Articles tagged with Machine Learning
The new tool, SnorCall, analyzes unsolicited calls to shed light on robocall trends and types of scams. It extracted information from over 232,000 robocalls, including phone numbers used in scams, helping regulators and law enforcement take action.
A machine learning model found that background parenchymal enhancement (BPE) on breast MRI is an indicator of breast cancer risk in women with extremely dense breasts. Women with dense breasts are at a higher risk of developing breast cancer compared to those with fatty breasts.
Researchers have developed a new explainable AI model to reduce bias and enhance trust in machine learning-generated decisions. The Pattern Discovery and Disentanglement (PDD) model can predict medical results with rigorous statistics and explainable patterns, leading to more reliable diagnoses and better treatment recommendations.
Researchers developed an AI model called OncoNPC that can analyze genetic data to predict cancer type and origin. The model accurately classified at least 40% of tumors with unknown origin, leading to a 2.2-fold increase in eligible patients for targeted treatments.
Critically ill children on ventilator support can experience patient-ventilator asynchrony (PVA), which worsens outcomes. Researchers are using machine learning to develop a common set of definitions and measurements for PVA in pediatric patients, aiming to minimize risks and improve clinical outcomes.
Researchers created a self-supervised AI model called GedankenNet that learns physics laws and thought experiments to reconstruct microscopic images. The model successfully reconstructed human tissue samples and Pap smears from holograms without relying on real-world experiments or data.
Researchers found that clinical strains of Aspergillus fumigatus differ significantly from environmental strains in amino acid synthesis. The fungus appears to shape the lung microbiome to its advantage, surviving on vital metabolites produced by other microorganisms.
AI-powered clinical decision support systems can enhance patient care, but doctors lack necessary skills to interpret and act on risk predictions. To address this gap, medical education and training need to incorporate explicit coverage of probabilistic reasoning tailored to CDS algorithms.
A radiomic-based model using T2-weighted MRI data achieved high accuracy in diagnosing pediatric Crohn disease, outperforming expert radiologists. The model was ensembled with clinical data to further improve performance.
Researchers developed a generative AI tool, AniFaceDrawing, to assist users in creating high-quality anime portraits. The tool uses a sketch-to-image framework and employs stroke-level disentanglement to match raw sketches with latent vectors of the generative model.
Researchers at University College London found that humans can only reliably detect fake speech 73% of the time, and this ability improves only slightly with training. The study's findings raise concerns about the potential for deepfakes to be used by criminals and nation-states to cause harm.
A machine learning system capable of learning diverse tennis skills from broadcast video footage has been created by a research team led by Simon Fraser University's Jason Peng. The system can generate long-lasting matches with realistic racket and ball dynamics between two physically simulated characters.
Researchers propose leveraging high-value information to overcome statistical and computational challenges in reinforcement learning. By accessing valuable observations, agents can improve strategies without trial and error, making the learning process more efficient and effective.
Researchers have discovered a way to utilize nonlinear scattering media for optical computing and machine learning. They created a novel theoretical framework involving third-order tensors, which can represent the complex relationships between input and output signals. This breakthrough has potential applications in real-world settings...
A study by University of Toronto researchers found that child language development and language evolution share a common cognitive foundation, based on a core knowledge base. The team built a computational model that predicts word meaning extension patterns across languages and time scales, highlighting the role of visual, associative,...
Researchers designed machine learning models to identify children at risk of self-harm, finding that incorporating more data points and diagnostic codes improved detection rates. The models were particularly effective for detecting underrepresented groups, such as Black and Latino youth.
A new study uses machine learning to analyze data from DrugAge, a database of chemical compounds modulating lifespan in model organisms. The researchers create four types of datasets to predict whether or not a compound extends the lifespan of C. elegans, using features such as compound-protein interactions and Gene Ontology terms.
An analysis of English Twitter data reveals a 17-fold increase in daily FGM conversations on International Day of Zero Tolerance, suggesting opportunities for social media education. At least 200 million women and girls have undergone FGM, leading to short- and long-term health consequences.
Hang aims to develop general-purpose robots that can handle complex physical interactions without requiring perfect input from sensors or extensive instructions. His project seeks to improve robotic manipulation tasks by reducing assumptions about how the robot acts in real-world conditions.
Researchers at Monash University developed a co-training AI algorithm that can effectively mimic human oversight in medical imaging. The algorithm achieved an average improvement of 3% compared to state-of-the-art approaches using limited annotated data, enabling AI models to make more informed decisions and uncover accurate diagnoses.
