Articles tagged with Machine Learning
A new AI-based chemical sensor can accurately detect specific gases in the air by analyzing temperature changes in a microbeam resonator. The device uses machine learning to differentiate between gases with varying thermal conductivities, achieving 100% accuracy in identifying helium, argon, and CO2.
The University of Tsukuba researchers developed a machine-learning model to predict undertriage in phone-based triage systems. The model identified risk factors such as age, sex, and comorbid conditions, which can be used to update protocols and improve patient outcomes.
Researchers used AI to automate the process of analyzing X-ray snapshots of materials, accelerating the technique by ten times on its own and 100 times with improved hardware. The new method can extract information from a range of previously inaccessible materials, including high-temperature superconductors and quantum spin liquids.
Researchers at CABBI used unmanned aerial vehicles with machine learning methods to select the best candidate genotypes in miscanthus breeding programs. The new method leverages high-resolution aerial imagery and three-dimensional neural networks to estimate crop traits such as flowering time, height, and biomass production.
A new nationwide study found that 34.7% of Canadians experienced severe loneliness in January 2021, with migrants and those experiencing job insecurity being most at risk.
Researchers developed an intelligent compaction technology that integrates into a road roller, assessing real-time the quality of road base compaction. This improves road construction, reducing potholes and maintenance costs, leading to safer and more resilient roads.
Researchers at University of Jyväskylä used machine learning to predict ACL injuries in elite athletes but found a low overall accuracy rate. The study analyzed the largest data set ever collected and provided valuable insights into the challenges of predicting injuries in individual athletes.
A team of researchers is developing a new method to evaluate the viability of donor kidneys using optical coherence tomography (OCT) scanning technology. OCT scans provide a more complete view of the organ's overall functional capacity, allowing for better matching with transplant patients. The goal is to increase kidney availability a...
In an experiment pitting human expertise against artificial intelligence, researchers found that the computer program correctly predicted six out of nine proteins capable of self-assembly. This breakthrough suggests machine learning can complement human intuition in biotechnology research.
Researchers used machine learning to track turbulent structures in fusion reactors, gaining detailed information on their behavior and heat flows. The approach enables more accurate engineering requirements for reactor walls and could lead to improved energy efficiency.
The US Department of Energy's Oak Ridge National Laboratory has developed a massive geographic dataset, USA Structures, using deep learning to forecast potential damage and accelerate emergency response. The dataset provides critical information on building outlines and attributes, enabling FEMA to prioritize response efforts.
Researchers at MIT have developed a machine-learning model that captures how sounds propagate through spaces, allowing for accurate visual renderings of rooms. This technique has potential applications in virtual and augmented reality, as well as improving AI agents' understanding of their environment.
A large study of over 5,800 tween children found that growing up in a socioeconomically disadvantaged household can have lasting effects on brain development, with different patterns of connections between brain regions observed. Parental education emerged as the most significant factor associated with variations in brain connections.
Researchers at WVU are developing software for robots to learn and adapt in real-time, inspired by the neural networks of electric fish. The goal is to enable robots to navigate different terrains autonomously without human supervision.
A new study reveals significant gender bias in Reddit discussions about female politicians, with more frequent use of given names and language related to their body or family. This perpetuates stereotypes and can affect voter perception, incentivizing female politicians to conform to online expectations.
Researchers used AI to predict compounds that could neutralize reactive oxygen species causing baldness. They successfully regenerated hair on mice using microneedle patches, providing a promising new treatment for androgenic alopecia.
Researchers at KAUST developed an inverse mixture-design approach using machine learning to create high-performance transport fuels. The model accurately predicted fuel properties and identified suitable blends, offering a promising solution to reduce greenhouse gas emissions.
Researchers from the University of Tokyo's Interactive Intelligent Systems Laboratory developed a new system called LookHere that incorporates natural hand gestures into the teaching process. This approach eliminates extraneous details and provides better input data for machines to create models, resulting in improved efficiency and ac...
A team from Massachusetts General Hospital developed an AI-based method to predict which patients with early-stage melanoma are most likely to experience a recurrence. Machine learning algorithms extracted predictive signals from clinicopathologic features, including tumor thickness and rate of cancer cell division.
Researchers at the University of Illinois developed an AI-powered system that uses a molecule-making machine to find optimal reaction conditions for synthesizing chemicals. The system doubled the average yield of a challenging class of reactions, paving the way for faster innovation and automation in biomedical and materials research.
