Articles tagged with Machine Learning
Researchers at Emory University used machine learning and fMRI to analyze a dog's brain activity while watching videos. The results show that dogs are more attuned to actions in their environment than to who or what is performing the action. This study offers proof of concept for decoding canine visual perception.
The Florida Summer Institute in Biostatistics and Data Science recruits students from underrepresented groups, providing a rigorous quantitative education. The program introduces college students to statistical methods and applications in biomedical research, highlighting health disparities and social drivers of health.
University of Minnesota scientists have developed a method to fine-tune traditional compressed sensing for high-quality images using modern data science tools and machine learning ideas. This approach closes the gap between traditional and deep learning methods, providing a new direction for MRI reconstruction research.
The Center for BrainHealth has launched three research projects to develop objective metrics of improved brain systems in response to interventions. These projects aim to determine the changes in the brain's physiology, structure, and function linked to gains in comprehensive psycho-social measurements over time.
Healthcare researchers caution against misusing AI algorithms in clinical research, highlighting concerns about bias, transparency, and data quality. The team advocates for evaluating ML methods against traditional statistical approaches and ensuring clinician decision-making is complemented, not replaced.
The University of California, San Diego is part of the National Institutes of Health's Bridge to Artificial Intelligence program, aiming to create comprehensive AI-ready datasets. The program will support researchers in developing interpretable and trustworthy AI technologies to improve human health.
Researchers developed a unified machine learning model that analyzes data from patients with and without treatment, predicting depression outcomes better than separate models. The approach enables personalized medicine by designing treatment plans specific to each patient's needs.
City digital twin technology is used to create synthetic training data for deep learning models, which are then trained on a combination of real and synthetic data. This approach yields promising results for architectural segmentation tasks, particularly for modern building styles.
Researchers find XAI methods improve AI performance and explain bias in data, enabling accurate applications like insurance decision-making. Implementing XAI helps manage power consumption and optimize AI systems for efficient Industry 4.0 growth.
A new method using machine learning corrects damaged DNA and unveils true mutation processes in tumour samples, helping early cancer detection and accurate diagnosis. The tool predicted over 90% of developing cancer processes, offering a significant advancement in cancer patient care.
Researchers from GIST developed an AI model that adjusts videogame difficulty based on player emotions, incorporating aspects such as challenge, competence, flow, and valence. The model has been verified to improve players' overall experience, regardless of their preference, and has potential applications in various fields beyond gaming.
Researchers developed an AI method to predict how well new COVID-19 variants infect human cells and evade antibodies. The system can analyze a million mutated variants, enabling the development of next-generation antibody therapies and vaccines that provide broader protection against potential future variants.
A team led by York University has developed a new technique to keep drinking water safe in refugee settlements using machine learning and ensemble forecasting systems. The approach can predict the probability of residual chlorine remaining in stored water, providing critical information for aid workers to ensure safe drinking water.
Researchers created IGLUE to score ML efficiency in 20 languages, addressing cultural bias and practical implications. The tool aims to improve solutions for visually impaired and reduce performance dropouts outside English-speaking contexts.
Machine learning helps researchers discover how bacterial populations adapt to environmental diversity by analyzing growth curves. The analysis reveals distinct decision-making components for lag, growth, and saturation phases, protecting the population from extinction.
Researchers at UMaine have developed a novel AI-based method for monitoring soil moisture in forests, using wireless sensor networks and machine learning to optimize energy efficiency. This approach could enable more efficient tracking of forest health, reducing costs and increasing reliability.
Researchers developed a new machine-learning method to understand force chains in jammed granular solids. The graph neural network approach can predict the position of force chains with high accuracy, even for complex systems and varying conditions.
Researchers at Drexel University have developed a computer model that uses machine learning algorithms to analyze the genetic sequence of the COVID-19 virus and predict the severity of new variants. The model provides an early warning system for public health officials, allowing them to prepare accordingly.
A large retrospective study found that visceral fat area from fully automated and normalized abdominal CT analysis predicts subsequent myocardial infarction or stroke in Black and White patients. The study suggests that body composition analysis using machine learning could be widely adopted to add prognostic utility to clinical practice.
A Brazilian research team has developed a novel method to sort specialty and standard coffee beans using multispectral imaging and machine learning. The technique, which does not require roasting or human intervention, uses images of the beans at different wavelengths to distinguish between quality levels.
