Articles tagged with Machine Learning
Researchers are developing a precision phage platform to restore microbiome balance and combat antibiotic-resistant diseases. The MIGHTY project aims to harness phages as targeted antimicrobials, leveraging AI and machine learning methods to identify effective phage combinations.
A new project aims to develop a computationally efficient model that accurately predicts how additive manufacturing process parameters influence the solidification microstructure of binary alloy solidification. This will enable optimization of additively manufactured parts with confidence in critical industries.
A new AI tool, SpectroGen, uses generative AI to quickly assess material quality by generating spectra in less than one minute. It can replace traditional methods that take several hours or days, improving productivity and efficiency in industries such as manufacturing and pharmaceuticals.
A new project aims to enhance workforce readiness in molecular bioscience by creating open-access resources and modules tailored to student needs. The Molecular Data Education Hub will host instructional materials and case studies for instructors to implement into their courses.
MetaSeg achieves the same segmentation performance as U-Nets but requires 90% fewer parameters, making medical image segmentation more cost-effective. The new approach leverages implicit neural representations to quickly adjust to new images and decode accurate labels.
The platform allows users to create personalized LLM-based chatbots, serving as a 24/7 teaching assistant or scraping campus websites to find needed resources. Illinois Chat has already been utilized in various departments and research groups, providing valuable feedback to make it more practical.
A new smartphone-based AI system accurately predicts avocado firmness and internal quality with high accuracy, enabling consumers to avoid overripe avocados. The technology has the potential to assess the ripeness and quality of other foods, reducing global food waste by 50% by 2030.
A new machine learning framework integrates experimental features with microbial biofilm data to optimize bioelectrodechlorination, predicting pollutant degradation rates with high accuracy. The approach reduces reliance on exhaustive laboratory testing while enhancing remediation efficiency and can be adapted for other bioelectrochemi...
Researchers at Texas A&M University have developed a new framework to protect industrial processing facilities from cyber threats. The framework identifies vulnerabilities, detects abnormal activity in real-time, and provides safeguards and mitigation strategies to maintain safe operations.
FastTrack uses machine learning force field and 3D PES sampling to predict atomic migration barriers accurately within minutes, overcoming limitations of traditional methods. It enables quantitative high-throughput screening of ion transport across vast material spaces.
Researchers at MIT have found a hidden atomic order in metals that changes their properties, including mechanical strength and heat capacity. The discovery reveals a new physical phenomenon explaining the persistent patterns and provides a simple model to predict chemical patterns in metals.
Researchers developed an AI-based generative approach to discovering technology opportunities from patent maps using machine learning. The system translates patent vacancies into human-readable text, enabling the identification of untapped technologies and facilitating innovation forecasting.
A new study uses general-purpose AI to classify real cosmic events and explain its reasoning without complex training. The model achieved approximately 93% accuracy and provided plain-English explanations for every classification.
The research team's Cord-Approx strategy coordinates drivers to assign different street parking spots using statistical predictions and an optimal matching algorithm. This approach significantly reduces parking search time, with drivers finding a spot in 6.7 minutes on average.
Florida International University researchers have developed a defensive system called SHIELD that can detect and neutralize cyberattacks on drones in real time. The system allows the drone to finish its mission while providing robust recovery mechanisms, making it crucial for securing commercial drone use across industries.
Researchers at MIT have developed a 3D-printable aluminum alloy that is five times stronger than traditionally manufactured versions. This breakthrough could lead to lighter and more efficient aircraft parts, such as fan blades in jet engines, reducing energy consumption and costs.
Paul Boutros, a pioneer in using AI and machine learning to analyze cancer data, is appointed as the new director of the National Cancer Institute-designated cancer center at Sanford Burnham Prebys. The cancer center will leverage computational tools to personalize therapies for cancer patients.
Scientists at MIT developed a method to predict how plasma in a tokamak will behave during rampdown, achieving high accuracy with limited data. This new model could significantly improve the safety and reliability of future fusion power plants.
A team of researchers from Worcester Polytechnic Institute has developed a new approach to producing hydrogen using plasma technology and metal alloys. The method reduces energy consumption and carbon emissions compared to traditional methods, making it more environmentally friendly and potentially affordable.
A team of researchers developed a computational method that can design intrinsically disordered proteins with desired properties. The work uses automatic differentiation to optimize protein sequences and leverages molecular dynamics simulations for precision. This breakthrough has the potential to reveal new insights into diseases like...
