Articles tagged with Machine Learning
The new tool will enhance the DLA's supply chain management capabilities, reduce operational disruptions, and bolster readiness. Quantum Research Sciences' technology will provide predictive capabilities and automate obsolescence management processes.
A groundbreaking AI model developed by researchers at Emory University accurately predicts the likelihood of blood transfusion in non-traumatic ICU patients, addressing longstanding challenges in predicting transfusion needs. The model achieved exceptional performance metrics, including an AUROC of 0.97 and an accuracy rate of 0.93.
Researchers developed a machine learning model using hyperspectral imaging to assess pre-harvest tomato quality, predicting key parameters like weight, firmness, and lycopene content. The approach enables real-time monitoring of fruit development, improving crop quality and reducing waste.
An English literature graduate has developed a new method for large language models to understand and analyze short text chunks, such as those on social media profiles. The method successfully grouped nearly 40,000 Twitter user biographies from accounts tweeting about US President Donald Trump into 10 categories.
Professors Scott Acton and Mathews Jacob of UVA's Charles L. Brown Department of Electrical and Computer Engineering were named to the IEEE Signal Processing Society's 2025 Class of Distinguished Lecturers for their groundbreaking work in signal processing, artificial intelligence, and medical imaging.
A new study assesses the historical knowledge of AI chatbots like ChatGPT-4 and finds they struggle with nuanced, PhD-level inquiry. The models performed best on legal systems and social complexity but struggled with topics such as discrimination and social mobility.
Zhu has made groundbreaking contributions to earthquake monitoring using deep-learning models like PhaseNet, DeepDenoiser, and GaMMA. His work has led to breakthroughs in seismic phase picking, denoising, and phase association.
A brain-computer interface has enabled a person with tetraplegia to control a virtual quadcopter by thinking about moving their unresponsive fingers. This technology provides unprecedented control, allowing the user to maneuver through a virtual obstacle course and potentially enabling remote work and social interactions.
Researchers at University of Birmingham have discovered three new protein biomarkers TFF3, LCN2, and CEACAM5 that show strong predictive potential for colorectal cancer. These biomarkers are linked to cell adhesion and inflammation, processes closely associated with cancer development.
Researchers at NIMS developed a next-generation AI device leveraging ion-controlled spin wave interference in magnetic materials, outperforming conventional devices by up to 10 times. The technology enables energy-efficient computations with minimal degradation when miniaturized, opening doors for various industrial applications.
The study utilizes infrared spectroscopy and a machine-learned protocol to map spectroscopic fingerprints to atomistic structures. The authors demonstrate the accuracy of their network in predicting local atomistic structures and energetic variations, enabling the tracking of dynamic C–C coupling on Cu surfaces.
Researchers at TU Graz are developing a self-learning AI system to position individual molecules quickly and autonomously, enabling the construction of highly complex molecular structures. The goal is to build logic circuits in the nanometre range using quantum corrals made from complex-shaped molecules.
Researchers introduced a novel approach to enhance reservoir computing, incorporating a generalized readout that offers improved accuracy and robustness compared to conventional methods. The new method uses a nonlinear combination of reservoir variables to uncover deeper patterns in input data.
Researchers have found evidence of songbirds forming social connections and potentially exchanging information about their migration routes through vocalizations. The study suggests that social cues play a significant role in shaping migration behaviors, particularly for young birds learning from observing other birds.
A new automated job hazard analysis tool promises to significantly reduce workplace accidents and improve safety in the construction industry. The University of South Australia's research team has built a 'knowledge graph' to predict hazards, which can be analysed in real-time to identify potential risks and control measures.
A new study by researchers at the University of Minnesota found that the benefits of corn-soybean crop rotation are extremely sensitive to climate change. The study suggests that increasing crop rotation can improve overall yields and highlight its potential as a climate adaptation strategy in the US Midwest.
A new study uses machine learning models to identify women experiencing severe subjective cognitive decline during the menopause transition, associated with aging, hypertension, obesity, and depression. This predictive model allows for early intervention to protect cognitive health, a novel guidance for interventions designed to preser...
DNNs have an inbuilt 'Occam's razor,' favouring simpler solutions that fit training data. This bias helps them generalize well on simple patterns but may struggle with complex data, aligning with real-world data characteristics.
Researchers developed an AI model to detect brain cancer spread in surrounding tissue using MRI scans, showing 85-per-cent accuracy. This non-surgical method offers insights into patients' cancer without aggressive surgery, potentially improving treatment and survival.
