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Abstract Detecting epileptic seizures from electroencephalogram (EEG) signals presents a significant challenge due to the nonlinear and non-stationary nature of brain activity. In this study, we propose a hybrid classifi...

Abstract Purpose This study introduces a new Convolutional Neural Network (CNN) architecture, EyeDiagNet, for the classification of fundus images into four classes: normal, cataract, diabetic retinopathy (DR), and glauco...

Abstract Learning to program is not just about writing code. It is a complex process involving trial and error, hesitation, corrections, and sometimes silence. And each student experiences it in their own way. Understand...

Abstract In response to the dual challenges of complex static obstacles and frequent dynamic disturbances in the construction site environment, this paper proposes a hybrid path planning strategy that integrates global s...

This paper presents DS-PTAM, a distributed architecture for the S-PTAM stereo SLAM system. This architecture is developed on the ROS framework, separating the localization and mapping tasks into two independent ROS nodes...

Abstract In the power operation scenarios, there are problems such as large differences in target scales, small targets susceptible to occlusion, and limited computational resources for edge devices. This leads to insuff...

Abstract The Error Diffusion Learning Algorithm (EDLA) is a learning scheme that performs synaptically local weight updates driven by a single, globally defined error signal. Although originally proposed as an alternativ...

Abstract Physical sciences are essential to the predictive modeling of complex environmental systems, particularly environmental monitoring and sustainability applications, as they provide the deterministic foundation re...

Abstract Traditional bridge structural condition assessments struggle to integrate the physical topological information of BIM models with the spatiotemporal evolution of long-term monitoring data, resulting in low asses...

Abstract The emergence of Internet-of-Things devices and expanding data processing demands have led to the popularization of edge computing. But energy efficiency challenges and security problems also remain difficult in...

Abstract Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems have significantly advanced data modelling capabilities and improved opportunities for extracting knowledge from vast and heterogeneo...

Abstract Skin diseases affect a considerable part of the world population, but it is not easy to diagnose these diseases on time because of the limited availability of dermatologists and the similarity of lesions. This r...

Abstract While multi-access edge computing (MEC) enables flexible task offloading, the inherent dynamism and complexity of real-world environments, accompanied by various uncertainties, exacerbate the challenges in offlo...

Abstract This research is concerned with the innovative use of AI in cross-cultural music learning, more particularly in the creation of a multimodal learning model that combines audio identification, image analysis, and...

Abstract Multi-label chest X-ray classification faces three critical challenges: (i) inadequate modeling of inter-pathology dependencies despite clinical co-occurrence patterns, (ii) severe class imbalance (11.2−47.6...

Abstract The ever-growing and increasing diversity of IoT ecosystems, the seamless interoperability, scale, and near real-time automation have remained grand challenges. This paper presents a solution enterprise semantic...

Abstract With the evolution of interactive art installations towards 4 K/8 K ultra-high-definition streaming and large-scale multi-point interaction, existing 5G networks face challenges in maintaining the "flow state"...

Abstract Smart home networks are a rapidly evolving part of the Internet of Things (IoT), where connected devices exchange information to automate energy use, enhance comfort, and improve user safety. Despite their benef...

Abstract This study seeks to address persistent challenges in traditional music education, particularly the subjectivity inherent in assessment criteria and the frequent delays in feedback, by harnessing the capabilities...