Articles tagged with Machine Learning
Researchers developed an algorithm to differentiate life-threatening gunshot events from non-life-threatening plastic bag explosion events. The study found that 75% of plastic bag pop sounds were misclassified as gunshot sounds, highlighting the need for a diverse dataset of similar sounds.
Researchers developed an AI technique to predict material properties using a small number of experiments, improving accuracy and facilitating digital transformation in materials development. The technique uses Bayesian optimization and incorporates measurement data into machine learning models.
Researchers at MIT and Google Brain developed a system that predicts how changing materials or designs will improve solar cell performance. The new simulator, called differentiable solar cell simulator, provides information on which changes will provide desired improvements, increasing the rate of discovery of new configurations.
DeepMind's neural network approach accurately describes electron interactions in chemical systems, overcoming long-standing challenges. The company's breakthrough enables researchers to explore material design, medicines, and catalysts at the nanoscale level.
A global community of hackers and threat modellers is needed to stress-test the harm potential of new AI products. Companies can harness techniques like red team hacking, audit trails, and bias bounties to prove their integrity and earn public trust. The industry faces a 'crisis of trust' if it doesn't adopt these measures.
A team from the University of Washington has developed a non-destructive 3D imaging method that can help doctors more accurately diagnose borderline cases of prostate cancer. The new approach uses 3D images to identify complex features in tissue samples, which can increase the likelihood of correctly predicting a cancer's aggressiveness.
The study leverages machine learning to tackle long-unsolvable problems in biological systems at the cellular level. By reducing data points, researchers can better analyze and model the impact of cells with high fidelity.
Researchers at the Max Planck Institute for Intelligent Systems developed an algorithm that analyzes gravitational wave data in seconds, rather than hours or months. The system, called DINGO, uses a deep neural network to infer properties of binary black-hole sources with high accuracy.
The University of California, Riverside, has been awarded a $980,000 grant from the Department of Energy to develop an AI-driven detector for the future Electron-Ion Collider. The team will use machine learning techniques to optimize detector design and achieve 'co-design,' a new concept in nuclear physics.
Carlos Ponce is studying the parts of the visual system that analyze shapes, using macaque monkeys as a model. He combines computational models with electrophysiology experiments to understand how neurons process visual information.
Researchers developed a new technology that tracks thousands of cells and determines the precise moment of death for any cell in the group. The approach was shown to work in rodent and human cells as well as within live zebrafish, and can be used to follow cells over weeks to months.
A new project led by University of Illinois researchers will develop machine learning models to predict the reactivity of thousands of organic contaminants in engineered and natural environments. This will help scientists better model pollutant fate and transport, leading to more accurate contaminant risk assessments.
A recent study uses machine learning to rapidly discover bacterial isolates with antifungal properties, identifying promising new compounds for crop protection. The approach analyzes thousands of microbial genomes at once, allowing researchers to identify novel beneficial microbes and bypass traditional screening tactics.
A new machine learning-based algorithm can predict stable material compounds much faster than traditional methods, opening up new avenues for research and discovery. The researchers identified several thousand potential new compounds using the computer, offering a promising breakthrough in materials science.
A study by the University of Toronto's Rotman School of Management found that predictive analytics can increase revenue by $500,000 to $1 million for manufacturers who invest in IT capital, educate their workforce, and implement high-efficiency manufacturing processes. The research team surveyed over 30,000 manufacturers and found that...
A team of researchers used deep learning to analyze cardiac images and identified genetic variations linked to aortic size. The findings may lead to the development of a polygenic score to identify individuals at high risk of aneurysm, as well as new drug targets for aortic enlargement.
Researchers aim to develop AI agents that reuse information, adapt quickly to new conditions and collaborate by sharing experiences. The goal is to enable machines to continually learn from their collective experiences and improve performance on novel and previous tasks.
A Michigan Tech-developed machine learning model uses probability to classify breast cancer shown in histopathology images and evaluate the uncertainty of its predictions. The model outperforms similar models and can measure uncertainty, promising time savings and referrals to human experts.
Computer scientists and mathematicians have used artificial intelligence to help prove or suggest new mathematical theorems in complex fields. The breakthrough uses DeepMind's AI processes to explore conjectures in mathematics, leading to a completely new theorem in knot theory.
A new computational method has been developed to accurately predict oxide reactions at high temperatures, even without experimental data. This approach combines quantum mechanics with machine learning to design clean carbon-neutral processes for steel production and metal recycling.
