Articles tagged with Machine Learning
Researchers find humans can consistently detect water temperature through its sound, even when not consciously aware of it. Machine learning algorithms help classify thermal properties with high accuracy, revealing a complex sensory mapping skill.
New research finds that AI explanations can fuel a perception of fairness without being grounded in accuracy or equity. Humans were more likely to override AI recommendations when explanations highlighted gender rather than task-relevance, but this did not improve decision-making accuracy.
A recent study published in Nutrition and Health suggests that following a Mediterranean diet can lower perceived stress levels. The research, conducted by Binghamton University, found an association between consuming Mediterranean diet components and reduced mental distress.
A novel application of repurposed COVID-19 rapid antigen tests combines lateral flow assays with machine learning to evaluate coagulation status in cardiovascular care. The approach enables clinicians to perform immediate and accurate anticoagulant dosing adjustments using existing resources.
Researchers at the Broad Institute of MIT and Harvard developed a machine-learning approach to design better AAVs for gene therapy. The tool helps engineer capsids with multiple desirable traits, such as targeting specific organs or working in multiple species. About 90% of predicted capsids successfully delivered cargo to human liver ...
Researchers have developed AI systems that use photos or videos to create simulations for training robots in real settings. The RialTo system generates highly accurate simulations of specific environments, while the URDFormer system creates generic simulations quickly and cheaply. These advancements aim to lower costs and increase acce...
Researchers developed motion capture wearables with integrated machine learning, combining elasticity with high computing capabilities. The devices outperformed previous iterations in handling elongation and stretching, and demonstrated accurate performance in tasks like sign-language recognition.
The Ariel Data Challenge 2024 aims to extract faint exoplanetary signals from noisy space telescope observations, with a focus on overcoming noise sources like 'jitter noise'. The competition offers a unique chance for data scientists and AI enthusiasts to contribute to cutting-edge research in exoplanet atmospheres.
Researchers developed a novel clustering technique that considers both basic characteristics and target material properties, enabling the categorization of over 1,000 oxides into material groups. This approach uses machine learning to predict target properties and incorporates basic feature information into the analysis.
A recent study uses machine learning to analyze 950 microbial genomes, identifying 2,194 potential toxins that could be used as new antimicrobials or biotechnological tools. The researchers also discovered four new toxins with enzymatic activities against different molecules.
A new study led by CU Boulder computer scientist Theodora Chaspari found that AI algorithms can be confused by natural variations in speech patterns between different genders and races. This can lead to underdiagnosis or misdiagnosis of mental health concerns like depression.
A research team at Iowa State University has developed artificial intelligence technology that can model and understand complex chemical reactions, including those involved in ammonia production. The technology uses reinforcement learning to identify the optimal reaction pathway, promising to reduce production costs and emissions.
Researchers developed an AI model called GROVER that treats human DNA as a text, learning its rules and context to draw functional information about the DNA sequences. The tool has the potential to unlock the genetic code and advance personalized medicine.
A new research consortium aims to improve the reliability of machine learning systems by using geometric methods to prevent adversarial attacks. The project, GeoMAR, will explore ways to feed neural networks with erroneous data during training to prepare them for real-world scenarios.
A new study found that heteroresistance, a phenomenon where a tiny fraction of bacteria remain resistant to antibiotics, is also present in fungal bloodstream infections in bone marrow transplant patients. The research identified the specific species of fungi responsible and developed a machine learning model to detect this type of inf...
Researchers have introduced a new AI calibration method called Thermometer, which enables efficient calibration of large language models for various tasks. This technique leverages temperature scaling to adjust a model's confidence and can generalize to new tasks without requiring additional labeled data.
Researchers at Mayo Clinic used AI to analyze electroencephalogram (EEG) tests, identifying subtle brain wave patterns characteristic of cognitive problems like Alzheimer's disease. This technology has the potential to provide healthcare professionals with a more accessible tool for early diagnosis in communities without easy access to...
A new mapping tool from North Carolina State University uses machine learning and open-source satellite imagery to model flooding in urban environments. The model creates maps that predict urban area flooding, helping officials make informed choices about flood resiliency and prevention resources.
A recent study published in Frontiers in Immunology highlights the crucial role of tissue-resident memory T cells in non-small cell lung cancer. The research found that these cells can significantly impact patient outcomes and guide personalized treatment strategies, particularly those involving immunotherapy.
Researchers found that adults' faces can be matched to their names at above-chance levels, but not in children. Machine learning algorithms revealed greater similarity between adult faces sharing the same name. The study suggests a 'self-fulfilling prophecy,' where social expectations shape physical appearance over time.
