{"text_format":"2","workbook":"xls","n_sheets":1,"sheets":["report2"],"not_rendered":{"charts":0,"images":0,"macros":0,"external_links":1,"defined_names":1,"comments":0,"pivot_tables":0},"unopened":[],"bounds":{"document_bytes":16777216,"sheet_cells":100000,"run_cost":"one","rows_first":2000,"rows_last":500,"rows_between":2000,"halvings":0,"cell_chars":16384,"cell_head_chars":4096},"cells_cut":0,"cells_raw":0,"read":"whole","date_system":"1900","formulas":"cached_only"}

{"sheet":"report2","index":0,"n_rows":30,"n_cols":5,"rows_kept":30,"cut":false,"hidden":false,"merged":[[0,2,0,3],[0,4,0,5],[1,2,1,3],[1,4,1,5],[2,2,2,3],[2,4,2,5],[3,2,3,3],[4,2,4,3],[5,2,5,3],[6,2,6,3],[7,2,7,3],[8,2,8,3],[9,2,9,3],[17,2,17,3],[18,2,18,3],[20,2,20,3]],"flags":["formulas_cached_only"]}
[null,"1.","Record Nr.",null,"UNINA9910983490803321"]
[null,null,"Autore",null,"Bhateja Vikrant"]
[null,null,"Titolo",null,"Signal Processing, Telecommunication and Embedded Systems with AI and ML Applications : Proceedings of 8th International Conference on Microelectronics Electromagnetics and Telecommunications (ICMEET 2023) / / edited by Vikrant Bhateja, V. V. S. S. S Chakravarthy, Jaume Anguera, Anumoy Ghosh, Wendy Flores Fuentes"]
[null,null,"Pubbl/distr/stampa",null,"Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2025"]
[null,null,"ISBN",null,"981-9784-22-0"]
[null,null,"Edizione",null,"[1st ed. 2025.]"]
[null,null,"Descrizione fisica",null,"1 online resource (513 pages)"]
[null,null,"Collana",null,"Lecture Notes in Electrical Engineering, , 1876-1119 ; ; 1281"]
[null,null,"Disciplina",null,"621.3815"]
[null,null,"Soggetti",null,"Electronic circuits"]
[{"$nulls":4},"Electronics"]
[{"$nulls":4},"Embedded computer systems"]
[{"$nulls":4},"Internet of things"]
[{"$nulls":4},"Electronic Circuits and Systems"]
[{"$nulls":4},"Electronics and Microelectronics, Instrumentation"]
[{"$nulls":4},"Embedded Systems"]
[{"$nulls":4},"Internet of Things"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Intro -- Contents -- About the Editors -- Learning-Based Traffic Classification for Software-Defined Networks -- 1 Introduction -- 2 Literature Survey -- 2.1 Payload-Based IP Traffic Classification -- 2.2 ML Classification -- 3 Proposed Solution -- 3.1 ML Model Training -- 3.2 Data Preparation -- 3.3 Data Clustering -- 3.4 Classification -- 3.5 Network Application Development -- 4 Result and Discussion -- 4.1 Network Traffic Using K-Means Clustering -- 4.2 Network Traffic Classification -- 4.3 Network Performance -- 5 Conclusion -- References -- AI-Based Wireless Display Data Extraction Using YOLO v5 Model -- 1 Introduction -- 2 Literature Survey -- 3 Proposed Idea -- 3.1 Dataset Collection -- 3.2 ESP 32 Module -- 3.3 Block Diagram -- 3.4 Modular Diagram -- 4 Implementation and Results -- 4.1 Experimental Setup -- 4.2 Results and Discussions -- 5 Conclusion and Future Work -- References -- Malicious Attack Detection Using Deep Learning in IoT Network -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Proposed System -- 3.2 Dataset Description -- 3.3 "]
[{"$nulls":4},"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."]
