This book compiles the proceedings of the Third International Conference on Next-Generation Computing and Information Systems (ICNGCIS 25). It combines high-quality research, practical insights, and scholarly debate spanning traditional domains like distributed computing, networks, and cybersecurity, alongside emerging areas such as AI, IoT, quantum security, and edge computing.
The proceedings include papers addressing currently relevant research issues such as smart contract security, interoperability in the metaverse, AI applications in healthcare, agriculture and related domains. The proceedings present findings with real-world implications for modern computing and information systems, addressing key challenges in design, deployment, operations, performance optimization, and limitation mitigation.
This book targets researchers from academia and industry, practitioners, students, technology enthusiasts, and general audiences seeking to understand cutting-edge applications, practical use cases, and core principles of modern computing and information systems.
This book combines high-quality research, practical insights, and scholarly debate spanning traditional domains like distributed computing, networks, and cybersecurity, alongside emerging areas such as AI, IoT, quantum security, and edge computing.
Section 1 Keynote addresses Record linkage; Multiobject tracking:
Challenges, opportunities and applications; The future computational camera;
AI for applications using Python language; Computing concepts for enhancing
artificial intelligence at the edge; LazyAI: Learning how and when not to
act; Section 2 Regular papers Post-quantum security for Fog computing-based
IoT systems against eavesdropping attacks; Intelligent traffic violation
detection using deep learning and YOLOv8n: System design and performance
analysis; Cross-device behavioral fusion in personal IoT for early stress
detection: A blockchain-enabled, privacy-preserving architecture; Real-time
hybrid AI framework for anomaly detection in GraphQL APIs under nested-query
attacks; Lightweight anomaly detection using TinyML models on simulated IoT
networks; Synergising fog and cloud computing: Intelligent task offloading
for real-time IoT applications; A learning-based traffic-sensitive optimal
route selection method in SDNs; Congestion-aware multi-agent path planning
for smart healthcare; Performance analysis of MFCC-GMM and MFCC-CNN
approaches for isolated word recognition in Dogri language; A
privacy-preserving federated meta-learning framework for rare disease
histopathology; Machine learning framework for diabetes mellitus
classification; Deep learning-based multichannel model for human activity
recognition technology from wearable devices; A hybrid SWIN transformer and
Model-Agnostic Meta-Learning framework for corticobasal degeneration
detection; Temporalspatial deep learning models for multi-class DDoS
detection: A comparative evaluation; Mach Zehnder interferometer-based
photonic matrix multiplication for AI acceleration devices; Hybrid imitation
learning framework for optimizing decision-making in consumer electronics
using lightweight AI; Hybrid AI framework for real-time traffic violation
detection and road safety enhancement; Leveraging the machine learning models
to predict the impact of rising electricity bill and its expense on the
customers annual disbursement; Cancer diagnosis prediction using
borderline-SMOTE balanced RNA-Seq data; Detection of lumpy skin disease using
machine learning-based approaches; Synergising fog and cloud computing:
Intelligent task offloading for real-time IoT applications; A learning-based
traffic-sensitive optimal route selection method in SDNs; Congestion-aware
multi-agent path planning for smart healthcare; Performance analysis of
MFCC-GMM and MFCC-CNN approaches for isolated word recognition in Dogri
language; A privacy-preserving federated meta-learning framework for rare
disease histopathology; Machine learning framework for diabetes mellitus
classification; Deep learning-based multichannel model for human activity
recognition technology from wearable devices; A hybrid SWIN transformer and
Model-Agnostic Meta-Learning framework for corticobasal degeneration
detection; Temporalspatial deep learning models for multi-class DDoS
detection: A comparative evaluation; Mach Zehnder interferometer based
photonic matrix multiplication for AI acceleration devices; Hybrid imitation
learning framework for optimizing decision-making in consumer electronics
using lightweight AI; Hybrid AI framework for real-time traffic violation
detection and road safety enhancement; Leveraging the machine learning models
to predict the impact of rising electricity bills and its expense on
customers annual disbursement; Cancer diagnosis prediction using
borderline-SMOTE balanced RNA-Seq data; Detection of lumpy skin disease using
machine learning-based approaches; DWT- and PCA-based color image
watermarking using genetic algorithm; Ensemble transfer learning framework
for early autism identification using structural MRI; A novel approach for
citrus greening disease severity assessment: Hybrid deep learning using
autoencoder and random forest; Deep learning in digital pathology for
prostate cancer: A comprehensive review of detection, segmentation, and
grading methodologies; DenCeptionNet-PD: A transfer learning-based ensemble
framework for early prediction of Parkinsons disease; A review of automatic
pronunciation mistake detector; DANN-based harmonization of multi-center fMRI
for ASD classification using ABIDE I and II; Early detection of lung cancer
using convolutional neural networks and DNA methylation biomarker analysis;
Deep learning pipeline for precise autoimmune disease prediction; AI-driven
legal assistant for legal empowerment in India: Leveraging Indian legal
datasets to promote access to justice
Ankur Gupta is currently serving as Director at the Model Institute of Engineering and Technology, Jammu (India), besides being a Professor at the Dept. of Computer Science and Engineering. He has 25+ years of experience spanning industry and academia. Prior to joining MIET, he worked as a Team Leader at Hewlett-Packard, India at Bengaluru. He has over 100 published research papers in international journals/conferences. He holds B.E (Hons.) CS and MS degrees from BITS, Pilani and PhD from NIT, Hamirpur. He has 24 patents granted and 75 patents filed at the Indian Patents Office. He is the inventor of the Performance Insight 360, quality analytics framework for higher education which has received several accolades. He has received the DSFT-FIST Grant in 2012, first in the private sector in J&K and received competitive grants over Rs. 2.5 Crore from various funding agencies. He is a recipient of the AICTE Career Award, faculty awards from IBM, EMC and Rs. 2 crores in funding from Govt. agencies. He is also Senior member IEEE and ACM and founder of the International Journal of Next-Generation Computing. His research interests are in cloud computing, P2P networks, network management, artificial intelligence and metaverse.
