work
3 Methodology
3.1 Proposed System
3.2 Dataset Description
3.3 Pre-processing
3.4 Deep Learning Algorithm
4 Results and Discussion
5 Conclusion and Future Works
References
Proposing a Machine Learning Approach for Cardiovascular Disease Prediction
1 Introduction
2 Problem Statement
3 Literature Survey
4 Proposed Methodology
5 Implementation and Results
6 Conclusion
References
Deep Learning-Based Automatic Skin Lesion Segmentation
1 Introduction
2 Literature Survey
2.1 Contributions
3 Dataset and Evaluation Metrics
4 Methods
5 Results and Discussion
6 Conclusion
References
Detection of COVID-19 CoronaVirus Using ResNet Deep Learning Technique
1 Introduction
2 Related Work
3 Material and Methods
4 Feature Extraction and Data Augmentation
4.1 Rotation
4.2 Transfer Learning Using a Convolutional Neural Network (CNN)
5 Feature Selection
6 Residual Network (ResNet)
6.1 Network Model ResNet50
6.2 Network Model ResNet101
6.3 Measures of Performance
7 Result Overview
8 Conclusion
References
Using Blue Whale Technology: An ML Edge Self-Adaptable Vehicle Slowdown Earliest Warning Information System
1 Introduction
2 Literature Review
3 Proposed Methodology
4 Light Detection and Ranging/High-Resolution Camera
5 Results and Discussion
6 Conclusion
References
Confluence of Machine Learning and Internet of Things for E-Healthcare Security
1 Introduction
2 Related Work
3 Proposed ML Based Framework for Medical Information Security
4 Result
5 Security Laws required for E-Healthcare
6 Conclusion
References
Hybrid Intelligent System for Improved Decision Support in Customer Churn Prediction for a Telecommunication Company
1 Introduction
2 Methodology
2.1 Dataset: Customer Churn Prediction for a Telecommunication Company
2.2 Evaluation Metrics for Measuring Decision Support Improvement
3 Case Studies and Experiments
3.1 Description of Datasets Used in Experiments
3.2 Implementation Details of the HIS
3.3 Interpretation of Findings and Insights
4 Results and Analysis
4.1 Analysis and Insights
5 Conclusion
References
A Study on the Detection and Different Methods of Classification of Arrhythmia Utilizing ECG Signal
1 Introduction
2 Methodology
2.1 Detection of Presence of Arrhythmia
2.2 Classification of the Arrhythmic Signal
2.3 Determination of the Accuracy of Different Classification Algorithms
3 Results and Analysis
4 Conclusion
References
Detection of Faults Based on Machine Learning Schemes in Wireless Sensor Networks
1 Introduction
2 Related Work
3 Problem Statement
3.1 Fault Taxonomy in WSNs
3.2 Challenges of Faults Detection in WSNs
4 Extra Trees-Based Fault Detection Approach
4.1 Proposed Approach
4.2 System Model of Fault Detection
5 Experimental Results and Discussions
5.1 Simulations
5.2 Original Dataset
5.3 Prepared Dataset
5.4 Results
6 Conclusion
References
Deep Learning-Based Anomaly Detection for Early Cancer Detection in CT Scans
1 Introduction
2 Related Work
2.1 Traditional Anomaly Detection in Medical Imaging
2.2 Deep Learning-Based Approaches in Cancer Detection
2.3 Deep Learning for Multi-modal Anomaly Detection
3 Data Collection and Preprocessing
4 Sample Dataset Description
5 Deep Learning Model Architecture
5.1 Architecture of the Convolutional Autoencoder
5.2 Encoder Architecture
5.3 Decoder Architecture
6 Training and Model Optimization
6.1 Training Data Preparation and Data Splitting
6.2 Hyperparameter Tuning
6.3 Training Process and Convergence Analysis
7 Experimental Results
7.1 Evaluation Metrics: Sensitivity, Specificity, AUC-ROC
7.2 Comparison with Baseline and Traditional Approaches
7.3 Visualizing Model Outputs and Detected Anomalies
8 Conclusion
References
Deep Learning-Based Improvement in Automated Diagnosis of Soft Tissue Tumours
1 Introduction
2 Methodology
2.1 Assessment of Model Efficiency
3 Deep Learning Soft Tissue Tumores Medical Images
3.1 Primary Soft Tissue Tumours
3.2 Soft Tissue Tumours Metastasis
3.3 Deep Learning Soft Tissue Tumours Based on Pathological
3.4 Discussions
4 Conclusions
References
Multi-modal Medical Image Fusion Using Wavelets and Morphological Filters for Diagnosis of Neurological Disorders
1 Introduction
1.1 Background
1.2 Literature Survey
2 Proposed Methodology for Multi-modal Fusion Using DWT and Morphological Filters
2.1 Input Source Images (Benchmarking Dataset)
2.2 Pre-processing Using Morphological Filters
