Articles tagged with Machine Learning
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.
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.
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.
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.
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.
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.
A new study improves AI diagnoses by penalizing algorithms for false negatives, which can be more urgent than accuracy. Researchers achieved significant improvements in precision and recall for chronic kidney disease and other conditions using cost sensitivity techniques.
A new algorithm has been developed to train spiking neural networks, mimicking the human brain's structure and function. This approach enables these powerful, fast, and energy-efficient systems to solve complex tasks like image classification with high precision.
A new visual analytics tool, Sibyl, was developed to help child welfare specialists understand machine learning predictions. The tool uses bar graphs to show how specific factors of a case contribute to the predicted risk that a child will be removed from their home within two years.
Pasqal has published a paper in the APS Physics journal presenting a new machine learning protocol called Quantum Evolution Kernel (QEK) for measuring similarity between graph-structured data on quantum computers. QEK is stable against detection error and comparable to state-of-the-art graph kernels on classical systems.
A team of researchers has developed an ensemble-based machine learning model that can predict how cancer patients will respond to certain drugs with high accuracy. The model was trained on data from 499 independent cell lines and validated against a clinical dataset containing seven chemotherapeutic drugs.
Researchers at Bar-Ilan University have developed a novel treatment method that destroys cancer cells by targeting the cytoskeletal protein WASp, which is unique in active hematologic cancer cells. The approach uses small molecule compounds identified through AI and machine learning to inhibit proliferation and destroy malignant cells.
Researchers developed an automated and accurate interpretation of chest CT scans using Machine Learning technique Multiple Instance Learning (MIL). The new framework, DA-CMIL, differentiates between COVID-19 and bacterial pneumonia with performance on par to state-of-the-art methods.
Researchers at Duke University used new tools to monitor neurons and analyze machine learning data to see how zebra finches practice their courtship calls. They found that a neurotransmitter called noradrenaline shuts down variability in the song, making it more precise when performed under pressure.
A recent study published in Nature Machine Intelligence challenges the long-held assumption that accuracy and fairness are mutually exclusive in machine learning. Researchers found that optimizing models for accuracy does not necessarily compromise fairness, particularly when adjustments are made to data, labels, and scoring systems.
Researchers at University of Pittsburgh and Prairie View A&M University developed an algorithm to repurpose cancer drugs for pulmonary hypertension, a devastating lung disease. Two compounds improved human cells and rodent markers, supporting broader drug-repurposing platform use.
Research at RMIT University uses Getty's top lists of editorial pictures to analyze daily investor sentiment, predicting stock market returns based on global mood. The algorithm produces a daily score from 10 popular photos, providing a quick snapshot of investment mood across developed and emerging economies.
Researchers at Osaka University developed a deep neural network to accurately determine qubit states despite environmental noise. The novel approach may lead to more robust and practical quantum computing systems.
Researchers at MIT develop a data-driven process using machine learning to optimize new 3D printing materials with multiple characteristics. The system lowers costs and lessens environmental impact by reducing chemical waste and suggesting unique chemical formulations that human intuition might miss.
The INRS team has developed an intelligent optical chip that uses autonomous learning approaches to generate optical waveforms, paving the way for further advances in telecommunications. The device can autonomously adjust to a user-defined target waveform with strikingly low technical and computational requirements.
Researchers used machine learning on UK Biobank data to create proxy measures for brain age, intelligence, and neuroticism traits. These indirect measurements strongly correlate with specific diseases or outcomes, offering a potential solution for mental health diagnoses.
A new study by MIT researchers has found that blind and sighted readers have sharply different takes on what content is most useful to include in a chart caption. The study created a four-level framework for evaluating charts, which could help develop more effective tools for automatically generating captions and alternative text.
Researchers at Johns Hopkins University have developed a non-invasive optical probe to understand the complex changes in tumors after immunotherapy. Using Raman spectroscopy and machine learning, they identified key features that indicate how tumors respond to treatment, showing promising results for predicting patient response.
Researchers have proved the existence of universal adversarial examples that can deceive multiple quantum classifiers. The study also reveals the universality aspect of adversarial attacks for quantum machine learning systems, providing valuable insights for future applications.
A recent study published in PLOS Medicine found that people with mental illnesses have poorer sleep quality compared to the general population. The study analyzed data from 89,205 participants and discovered significant differences in sleep patterns, including increased waking frequency and duration.
A new algorithm developed by Carnegie Mellon University researchers offers a powerful tool for illustrating genome folding in cell nuclei. The Higashi algorithm analyzes chromatin interactions using single-cell Hi-C technology, revealing detailed variations in genome organization from cell to cell.
Researchers developed an algorithm that leverages medical informatics to predict autism spectrum disorder (ASD) diagnoses in young children. The new approach uses diagnostic codes from past doctor's visits to calculate a risk score, identifying which patients are at risk of receiving a confirmed ASD diagnosis.
A team of researchers from the University of Illinois Urbana-Champaign used advanced machine learning to model the physico-chemical properties of a molten salt compound called FLiNaK, enabling accurate atomic-scale reproduction and prediction of behavior under specific reactor conditions. This computational framework can help character...
Functionalized metal-organic frameworks (MOFs) show improved hydrogen interaction, increasing storage capabilities by 15-80%. The study uses machine learning to predict binding energy and reduce computationally heavy calculations.
A novel machine learning approach has been developed to understand symmetry and trends in materials, enabling researchers to group similar classes of material together. The technique uses a large, unstructured dataset gleaned from 25,000 images to identify structural similarities and trends.
A new deep-learning algorithm, ECNet, has been developed to accelerate protein engineering by predicting the fitness of all possible sequences. By incorporating evolutionary history, ECNet outperforms current methods on several datasets and identifies novel mutants with improved fitness.
Researchers developed a novel machine learning algorithm to identify previously unknown air pollutant mixtures linked to poor asthma outcomes in children. The study found that early exposure to individual and mixed pollutants can lead to longer-term problems with asthma, affecting about seven percent of US children.