Researchers have developed a computational tool to compare large datasets and predict immune responses to disease, potentially leading to better vaccines. The new algorithm, designed by La Jolla Institute for Immunology scientists, uses machine learning to identify underlying patterns in immune system data.
The Fengyun-4A satellite in collaboration with a machine learning model generated a detailed PV resource map for China, providing new insights into the country's solar energy potential. This advancement sets a new standard for solar resource mapping, empowering decision-makers to make informed choices for a sustainable future.
A new editorial explores the potential of machine learning to enhance early cancer detection in primary care, leveraging extensive patient data and improving risk stratification accuracy. The authors emphasize the need for responsible implementation, collaboration, and validation across diverse populations.
Researchers developed a tool called SKILL that enables AI agents to learn 102 distinct tasks by sharing knowledge in parallel, reducing the time needed to master new skills. The technology has potential applications in medicine, education, and other fields where vast knowledge is required.
The study evaluates recent research on artificial intelligence-generated molecular structures from the perspective of medicinal chemists, recommending guidelines for assessing novelty and validity. Insilico Medicine's recommendations aim to improve the process of generating and evaluating novel AI-generated drugs.
Researchers have developed a new study showing that targeted electrical stimulation in patients with traumatic brain injury improved memory recall by 19%. The technology delivers the right stimulation at the right time, informed by the wiring of the individual's brain and that individual's successful memory retrieval.
A new geometric deep learning model called GFCN has been developed to detect stroke lesions in brain imaging scans. The model leverages rich geometric information to segment brain tissue and achieves higher segmentation performance than other neural network architectures.
Researchers at the University of Alabama created a detailed assessment of vulnerability to natural hazards across the continental US. The study found significant differences in vulnerability between neighboring blocks and identified top 10 states with high vulnerability rates, including Minnesota and Ohio.
Lero is recruiting 16 top international post-doctoral researchers for a €2.9 million fellowship program focusing on privacy, trust, inclusion and fairness in software expertise. The program will provide discipline-specific skills training and enhance career development.
A new AI approach has confirmed that the Maldivian government effectively enforced its ban on open trash burning and single-use plastics, eliminating toxic smoke plumes from satellite imagery. The tool, trained using transfer learning and image segmentation, achieved 88% accuracy in identifying plumes.
A joint research team has developed a novel approach combining machine learning with quantum-classical computational molecular design to accelerate the discovery of efficient OLED emitters. The optimal OLED emitter discovered is a deuterated derivative of Alq₃, which is both extremely efficient at emitting light and synthesizable.
Researchers at NIST and their colleagues used machine learning to identify abnormal cardiac rhythms in firefighters, achieving 97% accuracy. The Heart Health Monitoring model could lead to a portable heart monitor to detect early warning signs of heart trouble and prevent fatal cardiac events.
A new AI technology has been developed to generate artificial scientific data, allowing for faster and more efficient detection of material features. The AI uses generative adversarial networks to incorporate background noise and experimental imperfections into the generated data, making it virtually indistinguishable from real data.
Researchers used machine learning algorithms to explain how calpain-1 activity is modified on the molecular level, finding that lipid peroxidation products like MDA and HNE can increase or decrease activity. The study provides new insights into protein modification and its role in meat tenderness.
Researchers at Flinders University use machine learning to identify species interactions and predict which species are most likely to go extinct. By analyzing species traits and interactions, the algorithm can help plan interventions before extinctions occur.
A machine learning model developed by Duke Health researchers can differentiate normal cognition from mild cognitive impairment using retinal images from the eye. The model achieved a sensitivity of 79% and specificity of 83%, identifying specific features in OCT and OCTA images that signal cognitive impairment. This non-invasive metho...
A Lehigh University professor has received $4 million in NIH grants to develop an AI-driven approach for precision mental health diagnosis and care. The project aims to identify biomarkers in the brain that can predict treatment response and personalize interventions for patients with depression and other mental disorders.
A new machine learning-based simulation method called Materials Learning Algorithms (MALA) has been developed, enabling accurate electronic structure calculations at large scales. MALA achieves this by utilizing a hybrid approach that combines physics-based approaches with machine learning to predict the electronic structure of materials.
Researchers at Cornell University developed a new method that uses machine learning to visualize nanotextures in thin-film materials. This technique overcomes the challenge of preserving the sample, allowing for dynamic study of thin films and discovery of new morphologies.