[{"$nulls":4},"4.2 Transfer Learning Using a Convolutional Neural Network (CNN) -- 5 Feature Selection -- 6 Residual Network (ResNet) -- 6.1 Network Model ResNet50 -- 6.2 Network Model ResNet101 -- 6.3 Measures of Performance -- 7 Result Overview -- 8 Conclusion -- References -- Using Blue Whale Technology: An ML Edge Self-Adaptable Vehicle Slowdown Earliest Warning Information System -- 1 Introduction -- 2 Literature Review -- 3 Proposed Methodology -- 4 Light Detection and Ranging/High-Resolution Camera -- 5 Results and Discussion -- 6 Conclusion -- References -- Confluence of Machine Learning and Internet of Things for E-Healthcare Security -- 1 Introduction -- 2 Related Work -- 3 Proposed ML Based Framework for Medical Information Security -- 4 Result -- 5 Security Laws required for E-Healthcare -- 6 Conclusion -- References -- Hybrid Intelligent System for Improved Decision Support in Customer Churn Prediction for a Telecommunication Company -- 1 Introduction -- 2 Methodology -- 2.1 Dataset: Customer Churn Prediction for a Telecommunication Company -- 2.2 Evaluation Metrics for Measuring Decision Support Improvement -- 3 Case Studies and Experiments -- 3.1 Description of Datasets Used in Experiments -- 3.2 Implementation Details of the HIS -- 3.3 Interpretation of Findings and Insights -- 4 Results and Analysis -- 4.1 Analysis and Insights -- 5 Conclusion -- References -- A Study on the Detection and Different Methods of Classification of Arrhythmia Utilizing ECG Signal -- 1 Introduction -- 2 Methodology -- 2.1 Detection of Presence of Arrhythmia -- 2.2 Classification of the Arrhythmic Signal -- 2.3 Determination of the Accuracy of Different Classification Algorithms -- 3 Results and Analysis -- 4 Conclusion -- References -- Detection of Faults Based on Machine Learning Schemes in Wireless Sensor Networks -- 1 Introduction -- 2 Related Work."]
[{"$nulls":4},"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, "]
[{"$nulls":4},"AUC-ROC -- 7.2 Comparison with Baseline and Traditional Approaches -- 7.3 Visualizing Model Outputs and Detected Anomalies -- 8 Conclusion -- References -- Deep Learning-Based Improvement in Automated Diagnosis of Soft Tissue Tumours -- 1 Introduction -- 2 Methodology -- 2.1 Assessment of Model Efficiency -- 3 Deep Learning Soft Tissue Tumores Medical Images -- 3.1 Primary Soft Tissue Tumours -- 3.2 Soft Tissue Tumours Metastasis -- 3.3 Deep Learning Soft Tissue Tumours Based on Pathological -- 3.4 Discussions -- 4 Conclusions -- References -- Multi-modal Medical Image Fusion Using Wavelets and Morphological Filters for Diagnosis of Neurological Disorders -- 1 Introduction -- 1.1 Background -- 1.2 Literature Survey."]
[{"$nulls":4},"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."]
[{"$nulls":4},"4 Methodology -- 5 Implementation -- 6 Results and Discussions -- 7 Conclusion -- References -- Improving the Performance of Multimodal Biometric Recognition Using Machine Learning Techniques in Comparison with K-fold Cross Validation -- 1 Introduction -- 1.1 Literature Survey -- 1.2 Outline of the Proposed Approach -- 2 Experimental Analysis -- 2.1 Data Sampling -- 2.2 Face Recognition -- 2.3 BiLSTM Model (Bi-directional-Long Short-Term Memory) -- 2.4 Performance Metrics -- 2.5 Identification of Appropriate K Value -- 3 Conclusion -- References -- A Critical Analysis of the Implications of Employing Artificial Intelligence in Healthcare from an Ethical and Legal Perspective -- 1 Introduction -- 2 Healthcare Applications of AI -- 2.1 Instructions on Using AI in Healthcare -- 2.2 Development and Validation of Drugs -- 2.3 Applications for an off AI and Disease Diagnosis -- 2.4 Innovative AI-Powered Solutions for Treatment -- 3 Ethical and Legal Assessment -- 3.1 Ethical Thoughts About the Use of Artificial Intelligence -- 3.2 Law and Rules in Healthcare -- 3.3 Legal Considerations for AI in Healthcare -- 4 Conclusion -- References -- Design of an Automated Smart Waste Management Systems Using CNN "]
[{"$nulls":4},"-- 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."]
[{"$nulls":4},"3 Methodology."]
[null,null,null,"Sommario/riassunto","The book discusses the latest developments and outlines future trends in the fields of microelectronics, electromagnetics, and telecommunication. It contains original research works presented at the International Conference on Microelectronics, Electromagnetics and Telecommunication (ICMEET 2023), organized by Department of Electronics and Communication Engineering, National Institute of Technology Mizoram, India during 6 – 7 October 2023. The book is divided into two volumes, and it covers papers written by scientists, research scholars and practitioners from leading universities, engineering colleges and R&D institutes from all over the world and share the latest breakthroughs in and promising solutions to the most important issues facing today’s society."]