Articles tagged with Machine Learning
Researchers at Drexel University developed a machine learning model to predict Philadelphia's future energy use based on zoning decisions and building characteristics. The model uses two machine learning programs to tease out patterns from massive datasets and make projections about future energy consumption.
The National Science Foundation has awarded Columbia University a $20 million grant to establish the AI Institute for Artificial and Natural Intelligence (ARNI), an interdisciplinary center focused on connecting AI systems to brain research. ARNI will bring together top researchers from across the U.S. to advance neuroscience, cognitiv...
Researchers at EPFL have computationally designed novel protein binders that attach seamlessly to key targets, including the SARS-CoV-2 spike protein, using deep learning-generated 'fingerprints' to characterize millions of protein fragments. This method demonstrates therapeutic potential for rapidly designing protein-based therapeutics.
Nearly half of COVID-19 patients develop secondary bacterial pneumonia, which can lead to death. Machine learning analysis found that this complication offsets the mortality rate from the viral infection itself.
The UMD-led TRAILS institute will develop AI technologies that promote trust and mitigate risks through broader participation, new technology development, and informed governance. The institute aims to create AI systems that align with values and interests of diverse groups, leading to increased transparency, reliability, and accountab...
The CEBRA algorithm captures brain dynamics with high accuracy, allowing for the prediction of complex information such as visual stimuli and arm movements in primates. By analyzing brain signals alone, researchers can decode hidden structure in data, enabling new insights into how the brain processes information.
A new AI-powered model, PLPNet, has been developed to accurately diagnose tomato leaf diseases from digital photographs. The model achieved an accuracy of 94.5% while maintaining a speed of over 25 frames per second.
A deep learning model has been developed to classify cancer cells into distinct types, enabling accurate prediction of metastatic potential. The tool achieves high accuracy and is simple to use, making it a promising solution for medical practitioners.
A deep neural network developed by researchers at the University of California - Santa Cruz has been shown to accurately classify particle signals with 99.8% accuracy in real-time. The system can identify weak or noisy signals and pinpoint their source, making it suitable for point-of-care applications.
A study by University of Maryland School of Medicine found that AI algorithms used in medical imaging lack demographic data and bias evaluations. This can exacerbate existing health disparities worldwide. Researchers encourage future competitions to prioritize fairness among different groups.
A new machine learning model can automatically translate Akkadian text written in cuneiform into English, with the first version using Latin transliteration achieving satisfactory results. The program is effective for translating short sentences and can be used as part of a human-machine collaboration to correct and refine its output.
Researchers developed GAME-Net, a graph neural network that rapidly evaluates adsorption energy for large molecules like plastics and biomass. The model achieves accuracy comparable to density functional theory (DFT) while utilizing simple molecular representations.
Three high school students co-authored a paper using AI engine PandaOmics to discover new therapeutic targets for glioblastoma multiforme, a common and aggressive malignant brain tumor. The study identified three genes strongly correlated with both aging and glioblastoma as potential therapeutic targets.
Scientists measured brain waves in participants and artificial intelligence systems to reveal similarities in how the brain interprets speech. The study provides a window into the operation of AI systems, which have been advancing rapidly but remain largely opaque.
Researchers found that using positive trigger words can retrain large language models and result in less biased responses. The team analyzed GPT-2's responses to user prompts about different countries worldwide and found a significant impact on the types of adjectives used to describe citizens.
A team of scientists at SLAC and Argonne National Laboratory has developed an algorithm that pairs machine-learning techniques with classical beam physics equations to precisely predict a particle beam's distribution of positions and velocities. This detailed information will help improve experimental reliability, especially at higher ...
Researchers at the University of Pennsylvania School of Engineering and Applied Science have created a photonic device that provides programmable on-chip information processing without lithography. This breakthrough enables superior accuracy and flexibility for AI applications, overcoming limitations of traditional electronic systems.
A team of researchers from the Complexity Science Hub and Central European University created more-detailed poverty maps for Sierra Leone and Uganda, identifying poor areas with greater accuracy. The maps use a combination of survey information, satellite imagery, and social media data to provide a more accurate picture of wealth distr...
A new MIT deep-learning system can analyze the internal structure and properties of materials based solely on their surface conditions. The technique uses vast amounts of simulated data to generate reliable predictions, offering a promising solution for engineers seeking non-invasive insights into material properties.
Researchers at Sainsbury Wellcome Centre found that instinctual exploratory runs enable mice to learn a map of the world efficiently. The study demonstrates how biological brains can learn faster and more efficiently than AI agents by focusing on salient objects.
