Articles tagged with Machine Learning
Cleveland Clinic researchers successfully tested quantum computing's ability to simulate proton affinity, a fundamental chemical process critical to life. The study used machine learning applications on quantum hardware, achieving higher accuracy than classical computing in predicting proton affinity.
The CONCERTO Project aims to advance the understanding and modelling of the terrestrial carbon cycle, reducing uncertainty in climate predictions. By integrating Earth observation data and innovative models, the project will contribute to improved climate policy and global efforts towards carbon neutrality.
A new study developed an AI model that can predict whether bacteria will become antibiotic-resistant by analyzing their genetic data. The model shows that antibiotic resistance is more easily transmitted between genetically similar bacteria and mainly occurs in wastewater treatment plants and inside the human body.
A new framework developed by MIT researchers allows large language models (LLMs) to break down complex planning problems into manageable parts and find optimal solutions using software optimization tools. The framework achieves an 85% success rate on nine complex challenges, outperforming the best baseline.
A UC Irvine study found that machine learning algorithms using electronic health records can predict two-year dementia risk among American Indian/Alaska Native adults. The researchers identified novel predictors of all-cause dementia, including health service utilization.
A new machine learning-based method analyzes corporate reporting for responsibility and innovation, providing insights into company actions. Essi Nousiainen's research also explores blockchain-related trends in corporate reports.
Researchers developed new AI models, InstaNovo and InstaNovo+, to vastly improve accuracy and discovery in protein science. These models excel in tasks such as de novo peptide sequencing, identifying microorganisms, and discovering novel peptides, with implications for personalized medicine, cancer immunology, and beyond.
Researchers at NCSA and UICOMP have created an improved, automated screening method for anxiety and depression disorders using machine learning and acoustic analysis of verbal fluency data. The new tool offers promise for overcoming barriers to diagnosis and treatment.
Juan Gamella's mini-labs provide a flexible test environment for new AI algorithms, allowing researchers to test their performance beyond simulated data. The mini-labs help identify issues early on, enabling targeted improvements to underlying mathematical assumptions and algorithms.
Göttingen research team develops infomorphic neurons that learn independently and self-organize among neighboring neurons. This allows the smallest unit in the network to control its own learning, enabling novel machine learning approaches and a deeper understanding of brain function.
Crop mapping uses satellite imagery to create accurate crop type maps in various regions. The research team trained machines to recognize crops from satellite images, achieving high accuracy rates. However, models pre-trained on general image datasets performed better than those pre-trained on satellite images.
A new machine learning model improves risk assessment for patients with myelofibrosis seeking a transplant, identifying high-risk patients with a 40% chance of dying within a year. The open-access model provides a practical tool for clinicians to enhance shared decision-making with their patients.
A machine learning algorithm has been developed to diagnose coeliac disease with high accuracy, outperforming human pathologists in over 97 cases. The AI tool has the potential to speed up diagnosis and reduce delays in receiving an accurate diagnosis for patients suffering from this autoimmune disease.
Professor Yousung Jung's team uses LLMs to accurately predict and explain material synthesizability, overcoming limitations of existing methods. This technology is expected to accelerate material design and reduce development time for the semiconductor and secondary battery industries.
The study used AI analysis on over 4,200 clinical reports to identify key factors in autism diagnosis. Criteria related to repetitive movements and special interests were strongly linked to an autism diagnosis, while socialization criteria were not highly specific.
A study published in Biological Psychiatry identified the Shisa7 gene as a key driver of heroin addiction. The research team used machine learning to analyze brain tissue from human opioid users and found that modulating this gene's expression influenced heroin-seeking behavior and cognitive flexibility.
Researchers used large language models to analyze healthcare records of over 1,000 children with suspected autism, finding that current criteria prioritize socialization skills and not enough on interests and natural behaviors. The study suggests revising the criteria to focus more on repetitive behaviors and special interests.
Scientists have identified COVID-derived molecular mimics that trigger autoimmunity, with some associated with type 1 diabetes and multiple sclerosis. The study suggests people with specific genetics may be at higher risk of COVID-induced autoimmunity.
Researchers evaluate traditional precipitation phase partitioning methods and machine learning models, revealing near-freezing temperatures create inherent limitations in distinguishing between rain and snow. Accurate identification is critical for weather forecasting, hydrologic modeling, and climate research.
A new study from Duke University reveals that dopamine levels in the brains of baby birds increase with every effort they make while practicing their songs, regardless of success. This discovery has implications for understanding human learning and neurological disorders.