Researchers at MIT have developed a new approach to identify topological materials using machine learning and X-ray absorption spectroscopy. The method is over 90% accurate in identifying known topological materials and can predict properties of unknown compounds.
Researchers at FAU aim to empower amputees to maximize their individual potential for controlling the full dexterity of artificial hands using a novel bimodal skin sensor and machine learning algorithms. The project will develop customized prosthetic sockets and training programs to overcome limitations with current sensing technology.
Researchers at Penn State aim to create a minimally invasive system using machine learning and multimodal biosensing platforms to detect Alzheimer's disease in its early stages. The system has the potential to improve detection accuracy and reduce false positives.
A new project aims to create more race-inclusive AI in medicine by developing a distributed, inclusive data collection and learning framework that relies on smartphone apps. The framework uses federated learning, which allows models to be trained on device data while protecting user privacy.
A data visualization tool, TrafficVis, assists law enforcement in identifying patterns in online escort advertisements that indicate illegal activity. The tool analyzes millions of ads to highlight common phrasing or duplication among them, helping analysts direct investigations and identify human traffickers and victims.
Researchers developed a machine learning-based diagnostic method that combines genomic sequencing and analysis of patients' immune response for remarkable accuracy. The approach identifies and predicts sepsis cases with high accuracy, potentially exceeding current diagnostic capabilities.
Researchers developed an AI model that uses daily step counts to predict the likelihood of unplanned hospitalization during cancer therapy, offering personalized care and potential prevention of treatment interruptions. The model was tested on data from wearable consumer devices and showed strong predictive performance.
A novel algorithm uses near-infrared spectroscopy to estimate intracranial pressure (ICP) based on hemoglobin levels. The research validates the accuracy of this method using invasive ICP data.
Researchers have developed a deep learning algorithm that can accurately assess the stage of head and neck cancer using standard CT scans, outperforming expert radiologists. The algorithm demonstrated superior accuracy in measuring the extent of cancer spread, especially for patients with high-risk disease.
Researchers have developed a novel method for antibiotic resistance testing that can analyze bacterial cells in real-time, allowing for faster identification of susceptible and resistant bacteria. This breakthrough technology has the potential to transform microbial screening in clinical and research labs.
The platform enhances neurosurgical procedures by delivering precise treatment and diagnosis in deep brain tissue. Researchers successfully implanted the catheter in live sheep without damage or infection, paving the way for potential human trials within four years.
Researchers developed an AI-based model that combines artificial intelligence and weather forecast models to predict extreme wildfire danger with high accuracy. The new method can produce forecasts of extreme fire danger out to one week at finer scales (4km x 4km resolution), increasing its utility for fire suppression and management.
Researchers at MIT have developed a new method that uses optics to accelerate machine-learning computations on low-power devices. By encoding model components onto light waves, data can be transmitted rapidly and computations performed quickly, leading to over a hundredfold improvement in energy efficiency.
Researchers found that content overlap between critic and user reviews increases movie demand, particularly for movies with mediocre review ratings. The study suggests that producers and marketers can leverage this by engaging with both professional critics and general consumers to find commonalities in their reviews.
Researchers use machine learning to generate beta-barrel proteins that can detect metal ions in water, a potential solution for identifying pollutants like lead in waterways. The 'deep-fake' protein is designed to be ideal for binding specific metals and creating conductance differences.
FathomNet aggregates images from multiple sources to create a publicly available, expertly curated underwater image training database. The database uses artificial intelligence and machine learning to alleviate the bottleneck for analyzing underwater imagery, accelerating important research around ocean health.
A new AI app improved the accuracy of COVID-19 lateral flow test results, increasing sensitivity from 92% to 97.6%. The app was tested at UK Health Security Agency assisted test centers and found promising results in reducing false negatives.
A new AI model called CODE-AE can accurately predict the efficacy of novel drug compounds in humans, addressing a major challenge in drug discovery. The technique has been shown to improve accuracy and robustness over state-of-the-art methods, with potential applications in personalized medicine.
Omnipose, a deep learning software, can identify various types of tiny objects in micrographs with high precision, including bacteria of all shapes and sizes. It overcomes limitations of previous approaches by handling object overlap and detecting cell intoxication, making it a game-changer for biological image analysis.
