Articles tagged with Machine Learning
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...
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 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.
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.
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.
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.
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.
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.
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.
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 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.
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.
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 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.
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 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.
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.
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 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.
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.
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.
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.
Biochar, a carbon-rich material, is gaining attention for its ability to improve soils, clean water, and capture carbon. Machine learning models can predict biochar yield and pollutant removal efficiency with over 90% accuracy, accelerating its development.
A new study predicts an increase in western US wildfires sparked by lightning strikes, with 98% of the region seeing more risk days by 2060. The western US is expected to see a significant rise in lightning days, with areas like Oregon and Idaho experiencing up to 12 more days per summer.
Ves-GAN, an unsupervised vessel-targeted denoising framework, significantly improves LDCTA imaging by minimizing noise while preserving intricate vascular structures. This innovation enhances diagnostic accuracy in cardiovascular disease diagnosis, directly impacting patient outcomes.
A new AI-powered method has reduced antibody discovery time from weeks to under a day, offering a scalable approach that minimizes data bottlenecks and accelerates research. This breakthrough could transform pandemic response and therapeutic development, particularly during health emergencies where rapid response is critical.
Researchers at Mount Sinai are developing an AI-based predictive tool that can analyze complex sleep study data to predict cardiovascular event risk and treatment response among individuals with obstructive sleep apnea. The findings could help clinicians make more informed recommendations about OSA treatment for their patients.
Experts weighed in on the current state of artificial intelligence (AI) in cancer care, noting both its potential for improving efficiency and its challenges. Many expressed excitement over AI's possibilities for helping support an overburdened oncology workforce and accelerating the pursuit of new cures.
Researchers are decoding animal decision-making using glass knifefish, exploring the trade-off between gathering information and acting on it. The study, funded by the NIH, aims to understand how animals make decisions in uncertain environments and may lead to breakthroughs in robotics and medicine.
Researchers at Politecnico di Milano developed photonic chips for training physical neural networks, eliminating digitisation requirements. This allows for faster, more robust, and efficient network training using light signals.
Researchers have developed a new light-based chip that cuts power consumption for image recognition tasks by up to 100 times, using lasers and microscopic lenses fabricated onto circuit boards. This breakthrough enables faster performance and potentially strain-free AI systems.
Researchers have developed a silicon chip that uses light to perform convolution operations for AI, reducing energy consumption and increasing speed. The chip achieves near zero energy performance, leap forward for future AI systems.
The Global RETFound initiative aims to develop the world's first globally representative AI foundation model in medicine, addressing health inequalities and AI bias. The project involves a diverse dataset of over 100 million fundus photographs sourced from 65+ countries.
Researchers used AI models with integrated clinical and claims data to predict chronic kidney disease (CKD) progression to end-stage renal disease (ESRD). The study found that the models outperformed single data source models and reduced racial bias. The findings can inform likelihood and management of CKD, supporting targeted interven...
Researchers harness AlphaFold 3 to predict how T cells recognize peptides, opening avenues for precision immunotherapy and vaccine design. The approach enables in silico identification of immunogenic epitopes that could serve as vaccine targets.
A recent breakthrough in MXene nanomaterials could enable bespoke design tailored by AI. Researchers have discovered the thermodynamic forces underlying their unique structure and behavior, setting parameters necessary for AI-guided design.
A CISPA researcher has been awarded an ERC Starting Grant to tackle the issue of data leaks in large AI models. The project aims to develop new methods for protecting private training data, making it a crucial step towards ensuring trust in artificial intelligence.
Researchers at UCR have developed a method to preserve AI safeguards in open-source models by retraining internal structure to detect and block dangerous prompts. The approach avoids external filters or software patches, instead changing the model's fundamental understanding of risky content.
Researchers used AI to identify four times more earthquakes than earlier tools and pinpoint previously unknown faults in the region. The study expands seismicity recorded by monitoring stations from 2022 to 2025, revealing two faults converging under the town of Pozzuoli west of Naples.
The Wits MIND Institute has received a $1 million boost from Google.org, enabling it to drive next-generation breakthroughs in natural and artificial intelligence. The partnership aims to advance the scientific understanding of both natural and artificial intelligence, foster breakthrough research and technological innovation.