A recent study refines Siberia's land cover data using machine learning techniques, revealing a high-precision map that enhances climatic predictions. The new dataset improves assessments of carbon flux and ecosystem changes, providing essential insights for climate scientists.
A new approach to AI developed by Texas A&M University engineers mimics the human brain's neural processes, integrating learning and memory in a single system. This 'Super-Turing AI' has the potential to revolutionize the industry by reducing energy consumption and environmental impact.
A study using machine learning models found that people with IBD are at risk for premature death when developing other chronic health conditions earlier in life. Chronic conditions like arthritis, hypertension, and mood disorders were common causes of death among those with IBD.
A new real-time surface PM10 retrieval framework uses interpretable automated machine learning to provide accurate data across China. The framework demonstrates robust generalization and stability, outperforming previous studies in cross-validation and rolling iterative validation experiments.
Researchers at University of Toronto develop a new framework to optimize laser Directed Energy Deposition (AIDED) for higher quality and more reliable metal parts. The AIDED framework uses machine learning to predict optimal process parameters and enhance the accuracy and robustness of finished products.
A University of Cincinnati study found that machine learning models can aid clinicians in treating patients with spreading depolarizations (SDs), a condition that can cause significant brain damage. The algorithm was able to identify SD events with high sensitivity and specificity, detecting many events not identified by human scoring.
Researchers developed an automated MRI processing and machine learning software to diagnose Parkinson’s disease, reducing diagnostic time by up to 96%. The software uses diffusion-weighted MRI and a noninvasive biomarker technique to identify neurodegeneration in the brain, providing more precise diagnoses.
Researchers discovered plastic-degrading enzymes in landfills worldwide, suggesting a promising method for plastic recycling. The study identified 31,989 possible enzymes and predicted protein functions using machine learning and tertiary structure modeling.
Researchers developed a simple, inexpensive tool using robotics and artificial intelligence to analyze dried salt solutions from images. The method increases the accuracy of chemical analysis in scenarios where large samples are difficult to obtain, making it valuable for space exploration, law enforcement, and hospital use.
Researchers at Saarland University are developing leaner, customized AI models and techniques like knowledge distillation to reduce energy consumption. These smaller models enable small and medium-sized businesses to access powerful AI technology without a large technical infrastructure.
This special issue explores AI's applications across various subspecialties, including coronary interventions, structural heart disease, and cardiovascular imaging. It highlights the importance of responsible AI integration and addressing bias in decision support systems.
The collaboration aims to accelerate the development and commercialization of inait's innovative AI technology, using its unique digital brain AI platform. It will focus on joint product development, go-to-market strategies, and co-selling initiatives, initially targeting the finance and robotics sectors.
A machine learning model developed by a team of researchers from Washington University in St. Louis can accurately predict which adolescents with HIV are at risk of nonadherence to antiretroviral therapy. The model incorporates socio-behavioral and economic factors, including economic stability, education, and family structure.
Scientists identify the origin of magnetic moment enhancement in an iridium-doped iron-cobalt alloy through high-throughput X-ray measurements. The study reveals that Ir addition leads to increased electron localization and spin-orbit coupling, resulting in enhanced magnetic moments.
A new study challenges the long-held belief that fear is primarily communicated through facial expressions, suggesting instead that situational context plays a critical role in fear recognition. The research involved analyzing real-life fear reactions in videos and found that facial expressions alone fail to reliably signal fear.
Astronomers have created a detailed 3D map of dust in the Milky Way galaxy, providing new insights into the effects of dust on celestial observations. The map reveals unexpected properties of interstellar dust clouds, including a steepening extinction curve in areas of intermediate density.
Researchers developed a portable system using AI to spot cognitive impairment by measuring subtle differences in motor function. The device accurately identified 83% of participants with mild cognitive impairment (MCI), offering potential for early intervention and improved outcomes.
A Virginia Tech study reveals that machine learning models are inadequate in detecting critical patient conditions, missing 66% of injuries. The research emphasizes the need for more responsive models incorporating medical knowledge to improve healthcare outcomes.
Researchers have developed a novel technique to overcome the spurious correlations problem in AI by eliminating a small portion of the training data that contains hard-to-understand features. This approach improves performance even when conventional techniques are ineffective.
A new study from Tel Aviv University used AI tools to discover that 23.9% of people exercise to improve their appearance, while 18.9% prioritize physical health and 16.9% for mental well-being. The study also identified effective strategies for maintaining physical fitness, including creating exercise habits.
