Articles tagged with Machine Learning
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.
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.
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 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 ...
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.
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 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.
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.
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.
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 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.
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.
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 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.
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.
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 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.
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.
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.
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 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.
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.
Researchers used AI to analyze over 16,000 butterfly images, finding both males and females contribute to diversity among species. The study resolves a century-old debate between Charles Darwin and Alfred Russel Wallace on the role of natural selection in female evolution.
Researchers are working on improving the quality of high frequency wireless networks. Dr. Murat Yuksel is hoping to realize the dream of unimpeded communication at distances near and far. He is developing a smart wireless network system using machine learning, which can fine-tune the networks' efficacy.
Researchers found that AI models that analyze medical images can predict patient demographics with high accuracy but struggle to diagnose patients from diverse backgrounds. The models use demographic shortcuts, leading to incorrect results for women, Black people, and other groups.
Researchers developed a machine learning estimator to classify charge states in quantum dots, enabling automatic tuning of qubits. The estimator achieved high accuracy with visualizations revealing decision-making patterns, paving the way for scaling up quantum computers.
Researchers developed an AI model that accurately predicts metal yield strength by combining physical theory with machine learning. The model outperforms traditional methods, which often rely on extensive experimentation.
Researchers used AI to map slush on Antarctic ice shelves and found that 57% of all meltwater is held in slush, with a significant impact on ice shelf stability and sea level rise. This discovery could lead to more accurate predictions of ice sheet melting and collapse.
A recent study by Prof. Martin Bichler suggests that dividing Germany into several price zones may not reduce total power costs as expected. In contrast, nodal pricing shows promise in reducing costs by up to 9% due to efficient resource allocation and reduced re-dispatch measures.
Researchers from Florida Atlantic University developed a novel approach using wearable sensors and machine learning to assess balance. The method achieved high accuracy and strong correlation with ground truth balance scores, suggesting it is effective and reliable in estimating balance.
A Chinese research team introduced a novel two-stage framework using stacked transformers for multimodal sentiment analysis, improving the analysis of emotions expressed through modality combinations. The framework was tested on three open datasets and performed better than or as well as benchmark models.
Researchers at Boston University developed an AI model that analyzes speech patterns to predict the likelihood of Alzheimer's disease in patients with mild cognitive impairment. The model achieved an accuracy rate of 78.5% and could potentially revolutionize dementia screening, making it more accessible and efficient.
Researchers developed machine learning models predicting upper secondary education dropout from kindergarten age, using a 13-year longitudinal dataset. The study marks an advancement in early automatic classification, potentially leading to transformative changes in educational systems and policies.
Researchers at FSU used machine learning to analyze patterns in dried salt solution drops, identifying the chemical composition of different salts with accuracy. The tool has potential applications in lab safety testing, rapid screening for suspected drugs and low-cost blood analysis.
University of Texas at Dallas researchers develop AI model that can automatically reroute electricity in milliseconds to prevent power outages. The system uses machine learning to map complex relationships between entities in a power distribution network, enabling faster response times than human-controlled processes.
A new model developed by Flatiron Institute researchers proposes that individual neurons exert more control over their surroundings, which could be replicated in artificial neural networks. This updated model treats neurons as tiny 'controllers' and may lead to better AI performance and efficiency.
A new system named SQUID, a computational tool created by Cold Spring Harbor Laboratory scientists, helps interpret how AI models analyze the genome. It reduces background noise and leads to more accurate predictions about genetic mutations.
Scientists at UVA and Toyota Research Institute create language representations of driving behavior to enable robots to associate words with environmental interactions. This allows cars to provide guidance and adjust speed in challenging situations, improving safety and usability.
Researchers at Cold Spring Harbor Laboratory designed a new way for AI algorithms to move and process data more efficiently, inspired by the human brain. This design allows individual AI neurons to receive feedback and adjust on the fly, processing data in real-time.
Research suggests that large-language models could play a role in managing the energy grid, particularly in emergency response, crew assignments, and wildfire preparedness. However, significant challenges remain, including data availability, safety guardrails, and reliability, which must be addressed to ensure safe deployment.
Researchers at Bar-Ilan University have discovered a new scaling law that governs how artificial neural networks handle an increasing number of categories for identification. This law reveals how the identification error rate increases with the number of required recognizable objects, impacting AI latency and efficiency.
PSICHIC uses sequence data and AI to decode protein-molecule interactions with state-of-the-art accuracy, eliminating costly processes like 3D structures. The tool effectively screens new drug candidates and performs selectivity profiling, offering a more efficient and reliable approach to drug discovery.
A new machine-learning model using serum fusion-gene levels predicts HCC with an accuracy of 83-91%, significantly improving upon current biomarkers like serum alpha-fetal protein. This breakthrough tool may help identify patients at risk and monitor cancer recurrence, leading to improved survival rates.
A new study on learning has provided insights into the balance between habitual and goal-directed behaviors, with implications for AI development. The research suggests that a balance between these two types of behavior is necessary for efficient and adaptable decision-making in AI systems.
Researchers used machine learning to integrate high-throughput transcriptomic, proteomic, metabolomic, and lipidomic profiles to identify four distinct molecular profiles of Alzheimer's Disease. These profiles were associated with varying levels of cognitive function and neuropathological features.