Temperate savannas in eastern China have been mapped for the first time, revealing their geographical distribution and extent. The research provides precise information on the spatial characteristics of these ecosystems, supporting conservation and utilization efforts.
Researchers developed a machine learning model to identify defective products in semi-solid die casting by analyzing injection pressure. The model achieved high accuracy and revealed mechanisms behind defect formation, providing a foundation for optimizing manufacturing processes.
A new AI system analyzed electronic health records of long-COVID patients to identify four sub-populations with specific needs, including those with asthma or mental health conditions. The study found that these sub-populations require more specialized care and pointed toward updated profiles for hospitals to better address their needs.
The BiliSG app uses machine learning to analyze skin color and predict bilirubin levels in newborns, offering a convenient alternative to traditional testing methods. With 100% sensitivity, the app has shown promising results in monitoring neonatal jaundice and reducing the risk of brain damage.
Researchers at the University of New Hampshire developed an AI-powered algorithm to categorize over 706 million aurora images from NASA's THEMIS data set. This labeled database can help scientists better understand and forecast geomagnetic storms that disrupt vital communications and security infrastructure.
A pioneering new mathematical model developed by Oxford researchers could help assess the risks posed by AI and protect people's privacy. The method provides a robust scientific framework for evaluating identification techniques, including browser fingerprinting.
A series of small earthquakes in Surrey in 2018 and 2019 may have been triggered by oil extraction from a nearby well, according to a new study. The research used mathematical modeling to predict the frequency of earthquakes based on oil extraction timing and volume, finding a rough match with observed seismic activity.
TabPFN learns causal relationships from synthetic data, making correct predictions more likely than existing algorithms. The model requires fewer resources and data, making it ideal for small companies and teams.
Researchers developed an AI-based method to analyze CEO depression from vocal acoustic features in conference calls. The study found that CEOs with higher levels of depression tend to receive larger compensation packages and are more responsive to negative feedback.
Positive Phase 1 trial results suggest ISM5411's gut-restrictive property and favorable pharmacokinetic profile, validating its potential for treating inflammatory bowel disease. Insilico Medicine expects to initiate a Phase 2 proof-of-concept study in active ulcerative colitis patients.
A new AI model developed by researchers at Penn State College of Medicine can predict the progression of autoimmune disease among those with preclinical symptoms up to 1,000% more accurately. The GPS model integrates data from large genetic studies and electronic health records to identify individuals at high risk of disease progression.
A new method called Annotatability helps identify mismatches in cell annotations and better characterizes biological data structures. This approach enables more precise downstream analysis of biological signals, capturing cellular communities associated with target signals.
A team of researchers at the Indian Institute of Science (IISc) has developed a machine learning-based approach to predict material properties using limited data. By leveraging transfer learning and multi-property pre-training, they were able to improve model performance and extend its applicability to new materials.
A team of researchers developed a machine learning framework to streamline the discovery of high-performance ionic thermoelectric materials. The approach predicted Seebeck coefficients with high accuracy and identified critical molecular descriptors influencing material performance.
A new study uses machine learning to reduce time needed for calculating screening parameters in Koopmans functionals, enabling faster predictions of material spectral properties. Researchers trained a simple model using modest data and achieved accurate results, paving the way for studying temperature-dependent spectral properties.
Researchers developed a novel AI method using Disentangled Variational Autoencoder (D-VAE) for inverse materials design, making the process data-efficient and interpretable. The method was tested on high-entropy alloys, producing clear results that highlight influencing material features.
Current energy-hungry transformer-based systems contrast with Turing's idea of machines that develop intelligence naturally, like human children. AI systems can now perform tasks exclusive to human intellect, such as generating coherent text and discussing abstract ideas, but with limitations on sustainability and societal impact
Robert Johansson's Machine Psychology concept combines adaptive artificial intelligence with psychological learning principles to create a more intelligent AI system. The goal is to implement human-like intelligence in machines, enabling them to learn from experiences and apply knowledge across various situations.
Researchers create SciAgents framework to autonomously generate and evaluate promising research hypotheses in biologically inspired materials. The framework uses graph reasoning methods to organize relationships between scientific concepts, mimicking biological systems.
The Buck Institute and Phenome Health have been awarded up to $52M by ARPA-H to develop a groundbreaking research project that aims to predict and prevent diseases using advanced analytics and AI. The project, known as PATH, will utilize machine learning and digital wearables to create personalized recommendations for healthy aging.
Researchers developed two machine learning algorithms to determine whisky origin and identify strongest aromas, outperforming human experts. The algorithms accurately classified whiskies into American or Scotch categories with over 90% accuracy.