A new method for generating realistic images in driving simulations uses machine learning to improve visual fidelity. This enables better testing of driverless cars and study of driver distraction, ultimately enhancing safety and interaction between humans and AI on the road.
A team of Japanese researchers used reinforcement learning to study fluid mixing during laminar flow, achieving exponentially fast mixing without prior knowledge. The method also enabled effective transfer learning, reducing training time for new mixing problems, and has potential applications across various industries.
Researchers at Arizona State University have developed a machine learning model to predict melting temperatures for any compound. The model enables faster and more accurate calculations of melting points, which is critical for designing high-performance materials in various industries.
Rice University's ROBE Array algorithm slashes the size of DLRM memory structures, allowing training on 100 megabytes of memory and a single GPU. The method matches state-of-the-art DLRM training methods with improved inference efficiency.
A*STAR scientists have developed VarNet, an AI-based method that identifies cancer mutations in tumor samples with high accuracy. This breakthrough allows for personalized treatment strategies and better understanding of cancer.
Researchers at the University of Oldenburg and Fraunhofer IWES collaborate on a new project to develop more accurate wind flow simulations using artificial intelligence. The goal is to reduce computing times and enhance precision, ultimately accelerating innovation in wind turbine design.
ORNL researchers have won seven 2022 R&D 100 Awards for their advancements in materials science, machine learning, and energy storage. DuAlumin-3D, a high-strength aluminum alloy, and Gremlin, an AI system to identify weaknesses in machine learning models, are among the winning technologies.
A team of Brazilian researchers has developed a novel technique using light and artificial intelligence to identify the maturity stages of soybean seeds. The method utilizes chlorophyll fluorescence and machine learning algorithms to classify commercial seeds with high accuracy. This innovation avoids destroying seeds, which are then c...
A study reveals that over 50% of mammal food web links have disappeared due to animal declines, leading to a collapse of global ecosystems. Restoring extinct species to their historic ranges holds great potential to reverse these declines and restore food web complexity.
The special report outlines 12 suboptimal practices in data handling that can predispose machine learning systems to bias. The report presents strategies to mitigate these biases, including careful planning, multidisciplinary teams, and creating heterogeneous training datasets.
Researchers used satellite imagery and machine learning to pinpoint high-priority areas for deforestation action, reducing the target area by 160,000 km². The study reveals that only 37% of the last three years' deforestation rate was covered by official monitoring in the 11 municipalities.
Researchers developed a machine learning algorithm that can predict how different driving patterns affect battery performance, improving safety and reliability. The algorithm uses non-invasive probing to provide a holistic view of battery health, suggesting routes and driving patterns that minimize degradation and charging times.
Researchers at Washington University in St. Louis are evaluating the potential of AI to improve health outcomes and doctor well-being. Chenyang Lu's team has developed novel methods using deep learning to predict physician burnout and surgical outcomes, transforming clinical data into accurate predictions.
A machine learning model developed by Canadian and Slovenian researchers accurately predicted fall risk in lower limb amputees, achieving up to 80% accuracy. This breakthrough has significant implications for the development of smartphone-based fall detection systems.
NTU Singapore has launched the Algorand Centre of Excellence at NTU (ACE@NTU), a new research and education centre focused on developing and advancing blockchain technologies. The centre aims to become the nexus for blockchain education and research in Singapore and the region.
A proof-of-concept study developed three machine learning models to predict posttreatment recurrence in early-stage hepatocellular carcinoma patients. The models achieved high accuracy using imaging data alone, while combining clinical data did not significantly improve performance.
A team of researchers from Osaka University developed an AI algorithm to predict the risk of mortality for trauma patients. They analyzed a large dataset of patient information and blood markers to identify critical factors that guide treatment strategies more precisely.
Engineers developed a machine learning algorithm that can detect and correct wide variety of errors in real time, learning from other machines' experiences. The algorithm enables 'driverless' printers to work for multiple parts, materials, and printing conditions.
A new University of Illinois project aims to improve undergraduate students' ability to estimate their knowledge using artificial intelligence methods. The researchers will utilize machine learning to anticipate student performance and provide personalized feedback to enhance studying strategies.
Researchers used artificial intelligence to demonstrate the correlation between cytoskeleton organisation and nuclear position in eukaryotic cells. The study successfully predicted the presence and location of nuclei in over 8,000 cells with high accuracy, transforming the way scientists approach complex biological systems.
