Autonomous Cyber-Physical Systems (CPS) must be robust against potential failure modes, including physical degradations and software issues, and are required to self-manage contingency actions for these failures. Physical degradations often have a si...
Fault diagnosis of rotating machines is an important task to prevent machinery downtime, and provide verifiable support for condition-based maintenance (CBM) decision-making. Deep learning-enabled fault diagnosis operations have become increasingly p...
Wireless communications systems are traditionally designed by independently optimising signal processing functions based on a mathematical model. Deep learning-enabled communications have demonstrated end-to-end design by jointly optimising all compo...
Visual simultaneous localization and mapping (VSLAM) enhances the navigation of autonomous agents in unfamiliar environments by progressively constructing maps and estimating poses. However, conventional VSLAM pipelines often exhibited degraded perfo...
Selective laser melting (SLM) is recognized as one of the most promising metal additive manufacturing technologies for industrial deployment and has been applied to fabricate precision components for high-end equipment in sectors including aerospace...
Artificial intelligence (AI) acts as a vital part of enhancing network security using intrusion detection and anomaly detection. These AI-driven approaches have become essential components of modern cybersecurity strategies. Conventional IDS is based...
Wearable health monitoring systems (WHMS) are recognized as key enablers of continuous real-time physiological sensing in healthcare, eldercare, sports, and occupational safety. However, current devices face critical limitations due to their dependen...
Foodborne pathogens remain a major global concern, demanding rapid, accessible, and determination technologies. Conventional methods, such as culture assays and polymerase chain reaction, offer high accuracy but are time-consuming for on-site testing...
Reinforced Concrete (RC) structures experience progressive degradation over their service life due to mechanical loading and environmental exposure, leading to reduced bearing capacity and compromised structural safety. Incorporating discrete fibers...
Laser powder bed fusion (L-PBF) has emerged as a core metal additive manufacturing technology for high-end sectors, including aerospace and medical device manufacturing. However, melting anomalies that occur during fabrication accumulate layer by lay...
Multi-task learning is a statistical methodology that aims to improve the generalization performances of estimation and prediction tasks by sharing common information among multiple tasks. On the other hand, compositional data consist of proportions...
Integrating machine learning into additive manufacturing offers transformative opportunities to optimize material properties and design high-performance, fatigue-resistant structures for critical applications in aerospace, biomedical, and structural...
Smarter applications are making better use of the insights gleaned from data, having an impact on every industry and research discipline. At the core of this revolution lies the tools and the methods that are driving it, from processing the massive p...
Advanced manufacturing techniques, such as automated fiber placement and additive manufacturing enables the fabrication of fiber-reinforced polymer composite components with customized material and structural configurations. In order to take advantag...
The generation and prediction of daily human mobility patterns have raised significant interest in many scientific disciplines. Using various data sources, previous studies have examined several deep learning frameworks, such as the RNN and GAN, to s...
Background/Objectives: Observational learning enables children to acquire new skills by observing others’ actions. Attention is widely recognized as a key supporting process and consists of multiple components that develop substantially during...
Automatic modulation classification (AMC) is a key component of spectrum awareness, cognitive radio, and signal intelligence, enabling receivers to identify modulation schemes from noisy in-phase and quadrature (IQ) observations. Traditional approach...
The purpose of Graphic Design is to transfer information about design into reality and concerns the analysis, design and representation of mechanical components and assemblies. For the correct rendering of mechanical components, this discipline requi...
In deep learning, Long Short-Term Memory (LSTM) is a well-established and widely used approach for music generation. Nevertheless, creating musical compositions that match the quality of those created by human composers remains a formidable challenge...
Inducing learning strategies is a crucial component of intelligent tutoring systems. Previous research has predominantly focused on the induction of offline learning strategies. Although the existing offline learning strategy induction methods can al...
Edge AI, an interdisciplinary technology that enables distributed intelligence with edge devices, is quickly becoming a critical component in early health prediction. Edge AI encompasses data analytics and artificial intelligence (AI) using machine l...
Continual learning (CL) is a key technology for enabling data-driven autonomous guidance systems to operate stably and persistently in complex and dynamic environments. Its core goal is to enable the model to continuously learn new scenarios and task...
Cost prediction can provide a pharma supply chain industry with completing their projects on schedule and within budget. This paper provides a new multi-function Blockchain Technology-enabled Pharmaceutical Supply Chain (BT-enabled PSC) mathematical...
Intelligent infrastructure, including smart cities and intelligent buildings, must learn and adapt to the variable needs and requirements of users, owners and operators in order to be future proof and to provide a return on investment based on Operat...
In neural networks, a vital component in the learning and inference process is the activation function. There are many different approaches, but only nonlinear activation functions allow such networks to compute non-trivial problems by using only a s...
