Artificial intelligence
Researchers at the University of Cordoba have proposed two methodologies using artificial intelligence to predict extreme wind speeds, enhancing the efficiency of wind power in Spain. Wind energy accounted for 23.2% of the Spanish electricity system's energy injection last year, according to Red Electrica's 2024
Machine learning
Scientists at the Technical University of Munich have developed a novel solution to overcome the challenges of event-based depth prediction in autonomous vehicles, leveraging the spatiotemporal properties of event cameras to achieve superior performance in fast motion and low-light scenarios. By harnessing the power of event cameras and combining it
Machine learning
A recent study published in the Journal of Clinical Medicine has reported the development and evaluation of machine learning models to predict biochemical recurrence after robot-assisted radical prostatectomy. The research aims to improve the accuracy of prostate cancer treatment outcomes by leveraging machine learning algorithms and clinical data from 1125
Machine learning
Research by Noakhali Science and Technology University has utilized machine learning to identify significant factors contributing to panic-buying situations during the COVID-19 pandemic. The study proposes an AI model to predict panic-buying behavior, which could help mitigate inventory shortages and stabilize societal situations. Researchers employed a range of state-of-the-art classifiers
Decision making
Research has aimed to disentangle the cognitive mechanisms that support learning, particularly the roles of reinforcement learning (RL) and working memory (WM). A recent study used the reinforcement learning working memory Linear Ballistic Accumulator (RLWM-LBA) model to investigate how RL and WM contribute to learning and shape choice behavior. The
Exercise
Researchers have long known that people's intentions to exercise do not always translate into actual physical activity. A new study, published in the Frontiers in Psychology journal, aimed to bridge this gap by using machine learning to identify the key factors that contribute to the intention-behavior discrepancy. The
Decision making
Research has aimed to disentangle the cognitive mechanisms that support learning, particularly the roles of reinforcement learning (RL) and working memory (WM). A recent study used the reinforcement learning working memory Linear Ballistic Accumulator (RLWM-LBA) model to investigate how RL and WM contribute to learning and shape choice behavior. The
Machine learning
Researchers at Beijing University of Technology have made a breakthrough in machine learning, developing a multi-task learning (MTL) model that outperforms the traditional approach in various application scenarios. The new model, known as Multi-Task Parallel Model (MTPM), uses fuzzy neural networks and joint gradient descent algorithm to optimize parameters across
Machine learning
Researchers have made significant strides in developing algorithms that can accurately predict sarcopenia, a condition characterized by muscle wasting and weakness. A recent study published in Frontiers in Public Health found that unsupervised machine learning algorithms, specifically Gaussian Mixture Model (GMM), K-means clustering, and Otsu automatic threshold partitioning, outperformed MRI-based
Machine learning
Researchers from the Florida Institute of Technology have made a significant advancement in sensor research, developing a textile-based electroencephalography (EEG) system for passive brain-computer interfaces (pBCIs). This system uses a combination of EEG and machine learning to evaluate a user's cognitive and physiological state, with potential applications in
Machine learning
Researchers at King Saud University have sounded the alarm on the increasing inadequacy of traditional security mechanisms in the face of evolving cyberattacks. Artificial intelligence (AI) has emerged as a promising solution, but these AI-based Network Intrusion Detection Systems (NIDS) remain vulnerable to adversarial threats, particularly data poisoning attacks. In
Machine learning
Investigations into multidrug-resistant urinary tract infections (MDR UTIs) in patients with brain and spinal cord injuries have led to the development of a machine learning model that aims to predict the risk of developing such infections. Researchers from the Korea Advanced Institute of Science and Technology (KAIST) have analyzed data
