Data Science and Applications 1
Proceedings of ICDSA 2025, Volume 1
Herausgeber: Yadav, Rajendra Prasad; Saraswat, Mukesh; Prasad, Mukesh; Nanda, Satyasai Jagannath
Data Science and Applications 1
Proceedings of ICDSA 2025, Volume 1
Herausgeber: Yadav, Rajendra Prasad; Saraswat, Mukesh; Prasad, Mukesh; Nanda, Satyasai Jagannath
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This book gathers outstanding papers presented at the 6th International Conference on Data Science and Applications (ICDSA 2025), organized by Soft Computing Research Society (SCRS) and Malaviya National Institute of Technology Jaipur, India, from 16 to 18 July 2025. The book is divided into eight volumes, and it covers theoretical and empirical developments in various areas of big data analytics, big data technologies, decision tree learning, wireless communication, wireless sensor networking, bioinformatics and systems, artificial neural networks, deep learning, genetic algorithms, data…mehr
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This book gathers outstanding papers presented at the 6th International Conference on Data Science and Applications (ICDSA 2025), organized by Soft Computing Research Society (SCRS) and Malaviya National Institute of Technology Jaipur, India, from 16 to 18 July 2025. The book is divided into eight volumes, and it covers theoretical and empirical developments in various areas of big data analytics, big data technologies, decision tree learning, wireless communication, wireless sensor networking, bioinformatics and systems, artificial neural networks, deep learning, genetic algorithms, data mining, fuzzy logic, optimization algorithms, image processing, computational intelligence in civil engineering, and creative computing.
Produktdetails
- Produktdetails
- Lecture Notes in Networks and Systems Nr.1721
- Verlag: Springer International Publishing AG / Springer-Verlag GmbH
- Artikelnr. des Verlages: 89576830
- Erscheinungstermin: 12. Dezember 2025
- Englisch
- Abmessung: 235mm x 155mm
- ISBN-13: 9783032107527
- ISBN-10: 3032107520
- Artikelnr.: 75577206
- Herstellerkennzeichnung
- Springer-Verlag GmbH
- Tiergartenstr. 17
- 69121 Heidelberg
- ProductSafety@springernature.com
- Lecture Notes in Networks and Systems Nr.1721
- Verlag: Springer International Publishing AG / Springer-Verlag GmbH
- Artikelnr. des Verlages: 89576830
- Erscheinungstermin: 12. Dezember 2025
- Englisch
- Abmessung: 235mm x 155mm
- ISBN-13: 9783032107527
- ISBN-10: 3032107520
- Artikelnr.: 75577206
- Herstellerkennzeichnung
- Springer-Verlag GmbH
- Tiergartenstr. 17
- 69121 Heidelberg
- ProductSafety@springernature.com
Dr. Satyasai Jagannath Nanda is an associate professor in the Dept. of Electronics and Communication Engg., Malaviya National Institute of Technology Jaipur since Dec. 2023. He joined as an assistant professor at MNIT Jaipur in June 2013. He received the PhD degree from School of Electrical Sciences, IIT Bhubaneswar in 2013 and M. Tech. degree from Dept. of Electronics and Communication Engg., NIT Rourkela in 2009. He received the B.E. degree in Electronics and Telecommunication Engg. from Institute of Technical Education and Research (ITER), Bhubaneswar in the year 2006. He was the recipient of Canadian Research Fellowship- GSEP, from Dept. of Foreign Affairs and Intern. Trade (DFAIT), Govt. of Canada for the year 2009-10. He was awarded Best PhD thesis award at SocProS 2015 by IIT Roorkee. Prof. Rajendra Prasad Yadav is currently working as a Professor-HAG in the Department of Electronics and Communication Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan, India. He has more than four decades of teaching and research experience. He was instrumental in starting new B.Tech, M.Tech courses and formulating PhD Ordinances for starting research work in Rajasthan Technical University (RTU) Kota and other affiliated Engg colleges as Vice Chancellor of the University. He has served as HOD of Electronics and Comm. Engg., President Sports and Library, Hostel warden, Dean Student Affairs at MNIT Jaipur. He was also the Chief Vigilance Officer of MNIT Jaipur. Prof. Yadav received the PhD degree from MREC Jaipur and M.Tech degree