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Machine learning has evolved into a potent tool for analysing patterns and making predictions from complex data. In this machine learning era, we employed neural network techniques to estimate the parameters of statistic...

Presents corrections to the paper, (Demand Forecasting in Data-Scarce and Resource-Restricted Environments).

Autonomous microrobotic navigation in life sciences is highly challenging due to environmental disturbances, unpredictable phenomena, diverse obstacles, and dynamic changes. These significantly limit motion of microrobot...

Federated learning (FL) in mobile edge computing (MEC) operates over wireless links with time-varying bandwidth, latency, and packet loss, which makes conventional full-model synchronization inefficient and vulnerable to...

Even with the introduction of additional security features, the progression toward 6G and Open Radio Access Network (O-RAN) architectures expands security risks, increasing both the scale and complexity of mobile network...

High accuracy positioning has become a key capability for fifth generation (5G) mobile networks and their future generations; however, the achievable performance is still severely limited by network time synchronization,...

Reliable beat-level arrhythmia detection from electrocardiogram (ECG) signals is essential for continuous patient monitoring. However, many existing deep learning models remain computationally expensive and are sensitive...

Attacks on network components and devices pose a significant threat to service continuity, necessitating robust detection mechanisms. This paper presents a Distributed Denial of Service (DDoS) attack detection framework ...

The transport industry has recently shown significant interest in unmanned surface vehicles (USVs), specifically for port and inland waterway transport. These systems can improve operational efficiency and safety, which ...

Adaptive ability is essential for a quadrotor to fly in a complex environment with various situations thus a lightweight motion planner is necessary to ensure the quadrotor’s maneuverability. At present, the ESDF-...

Integration of Photovoltaic (PV) generation into oil and gas power networks has transformed fault behaviour by creating highly complex sensitivity challenges for conventional Overcurrent Relay (OCR) coordination and trip...

Spark detection in distribution line videos remains challenging due to weak visual saliency, multi-scale variation, and complex environmental interference. To address these issues, this paper presents an enhanced Swin Tr...

In recent years, electric bicycles have emerged as a novel and sustainable mode of transport, offering individuals and groups alike an active and environmental-friendly way to get around. It is evident that active transp...

Passive SONAR has attracted significant attention in covert operations, enabling vessel detection and classification through spectrogram analysis of received signals without active transmission. However, in realistic oce...

This paper proposes beam training algorithms for robust line-of-sight (LoS) multi-input and multi-output (MIMO) systems with subarray-based beamforming functionality at both the transmitter (TX) and receiver (RX). In gen...

This paper introduces CONV_GBERT, a hybrid deep learning framework for robust sentiment classification. The model integrates the bidirectional contextual understanding of BERT, the generative capabilities of GPT, and the...

Intelligent tire systems (ITS) enable direct sensing of tire and road interaction using sensors mounted inside the tire, providing information that is difficult to obtain from conventional vehicle-mounted sensors. Howeve...

Public service organisations, particularly universities, lack structured methods to evaluate their operational readiness for digital transformation, hindering strategic planning in the Industry 4.0 era. This paper addres...

Efficient UAV path planning in large and dynamic environments requires adaptive strategies that account for varying regional importance. Traditional coverage or Reinforcement Learning-based methods often suffer from redu...

Electrochemical migration (ECM) is a major concern in modern electronics because miniaturization has reduced insulation distances and electronics are now operated in harsher environments than before. Previous studies on ...

This study focuses on the explainability of AI operations for diagnosing degradations in cloud applications. Log data can provide essential contextual information on misbehaving applications in monitoring, evaluated agai...

This research describes a deep learning-based artificial neural network (ANN) approach for the detection, classification, and localization of series faults (open conductor faults) in power distribution networks. The ANN ...

Modern edge–cloud deployments integrate devices, edge servers, private networks, and cloud platforms into heterogeneous, multi-layer systems. Each layer exposes distinct metrics, measurement granularities, and tim...

Accurate velocity estimation plays a key role in ensuring precise motion control and safe navigation of ground robots. Ground-view event cameras provide a reliable sensing modality for this task. They exploit the static ...