Articles tagged with Machine Learning
Researchers developed a machine learning algorithm to identify cough sounds and determine if someone has pneumonia, aided by room impulse responses. The algorithm can work in any environment, facilitating non-face-to-face treatment and reducing medical costs.
A new paper by researchers at the University of Birmingham argues that a 'one size fits all' approach to treating early psychosis may not be effective. Instead, they propose using machine learning techniques to deliver tailored treatment plans that address individual needs and improve outcomes.
Researchers at Klick Applied Sciences have created a machine learning model to predict diabetes onset in patients using just 12 hours of data from continuous glucose monitors. The study showed high accuracy in identifying prediabetes, healthy patients, and those with Type 2 diabetes, offering a potential tool for early disease prevention.
Researchers developed a new approach to analyze coercivity in soft magnetic materials using machine learning and data science. The method condenses relevant information from microscopic images into a two-dimensional feature space, visualizing the energy landscape of magnetization reversal. This study showcases how materials informatics...
A study led by Kyoto University researchers found that AI-generated haiku poems, created without human intervention, were often indistinguishable from those penned by humans. In contrast, human-AI collaboration produced more creative works.
Researchers developed a method to measure overall fitness using wearable devices, outperforming current consumer smartwatches and fitness monitors. The model uses machine learning to predict VO2max during everyday activity, providing accurate predictions based on heart rate and accelerometer data.
Scientists used AI-driven PandaOmics platform to analyze gene expression datasets from DNA repair diseases, identifying biomarkers associated with treatment response. The study focused on genes that stratify cancer patients by survival outcomes, providing potential targets for personalized therapies.
A new AI method has analyzed substance use trends among Canadian high schoolers, identifying factors such as large weekly allowances and low physical activity that increase the risk of transitioning to multiple substance use. The study found that once students start using substances, it is rare for them to stop, highlighting the need f...
Researchers developed a new method to detect lung cancer using machine learning and statistical techniques, identifying 7 specific volatile organic compounds in breath samples. This breakthrough could lead to earlier diagnosis and improved treatment outcomes for patients.
A neural network trained using a diverse dataset outperforms conventionally trained algorithms by reducing bias in artificial intelligence. The use of images from low-resource populations boosts the object recognition performance of machine learning systems.
A new machine learning fusion model has been developed to diagnose ovarian cancer more accurately by combining ultrasound and photoacoustic tomography imaging. The model achieved an accuracy of 90% in detecting ovarian lesions, outperforming previous methods.
A recent study published in Nature Computational Science reveals that specific regions of the brain process both individual and combined words, while others focus solely on individual words. The research could contribute to the development of wearable neurotechnology devices that can decode language directly from brain activity.
A Penn State research team developed a novel analytical platform using machine learning to selectively measure multiple biomolecules, saving space and reducing complexity. The sensor can detect small quantities of uric acid and tyrosine, important biomarkers associated with various diseases, in saliva and sweat.
The researchers have developed an AI algorithm called M3GNet that can predict the structure and dynamic properties of any material. The algorithm was used to create a database of over 31 million yet-to-be-synthesized materials with predicted properties, facilitating the discovery of new technological materials.
A Rutgers researcher has created a machine learning model that can estimate arsenic contamination in private wells without sampling the water. The model identifies geological bedrock type and soil type as primary contributors to higher arsenic concentrations, highlighting the need for targeted well testing programs.
A Cornell-led collaboration used machine learning to predict Alzheimer's progression in cognitively normal and mildly impaired individuals. The modeling showed that MRI scans are most informative for asymptomatic cases, while PET scans are more effective for those with mild cognitive impairment.
A new study using artificial intelligence has found that a simple eye test can accurately predict the risk of heart disease. The researchers developed an algorithm that can analyze retinal images to assess cardiovascular health, providing a non-invasive alternative to traditional risk scores.
Researchers developed a new machine-learning framework that enables cooperative or competitive AI agents to consider the future behaviors of all agents, not just their teammates or competitors. This framework, FURTHER, uses two modules: an inference module and a reinforcement learning module, to enable agents to adapt their behaviors a...
