An artificial intelligence (AI) deep learning tool that estimates the malignancy risk of lung nodules achieved high cancer detection rates while significantly reducing false-positive results. Results ...

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 ...

Traffic prediction is the core of intelligent transportation system, and accurate traffic speed prediction is the key to optimize traffic management. Currently, the traffic speed prediction model ...

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 ...

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: The rapid proliferation of the Internet of Things (IoT) and Industrial IoT (IIoT) has revolutionized industries through enhanced connectivity and automation. However, this expansion has ...

Introduction: Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by challenges in communication, social interactions, and repetitive behaviors. The heterogeneity of ...

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: 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 ...