Developing a technique to create conductive polymer wire connections between electrodes enables artificial neural networks that overcome the limits of traditional computer hardware. The approach allows researchers to control and train the network using small voltage pulses.
Researchers at Nagoya University developed an AI-based technique to predict crystal orientation in polycrystalline materials, revolutionizing the industry. The method uses optical photographs and reduces measurement time from 14 hours to 1.5 hours, enabling large-area materials analysis.
Researchers created a model to forecast eucalyptus leaf health based on temperature data, explaining over 80% of observed damage. This approach has the potential to identify suitable planting regions worldwide.
A team of researchers from Kyoto University and international institutions has developed a mathematical solution to the temporal asymmetry of nonequilibrium disordered Ising networks. This breakthrough offers insights into the behavior of biological systems, machine learning, and AI tools.
Researchers develop predictive model to control gene expression with high accuracy, enabling precise modulation of gene dosage. The AI-powered tool uses deep learning to predict on- and off-target activity of RNA-targeting CRISPRs, holding promise for treating viral infections and developing new therapies.
A new study by researchers at NYU and the New York Genome Center combines deep learning with CRISPR screens to control human gene expression. The model predicts on- and off-target activity of RNA-targeting CRISPRs, enabling precise gene controls for developing new therapies.
A team of scientists identified the dorsal medial prefrontal cortex as a key region in predicting rumination, which is linked to depression. The study's findings suggest that dynamic connectivity between brain regions can be used to decode rumination patterns.
The IceCube Neutrino Observatory has produced an image of the Milky Way using neutrinos for the first time. The high-energy neutrinos were detected from the galactic plane, confirming what is known about our galaxy and cosmic ray sources.
A new study from Aarhus University has found that applying AI predictions of protein structures enhances the CRISPR technology, making the cuts in a patient's DNA more precise. This discovery may lead to better treatments for patients with genetic disorders and potentially develop cures for various genetic diseases.
The IceCube Neutrino Observatory has produced an image of the Milky Way using neutrinos, revealing it is a neutrino desert. The observation suggests the galaxy produces significantly fewer high-energy neutrinos than distant galaxies.
A soft robotics glove with integrated sensors and AI can aid patients in relearning daily tasks after neurotrauma, including playing music. The glove provides hand guidance, amplifying dexterity and motor skills.
A new machine learning model developed by University of Pittsburgh researchers uses electrocardiogram (ECG) readings to diagnose and classify heart attacks faster and more accurately than current approaches. The model improves risk assessment, helping patients receive appropriate care without delay.
Researchers developed AEGIS, the first technique to detect backdoor attacks in robust machine learning models. It improves the trustworthiness of artificial intelligence by analyzing robust models and detecting mixed input distributions for poisoned classes.
New smart pants based on fiber optic sensors can track various types of physical activities in the clinic or at home, detecting signs of distress. The sensing approach achieved 100% accuracy in classifying activities and has several advantages, including low-cost and reliability.
A new theoretical proof shows that overparametrization enhances performance in quantum machine learning, allowing for enhanced learning and classification tasks. The Los Alamos team developed a framework to predict the critical number of parameters at which a quantum machine learning model becomes overparametrized.
A research team at the University of Tsukuba has developed a secure AI technology that allows for the shared analysis of personal and identifiable data from multiple organizations. This will enhance the accuracy of AI analysis, particularly in disease prediction and educational effectiveness.
Research highlights how generative AI models encode biases and negative stereotypes in their users, particularly marginalised groups. This can lead to the mass generation and spread of nonsensical information, influencing human beliefs and perpetuating existing inequalities.
BioAutoMATED is an all-in-one AutoML platform designed for biologists, enabling easy analysis and interpretation of biological sequences. The platform uses three existing AutoML tools to generate models that can predict biological functions from sequence information.
Researchers at the University of Washington created an app called FeverPhone that uses existing phone sensors and screens to estimate whether people have fevers. The app was tested on 37 patients in an emergency department and showed accuracy comparable to consumer thermometers, with potential for early intervention in viral outbreaks.
Researchers applied machine learning to brain responses and achieved near-perfect classification accuracy for songs that may become hits. The approach, called 'neuroforecasting,' uses data from a small group of people to predict population-level effects without needing to measure the brain activity of hundreds of people.
Researchers developed Precious1GPT, a multimodal transformer-based approach for aging clock development and feature importance analysis. The model utilizes methylation and transcriptomic data to predict biological age and identify disease-related genes, providing a pathway for therapeutic drug discovery.