Researchers developed a new imaging method using multiphoton microscopy to rapidly identify pancreatic neuroendocrine tumors with high accuracy. Machine learning algorithms achieved 80.6% accuracy, while convolutional neural networks outperformed with accuracies ranging from 90.8% to 96.4%.
A new study from the University of Gothenburg reveals that trawling restrictions have led to a significant increase in marine life, particularly among filter-feeding species like mussels and soft corals. However, heat-sensitive species are declining at shallow depths due to warmer water temperatures, driven by climate change.
A machine learning approach enhances the treatment of livestock manure, predicting phosphorus distribution and recovery. The process converts biowaste into hydrochar and a nutrient-rich liquid, reducing environmental pollution and supporting sustainable agriculture.
Researchers argue that AI can strengthen pandemic preparedness by detecting emerging diseases earlier. By combining data from humans, animals, and the environment, AI can reveal patterns and provide insights into potential pathogens.
Researchers at Tohoku University utilized AI to develop strategies for water treatment and air pollution control. The study highlights innovative approaches using machine learning in material screening and global distribution simulation of pollutants.
A new 'future-guided' AI method developed at the University of California, Santa Cruz, has shown significant improvements in predicting seizures using brain wave data. The technique operates with two deep learning models working together, improving predictions further into the future by transferring knowledge.
Liberate AI project develops an AI model capable of predicting long-term outcomes and potential complications in ischemic stroke patients. The model will be trained using Swarm Learning technology, aiming for explainability and transparency, while striking a balance between these features and accuracy.
A new AI tool called Waldo can scan social media data to discover personal reports of harmful side effects of popular health products. The tool achieved an accuracy of 99.7% in detecting adverse events, outperforming general-purpose chatbots.
Researchers at Champalimaud Centre for the Unknown used machine learning techniques to show that mice's facial movements reflect their hidden thoughts. This discovery could offer unprecedented insight into brain function and potential new research tools.
A new clinical model integrating CT signatures predicts HCC risk more accurately than existing models, stratifying patients into high-risk and low-risk groups. The study enhances individualized surveillance and improves patient outcomes for cirrhosis patients.
The European Lighthouse on Secure and Safe AI (ELSA) network has grown to include 41 members, expanding its expertise in pressing AI research topics and fostering knowledge exchange. The network's founding members are renowned for their work on Safety, Security, Artificial Intelligence, and Machine Learning.
Researchers at Institute of Science Tokyo developed a new framework for generative diffusion models by reinterpreting Schrödinger bridge models as variational autoencoders. This approach reduces computational costs and prevents overfitting, enabling more efficient generative AI models with broad applicability.
Researchers have developed a new method to boost energy transfer in magnesium batteries using amorphous materials. The approach uses machine learning to simulate the behavior of ions within these materials, leading to significant improvements in rate of energy transfer.
Using embryoid models made of stem cells, researchers uncovered important processes mimicking the first moments of human development. The team applied AI neural networks to thousands of images collected with confocal fluorescent microscopy to detect features and protein marker expression data.
Researchers developed an AI-based system to streamline medical image segmentation, allowing users to mark areas of interest and predict segmentations with decreasing user input. The tool has been shown to outperform state-of-the-art tools in image segmentation tasks, enabling faster study completion and cost reduction.
A cross-sectional study found that FDA-cleared AI-enabled medical devices lack standardized efficacy, safety, and risk assessment, highlighting the need for dedicated regulatory pathways and post-market surveillance to address these challenges.
The book offers novel human-centered AI techniques to address social intelligence challenges, including multimodal approaches and explainable AI designs. Researchers can integrate artificial and human intelligence to tackle complex social challenges.
Researchers developed a platform called CRESt that incorporates insights from literature, chemical compositions, and imaging to optimize materials recipes. CRESt uses robotic equipment for high-throughput testing and large multimodal models to further optimize materials recipes.
Forestry professionals express concerns about AI's impact on land-management decisions and policy, citing 'black box' problems and data quality issues. However, they see potential for AI to support tasks like data analysis and task automation.
Researchers at Chalmers University of Technology have developed new simulation methods using machine learning to understand halide perovskites, a promising material for efficient solar cells. The study provides insights into the structure and behavior of formamidinium lead iodide, helping to address its instability issues.
Scientists at Seoul National University have developed a framework to manipulate emergent behavior in animal groups and robot swarms. The approach uses physics-informed AI to learn local interaction rules, enabling the control of collective patterns such as rings, clumps, and flocks.