2.3 Wavelets
2.4 Image Fusion
3 Fusion Metrics Used for IQA
3.1 Entropy
3.2 Mutual Information
4 Experimental Results
4.1 Simulation Set-Up
4.2 Simulation Results
5 Conclusion
References
Machine Learning Approach of Stent Placement for Coronary Artery Disease Patients-A Hypothetical Approach
1 Introduction
2 Proposed Methodology
2.1 Example
3 Results
4 Discussion
5 Conclusion
References
Non-Local Means Filter-Based Unsharp Masking Model for Mammogram Enhancement
1 Introduction
2 Related Works
3 Non-Local Means Filter
4 Proposed NLM Filter-Based UM Model
5 Experimental Framework for Mammogram Enhancement
6 Results
7 Conclusion
References
Predominant Music Genre Classification Using Machine Learning Approach
1 Introduction
1.1 Dataset Description
2 Literature Review
3 Methodology
3.1 Audio Pre-processing
3.2 Feature Extraction
3.3 Data Visualization
4 Analysis
5 Comparative Study
6 Conclusions and Future Works
References
Vehicle Detection and Classification Using Intelligent Systems
1 Introduction
1.1 Application
2 Related Work
2.1 Data Annotation
3 System Architecture
3.1 Model
3.2 Single Shot Detector
4 Results
4.1 Testing Results
5 Conclusion
References
An Investigation into Chronic Kidney Disease Based-on Classification Model
1 Introduction
2 Literature Review
3 Methodology
3.1 Dataset
3.2 Classification Models
3.3 Data Preprocessing
4 Experiments and Results
5 Conclusion
References
Human Activity Recognition Using Machine Learning Models
1 Introduction
2 Related Work
3 Dataset
4 Methodology
5 Implementation
6 Results and Discussions
7 Conclusion
References
Improving the Performance of Multimodal Biometric Recognition Using Machine Learning Techniques in Comparison with K-fold Cross Validation
1 Introduction
1.1 Literature Survey
1.2 Outline of the Proposed Approach
2 Experimental Analysis
2.1 Data Sampling
2.2 Face Recognition
2.3 BiLSTM Model (Bi-directional-Long Short-Term Memory)
2.4 Performance Metrics
2.5 Identification of Appropriate K Value
3 Conclusion
References
A Critical Analysis of the Implications of Employing Artificial Intelligence in Healthcare from an Ethical and Legal Perspective
1 Introduction
2 Healthcare Applications of AI
2.1 Instructions on Using AI in Healthcare
2.2 Development and Validation of Drugs
2.3 Applications for an off AI and Disease Diagnosis
2.4 Innovative AI-Powered Solutions for Treatment
3 Ethical and Legal Assessment
3.1 Ethical Thoughts About the Use of Artificial Intelligence
3.2 Law and Rules in Healthcare
3.3 Legal Considerations for AI in Healthcare
4 Conclusion
References
Design of an Automated Smart Waste Management Systems Using CNN
1 Introduction
2 Literature Survey
3 Automated Smart Waste Management System
4 Results Analysis
5 Conclusion
References
An Analogy of Machine Learning Algorithms for Diabetes Call
1 Introduction
2 Literature Survey
3 Proposed Methodology
3.1 Data Preprocessing
3.2 Missing Value Identification
3.3 Outlier Identification and Removal
3.4 Feature Choice
3.5 Normalization
3.6 Dataset Train and Test Methods
4 Results
5 Conclusion
References
Identifying and Categorizing Skin Disorders by Using CNN to Diagnose Five Prevalent Skin Disease from Skin Images
1 Introduction
2 Literature Survey
3 Methodology
3.1 Data Set
3.2 CNN for Skin Disease Classification
4 Results and Discussion
5 Conclusion
References
Improving Realism in Face Swapping Using Deep Learning and K-Means Clustering
1 Introduction
2 Literature Review
3 Proposed Methodology
4 Results and Discussion
5 Conclusion
References
Unveiling Deepfake Images with a CNN-Based Approach
1 Introduction
2 Literature Survey
3 Methodology
3.1 Machine Learning Techniques Using Convolutional Neural Networks
4 Results
5 Conclusion and Future Scope
References
Deep Learning for Multi-class Thyroid Nodules Classification
1 Introduction
2 Literature Survey
3 Proposed Methodology
3.1 Dataset
3.2 Classification Method
3.3 Hyperparameters Used in Training the Models
3.4 Pre-processing
3.5 Transfer Learning
3.6 Training
4 Results and Discussions
5 Performance
6 Conclusion
References
Real-Time Sign Language Recognition System for Physically Challenged Community Using Deep Learning Technique
1 Introduction
2 Related Work
3 Proposed Model
3.1 Steps Involved
3.2 System Architecture
4 Implementation and Results
5 Conclusion
