Centre for Artificial Intelligence and Robotics

Energy Efficient Federated Graph Reinforcement Learning and Digital Twin Assisted Demand Response for Blockchain-Enabled Self-Healing Smart Grids

Abstract Smart grids today are highly decentralized and data-driven, with the growing incorporation of renewable energy resources, electric vehicles, distributed energy systems and intelligent edge devices. Energy efficiency, operational resilience, privacy protection and secure...

Isolation and Characterization of Lytic Bacteriophages Targeting Clinical Staphylococcus Other Than S. aureus

Abstract Staphylococcus spp., other than S. aureus (SOSA, formerly CoNS), are opportunistic pathogens often linked to biofilm infections on indwelling devices. Their rising antibiotic resistance highlights the need for novel therapies. Bacteriophages are promising due to their sp...

An explainable multi-modal graph learning framework with attention-based GCN-GAT for EEG-based depression detection

Major Depressive Disorder (MDD) is phenotypic and neurophysiologically heterogeneous, and developing reliable computational models for it requires careful control of information leakage, dataset heterogeneity, and contributions from different information sources. In this study, a...

Adaptive timestamp based concurrency control for efficient transaction processing in permissioned blockchains

Efficient concurrency control is key on permissioned blockchain platforms like Corda, where transaction volumes are high and contention is rife. In some hotspot situations, conventional concurrency control mechanisms such as Two-Phase Locking, Optimistic Concurrency Control, Mult...

A robust CurvLaneNet-YOLO framework for real-time lane curvature estimation and vehicle detection in intelligent transportation applications

Abstract Current road safety systems face challenges in detecting curved roads and vehicles under varying lighting and weather conditions, leading to lane departure and collision risks. To address this, we propose CurvLaneNet-YOLO, a deep learning framework based on YOLOv8 that s...

A partially supervised joint segmentation method for farmland and rural roads from single-class annotated remote sensing datasets

Accurate segmentation of co-existing land-cover objects in high-resolution remote sensing imagery is essential for precision agriculture and rural planning. Although multi-class datasets are increasingly available, many high-quality annotations remain task-specific, where only a...