A University of Groningen team created two machine learning models to predict app removal risks, achieving accuracy rates of up to 79.2%. The models can help developers avoid bans and users protect their data.
Researchers developed a machine-learning model to predict heat capacity of MOFs, enabling more efficient applications in energy and climate change. The model's accuracy was improved by removing solvent from pores during synthesis.
A recent study analyzed the records of over 33,000 dementia patients in Wales and found that those closest to their diagnosis were taking an average of three or more medications. This suggests a possible link between polypharmacy and the development of dementia.
A study published in the Canadian Journal of Cardiology found that smartwatch health apps detecting atrial fibrillation generated a high rate of false positives and inconclusive results, especially in patients with certain cardiac conditions. Better algorithms and machine learning may help improve the accuracy of these devices.
A team of Illinois Tech researchers used machine learning to estimate the age and gender of individual users with high accuracy, raising questions about data security and privacy. The study highlights the need for better regulations and best practices to protect personal information from being misused.
Researchers successfully taught human and mouse neurons to play the video game Pong in real-time, showcasing their ability to exhibit sentience and adapt to a changing environment. The study's findings have potential applications in disease modeling, drug discoveries, and expanding our understanding of brain function.
Researchers from McGill University and MIT developed an AI system that can learn the rules and patterns of human languages on its own. The model automatically generates higher-level language patterns that can be applied to different languages, achieving better results.
Researchers used AI to analyze seismic signals and predict future fault friction and next failure time with high resolution in laboratory earthquakes. The technique goes beyond previous work by predicting the future state of the fault's physical system.
Researchers developed a smart mouthguard that translates complex bite patterns into instructions to control devices such as computers, smartphones and wheelchairs. The device achieves 98% accuracy and has the potential to support individuals with limited dexterity or neurological disorders.
Researchers developed a generatively designed patient-specific bone fixation device using Generative Design technology. The implants are tailored to each patient's anatomy and biomechanical needs, resulting in lighter, less prominent, and minimally invasive designs that promote faster healing and reduced revision surgery.
A University at Buffalo-led research team has been awarded a $5 million grant to develop digital tools that can help older adults recognize and protect themselves from online deceptions. The project, DART, aims to reduce online fraud among older adults, who lose billions of dollars each year due to scams.
Researchers developed algorithms that can predict volleyball players' actions with over 80% accuracy, combining visual data with hidden variables. The algorithms also showed promise in predicting multiple actions across sequences of up to 44 frames.
Researchers found that machine learning models outperformed traditional risk prediction models in predicting suicide-related outcomes. These models can identify patterns associated with suicide risk and have been shown to correctly predict 66% of people who would experience a suicide outcome.
Researchers aim to create a unified database network for battery data, facilitating AI analysis and predictions. The Battery Data Genome will collect data across the entire battery lifecycle, from discovery to deployment, with uniform standards for metadata.
Researchers at FAU's Schmidt College of Medicine are developing a pilot program to tackle health disparities by leveraging electronic health records, artificial intelligence, and machine learning. The project aims to improve AI/ML delivery and research operations in community health centers and federally qualified health centers.
A new study found an association between college student FoMO and maladaptive behaviors such as academic misconduct, drug and alcohol use, and breaking the law. Brief FoMO assessments may be valuable risk prediction tools for counselors assisting students in transition to college.
Researchers used machine learning algorithms to optimize climate models, increasing their accuracy and detail. By applying Generative Adversarial Networks (GANs) to climate simulations, the team was able to improve the models' ability to represent extreme precipitation events.
Researchers developed a new technique that enables on-device training using less than a quarter of a megabyte of memory, reducing the need for powerful computers and central servers. This approach preserves privacy by keeping data on the device, making deep learning more accessible for low-power edge devices.
Researchers developed a non-invasive algorithm to identify patients with compensated cirrhosis at highest risk for severe complications. The online calculator uses widely available laboratory parameters and is simple, non-invasive, and cost-effective.
A machine learning model developed by Carnegie Mellon University can accurately predict how stay-at-home orders affect people with multiple sclerosis. The model uses passively gathered smartphone and fitness tracker data to identify factors that signal changes in health.
The new computer chip uses a transistor-free design that eliminates data transfer time and minimizes energy consumption. It offers up to 100 times faster performance than conventional computing architectures, making it ideal for AI applications.