Articles tagged with Machine Learning
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.
A comprehensive analysis of Bluesky reveals its structure, polarization, and political leanings. The platform's users, particularly those from X (formerly Twitter), exhibit left-leaning tendencies, as evident in the majority of shared links and content. The study also highlights how Bluesky diverges on the Israel-Palestine conflict topic.
A new machine-learning approach to mapping poverty has been developed by Cornell University researchers, aiming to help policymakers and NGOs better identify the poorest populations in poor countries. The approach uses national surveys and Earth observation data to create actionable terms for policymakers, outperforming previous methods.
A new AI-powered tool helps predict traumatic brain injury outcomes based on documented assault scenarios, providing an evidence-based approach to improve investigation accuracy. The model achieved remarkable prediction accuracy for TBI-related injuries, integrating mechanical biophysical data with forensic details.
Researchers leveraged a large language model to classify artwork types without pre-prepared training data, achieving high accuracy. This approach enables performance comparable to conventional machine learning methods while reducing human effort and time required for data organization.
Researchers at Columbia University developed a way for robots to autonomously model their own 3D shapes using a single camera, enabling them to understand and adapt to their movements. This breakthrough allows robots to overcome damage to their bodies, making them more reliable and resilient for various applications.
Researchers developed ItpCtrl-AI, a transparent AI framework that reads chest X-rays like a radiologist, providing accurate diagnoses and increasing trust in medical technology. The framework uses a gaze heat map to show the computer where to search for abnormalities and what section of the image requires less attention.
Researchers from Aarhus University found a machine-learning algorithm that can predict patients with high risk of developing schizophrenia or bipolar disorder within the next five years. The study analyzed electronic health record data and identified key factors, including words describing symptoms in clinical notes.
Ben Jones, a UTA physicist, has been recognized for his contributions to developing advanced instruments used in particle physics research. His work focuses on uncovering the origin of neutrino mass and sheds light on fundamental physics at extremely small scales.
The partnership aims to develop AI-driven tools to improve investment decisions, enhance system stability through intelligent forecasting, and deploy smart optimisation algorithms. The collaboration seeks to address key challenges in smart energy storage by integrating Trinasolar's expertise with NTU's leading research.
Ebru Demir aims to study how groups of AI-driven microswimmers move in biological fluids for potential applications in drug delivery, fertility treatments, and other medical fields. Her research combines artificial microswimmers with machine learning to uncover the underlying physics governing their movement.
A study published in Science Bulletin has created a comprehensive tree density map of China, estimating 142.6 billion trees across the country, with varying densities by region and ecosystem type.
Researchers at Cornell University have developed a new method using underwater microphones and machine learning to estimate the population of North Atlantic right whales. The study demonstrates how this method can aid in the conservation of these critically endangered species by providing real-time monitoring capabilities. By analyzing...
A recent study has identified that the neuronal subtype responds best to immunotherapy, while other subtypes exhibit lower response rates. The researchers developed a machine-learning algorithm using large public data sets to predict treatment response based on tumor mutational burden and immune cell infiltration.
Researchers used machine learning to predict diagnostic transition to schizophrenia and bipolar disorder, with schizophrenia being notably easier to forecast. By analyzing routine clinical data, the study suggests a possible approach for early intervention and personalized treatment plans.
Researchers at the University of Navarra present a new prediction methodology to improve fairness and reliability of AI models used in critical decision-making. The methodology optimizes parameters of reliable machine learning models, reducing inequalities related to sensitive attributes.
Researchers are using machine learning to enable autonomous control of particle accelerators, opening up new possibilities for commissioning and operating high-power accelerators. The technology has been successfully applied to the CAFe2 superconducting segment, achieving global trajectory adaptive control.
Researchers created printed fabric sensors that can detect tiny skin movements for sleep disorder monitoring. The smart pyjamas achieved an accuracy of 98.6% in identifying six different sleep states, including nasal and mouth breathing, snoring, and teeth grinding.
Researchers have developed software tools that analyze student behavior in real-time, assessing how well students develop and use collaborative problem-solving skills. The tools can modify the game to improve learning outcomes by identifying patterns of behavior associated with specific learning outcomes.
Researchers developed an AI system to control airflow on wing surfaces, reducing turbulent separation bubbles by 9%. The system uses deep reinforcement learning to adapt to airflow dynamics and enhance the effectiveness of experimental technologies.
A new study from the University of South Australia found that most people trust AI in situations where the stakes are low, such as music suggestions. However, those with poor statistical literacy or little familiarity with AI were just as likely to trust algorithms for trivial choices as they were for critical decisions. The study also...
A new study by the University of Birmingham found that everyday stress, loneliness, and childhood trauma are significant risk factors for dissociation in teenagers and young adults. The research identified four key risk factors, with everyday stress being the most significant.
Researchers found a remarkable X-ray flash in archived Chandra Observatory data, hinting at possible explanations: X-ray burst, magnetar flare, or new cosmic event. The discovery showcases the potential of AI for scientific breakthroughs in astronomical archives.
Researchers used a remote seismic station to detect and characterize an earthquake swarm in American Samoa, producing a new catalog of events. The technique employed, combining single-station data with deep-learning models, can be useful in sparse monitoring regions.