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Football is the world’s most popular sport, and evaluating the market value of players is crucial for clubs and managers in making informed decisions regarding transfers, contracts, and financial planning. This st...

The recent Zero-Trust Architecture (ZTA) is progressively adopted to the develop network security by assuming no implicit trust within or outside an organization’s boundary. Though, ZTA faces substantial challenge...

Background: Non-traumatic osteonecrosis of the femoral head (ONFH) can progress to severe osteoarthritis without early intervention. The modified light bulb (MLB) and multiple drilling (MD) techniques aim to preserve the...

This paper addresses the growing need for implementing intelligent Natural Language Processing (NLP) systems on low-power, memory-limited devices such as Raspberry Pi, mobile phones, and IoT edge hardware. As edge comput...

The COVID-19 pandemic has placed enormous strain on providing health care services internationally while reinforcing the argument for the need to strengthen forecasting techniques. Existing forecasting methods have drawb...

BackgroundThere is limited understanding regarding the factors that predict recreational therapy engagement among veterans in long-term care facilities. We aimed to develop and validate machine learning models to predict...

Abstract Proposed research presents a data-driven framework for forecasting municipal solid waste (MSW) generation and emission dynamics in Erode City, India, by employing supervised machine learning algorithms. Leveragi...

The prediction and design of photonic features have traditionally been guided by theory-driven computational methods, spanning a wide range of direct solvers and optimization techniques. Motivated by enormous advances in...

The integration of the Internet of Things (IoT) into healthcare would mean a revolutionized approach in patient monitoring, diagnosis, and treatment, making this quite some development in healthcare delivery. This review...

Abstract Unmanned Aerial Systems (UAS) have become increasingly integral to industries such as agriculture, surveillance, transportation, and entertainment, raising significant concerns about their vulnerability to cyber...

Abstract The primary objective of this study is to develop and validate robust data-driven models for accurately predicting bacterial growth inhibition induced by cerium oxide nanoparticles across different bacterial str...

Abstract Accurate prediction of compressive strength is important for the efficient design of sustainable concrete incorporating palm oil fuel ash (POFA). This study developed a hybrid artificial neural network model opt...

The safety concept primarily examines the most fatal (resulting in dead passengers) accidents of aviation history in this study. The primary causes of most fatal accidents are; human, technical, and sabotage/terrorism fa...

The global aviation industry plays a critical role in economic development and international connectivity. However, it also contributes significantly to environmental challenges, particularly through fuel consumption and...

Mental health questions can be tackled through machine learning (ML) techniques. Apart from the two ML methods we introduced in our previous paper, we discuss two more advanced ML approaches in this paper: support vector...

Abstract Considering crop yield prediction as critical to optimizing agricultural practices and food security, this question is critical to U.S. agricultural planning and regional food security; relevant research on corn...

Calibration of low-cost air quality sensors (LCSs) for total volatile organic compound (TVOC) and benzene, toluene, ethylbenzene, and xylenes (BTEX) quantification remains challenging due to the sensors' cross-sensitivit...

Federated learning (FL) is a decentralized machine learning (ML) approach that can be used for intrusion detection in Internet of Things (IoT) devices. It involves the local training of AI models and their aggregation at...

The necessity for sophisticated recommender systems in the movie recommendation sphere has become particularly pronounced, generating a more personalized movie recommendation due to people nowadays who like to watch movi...

Machine-Learned Likelihood (MLL) is a method that, by combining modern machine-learning techniques with likelihood-based inference tests, allows estimating the experimental sensitivity of high-dimensional data sets. Here...

This paper presents a prototype man–machine dialogue system specifically designed for Arabic, addressing the growing need for voice-based interaction in under-resourced linguistic contexts. Arabic poses particular chal...

ABSTRACT Graph contrastive learning (GCL) has emerged as a dominant paradigm for self‐supervised representation learning for attributed graph data. However, existing GCL methods heavily rely on empirical graph data aug...

The rapid growth and increasing complexity of Internet of Things (IoT) networks require efficient real-time monitoring and anomaly detection mechanisms. Traditional machine learning approaches often struggle to handle th...

Mobile Ad-Hoc Networks (MANET) is a type of ad-hoc networks which use less infrastructure, that means the nodes in this network forward the massages without the need of infrastructure such as routers, switches etc. One o...