Next-Generation Computing and Information Systems

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; Temporal-spatial 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; Temporal-spatial 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 Parkinson's 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