from IIT Delhi. Dr Mukesh Prasad is a Senior Lecturer at School of Computer Science in the Faculty of Engineering and IT at UTS who has made substantial contributions to the fields of machine learning, artificial intelligence and the internet of things. Mukesh’s research interests include also big data, computer vision, brain computer interface, and evolutionary computation. He is working also in the evolving and increasingly important field of image processing, data analytics and edge computing, which promise to pave the way for the evolution of new applications and services in healthcare, biomedical, agriculture, smart cities, education, marketing and finance. His research has appeared in numerous prestigious journals, including IEEE/ACM Transactions and conferences, he has written more than 100 research papers. Prof. Mukesh Saraswat is a Professor and Associate Dean (Innovation) at Jaypee Institute of Information Technology (JIIT), Noida, India. Prof. Saraswat obtained his Ph.D. in Computer Science & Engineering from ABV-IIITM Gwalior, India. He has more than 20 years of teaching and research experience. He has guided 04 Ph.D. students and presently guiding 04 Ph.D. students. He has published more than 80 journal and conference papers in image processing, pattern recognition, and soft computing. He was part of a successfully completed project funded by SERB, New Delhi on image analysis. He has been an active member of many organizing committees for various conferences and workshops. He is also a faculty in charge of Jaypee Youth Club at JIIT, Noida. He is also a guest editor of the Array, Journal of Swarm Intelligence, SN Computer Science, and Journal of Intelligent Engineering Informatics. He is one of the General Chairs of the International Conference on Data Science and Applications. He is also an Advisory Board Member of the Journal MethodsX. He is also a series editor of the SCRS Book Series on Computing and Intelligent Systems (CIS). He is an active member of ACM, CSI, and SCRS Professional Bodies.
Emerging Threats and Mitigation Strategies in Biometric Authentication Systems.
Predicting the Effectiveness of B2B Marketing Strategies using Deep Learning: A Multi
Target Regression and Classification Approach.
Machine Learning Based Feature Extraction and Stress Detection using Different Dataset.
IoT
based Smart Grid: Architecture, Security Challenges and Mitigations.
An IoT
Enabled Multi
Agent Framework for Real
Time Emergency Monitoring: A Context
Aware Approach.
CSRANet A Deep Supervised Residual Attention Model for Coronary Stenosis Detection.
Swin
SAINT: A Hybrid Transformer Framework for Multi
Modal Skin Cancer Detection using Structured Metadata and Dermoscopic Images.
Advanced Tuberculosis Detection and Treatment Optimization using Moth Search Algorithm with Stacked Decision Tree Structures.
IDCNN: Efficient Multi
Class Intrusion Detection on Imbalanced Data in Software Defined Network.
Personalized Drug Recommendation using ClinicalBERT
Augmented Graph Neural Networks.
Combatting Political Fake News: Detection & Prevention of Disinformation on Social Media with Generative AI.
Secure Anomaly Detection for Electric Vehicle Batteries: A Hybrid Federated Split Learning Approach.
Adaptive and Interactive Learning using LLMs and Machine Learning Models for English Mastery
Interactive English Mastery.
ARUNet6L: A Deep Learning Based Hybrid U
Net Model Integrated with Attention and Residual Connection for Optimizing Image Segmentation of Mitochondria.
Gesture Synthesis for Sign Language using Generative Adversarial Networks.
Predicting the Effectiveness of B2B Marketing Strategies using Deep Learning: A Multi
Target Regression and Classification Approach.
Machine Learning Based Feature Extraction and Stress Detection using Different Dataset.
IoT
based Smart Grid: Architecture, Security Challenges and Mitigations.
An IoT
Enabled Multi
Agent Framework for Real
Time Emergency Monitoring: A Context
Aware Approach.
CSRANet A Deep Supervised Residual Attention Model for Coronary Stenosis Detection.
Swin
SAINT: A Hybrid Transformer Framework for Multi
Modal Skin Cancer Detection using Structured Metadata and Dermoscopic Images.
