This book constitutes proceedings of the International Conference on Health Informatics, Intelligent Systems, and Networking Technologies (HINT'24). This book includes contributions from experts, researchers, and practitioners from diverse fields to explore the intersection of health, technology, and networking. This conference aims to foster collaboration and knowledge exchange, pushing the boundaries of innovation to enhance healthcare systems globally. The book focuses on topics such as 6G & 5G in healthcare, Internet of Things (IoT) applications, and data communication standards shedding light on the transformative impact of networking technologies on healthcare infrastructure. This book is a beacon for progress, paving the way for a more interconnected, intelligent, and informed healthcare ecosystem.
Part 1: Health Informatics.- Integrating Behavioral, Biometric, and
Environmental Data for Health Insights.- Revolutionizing Antifungal
Treatment: Machine Learning Insights into Candida Species Antimicrobial
Resistance Patterns for Informed Clinical Decisions.- FutureGlycemics:A
Comparative Study Of Diverse Machine Learning Models For Diabetes Prognosis.-
Eating Disorder Detection and Classification using Machine Learning.-
Decoding Diabetic Retinopathy: A Comprehensive Analysis through Diverse Deep
Learning Algorithms.- Application of Deep Learning Techniques to Perform
Multi-class Skin Disease Classification.- Eeg Based Emotion Recognition Using
Genetic Algorithms Optimized Ensemble Model.- Deep Learning for classifying
Mild Traumatic Brain Injury for the need of CT scans using EEG signals.-
Efficient Deep Learning Models for Automated Diagnosis of Tuberculosis Using
Chest X-ray.- Prediction of Chronic Disease using Deep Ensembled Machine
Learning Approach.- Decoding Health Informatics Patents: Investigating Topic
Models for Patent Information Retrieval.- Analysis of Foramen Magnum from
Skull Images Using Image Processing Techniques.- Early Prediction of Thyroid
Disease through the Integration of Machine Learning Algorithms and Particle
Swarm Optimization-Based Feature Extraction: A Novel Approach for Improved
Diagnosis.- Detection of Forgery in Medical Images using Deep Learning.-
Improving Brain Tumor Segmentation from MR Images Through Integration of
Preprocessing Techniques.- Sleep Disorders and Heart Disease: A Machine
Learning Exploration.- Classification of Alzheimers Disease From EEG Signals
using Ensemble Learning and Deep Learning approaches.- Detection of Brain
Tumor from MR Images Using Region Based Convolutional Neural Network (RCNN).-
Part II: Intelligent Systems.- Comprehensive Performance Analysis of PySpark
and Pandas for Classification and Clustering Task.- Enhancing Women's Safety:
An Implementation and Integration Framework for Real-Time Safety Systems.- A
Comprehensive Study of Image Deduplication Techniques Addressing Challenges
and Advancements in Storage Optimization.- Shadow Detection through YCbCr
Color Space for Enhanced Scanned Document Quality.- Recommending Fashions
using Computer Vision based Artificial Intelligent approaches - A
comprehensive analysis.- Smart Farming Solutions: Advanced Techniques in
Clas-sification of Weeds Using Image Processing and Machine Learning.-
ResFruitGrader: Leveraging Residual Networks for Advanced Fruit Quality
Grading Systems.- A/B Testing for Beginners: Reusable Controlled
Experiments.- NeuroNet: Early detection of Alzheimer's Disease using Deep
Learning.- From Dataset Creation to Recognition Precision: A Comprehensive
Study on Machine Learning Algorithms for Tulu Script.- SmartEval: Advancing
Automated Evaluation of Textual Answer Scripts with Transformer Models.-
Leveraging Pretrained Super-Resolution Models for Deep Steganography in YUV
Color Space.- Intelligent Attention-based transformer models for Text
Extraction: A proof of concept.- Part III: Networking Technologies.- The
Threat Triplet: RATs, Keyloggers And Registry Keys A Proof of Concept
Analysis.- Improving the Transmission Performance to Minimizing the Latency
in Mobile Adhoc Network.- Enhancing Autonomous Navigation for Visually
Impaired Individuals Using Real-Time Object Detection and Voice Feedback
