Advances in Distributed Computing and Machine Learning

Proceedings of ICADCML 2026, Volume 2
Erscheinungsjahr 2026
ISBN: 978-3-032-25086-5
Verlag: Springer Nature Switzerland AG

Buch, Englisch, 360 Seiten, Format (B × H): 155 mm x 235 mm

Reihe: Lecture Notes in Networks and Systems

This book is a collection of peer-reviewed best-selected research papers presented at the Seventh International Conference on Advances in Distributed Computing and Machine Learning (ICADCML 2026), organized by Department of Computer Science and Engineering, National Institute of Technology, Jamshedpur, India, during January 15–16, 2026. This book presents recent innovations in the field of scalable distributed systems in addition to cutting-edge research in the field of Internet of Things (IoT) and blockchain in distributed environments. The work is presented in two volumes.

Zielgruppe

Research

Autoren/Hrsg.

Mishra, Alekha Kumar

Tripathy, Asis Kumar

Sahoo, Jyoti Prakash

Pradhan, Jitesh

Li, Kuan-Ching

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik
• Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Kybernetik, Systemtheorie, Komplexe Systeme
• Mathematik | Informatik EDV | Informatik Computerkommunikation & -vernetzung

Inhaltsverzeichnis

Interpretable Satellite Image Analysis Using Retrieval Augmented Generation and Vision Language Models.- DL IndiLang A Deep Learning Framework for Indian Language Categorization.- A Hybrid Intelligent Model for Predicting Flight Departure Delay in Indian Aviation Sector.- A Hybrid CNN Transformer Model with Spatial Attention for Brain Tumor Classification.- A Framework for Voice Synthesizer Using User Provided Notations and Lyrics for Indian Classical Music with Efficient Preprocessing Pipeline.- Hybrid Shallow CNN Model for Image Splicing Detection.- Anti Rumor Context Integration A Novel RAG System for Automated Factual Correction.- Leveraging Large Language Models LLMs for Enhanced Assessment and Interactive Learning Advancements in AI Based Educational Tools.- Leveraging GitHub Repository Insights and Profile Analytics for Career Aligned Placement Prediction Using Random Forest Classifier and KNN.- VideoQA An Explainable Video Question Answering on Multi Camera Video Analysis.- Diffusion Based Super Resolution for Enhanced Diabetic Retinopathy Grading Using Fundus Images.- XAI Enabled Fraud Detection.- Novel Sustainable Conditions of Jewish Population Based Optimization Algorithm.- Enhancing House Price Prediction Through Multimodal Feature Fusion of Visual and Structural Attributes.- A Retinex Inspired Deep Learning Framework for Real Time Low Light Image Enhancement.- An Interpretable AI Approach for Predictive Vehicle Maintenance Using XGBoost with Explainable AI.- SmartGrid MMF A Multi Module Framework for Forecasting and Fault Detection.- MLTRP XAI Machine Learned Based Trustworthy Routing Protocol for Wireless Sensor Networks with Explainable AI Integration.- A Hybrid Approach for Enhanced Early Detection and Localization of Brain Tumors.- Texture Based Feature Extraction and CBAM Enhanced U Net for Automated Knee Osteoporosis Detection.- Legacy Oracle Systems to Snowflake Cloud Data Warehouse Migration of Healthcare Data Management.- Dual Dimensional Transformer in Remote Sensing A Hyperspectral Image Classification Framework.- Towards Smart Agriculture An Edge Based CNN and Transformer Architecture for Potato Leaf Disease Detection.- Intelligent QNN Driven Quantum Optimization for Real Time RIS Resource Allocation in 6G Networks.- A Deep Learning Based Framework for Crop Yield Prediction from Remote Sensing Data Using DCGAN Augmentation.- Anomalies in Google Play Data Safety Section Sharing Collection and Compliance Risks.- Depth Guided ByteTrack Framework for Accurate Apple Counting and Tracking in Multilane Orchard Environments.- Prediction of Thyroid Disease Classification Using Transfer Learning Model An Application of Machine Learning in Medicine.- An Imperceptible Image Steganography Framework Using Hybrid Saliency Maps and Chaotic Encryption.

Über Autor(innen)

Alekha Kumar Mishra is working as a faculty member in the department of computer science and engineering, NIT Jamshedpur, India. He has received his PhD degree from NIT Rourkela, India in the year of 2014. He has also received his MTech degree in Information Security from NIT Rourkela in the year 2009. He has over 8 years of teaching experience from NIT Jamshedpur, VIT Vellore, and SIT Bhubaneswar. His research interests include IoT, Network Security, Security Threat Modeling and Analysis, Energy-efficient Routing in Low-Powered Lossy Networks, and Cybersecurity threat detection.

Asis Kumar Tripathy is a Professor in the School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, India. He completed his Ph.D. from the National Institute of Technology, Rourkela, India and MTech from IIIT Bhubaneswar, India. His areas of research interests include wireless sensor networks, cloud computing, Internet of things and advanced network technologies. He has several publications in refereed journals, reputed conferences and book chapters to his credit. He has served as a program committee member in several conferences of repute. He has also been involved in many professional and editorial activities. He is a senior member of IEEE and a member of ACM.

Jyoti Prakash Sahoo is a Senior Member, IEEE, and an experienced Assistant Professor with a demonstrated history of working in engineering education. Currently, he is working in the Dept of Computer Science and Information Technology, Institute of Technical Education and Research, Siksha ’O’ Anusandhan (Deemed to be University) for the last 10 years. Prior to joining Siksha ’O’ Anusandhan, he also worked as an Assistant Professor with CV Raman College of Engineering, Bhubaneswar (now C. V. Raman Global University). He is having more than 12 years of academic and research experience in Computer science and engineering education. He has published several research papers in various international journals and conferences. He is also serving many journals and conferences as an editorial or reviewer board member. He is having expertise in the field of Cloud computing and Machine learning.

Jitesh Pradhan is currently working as an Assistant Professor in Department of Computer Science and Engineering at NIT Jamshedpur. He has done his master’s and PhD from IIT Dhanbad. He has 9+ years of research experience in the field of Image Processing and Artificial Intelligence. He has published more than 50 research articles in International Journals and Conference. He has published 2 patents and is currently part of four externally funded projects on application of AI in Healthcare, Video Processing, and Image Processing. He is also guest editor of a Q1 springer journal. He is an active reviewer of 20+ renowned international journals. His research area includes Image Processing, DNA Computing, NLP, Machin Learning, Deep Learning, Feature Engineering, and Medical Image Analysis.

Kuan-Ching Li is currently appointed as Distinguished Professor at Providence University, Taiwan. He is a recipient of awards and funding support from several agencies and high-tech companies, as also received distinguished chair professorships from universities in several countries. He has been actively involved in many major conferences and workshops in program/general/steering conference chairman positions and as a program committee member and has organized numerous conferences related to high-performance computing and computational science and engineering.