The partnership aims to provide cutting-edge professional development to public sector and critical infrastructure leaders across Canada, equipped with strategic skills to outthink and outmaneuver threats. The initiative strengthens Canada's cyber resilience by enhancing national cyber resilience.
Researchers at FAU have developed a smarter AI framework that can manage complex systems with unequal levels of authority and adapt to imperfect information. The framework, based on reinforcement learning and game theory, reduces unnecessary computation while maintaining system stability and optimal strategy outcomes.
A multidisciplinary team led by Natasha Vermaak investigates developing structural materials resistant to high-frequency thermomechanical loads for rotating detonation engines. The project aims to address the lack of established materials solutions for extreme thermomechanical loadings, enabling advancements in propulsion systems.
A wearable device called a-Heal optimizes each stage of the wound healing process using AI and bioelectronics, delivering medication or an electric field for personalized treatment. Initial preclinical results show the device speeds up the healing process by 25% compared to standard care.
Researchers at MIT developed a technique called SCIGEN that steers generative AI models to create promising quantum materials by following specific design rules. The approach led to the synthesis of two actual materials with exotic magnetic traits.
A recent study demonstrates significant gains in forecast accuracy using a hybrid Shanghai Typhoon Model, which combines the strengths of both physics-based and machine-learning weather prediction models. The model achieved substantially lower track errors than state-of-the-art models during Typhoon Danas in 2025.
Researchers developed MoBluRF, a two-stage motion deblurring method for NeRFs, achieving high-quality 3D reconstructions from ordinary blurry videos. The framework outperforms state-of-the-art methods and is robust against varying degrees of blur, enabling smartphones to produce sharper and more immersive content.
A new study from IU researchers found that simple screening questionnaires performed better than advanced machine learning methods in measuring social needs. However, each method had gaps, particularly with financial strain, which was harder to identify.
A deep learning model achieved up to 98% accuracy in distinguishing autistic from neurotypical participants, providing clear insights into brain regions most influential to its decisions. The model could benefit autistic people and clinicians by offering accurate and explainable results to inform assessment and support.
A new AI model trained on routine ECG data accurately predicts patients at risk for life-threatening complications after surgery, outperforming current risk scores. The model achieves 85% accuracy and has the potential to transform decision-making and risk calculation for both patients and surgeons.
Researchers developed an AI-driven algorithm that can predict nearly 70% of hot flashes before they're felt. The Embr Wave wearable device will incorporate this technology to mitigate symptoms and provide meaningful relief.
Researchers developed computational tools to analyze ecological data, identifying functionally equivalent species across different ecosystems. These tools use optimal transport distances to compare network structures, allowing for large-scale monitoring of ecosystem health and guiding conservation efforts.
Researchers are developing a detection system to identify pre-ignitions in hydrogen internal combustion engines (H2-ICE) using machine learning algorithms and onboard sensors. The project aims to address the challenges associated with H2-ICE pre-ignition, which can degrade engine performance and compromise its mechanical integrity.
Researchers developed a custom speech recognition system trained on Supreme Court hearings, reducing transcription errors by up to 9% compared to leading commercial tools. The AI tool semantically matches paragraphs with timestamps, allowing users to scroll through judgements and instantly watch relevant exchanges from the hearing.
A new study developed a flood-forecasting AI that can be tuned for any country, reducing errors in national flood prediction programming. The hybrid model combining the AI with the National Water Model was four to six times more accurate, improving forecast accuracy and potential economic impacts of floods.
Moffitt researchers develop machine learning model to predict urgent care visits for lung cancer patients, incorporating patient-reported outcomes and wearable sensor data. The study shows improved prediction accuracy and provides insights into the interaction of symptoms, sleep quality, and lab results with risk.
The TCT AI Lab equips clinicians with skills to integrate AI into clinical practice, advancing precision, efficiency, and patient outcomes. The initiative is made possible by a generous grant from the Jon DeHaan Foundation.
Researchers from Bielefeld University explore how humans and machines learn differently, highlighting the importance of bridging cognitive science and AI research. The study shows that machines generalize differently than humans, making it crucial for successful human-AI collaboration.
A new paper highlights the importance of human historians in capturing emotional complexity behind world events as AI struggles to accurately represent Holocaust survivors' experiences. Historians possess skills that AI lacks, including the ability to capture human suffering and preserve fracture and silence.