Timely and accurate fault diagnosis of ship oil purifiers is essential for maintaining the operational reliability of a degree-4 maritime autonomous surface ship (MASS). Conventional approaches rely on manual feature engineering or simple machine lea...
Despite the rapid rise in the number of blended education studies after the COVID-19 pandemic, the topics of self-regulated and mobile-mediated learning in blended tertiary education environments is an open research issue. The purpose of this pilot s...
Power equipment defect detection is essential for ensuring reliable power grid operation. Existing power equipment defect detection methods typically formulate the task as multi-class object detection, but their performance is limited by long-tailed...
Over recent years, machine learning and artificial intelligence have become critical components of many cancer detection tests, in particular multi-omic tests such as spectroscopic liquid biopsies. The complexity and multi-variate nature of spectral...
Retrieving waveguiding properties of plasmonic metal nanowires (MNWs) through numerical simulations is time- and computational-resource-consuming, especially for those with abrupt geometric features and broken symmetries. Deep learning provides an al...
In this study, we analyze how crop management will benefit from the Internet of Things (IoT) by providing an overview of its architecture and components from agronomic and technological perspectives. The present analysis highlights that IoT is a matu...
Federated Learning (FL) is emerging as an encouraging paradigm for AI model training in healthcare that enables collaboration among institutions without revealing sensitive information. The lack of transparency in federated models makes their deploym...
This study presents a reinforcement learning–based approach to optimize replenishment policies in the presence of uncertainty, with the objective of minimizing total costs, including inventory holding, shortage, and ordering costs. The focus is...
Vehicle-to-grid services make use of the aggregated capacity available from a fleet of vehicles to participate in energy markets, help integrate renewable energy in the grid and balance energy use. In this paper, the critical components of such a ser...
While previous studies have examined sustainable leadership and innovation separately, limited attention has focused on their systemic interconnection. Building on established frameworks, this study adopts a systems perspective to explain how sustain...
A long-standing research goal is to develop computing technologies that mimic the brain’s capabilities by implementing computation in electronic systems directly inspired by its structure, function, and operational mechanisms, using low-power,...
In reinforcement learning (RL), precise observations are crucial for agents to learn the optimal policy from their environment. While Unity ML-Agents offers various sensor components for automatically adjusting the observations, it does not support h...
This paper presents a novel synthetic learning-based approach for solving the component-to-slot assignment problem in robotics using a SCARA robot. The method uses a fully simulated environment that generates and annotates scenes based on rules and v...
Component-based software architectures are essential for building modular, maintainable systems across diverse domains including embedded systems, scientific computing, and distributed applications. However, existing component frameworks (CORBA, COM,...
Wind power is an essential component of renewable energy. It enables the conservation of conventional energy sources such as coal and oil while reducing greenhouse gas emissions. To address the stochastic and intermittent nature of ultra-short-term w...
The main activity of social robots is to interact with people. In order to do that, the robot must be able to understand what the user is saying or doing. Typically, this capability consists of pre-programmed behaviors or is acquired through controll...
Rolling bearing fault diagnosis is critical for the reliable operation of rotating machinery. However, many existing deep learning-based methods rely on complex signal preprocessing and lack interpretability. This paper proposes an adversarial autoen...
One of the common methods for implementing the condition-based maintenance of rotating machinery is vibration analysis. This tutorial describes some of the important signal processing methods existing in the field, which are based on a profound under...
In this article, an adaptive neural network (ANN) controller based on feature augmentation (FA) is designed for quadrotors. The proposed controller consists of two components: a position sub-controller and an attitude sub-controller. We use the ANN t...
Over the past few decades, the mathematical community has accumulated a significant amount of pure mathematical data, which has been analyzed through supervised, semi-supervised, and unsupervised machine learning techniques with remarkable results, e...
The design, implementation and evaluation of a year 1 pharmacy-integrated learning component, using the World Health Organisation’s (WHO) analgesic ladder as a scaffold for case-based learning, is described. A novel aspect of the integrated com...
Accurately predicting rice yield and its components is crucial for optimizing agricultural practices and ensuring food security. Traditional methods of assessing crop status wwcan be time-consuming and labor-intensive. This study investigated the use...
Researchers are interested in task design in distance learning. This task design is critical in emergency education that uses distance learning. The present research investigated mathematics and science teachers’ task design in distance learnin...
Predictive maintenance (PdM) has the potential to reduce industrial costs by anticipating failures and extending the work life of components. Nowadays, factories are monitoring their assets and most collected data belong to correct working conditions...
Point of Interest (POI) data play a critical role in enabling location-based services (LBS) by providing intrinsic attributes, including geographic coordinates and semantic categories, alongside a spatial context that reflects relationships among POI...
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