Deep neural networks (DNNs), the machine learning algorithms underpinning the functioning of large language models (LLMs) and other artificial intelligence (AI) models, learn to make accurate ...

Research Unit of Health Sciences and Technology, Faculty of Medicine, University of Oulu, Aapistie 5 A, 90220 Oulu, Finland Research Unit of Disease Networks, Faculty of Biochemistry and Molecular ...

Electric Vehicle (EV) cost prediction involves analyzing complex, high-dimensional data that often contains noise, multicollinearity, and irrelevant features. Traditional regression models struggle to ...

1 Department of Agricultural and Biological Engineering, University of Florida, Gainesville, FL, United States 2 Department of Horticultural Science, North Carolina State University, Raleigh, NC, ...

1 Department of Computer Science, The University of Zambia, Lusaka, Zambia. 2 Department of Clinical Care, Levy University Teaching Hospital, Lusaka, Zambia. This paper focuses on the use of YOLOv12 ...

1 Department of Neuroscience, Institute of Psychopathology, Rome, Italy. 2 Department of Computer Engineering (AI), University of Genova, Genova, Italy. Mental disorders, including depression, bipolar ...

Objective: To develop a deep learning (DL) model for carotid plaque detection based on CTA images and evaluate the clinical application feasibility and value of the model. Methods: We retrospectively ...

Abstract: With the rapid growth of Internet of Things (IoT) technology, there is an increase in the dangers of malware attacks. The underlying threat in these attacks is invading people's privacy and ...

Abstract: Most machine learning-based malware detection systems use various supervised learning methods to classify different instances of software as benign or malicious. This approach provides no ...