A collaboration of scientists, ethicists, and researchers aims to create a consensus definition for diverse intelligent systems, including AI, LLMs, and biological intelligences. The proposed approach will provide a common language for recognizing, predicting, manipulating, and building cognitive systems.
A new device called computational random-access memory (CRAM) reduces energy consumption for artificial intelligence applications. CRAM enables true computation in and by memory, breaking down the bottleneck in traditional computing architecture.
A new study published in PLOS Digital Health found that machine learning models can reliably predict the disability progression of multiple sclerosis. The models were trained on data from 15,240 adults with at least three years of MS history and had an average accuracy of 0.71 ± 0.01.
Researchers at UVA School of Engineering and Applied Science developed artificial compound eyes that mimic praying mantis vision, offering improved depth perception and reduced power consumption by over 400 times compared to traditional systems.
A new AI model can identify certain stages of ductal carcinoma in situ (DCIS), a type of pre-invasive breast cancer, that are likely to progress to invasive cancer. The model uses imaging and machine learning to analyze tissue samples and determine the stage of DCIS based on cell arrangement and organization.
A study by Stanford University researchers reveals a previously unknown relationship between Sahara dust plumes and hurricane rainfall. Thicker dust plumes can lead to heavier rainfall, while thinner ones may suppress hurricane formation over the ocean.
A new method combines machine vision, deep learning, and nonlinear conversion to increase information capacity in machine learning-based ultra-accurate information networks. The system can achieve low bit error rates and high data recognition accuracy even with complex light fields.
High levels of formaldehyde and aldehydes are emitted from new cars on hot summer days, exceeding national safety limits. A machine learning model has been developed to predict in-cabin concentrations of volatile organic compounds, potentially informing exposure assessments and intelligent car systems.
Researchers found that large language models perform poorly in high-stakes situations despite being better than smaller models, due to misalignment with human generalization function. Human generalization, which involves forming beliefs about others' abilities, plays a significant role in LLM performance and deployment.
A machine learning algorithm was trained to predict individuals with functional neurological disorder (FND) by analyzing their brain structure. The algorithm achieved significant above-chance accuracy in classifying FND participants against healthy controls and psychiatric samples, highlighting the importance of considering both brain ...
The University of Leicester is developing a method to shrink artificial intelligence algorithms for smarter spacecraft. The REALM project aims to demonstrate streamlined machine learning algorithms suitable for limited spacecraft power and computing performance.
A new automated system of monitoring and classifying persistent vibrations at active volcanoes can eliminate hours of manual effort. The system, based on machine learning, documents volcanic tremor, a continuous seismic signal indicating underground movement of magma or gas.
A study found that large language models, despite accuracy in medical exams, fail to consistently request necessary examinations and often deviate from treatment guidelines. In comparison to human doctors, AI diagnoses achieved lower accuracy rates, highlighting concerns about their suitability for everyday clinical practice.
Pusan National University researchers introduced FLIT-SHAP, an explainable machine learning approach that breaks down pollutant effects in mixtures. The tool revealed significant synergistic and antagonistic interactions, challenging current approaches to regulating pollutants.
Researchers at USC developed a new method to accurately predict wildfire spread using satellite data and artificial intelligence. The model offers a potential breakthrough in wildfire management and emergency response, providing more precise and timely data for firefighters and evacuation teams battling wildfires.
Researchers at Duke University have broken through the performance wall of adaptive radar systems using convolutional neural networks, paralleling computer vision. They've released a large open-source dataset for other AI researchers to build upon their work, aiming to tackle industry needs like object detection and tracking.
A new study uses machine learning to analyze the genetic diversity of two amphibian species, finding that different processes shaped their evolution. The research suggests that population demographic events and contemporary landscape factors played a significant role in shaping the genetic variation of these species.
Researchers have developed PrISMa, a novel platform that seamlessly connects materials science, process design, techno-economics, and life-cycle assessment to identify effective and sustainable carbon capture solutions. The platform has been tested on over 60 real-world case studies, providing valuable insights for stakeholders.
The study uses machine learning framework to estimate global rooftop area growth, projecting a 20-52% increase by 2050. Rooftop solar power holds significant potential for emerging economies, driving sustainable development and prosperity.
Researchers used machine learning to create highly detailed maps of individual trees, providing valuable information for conservation efforts and ecological projects. The algorithm achieved high accuracy in classifying common tree species, with strengths shown in areas with open space and lower species diversity.