A research team developed an AI framework that analyzes protein interactions to predict effective and low-toxicity cancer drug combinations. The framework, GraphSynergy, outperforms conventional models in identifying synergistic combinations.
Researchers found that major COVID-19 models were wrong and not very useful in predicting the pandemic course. They used physics-informed neural networks (PINNs) to capture changing parameters and make predictions with improved accuracy.
Researchers used machine learning to identify patterns in knot theory and representation theory, suggesting new connections that mathematicians were able to prove. This collaboration demonstrates the potential of AI as a tool for guiding intuition in mathematical research.
A new artificial intelligence framework called TinNet combines machine-learning algorithms and theories to identify new catalysts for efficient energy production. By understanding how catalysts interact with different intermediates, researchers can design robust catalytic processes that improve daily life.
Researchers developed transformational machine learning (TML) to learn from multiple problems and improve performance while learning. TML out-performs current machine learning methods for drug design, accelerating the identification and production of new drugs.
Researchers have developed a new AI technique, MuSIC, which combines microscopy, biochemistry techniques and artificial intelligence to reveal approximately 70 components contained within a human kidney cell line, half of which had never been seen before.
Researchers used AI to optimize multiple properties of flow batteries, finding molecules that store a lot of energy and remain stable. The study uses quantum chemistry-guided multiobjective Bayesian optimization to identify promising candidates.
A KAUST team developed an improved method for detecting malicious intrusions using deep learning, achieving accuracy rates of up to 99% in simulations of different kinds of attacks. This stacked deep learning approach promises an effective defense against cyberattacks and could prevent outages in critical infrastructure.
Researchers develop a methodological framework to extract and monitor information from consumer reviews, providing actionable insights on product attributes and their benefits. The study also extends sentiment analysis by demonstrating hierarchical sentiment analysis, enabling managers to generate tailored dashboards and inform decisions.
A new 'image analysis pipeline' called TDAExplore gives scientists rapid insight into how cells are changed by disease, using a combination of microscopy, topology, and artificial intelligence. This approach can provide objective information on cell changes, such as the movement of proteins like actin, even with limited training data.
A new web-based application uses machine learning to predict lupus nephritis treatment response, considering various disease indicators. The tool may help physicians identify patients at risk of poor outcomes, enabling them to provide targeted care and preserve as much kidney function as possible.
A new study by USC researchers uses GANs to generate synthetic neurological data that can be fed into machine-learning algorithms to improve BCI usability. This approach improved BCI training speed by up to 20 times and enabled rapid adaptation to new subjects.
Researchers developed a technology that accurately detects lies by analyzing facial muscle contractions, achieving a success rate of 73%. The study identified two distinct groups of 'liars' based on cheek muscle and eyebrow activation, with potential implications for real-life deception detection.
Machine learning enables better understanding of climate-induced hazards, predicting floods and landslides with high accuracy. The technology combines diverse data sources to assess risk extent, considering both triggering hazards and socio-economic vulnerability.
The research team developed a technology for remotely assessing the condition of a building during an earthquake based on the readings from the building's seismometer. The new method uses CNN machine learning to quickly assess damage levels and determine if a building can continue to be used.
Researchers developed EDS-HAT, an AI-powered system combining machine learning and whole genome sequencing to detect clusters of similar infections in real-time. The system identified 99 clusters of infections and prevented potential transmissions in 65.7% of cases, saving the hospital $692,500.
Researchers from Okayama University developed an AI-powered image classifier to simplify and speed up the task of image analysis in cell biology. The system achieved high detection accuracy for mitotic cells in plant species, demonstrating its potential for non-experts to use.
A new study used machine learning to predict the zoonotic capacity of 5,400 mammal species, identifying those at high risk of transmitting SARS-CoV-2. The model, which combined data on biological traits with ACE2 receptor information, predicted 72% accuracy and identified numerous additional species with potential to transmit the virus.
International researchers used machine learning to forecast marsh establishment under various environmental conditions, revealing that controllable local factors are more important than global climate change. The study suggests smart management of tidal flats can counteract threats and strengthen wetlands.
Rice University computer scientists have discovered an inexpensive way to implement rigorous personal data privacy in large databases for machine learning. Using locality sensitive hashing, their RACE method creates small summaries of enormous databases while scaling for high-dimensional data.