Researchers used machine learning to identify bacterial environmental pH preferences from genomic data, enabling quicker growth of finicky bacteria in labs. This technique may also improve agricultural practices by ensuring inoculants are adapted to local pH levels, supporting native prairie restoration and plant growth.
A team of researchers from Carnegie Mellon University has developed an AI-based system to help clinicians make decisions quickly and precisely in the ICU. The system, called the AI Clinician Explorer, provides recommendations for treating sepsis based on data from over 18,000 patients.
A team of IUPUI researchers has developed an AI-powered approach to classify insect species, tackling the challenge of discovering new species. The method uses deep hierarchical Bayesian learning to distinguish between known and unknown species, providing insight into their taxonomy and ecosystem impacts.
Scientists are developing a high-resolution landslides susceptibility map to forecast future landslides in eastern Oklahoma. The project uses remote sensing data, machine learning, and LiDAR topographic data to understand the causes, mechanics, and associated hazards of landslides.
Recent research on local holographic transformations has improved the tractability of counting problems, enabling new approaches to complexity classification. The study reveals the potential of these transformations as a tool for proving hardness in various frameworks.
A team of researchers developed a self-checking deep learning system that accurately extracts information from gravitational-wave data. The algorithm, called DINGO, has been trained to interpret real data and can cross-check its own results for accuracy.
A machine learning model combines clinical and genetic data to predict the number of eggs retrieved in patients undergoing ovarian stimulation. This study aims to improve IVF procedures by providing personalized predictions, potentially increasing success rates.
A team of experts identified 29 sources of bias in AI/ML models for medical imaging, including data collection, preparation, and deployment. The study provides a comprehensive roadmap for mitigating these biases and ensuring fairness, equity, and trust in AI/ML models.
Researchers develop imaging-based computer algorithms to boost crop-breeding data using self-supervised contrastive learning methods, outperforming conventional supervised approaches. The study uses wheat as a model crop and finds that these new methods can improve plant phenotyping accuracy and scalability.
A recent study analyzing 34 Hollywood films found that stereotypical gender roles persist, with men depicted as aggressive and powerful, and women as loving and caring. However, the analysis also showed a significant increase in female representation over the past two decades.
Researchers at Argonne National Laboratory have developed a self-driving laboratory called Polybot, which automates electronic polymer research and frees scientists' time to work on tasks only humans can accomplish. The tool combines AI and robotics to streamline experimental processes and accelerate discovery.
Researchers at EPFL have developed a novel imaging technique using cryogenic transmission electron tomography and deep learning to visualize the nanostructure of platinum catalyst layers in fuel cells. This breakthrough reveals the heterogenous thickness of ionomer, a crucial component that influences catalyst performance.
Researchers at the University of Georgia have confirmed evidence of a previously unknown planet outside our solar system using machine learning tools. The discovery highlights the potential for artificial intelligence to enhance scientists' work and speed up analysis, with the potential to dramatically expand exoplanet discoveries.
A machine learning framework predicts and quantifies chromosome synthesis difficulties, providing guidance for optimizing design and synthesis processes. The model achieved high accuracy and predictive ability, enabling the development of a Synthesis difficulty Index to explain causes of synthesis difficulties.
Researchers trained a machine learning program using data from over 5 million direct messages, annotated by 150 adolescents who experienced uncomfortable or unsafe conversations. The technology can quickly flag risky DMs and is intended to address rising trends of child sexual exploitation.
Researchers used a machine learning model to simulate the behavior of hydrogen atoms at high pressures, discovering a new phase that was missed by previous theories and experiments. The discovery has sparked further investigation into the properties of solid hydrogen under extreme conditions.
Researchers tracked social grooming behavior in wild baboons using collars-mounted accelerometers, identifying and quantifying giving and receiving grooming with high accuracy. The study's findings have important implications for the study of social behavior in animals, particularly non-human primates.
A large-scale registry study in Finland identified factors associated with COVID-19 vaccination uptake, including low labor income and mental health issues. The study found that individuals in vulnerable positions were the least likely to vaccinate, highlighting the need for targeted vaccination campaigns.
Researchers developed machine-learning algorithms to generate proteins with specific structural features, enabling the creation of biologically inspired materials. The models can produce millions of new protein ideas in a few days, allowing scientists to explore unique applications.
Researchers at Bar-Ilan University have discovered that efficient learning on artificial shallow architectures can achieve the same classification success rates as deep learning architectures, but with less computational complexity. This breakthrough has significant implications for the development of unique hardware and advanced GPU t...