References
3D E-commerce Using AI Depth Algorithm Augmented Reality
1 Introduction
2 Tools and Methodology
3 Working Procedure
4 Literature Review
5 Proposed System
6 Advantage and Cost Analysis
7 Result and Analysis
7.1 Output Screenshots
8 Conclusion
9 Future Work
References
A Comprehensive Study on the Usage of Different Datasets, Machine Learning and Deep Learning Techniques in Brain Tumor Classification
1 Introduction
2 Research Methodology
3 Benchmark Datasets
3.1 BRATS Dataset [5]
3.2 TCGA-GBM (The Cancer Genome Atlas-Glioblastoma Multiforme) Dataset [6]
3.3 MICCAI BraTS 2020 Dataset [7]
3.4 Center for Biomedical Image Computing and Analytics (CBICA) TCGA-GBM Dataset [8]
3.5 LGG-1p/19q Deletion Dataset [9]:
3.6 RESECT Dataset [10]
3.7 Machine Learning Algorithms
4 Results
5 Conclusion
References
Predominant Musical Instrument Identification Using Deep Hybrid Neural Networks
1 Introduction
1.1 Motivation
1.2 Our Contributions
1.3 Article Organization
2 System Architecture
2.1 Feature Extraction
2.2 Audio Preprocessing
2.3 Network Architecture
2.4 Training Setup
3 System Evaluation
3.1 IRMAS Dataset
3.2 Testing Setup
3.3 Performance Evaluation
4 Experiment Results and Performance Analysis
4.1 Performance Evaluation of Proposed Model
4.2 Performance Comparison with Existing Model Algorithms
5 Conclusion
References
An Analysis of ML-Based Intelligent IDS for Wireless Sensor Networks
1 Introduction
1.1 Applications of WSN
1.2 Applications of IDS
2 Literature Survey
2.1 Challenges and Opportunities
2.2 Common ML Techniques Used for Intrusion Detection in WSN
2.3 Machine Learning (ML) Approach
2.4 Evaluation Parameters of IDS WSN Using ML Approach
3 Related Work and Comparative Analysis
4 Conclusion
5 Future Scope
References
Design of Hybrid Approach for Cloud Resource Allocation Using Genetic Algorithms and ML Techniques
1 Introduction
2 Related Work
3 Problem Formulation and Description
3.1 System Model
3.2 Mathematical Modeling
4 Results and Discussion
5 Conclusion and Future Scope
References
Evaluating the Effectiveness of Machine Learning, Deep Learning, and Evolutionary Algorithms in Intrusion Detection Systems
1 Introduction
2 Related Work
3 Application of ML and DL in IDS
4 Evolutionary Algorithm Approach for IDS
5 Benchmark Datasets for Intrusion Detection
6 Evaluation Matrix
7 Future Direction for Research on ML and DL-Based IDS
8 Conclusion
References
An Optimization of Healthcare Operation Management Using Machine Learning
1 Introduction
2 Related Work
3 Proposed System
3.1 Workflow with Machine Learning
3.2 Prediction of Wait Time of Patient
3.3 Optimal Feature Section
3.4 Support Vector Machine
4 Results and Discussion
4.1 Operation Model in Healthcare
4.2 Scalability and Adaptability
5 Conclusion
References
Optimizing Fine-Tuning Strategies for Diabetic Retinopathy Detection: A Comparative Evaluation of ResNet, Inception, and DenseNet
1 Introduction
2 Literature Review
3 Methodology
4 Results and Outputs
5 Conclusion and Future Scope
References
Blockchain-Based Internet of Things: Machine Learning Suspicious Object Traceability System
1 Introduction
2 Literature Review
3 Problem Statement
4 Proposed Research Methodology
5 Result and Discussion
5.1 Noise Removal for Gaussian Filter and Other Techniques
5.2 Accuracy Evaluation
5.3 Confusion Matrix Proposed Work
5.4 Comparison of Accuracy Parameters
6 Conclusion
7 Future Scope
References
Android Application for Soil Insect Detection Using CNN
1 Introduction
2 Related Works
3 Methodology
4 Results and Discussions
5 Conclusion
References
Detection of Heart Failure Using ML Algorithm
1 Introduction
1.1 Categorization of HFs
2 Recorded Incidence of HFs
3 Clinical Presentation of HFs
4 Diagnosis of HFs
5 Predictor of Bad Outcome and High Mortality Rate
6 Management of HF
6.1 Victim Management of HF
6.2 Out-Patient Management of HF
6.3 Sex Differences in HF Incident
7 Results and Discussions
7.1 Analysis Using k-nearest Neighbors and Logistic Regression
8 Conclusions and Future Directions
References
Maternal Health Risk Analysis and Classification Using Random Forest Model with Hyperparameter Tuning
1 Introduction
2 Literature
3 Proposed Model
3.1 Random Forest
3.2 Hyperparameter Tuning
4 Experimental Results
5 Conclusion
References

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