Advanced Tuberculosis Detection and Treatment Optimization using Moth Search Algorithm with Stacked Decision Tree Structures.
IDCNN: Efficient Multi
Class Intrusion Detection on Imbalanced Data in Software Defined Network.
Personalized Drug Recommendation using ClinicalBERT
Augmented Graph Neural Networks.
Combatting Political Fake News: Detection & Prevention of Disinformation on Social Media with Generative AI.
Secure Anomaly Detection for Electric Vehicle Batteries: A Hybrid Federated Split Learning Approach.
Adaptive and Interactive Learning using LLMs and Machine Learning Models for English Mastery
Interactive English Mastery.
ARUNet6L: A Deep Learning Based Hybrid U
Net Model Integrated with Attention and Residual Connection for Optimizing Image Segmentation of Mitochondria.
Gesture Synthesis for Sign Language using Generative Adversarial Networks.
Emerging Threats and Mitigation Strategies in Biometric Authentication Systems.
Predicting the Effectiveness of B2B Marketing Strategies using Deep Learning: A Multi
Target Regression and Classification Approach.
Machine Learning Based Feature Extraction and Stress Detection using Different Dataset.
IoT
based Smart Grid: Architecture, Security Challenges and Mitigations.
An IoT
Enabled Multi
Agent Framework for Real
Time Emergency Monitoring: A Context
Aware Approach.
CSRANet A Deep Supervised Residual Attention Model for Coronary Stenosis Detection.
Swin
SAINT: A Hybrid Transformer Framework for Multi
Modal Skin Cancer Detection using Structured Metadata and Dermoscopic Images.
Advanced Tuberculosis Detection and Treatment Optimization using Moth Search Algorithm with Stacked Decision Tree Structures.
IDCNN: Efficient Multi
Class Intrusion Detection on Imbalanced Data in Software Defined Network.
Personalized Drug Recommendation using ClinicalBERT
Augmented Graph Neural Networks.
Combatting Political Fake News: Detection & Prevention of Disinformation on Social Media with Generative AI.
Secure Anomaly Detection for Electric Vehicle Batteries: A Hybrid Federated Split Learning Approach.
Adaptive and Interactive Learning using LLMs and Machine Learning Models for English Mastery
Interactive English Mastery.
ARUNet6L: A Deep Learning Based Hybrid U
Net Model Integrated with Attention and Residual Connection for Optimizing Image Segmentation of Mitochondria.
Gesture Synthesis for Sign Language using Generative Adversarial Networks.
Predicting the Effectiveness of B2B Marketing Strategies using Deep Learning: A Multi
Target Regression and Classification Approach.
Machine Learning Based Feature Extraction and Stress Detection using Different Dataset.
IoT
based Smart Grid: Architecture, Security Challenges and Mitigations.
An IoT
Enabled Multi
Agent Framework for Real
Time Emergency Monitoring: A Context
Aware Approach.
CSRANet A Deep Supervised Residual Attention Model for Coronary Stenosis Detection.
Swin
SAINT: A Hybrid Transformer Framework for Multi
Modal Skin Cancer Detection using Structured Metadata and Dermoscopic Images.
Advanced Tuberculosis Detection and Treatment Optimization using Moth Search Algorithm with Stacked Decision Tree Structures.
IDCNN: Efficient Multi
Class Intrusion Detection on Imbalanced Data in Software Defined Network.
Personalized Drug Recommendation using ClinicalBERT
Augmented Graph Neural Networks.
Combatting Political Fake News: Detection & Prevention of Disinformation on Social Media with Generative AI.
Secure Anomaly Detection for Electric Vehicle Batteries: A Hybrid Federated Split Learning Approach.
Adaptive and Interactive Learning using LLMs and Machine Learning Models for English Mastery
Interactive English Mastery.
ARUNet6L: A Deep Learning Based Hybrid U
Net Model Integrated with Attention and Residual Connection for Optimizing Image Segmentation of Mitochondria.
Gesture Synthesis for Sign Language using Generative Adversarial Networks.