Scientists develop AI-assisted digital twin model that can adapt and control physical machines, like robots or autonomous cars. The model, called Intelligent Acting Digital Twin (IADT), enables digital twins to interact with the physical world, make decisions, and act autonomously.
Researchers developed an AI model that predicts fluidity of videoconferences based on conversational turn-taking, facial actions, and body movements. The model accurately gauged conversations with long gaps in turn-taking as less enjoyable than those with more dynamic exchanges.
Researchers at Yokohama National University have developed a tiny, low-weight robot that can act independently and with ultra-high precision in extreme environments. The Holonomic Beetle 3 (HB-3) integrates piezoelectric actuators with autonomous technology for precise manipulation tasks, addressing industries such as laboratory automa...
Researchers used AI to analyze 126 individuals with generalized anxiety disorder (GAD) and identified key variables predicting recovery and nonrecovery. Higher education level, older age, and positive affect were most important for recovery, while depressed affect and medical visits proved most important for nonrecovery.
Researchers at Technical University of Munich developed a new AI training method that significantly reduces energy consumption. The approach uses probabilities to determine parameters, making the training process 100 times faster while maintaining accuracy comparable to existing procedures.
The research analyzed satellite images and machine learning to detect forest loss in Ukraine during the war. The study found that Ukraine lost over 800 km² of forests in 2022 and 772 km² in 2023, mainly due to fires in war-torn regions.
Insilico Medicine has deployed the first bipedal humanoid AI scientist, Supervisor, to aid in data acquisition and generation for training embodied AI systems. The humanoid will assist with lab tours, telepresence, tracking, and supervision, bridging the gap between human-free fully-autonomous robotics facilities.
A new AI prediction model, UNAFIED, uses machine learning to predict whether a patient has or might develop detectable AFib within two years. The non-invasive approach provides a practical option for proactive screening of patients at elevated risk for AFib.
A new machine learning algorithm can fully characterize systems of merging neutron stars in under a second, compared to traditional methods which take around an hour. This allows for rapid localization of the source and pointing of telescopes towards the merging neutron stars.
A symposium at Lehigh University aims to explore challenges and solutions for improving AI's reliability, inclusivity and ethical impact in healthcare. The event will bring together researchers, clinicians and industry experts to foster cross-disciplinary collaboration and promote people-centered AI-enabled healthcare systems.
The Center for Open Science has launched the Predicting Replicability Challenge to accelerate the development of methods for evaluating research credibility. Teams will compete to develop algorithmic approaches that can accurately predict the likelihood of research claims being successfully replicated, with cash prizes ranging from $3,...
Researchers at the University of Kansas are partnering with regional high schools to train about 500 students in AI coding and microelectronics. The program aims to develop a workforce that can specialize in AI and microelectronics, with a focus on community-centered projects and altruistic goals.
Researchers have identified novel mosquito repellents with high success rates from natural sources, including food and flavoring materials. The team's machine learning-based cheminformatics approach also pinpointed pyrethroid analogs up to 100 times more effective than existing industry standards.
A recent survey highlights the transformative role of machine learning in unlocking the potential of big data, revealing hidden patterns and driving innovation across industries. Real-world case studies demonstrate how ML can revolutionize decision-making and operational efficiency.
Studies show that AI chatbots, when exposed to traumatic content, exhibit increased anxiety levels. However, researchers at the University of Zurich have found that therapeutic prompts can significantly reduce these elevated anxiety levels in language models like ChatGPT.
Researchers at SwRI and U-M have created a new methane flare burner using additive manufacturing and machine learning that eliminates 98% of methane vented during oil production. The burner's design, with a complex nozzle base and impeller, allows for efficient combustion even in challenging crosswind conditions.
A study developed by the US Department of Energy's Thomas Jefferson National Accelerator Facility aims to lower data center costs using machine learning. The Digital Data Center Twin (DIDACT) system detects anomalies and diagnoses their source using AI continual learning, reducing downtime for scientists processing data from experiments.
A new framework combines machine learning and blockchain to boost security, protecting data and computations. The MLOB framework achieves significant security enhancements while maintaining accuracy.
A team of New York University scientists created a computer model that can represent and generate human-like goals by learning from how people create games. The AI model successfully captured the ways humans develop new goals and generated its own playful goals indistinguishable from human-created ones.
Researchers at Cornell University have developed a new method for mapping poverty using national surveys, big data, and machine learning. The approach translates Earth observation data into actionable terms for policymakers, providing more accurate estimates of poverty lines.