Articles tagged with Machine Learning
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.
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.
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 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.
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 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 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.
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.
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.
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.
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.
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 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 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.
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 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.
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.
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.
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.
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.
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.
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.
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.
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 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.
A new machine learning algorithm analyzed high-resolution digital images of herbarium specimens, revealing that factors other than climate have a strong effect on leaf size within a plant species. The study also demonstrates how AI can be used to transform static specimen collections and quickly document climate change effects.
Generative AI raises fundamental questions about the creative process and human's role in it. Researchers highlight gaps in understanding perceptions of AI-generated content, ownership, credit, labor economics, and media ecosystem impact.
Researchers developed a new method for controlling lower limb exoskeletons using deep reinforcement learning, enabling more robust and natural walking control. The system has the potential to benefit users with spinal cord injuries, multiple sclerosis, stroke, and other neurological conditions.
A new research paper challenges the idea that unlimited trials are needed to learn safe actions in unfamiliar environments. The team presents a fresh approach that ensures learning safe actions with complete confidence while managing tradeoffs between optimality and exposure to unsafe events.
A new approach to enhance artificial intelligence-powered computer vision technologies has been developed by UCLA researchers, adding physics-based awareness to data-driven techniques. This hybrid methodology aims to improve how AI-based machinery sense, interact, and respond to their environment in real time.
A new research project at University of Leicester will develop a digital 'twin' of the UK using artificial intelligence and big data to inform environmentally-friendly land use. The project aims to reduce emissions from cattle and sheep farming, which contribute around 10% of the UK's emissions.
Research by Francis de Véricourt and Huseyin Gurkan found that trust in machines' decision-making ability is key to effective learning. They discovered that biased learning occurs when humans override algorithmic decisions without observing the machine's correctness, leading to incorrect usage of machines in decision making.
A machine-learning study has found that individual characteristics, including age and weight, determine which drug combinations most effectively reduce COVID-19 recurrence rates. The study used real-world data from a hospital in China and identified unique treatment combinations for different demographic groups.
Researchers have developed a new AI model that can quickly screen large libraries of potential drug compounds against target proteins. The ConPLex model uses language analysis to match potential drugs with proteins without needing to calculate molecular structures, enabling fast screening of over 100 million compounds per day.
MethaneMapper is an artificial intelligence-powered hyperspectral imaging tool that can detect real-time methane emissions and trace them to their sources. With a performance accuracy of 91%, it has the potential to revolutionize the way we monitor oil and gas operations and curb climate change.
A group of educators is urging caution when using artificial intelligence (AI) in special education, highlighting its potential to benefit students with disabilities. The authors emphasize the need for careful consideration of AI's uses and limitations, including information literacy, consent, and critical thinking.
A chemist at the University of Kansas has developed a digital tool that can spot scientific text generated by ChatGPT with 99% accuracy. The tool, which was published in Cell Reports Physical Science, uses human insight and intuition to identify key differences between human-written and AI-generated texts.
Researchers at NYU Grossman School of Medicine have developed an AI tool called NYUTron that can accurately estimate patients' risk of death, length of hospital stay, and other factors important to care. The tool achieved impressive results in predicting readmission rates, improving upon standard methods by up to 7%.
A team of researchers developed a tool to identify AI-generated academic science writing with high accuracy, using characteristics such as predictability, paragraph structure and vocabulary. The model outperformed existing AI text detectors and has potential applications in assessing student essays.
Researchers developed a new AI framework that significantly improves the ability to analyze team communication, enabling adaptive training technologies to facilitate effective team collaboration. The framework performed substantially better than previous AI technologies in classifying dialogue and following information flow.
Researchers at Mount Sinai have developed an AI model called HeartBEiT that can analyze electrocardiograms as language, enabling more accurate diagnoses. The model outperformed established methods in comparison tests and demonstrated improved performance with lower sample sizes.
Optical memristors have the potential to transform high-bandwidth neuromorphic computing, machine learning hardware, and artificial intelligence. However, scalability is a significant challenge that needs to be addressed to unlock their full potential.
A new study by Carnegie Mellon University researchers finds that crime risk assessment tools, such as machine learning models, are prone to cohort bias when ignoring social change dynamics. This bias can generate inequality in the criminal justice system, distinct from racial bias.
A recent study has found that microbes play a crucial role in storing carbon in the soil, with a four-fold greater importance than other processes. This breakthrough could lead to improved soil health and increased food security through targeted farm management practices.
Researchers developed a method using artificial intelligence to track changes in brain synapses, enabling better understanding of how connections change with learning, aging, injury and disease. Machine learning was leveraged to enhance image quality, allowing for detection and tracking of individual synapses.
A new framework developed by Carnegie Mellon University researchers significantly reduces consumers' privacy risk while preserving advertisers' utility in mobile location data analysis. The framework uses machine learning to quantify personalized privacy risks and performs personalized data obfuscation.
Researchers trained a robotic chef to watch and learn from cooking videos, enabling it to identify ingredients and actions. The robot recognized 93% of the correct recipe from 16 videos, including variations and new recipes, showcasing its potential for automated food production and cost-effective deployment.
Researchers updated their protein localization prediction model, MULocDeep, to provide more targeted predictions for biological discoveries. The tool helps researchers design more effective experiments and advance scientific discoveries related to drug development and treating diseases like epilepsy.
Researchers develop AI-based method to quantify cracking patterns in reinforced concrete structures, enabling more accurate and efficient assessments of structural damage. The approach uses graph theory and machine learning algorithms to create a unique 'fingerprint' for each set of cracks, allowing for quick and consistent evaluations.
Researchers at North Carolina State University have developed a new methodology called Patch-to-Cluster attention (PaCa) that addresses the challenges of vision transformers. PaCa improves ViT's ability to identify, classify, and segment objects in images while reducing computational demands and enhancing model interpretability.