The study found that individuals with accelerated biological ageing had poorer health outcomes, while those with decelerated ageing had weaker links to good health. Metabolomic ageing clocks have the potential to identify early signs of declining health and inform preventative strategies.
A new study by UCL researchers found that AI systems amplify human biases, leading to a snowball effect where small initial biases increase the risk of human error. The researchers demonstrated real-world consequences, including overestimating white men's likelihood of holding high-status jobs and underestimating women's performance.
Physicists from the University of Konstanz have created a solution using microrobots and counterfactual rewards to ensure fair distribution of load in collective tasks. The approach enhances efficiency and provides insights into improving teamwork in various collective systems.
Researchers at KAIST developed a new method to learn without weight transport, enabling faster and more accurate learning. By pre-training with random noise, the team showed that neural networks can achieve high learning efficiency and solve the weight transport problem.
Researchers developed a unique numerical decision-making framework for solar panel protection against extreme weather conditions. The framework treats individual panels as independent decision-makers, identifying creative solutions to reduce stress and minimize damage during high-wind events.
Researchers developed a new benchmark for health care using reinforcement learning, which shows promise in managing chronic or psychiatric diseases. However, current methods are data-hungry and fail to perform accurately when tested on real-world data.
Researchers at MIT have developed Boltz-1, an open-source AI model that achieves state-of-the-art performance in predicting biomolecular structures. The model surpasses AlphaFold3, which is limited to academic research and commercial use, by incorporating new algorithms and improving prediction efficiency.
A new tool developed by Penn State researchers uses computer vision and artificial intelligence to analyze placenta images, detecting abnormalities and risks such as neonatal sepsis. The PlacentaCLIP+ model has the potential to transform neonatal and maternal care in low- and high-resource settings.
Researchers achieved near-perfect accuracy in detecting Parkinson's disease by analyzing brain responses to emotional situations. The study identified distinct patterns in how patients processed emotions, enabling accurate differentiation between patients and healthy controls.
A multimodal machine learning model outperformed clinical and genomic models in predicting outcomes for HR-positive, HER2-negative breast cancer patients receiving CDK4/6 inhibitor combinations. The model integrated clinical and genomic factors to identify high-risk patients with a 6.5-fold difference in hazard ratio.
Researchers at Graz University of Technology developed a new database to improve speech recognition of Austrian German using speech data from 38 speakers. They found that traditional HMM-based systems are more robust for short sentences and dialectal language, while transformer-based models excel with longer sentences and context.
Insilico Medicine has received its first clinical milestone payment of $10 million from Exelixis for XL309, a selective USP1 inhibitor discovered with the company's AI platform. The drug is being developed for advanced solid tumors and has shown efficacy in preclinical studies.
A team of researchers has developed a novel technique to steal artificial intelligence (AI) models by monitoring electromagnetic signals. The method allows attackers to recreate the high-level features of an AI model with 99.91% accuracy, potentially undermining intellectual property rights and exposing sensitive data.
Researchers developed a deep learning model that classifies pancreatic cancer into molecular subtypes using histopathology images, achieving high accuracy and rapid turnaround time. The AI tool has the potential to improve patient outcomes by enabling timely and tailored treatment strategies.
Researchers at Drexel University developed an AI tool using large language models to identify and suggest alternative words that stigmatize people with substance use disorder. This framework aims to educate users and preserve supportive spaces in online forums.
A new technique identifies and removes specific points in a training dataset that contribute most to a model's failures on minority subgroups. This approach maintains the overall accuracy of the model while improving its performance regarding underrepresented groups.
Silvia Blemker, a University of Virginia biomedical engineer, has been elected Fellow of the National Academy of Inventors (NAI) for her work on muscle health. Her patented technology, Image-based Identification of Muscle Abnormalities, uses advanced imaging and analytics to provide detailed insights into muscle health.
Researchers at Pusan National University developed a hybrid model to predict metal wear in magnesium alloys, enabling safer, lighter designs. The model combines machine learning and physics to improve fatigue life prediction, offering greater predictive reliability for enhanced safety and longevity.
A groundbreaking AI model called NitroFusion creates images in seconds using modest hardware, eliminating the need for large computing resources. The open-source technology enables creative professionals and individuals to produce high-quality images affordably.
Researchers from Bar-Ilan University discover that classifying objects together through Multi-Label Classification can yield better results than detecting individual objects. This new method allows networks to learn correlations between object combinations, making them more recognizable in real-life applications such as autonomous vehi...