A new method, XTEC, uses machine learning to analyze large volumes of X-ray data, revealing previously hidden structural changes in materials. This accelerates materials discoveries and unlocks new properties of temperature-sensitive devices.
Researchers developed a Flashover Prediction Neural Network (FlashNet) model to forecast deadly fire events, beating other AI-based tools with up to 92.1% accuracy across various building floorplans. The model's performance improved when given real-world data, highlighting its potential for saving firefighter lives.
MIT researchers developed a method to create 3D-printed materials with tunable mechanical properties and embedded sensors, enabling real-time feedback on movement and interaction. The sensing structures use air-filled channels that deform when moved or squeezed, providing accurate feedback for robotics and wearable devices.
Two OU research groups received nearly $2 million in funding to develop new surveillance methods and predict the next avian influenza pandemic. The projects focus on integrating data from multiple sources to detect early signals of disease spread.
Researchers used natural language processing and machine learning to analyze nearly 35,500 death records, identifying the most common substances involved in overdose deaths. The system reduced data processing time by months, allowing for more rapid public health responses and interventions.
Researchers successfully taught microrobots to swim via deep reinforcement learning, allowing them to adapt to changing conditions and perform complex maneuvers. The AI-powered swimmers can navigate toward any target location on their own, showcasing their robust performance in fluid flows and uncontrolled environments.
Researchers from Aarhus University are developing a new approach to turbulence modelling using physics-constrained machine learning to accurately simulate complex turbulent systems. The goal is to reduce computational costs while maintaining accuracy, enabling more efficient designs and predictions in various fields.
Physicists have created a way to simulate quantum entanglement between interacting particles using neural networks and fictitious 'ghost' electrons. This approach enables accurate predictions of molecule behavior, which could lead to breakthroughs in pharmaceutical development and material design.
A new study used artificial intelligence to create the largest global map of insect diversity, highlighting areas with high ant biodiversity and potential conservation priorities. The researchers also found that only a low percentage of these areas are protected.
A new study used satellite data to determine the effect of fuel regulations on sulfur pollution from cargo ships. The research team found significant changes in pollution after regulations went into effect, and their data can contribute to understanding how pollutants interact with clouds and affect global temperatures.
Researchers at MIT have developed a machine-learning system that uses computer vision to monitor the 3D printing process and correct errors in real-time. The system successfully printed objects more accurately than other 3D printing controllers, enabling engineers to incorporate novel materials into their prints with ease.
A team of Chan Zuckerberg Biohub scientists developed a deep-learning method, dubbed
A new study introduces a novel epigenetic predictor, PCBrainAge, that captures aging heterogeneity across multiple brain regions. The tool demonstrates stronger associations with AD dementia and pathologic AD compared to existing age predictors.
Researchers from Aarhus and Berlin have developed an algorithm that can predict how complex molecules will bind to the surface of catalysts. This is achieved through a machine-learning approach inspired by 3D Tetris, allowing computers to quickly identify promising catalysts.
MIT researchers have developed a new type of programmable resistor that enables analog deep learning, which promises faster computation with reduced energy usage. The device can process complex AI tasks like image recognition and natural language processing, paving the way for integration into commercial computing hardware.
Researchers developed a machine learning method to predict material structure, overcoming a key bottleneck in materials science. The approach accurately predicts the structure of materials with five times the efficiency of current methods, paving the way for advances in battery technology and photovoltaics.
The new AI system uses associative learning to detect similarities in datasets, reducing processing time and computational cost. By leveraging optical parallel processing and light signals, the system can identify patterns and associations more efficiently than conventional machine learning algorithms.
A team of researchers at Osaka University has created a machine learning system that can virtually remove buildings from a live view, streaming in real-time on a mobile device. This technology can help accelerate the process of urban renewal based on community agreement, reducing conflicts and delays.
A recent study by Renmin University of China found that a good media reputation consistently reduces the risk of termination for administrative agencies. The research analyzed over 4.95 million articles published between 1949 and 2019 in the People's Daily, an official newspaper of the Chinese Communist Party Central Committee.
Researchers from Georgia State University developed an AI model that can analyze large amounts of brain imaging data to identify novel patterns linked to mental health conditions. The model was trained on datasets of over 10,000 individuals and showed promise in predicting Alzheimer's disease, schizophrenia, and autism risk.