A new model, PAG-STAN, predicts short-term origin-destination demand in urban rail transit systems with remarkable precision. The model improves interpretability and enhances training efficiency through a masked physics-guided loss function.
UCF researchers George Atia and Yue Wang received a $1.2 million DARPA grant to develop AI-based technologies that can help autonomous systems adapt to unknown variables and overcome simulation-to-real gap issues.
Researchers at Weill Cornell Medicine used machine learning to define three subtypes of Parkinson’s disease, each with distinct driver genes and molecular mechanisms. These subtypes may suggest customized treatment strategies for patients, potentially targeting specific drugs such as metformin to slow down progression.
Scientists have developed a new technique that leverages X-ray photon correlation spectroscopy, artificial intelligence, and machine learning to create unique 'fingerprints' of materials. These fingerprints can be analyzed by neural networks to yield new information about material behavior under stress and relaxation.
A new AI technique uses physics-informed machine learning to create high-fidelity atmospheric transmission profiles without prior knowledge, enabling accurate remote sensing data correction. This approach enhances remote sensing capabilities for tasks like target detection, requiring limited data and computational resources.
Researchers identified six clinical subtypes in older adults starting long-term care in Japan, including cardiac disease, respiratory disease/cancer, and insulin-dependent diabetes, which incur higher mortality risks and worsen care needs. These findings can inform optimal interventions for each subtype and influence healthcare policy.
Researchers at Max Planck Institute propose a new method for implementing neural networks with optical systems, which could lead to faster and more energy-efficient alternatives. The approach allows for parallel computations in high speeds limited by the speed of light, and can be applied to various physically different systems.
The Grid Event Signature Library provides an online collection of anonymized datasets containing waveforms, enabling utilities and research institutions to understand the increasingly complex grid. Machine learning can be trained to recognize waveforms that provide early warnings of equipment malfunction, preventing blackouts and damage.
A UCLA-led team created a machine-learning model that can accurately predict short-term CRRT survival, providing a data-driven tool for clinical decision-making. The study aims to improve patient outcomes and resource use by serving as a basis for future clinical trials.
A Danish study of over 100,000 children used machine learning to identify at-risk kids earlier, potentially improving child maltreatment detection and social worker decision-making. The findings suggest that predictive risk models could enhance outcomes for these vulnerable children.
Researchers developed a machine learning approach to identify potential subtypes in diseases, significantly enhancing disease classification and treatment strategies. The model uncovered 515 previously unannotated disease subtypes.
Researchers at University of Cambridge developed machine-learning tool to identify drug-resistant Salmonella Typhimurium bacteria from microscopy images. The algorithm correctly predicted resistance or susceptibility without culturing the bacteria, reducing diagnosis time from days to hours.
Researchers create an analog system that can learn complex tasks like XOR relationships and nonlinear regression, using local learning rules without centralized processor. The system is fast, low-power, and scalable, offering a unique opportunity for studying emergent learning.
A study published in JMIR Medical Informatics found that machine learning can accurately classify patients into differing levels of opioid use disorder (OUD) risk, demonstrating substantial agreement with clinicians' reviews. The research suggests that this technology can enhance personalized and safer care for patients early in opioid...
The college will develop a Knowledge-enhanced Antidepressant Recommendation Dialogue System (KARDS) to engage users in a conversation to identify the appropriate antidepressant medication. The AI chatbot aims to address medication needs of Black and African Americans with depression, a gap in current management.
A new machine learning-based method uses 3D structure of protein backbone with large language models to predict molecular changes that lead to better antibody drugs. The approach resulted in a 25-fold improvement against a virus, outperforming traditional methods that rely on generating huge amounts of data about protein sequences.
Researchers at UAB have developed a method to assess cardiac dynamics in fruit flies using deep learning and high-speed video microscopy. The study uses this approach to analyze the effects of aging and dilated cardiomyopathy on heart function, with potential applications for human cardiovascular research.
Scientists have developed a machine learning program that can identify blobs of plasma in outer space known as plasmoids. The program will analyze data from NASA's Magnetospheric Multiscale (MMS) mission to better understand magnetic reconnection and its effects on the electrical grid.
Researchers developed a one-dimensional convolutional neural network (1D CNN) to compensate for errors due to sample location variations. The model achieved high accuracy, reducing mean absolute error to 0.695% and mean squared error to 0.876%.
The Mount Sinai researchers have developed a comprehensive epidemiological dataset for youth diabetes and prediabetes research, derived from NHANES data collected from 1999 to 2018. The newly launched POND portal aims to facilitate an understanding of factors that may influence youth diabetes risk.