Machine learning - Strategic Insights Report (Page 6)

Machine learning

Multimodal Machine Learning for Predicting Perioperative Safety Indicators in Spinal Surgery

Researchers from the Albert Einstein College of Medicine have developed a novel machine learning architecture that integrates structured electronic health records (EHRs) with unstructured free-text inputs to predict perioperative safety indicators for spinal surgery patients. This preoperative multimodal model demonstrated superior performance in predicting extended length of stay, 90-day reoperation,

Machine learning

Advancements in Nano-Manipulation Technologies: From Manual to Autonomous Control

Researchers have made significant progress in nano-manipulation technologies since Richard Feynman's vision of atomically precise manufacturing. Three foundational pillars have enabled these advancements: observation, construction, and manipulation. Key platforms include scanning tunneling microscopy (STM), atomic force microscopy (AFM), optical tweezers, and scanning electron microscopy (SEM). The SEM, in

Machine learning

Salk Neuroscientist Terrence Sejnowski Wins 2025 NIH Director's Pioneer Award

Terrence Sejnowski, a renowned neuroscientist at the Salk Institute, has been selected to receive a 2025 NIH Director's Pioneer Award. The prestigious award honors scientists proposing exceptionally creative and high-risk, high-reward research. Sejnowski, a professor and head of the Computational Neurobiology Laboratory, will receive $3.5 million over

Artificial intelligence

AI-Powered Wind Speed Prediction Enhances Renewable Energy Efficiency in Spain

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

Exercise

Closing the Intention-Behavior Gap in Physical Activity Through Explainable Machine Learning

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

Neural Dynamics of Decision-Making During Learning Revealed

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

Decision making

Neural Dynamics of Decision-Making During Learning Revealed

Machine learning

Multi-Task Learning Model Outperforms Traditional Approach in Application Scenarios

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

Unsupervised Machine Learning Algorithms Outperform MRI-Based Counterparts in Sarcopenia Prediction

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

Machine Learning Model Predicts Multidrug-Resistant UTIs in Brain and Spinal Cord Injury Patients

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

Machine learning

AI-Based Network Intrusion Detection Systems Vulnerable to Adversarial Threats

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

Novel Approach for Sustainable Construction Using Mathematical Simulation and Machine Learning

A groundbreaking study on sustainability research has been conducted by Northern Border University, Saudi Arabia, introducing a novel approach for sustainable construction using mathematical simulation and machine learning. According to the research, a new mathematical simulation has been developed to create sustainable, intelligent, and resilient infrastructure, utilizing the self-sensing characteristics

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