A recent study identified over 182,000 small seismic events in South Korea, with 135,000 related to mining explosions. The researchers used machine learning techniques to analyze data from 421 seismic stations and found distinct patterns that allowed them to distinguish between microseismic events and earthquakes.
Researchers successfully applied reinforcement learning to protein design, creating proteins with improved antibody generation and accurate nano-structures. The approach may lead to more potent vaccines and novel applications in regenerative medicine.
Researchers developed a machine learning model that predicts strokes more accurately than current systems, using data available at hospital arrival. The model achieved high precision and sensitivity, outperforming existing scales.
Researchers have developed a computer-assisted method to automate the assessment of speech severity in ataxia patients, achieving an 80% hit rate. The new methodology leverages artificial intelligence and could simplify procedures for determining ataxia severity, facilitating research and clinical practice.
A recent study found that the Epic sepsis model's accuracy in predicting sepsis onset depends on hospital factors such as sepsis incidence and multiple health conditions. The model performed worse in hospitals with higher rates of these conditions, suggesting it may be more useful in lower-acuity settings.
A new Dartmouth study examines how changes in precipitation and temperature due to global warming affect streamflow and flooding in the Northeast. The research finds that a warmer climate will lead to increased streamflow and higher flood risk, particularly if soils become wetter and more prone to heavy rainfall events.
A new machine learning study found that habit formation varies in time for different behaviors, such as gym-going and hand-washing. The study analyzed data from over 30,000 gymgoers and 3,000 hospital workers, revealing factors like past behavior and time since last visit played significant roles.
Using high-throughput experiments and machine learning-based algorithms, researchers forecast the behavior of hybrid perovskites with high accuracy. The study aims to find materials that combine high-efficiency performance with resilience to environmental conditions.
A machine learning program can spot risky conversations on Instagram by analyzing metadata clues, such as conversation length and participant engagement. The system was 87% accurate in identifying risky chats using sparse and anonymous details from over 17,000 private chats.
Researchers developed ProTac, a soft robotic link with multimodal perception to improve human-robot interactions. The device incorporates tactile and proximity sensing capabilities, enabling robots to react safely and predictably to physical contact.
A machine learning model has been developed to predict a patient's risk for a sleep disorder using demographic, lifestyle, physical exam results, and laboratory values. The model identified depression, weight, age, and waist circumference as the greatest predictors of a sleep disorder diagnosis.
A new VR locomotion system, Seamless-walk, offers a natural and comfortable experience without equipment or body pose recording. It uses high-resolution foot pressure imprints and machine learning to estimate the user's direction and movement speed.
Researchers at the University of Cambridge designed a soft robotic hand that can grasp a range of objects using passive movement and tactile sensors. The hand successfully grasped 11 of 14 objects in tests, including a peach, computer mouse, and roll of bubble wrap, demonstrating its ability to predict when it might drop an object.
Researchers at the University of São Paulo used artificial intelligence and Twitter to develop a database and models that can detect depression and anxiety before clinical diagnosis. The study found that BERT performed best in predicting depression and anxiety, with a statistically significant difference from LogReg.
A new study from Weill Cornell Medicine has identified four clinically distinct groups of people with autism spectrum disorder, each with unique brain connection patterns and behavioral traits. The findings highlight the potential for personalized therapies tailored to individual subgroups, which may lead to more effective treatments.
A new machine learning model estimates optimal treatment timing for sepsis, taking into account vital signs and lab test results to predict patient survival. The model was trained on a dataset of over 14,000 individuals with sepsis and showed improved outcomes when actual treatment matched the recommended timeline.
Researchers found that simultaneous learning of two tasks enhances a deep-learning model's performance on retrieving precipitation information from satellite data. The new framework uses multi-task learning to improve current estimates of precipitation, outperforming existing approaches.
A Swansea University study reveals the potential of machine learning in identifying Ankylosing Spondylitis (AS) patients, reducing diagnosis delays from eight years to earlier. The research uses a national data repository to develop a predictive model for AS detection, empowering GPs to refer patients more efficiently.
Researchers at CZ Biohub SF developed a new diagnostic method for LRTI in children, leveraging machine learning and genomics to identify pathogens with high accuracy. The method uses metagenomic sequencing data to analyze gene expression and microbial abundance, providing a more holistic approach to diagnosing the condition.
A new study published in Neurology shows that younger brain age is associated with superior post-stroke outcomes, suggesting a potential biomarker for stroke rehabilitation. The research team used multi-site data sets and 3D brain structural MRIs to analyze the relationship between brain age and stroke recovery.