The University of Manchester's Centre for Robotics and AI will focus on interdisciplinary research, exploring applications of robotics in extreme environments and the intersection of AI and humanity. The centre aims to develop robots that can work safely in hostile zones, such as nuclear decommissioning sites.
Researchers developed an AI model to analyze spatial and temporal gait parameters, identifying key features for diagnosing Parkinson's. The model achieved high diagnostic accuracy, reducing the probability of error in clinical assessments.
A research team developed an optical chip that can train machine learning hardware, improving AI performance and reducing energy consumption. This innovation uses photonic tensor cores and electronic-photonic application-specific integrated circuits to speed up the training step in machine learning systems.
Researchers from the University of Johannesburg deployed Few Shot Learning (FSL) for NIALM, a non-intrusive appliance load monitoring system. FSL requires only 7 test images to recognize appliances with 97.83% accuracy, making it faster and more cost-effective than traditional Machine Learning.
Researchers from Kessler Foundation are enrolling participants in a national trial testing a breakthrough device for improving recovery after stroke. The EMAGINE Stroke Recovery Trial pairs therapeutic exercise with electromagnetic stimulation to enhance brain and spinal cord function.
Researchers demonstrate that tetraplegic users can operate mind-controlled wheelchairs in a cluttered environment after training, with improvements in accuracy and brain activity patterns. The study highlights the importance of long-term training and neuroplastic reorganization for successful brain-machine interface control.
Researchers used weather radar to track bird movements and found peak roosting stages shifting earlier due to warmer temperatures. This shift may lead to a shortened pre-migratory season, impacting birds' survival during migration.
Researchers at Oak Ridge National Laboratory have discovered genetic markers for autism, developed recyclable composites to drive the net-zero goal, and created a tool for real-time building evaluation. Additionally, they have made significant progress in growing hydrogen-storage crystals using a novel nano-reactor material.
A study by University of Minnesota researchers found that personalized federated learning may offer an opportunity to develop both internal and externally validated algorithms. This technique enables multiple parties to train AI models collaboratively without exchanging or centralizing data sets, protecting sensitive medical informatio...
This study employs machine learning to analyze existing experimental results and predict the device performance of metal halide perovskite solar cells. The authors applied shapley additive explanations (SHAP) analysis to understand the correlations between fabrication processes, composition, and device performance.
Researchers developed an algorithm using amino acid sequences of proteins called T cell receptors to predict patient response to treatment, providing insights into the biology behind an effective response. The algorithm, DeepTCR, identified patterns that are predictive of patient response as accurately as known biomarkers.
Researchers used machine learning to analyze high-frequency oscillations in patients' brains during deep sleep, distinguishing between epileptogenic and non-epileptogenic regions. The study achieved an 85% accuracy rate, suggesting a promising method for predicting seizure outcomes.
Researchers modelled relationship between plant diversity and environmental conditions, capturing how diversity varies along environmental gradients. The models predict highest concentrations of plant diversity in environmentally heterogeneous tropical areas like Central America and the Amazonia.
Researchers developed a method to learn complex Boolean systems, enabling faster and more accurate diagnoses of urinary diseases, cardiac conditions, and financial risks. The technique uses optimal causation entropy to narrow down correct solutions and turn complex diagnostic processes into decision trees.
A new study published in The Journal of Nuclear Medicine found that a PET/MRI machine learning model can reliably distinguish between patients with and without lymph node metastases. This breakthrough technology has the potential to eliminate sentinel lymph node biopsy, a common procedure for breast cancer treatment.
Researchers induced brain-like sleep in an artificial spiking neural network, enabling it to retain previously learned information. This breakthrough helps the model avoid catastrophic forgetting and improve its overall performance over time.
Researchers created a new set of standards, called FAIR, to manage AI models, making them findable, accessible, interoperable and reusable. This standardization enables cross-pollination across teams and reduces duplication of effort, ultimately facilitating scientific discovery.
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 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 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 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.
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.
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.
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.
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 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 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...
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.