System.- Gaussian Differential Privacy Federated Learning to Identify Ocular
Diseases.- Enhancing Virus Detection Through Advanced Aggregation of
Low-Level Features in Medical Imagery.- Enhancing ASV Security: An Integrated
Ensemble Detector for Logical Access and DeepFake Attacks.- Detecting Forgery
in War Footage for Information Accuracy.- A Hyperledger-based Chaincode for
Registration and Authorization in Secure Parking Systems.- An EEG-Based
Brain-Computer Interface Approach for Enhanced Interaction with Digital
Devices.- A Comparative study of performance in Spectrum Utilization during
spectrum sensing between CR-VANET and CR-VANET-QL.- Part IV: Bigdata and Data
mining.- Streamlining Banking Operations: A Computer Vision-based Queue
Management System for Improved Customer Service.- Enhancing Educational
Environments: Object Recognition and Relationship Inference through YOLO in
Education 5.0.- Unlocking Imagination: Story Generation from Autistic
Childrens artwork using a fusion of Computer Vision and NLP.- Image
Recognition for Medicinal Plants using Deep Neural Networks.- Text-based
Emotion Recognition with Hybrid Feature Selection and Ensemble
Classification: A BERT and RoBERTa Approach.- Segmentation Techniques for
Automatic Foreground and Background Area Separation in Multi-Temporal
Sentinel-1A Imagery: A Comparative Study.- 64_Keyword Extraction in Learning
Managemnt System (LMS) using Natural Language Processing (NLP).- WoS
Bibliometric and Content Analysis on Handwritten Hindi Characters
Recognition.- Exploring Sleep Quality: Influential Factors and Implications.
Andrew Jeyabose is an associate professor in the Department of Computer Science and Engineering (CSE) at Manipal Institute of Technology (MIT), Manipal, India, and recently began his postdoctoral research at the University of North Carolina at Chapel Hill in 2024. He received his Ph.D. in 2021 from Vellore Institute of Technology (VIT), Vellore, India, and completed his Bachelor of Engineering (B.E.) in CSE in 2011 and Master of Engineering (M.E.) in 2013 from Anna University, Chennai, India. He is an active researcher who has published over 70 scientific research articles in reputed journals and conferences. He has also served as a speaker at many prestigious conferences worldwide. With over 11 years of teaching experience at undergraduate (UG) and postgraduate (PG) levels, he has supervised numerous projects at various academic levels. His research interests include data privacy, healthcare data analysis, deep learning, machine learning, computer vision, and blockchain technologies.
Valentina Emilia Balas is a full professor in the Department of Automatics and Applied Software at the Faculty of Engineering, Aurel Vlaicu University of Arad, Romania. She holds a Ph.D. Cum Laude in Applied Electronics and Telecommunications from the Polytechnic University of Timisoara. Dr. Balas is the author of over 400 research papers in refereed journals and international conferences. Her research interests include intelligent systems, fuzzy control, soft computing, smart sensors, information fusion, and modeling and simulation. She is the editor-in-chief of the International Journal of Advanced Intelligence Paradigms (IJAIP) and the International Journal of Computational Systems Engineering (IJCSE). Dr. Balas is a member of EUSFLAT, ACM, a senior member of IEEE, and an active member of several IEEE Technical Committees, including TC EC, TC-FS (IEEE CIS), and TC SC (IEEE SMCS).
Steven L. Fernandes began his postdoctoral research at the University of Alabama at Birmingham. There, he worked on NIH-funded projects. He also conducted postdoctoral research at the University of Central Florida. This research included working on DARPA, NSF, and RBC-funded projects. His publications include research articles in highly selective artificial intelligence venues. Dr. Fernandes is a senior IEEE member and AWS educator. His current area of research is focused on developing artificial intelligence techniques to extract useful patterns from big data. This includes robust computer vision applications using deep learning and computer-aided diagnosis using medical image processing.