Researchers identified 166 prognostic biomarkers from long non-coding RNAs, with one biomarker, HOXA10-AS, showing high effectiveness in categorizing gliomas as low- or high-risk. The study provides potential therapeutic targets and insights into cancer biology.
Researchers at Massachusetts General Hospital developed an AI-based method to predict atrial fibrillation risk based on electrocardiogram data. The method was highly predictive, especially in subsets of individuals with prior heart failure or stroke, and could serve as a pre-screening tool for patients at risk.
Researchers at Peking University developed a non-invasive exhaled breath screening system for COVID-19, identifying key breath-borne VOC biomarkers in just 5-10 minutes. The technology reduces transmission risk by providing quick screenings for false negatives and hospital discharges.
A new machine learning-based approach enhances student engagement in online environments. The algorithm detects when students disengage, prompting interventions to improve learning outcomes.
Researchers at the University of Bern have developed an approach called 'evolving-to-learn' (E2L) that enables computers to discover mechanisms of synaptic plasticity, leading to improved learning capabilities. The algorithm was tested in three scenarios and successfully solved new tasks by mimicking biological evolution.
Researchers have developed a 'time machine' framework that uses artificial intelligence to learn from past environmental changes and predict future biodiversity loss. This framework can help decision-makers prioritize conservation approaches and mitigation interventions, leading to more effective management of ecosystem services.
Researchers have developed an autonomous robot that can open its own doors and find nearby outlets to recharge. The innovation addresses a significant challenge in robotics, enabling helper robots to work independently without human assistance.
Researchers developed a new method to predict stress at atomic scale using machine learning, enabling accurate predictions of grain boundary stresses in actual metal specimens. This breakthrough advances the field of mechanics of materials and enables scientists to engineer stronger and more heat-resistant metals.
Researchers developed AfriBERTa, a neural network model that achieves state-of-the-art results for low-resource African languages. The model works with 11 languages spoken by over 400 million people and requires only one gigabyte of data, compared to thousands for existing models.
A recent study published in PRX Quantum reveals that quantum machine learning algorithms are hindered by excessive entanglement, leading to a phenomenon known as barren plateaus. By limiting depth and connectivity, researchers propose a solution to avoid these regimes and successfully train quantum neural networks.
Researchers from academia and industry will converge at Lehigh University to discuss innovative solutions for optimizing efficiency and resiliency in the global supply chain. The workshop aims to leverage machine learning for prescriptive analytics, enabling proactive optimization of supply chain operations.
FAU researchers custom-build multi-sensor tag combined with AI to observe goliath grouper species in the wild. The study identified 13 behaviors, including hovering, forward swimming, and vocalizations, using video footage from the tags.
Researchers used reinforcement learning to control a small particle moving in a double-well system, achieving accurate control despite noisy measurements. The method shows promise for future applications in quantum technologies and AI.
Researchers at Penn discovered a new suite of antimicrobial peptides, hidden within the human genome, which showed promising natural antibiotic potential. The peptides displayed antimicrobial activity against various pathogens, including E. coli and staph infection-causing bacteria.
A new machine learning-based algorithm has been developed to identify adolescents who have experienced suicidal thoughts and behavior. The algorithm, applied to a large dataset of survey responses from over 179,000 high school students in Utah, shows high accuracy in predicting individual adolescents at risk.
The Gutenberg Gait Database provides a reference set of data for healthy individuals to diagnose and treat gait disorders. The database, compiled from 350 volunteers aged 11-64, offers processed raw data and ready-to-use data for orthopedic institutes and research organizations.
A new study explores the problem of shortcuts in a popular machine learning method and proposes a solution that can prevent shortcuts by forcing the model to use more data. By removing simpler characteristics and asking the model to solve the task two ways, researchers reduce the tendency for shortcut solutions and boost performance.
Research reveals brief DBS exposure triggers significant brain state changes and sustained antidepressant response. A decrease in beta power is identified as a novel biomarker for DBS treatment optimization.
Researchers at University of Missouri and University of Chicago develop an artificial material that can respond to its environment, make decisions, and perform actions not directed by humans. The material uses a computer chip to control information processing and convert energy into mechanical energy.
Astrocytes play a crucial role in self-repairing the brain and may hold the key to creating energy-efficient artificial intelligence. By emulating astrocyte functions in hardware devices, researchers aim to reduce power consumption and increase fault resilience.