Articles tagged with Machine Learning
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.
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 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.
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.
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.
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 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.
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.
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 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.
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...
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.
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.
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.
A global study surveyed 14,000 patients across 43 countries, finding that those in poorer health were more likely to reject AI. Patients preferred explainable AI and wanted clinicians to make final decisions.
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.
A new study uses machine learning to optimize the adsorption capabilities of biochar for dye removal, identifying optimal conditions for maximum efficiency. This research has significant implications for addressing water pollution and achieving environmental sustainability.
Researchers developed an AI model that makes individualized treatment recommendations for atrial fibrillation patients, potentially revolutionizing stroke and bleeding prevention. The model reclassified up to half of AF patients from receiving anticoagulants under current guidelines.
Researchers at EPFL developed BindCraft, an open-source AI platform that uses AlphaFold2 to generate novel binders with desired functional properties. The platform reduces the need for high-throughput screening and makes protein design more democratized.
A new AI tool has identified over 1,000 'questionable' scientific journals, including those that charge hundreds or thousands of dollars to publish research without proper vetting. The AI system uses six criteria to evaluate journals, including editorial board composition and website quality.
A former diplomat warns that algorithms lack empathy and intuition, which are essential for successful negotiations. However, AI can streamline diplomacy and amplify human aspirations when used carefully. Diplomats need training in AI ethics and global cooperation to ensure equal access and deployment.
An interdisciplinary team analyzed data from 31 sub-Saharan African countries, identifying key socioeconomic factors associated with maternal and child health services use. The study found that socioeconomic status plays a strong link in accessing these life-saving services.
A team of computer scientists at UC Riverside has developed a method to erase private and copyrighted data from artificial intelligence models without needing access to the original training data. The approach enables AI models to 'forget' selected information while maintaining functionality with remaining data.
SMU's latest project aims to improve AI-driven defect detection in aircraft surface inspections, tackling challenges like inconsistent lighting conditions and limited training data. The project uses knowledge from large vision-language models to spot defects accurately and adapt to changing environments.
Researchers Marie Teich and Wilmer Leal develop a formal framework to analyze metaphors, confirming they are enduring linguistic and cognitive structures. The study reveals two significant metaphorical processes: mappings from concrete to abstract topics and the emergence of new mappings between domains.
A new study published in Clinical Gastroenterology and Hepatology shows that AI analysis can accurately identify mucosal ulceration as well as human reviewers, providing a more objective and reproducible assessment of Crohn's disease. The findings have potential implications for education, drug development, and automated care.
Researchers at the University of Vaasa developed smart packaging that can detect subtle color changes in printed packages, enabling cost-effective solutions for industries like food and beverage, healthcare, and logistics. This technology provides a human-eye accurate and environmentally friendly alternative to electronic sensors, pavi...
ConcreteSC technology achieves significant speed boosts and improved efficiency in next-generation wireless networks. The innovation integrates user tasks into communication processes, reducing computational complexity and increasing semantic meaning.
A new Mayo Clinic AI tool, UNISOM, has shown promising results in identifying early signs of clonal hematopoiesis of indeterminate potential (CHIP), a condition that raises the risk of leukemia and heart disease. The tool helps detect CHIP-related mutations in standard genetic datasets, opening new avenues for research and discovery.
Simpler, physics-based models can generate more accurate predictions than state-of-the-art deep-learning models for certain climate scenarios. However, simple models are more accurate when estimating regional surface temperatures, while deep-learning approaches excel at local rainfall estimation.
A new reporting checklist introduces key methodological details to improve reproducibility and transparency in AI-based automated image analysis. The guidelines will support clear communication of methods and reduce cognitive bias, promoting the translation of AI tools into routine pathology workflows.
Researchers at Uppsala University developed an AI model that can accurately predict battery ageing, leading to longer life and enhanced safety for electric vehicle batteries. The model reduces the need for sensitive vehicle data and provides a detailed picture of chemical processes inside batteries.
Researchers developed a systematic solution combining high-throughput calculations and machine learning to find high-performance materials. Thermal expansion enhances thermoelectric performance by reducing lattice thermal conductivity and increasing the Seebeck coefficient.
A new Concordia study reimagines parcel delivery by integrating electric vehicles, autonomous delivery robots, and self-service lockers to prioritize high-value customers like Amazon Prime members. The innovative model cuts route and vehicle use costs by up to 53% compared to traditional methods.
Researchers developed a dynamic nomogram to predict long-term survival in patients with brain abscess, identifying key predictors such as age, Karnofsky performance status, and hemoculture results. The model offers an interactive tool for individualized risk assessment, facilitating better treatment decisions and improving outcomes.
The new book, Monster Transformation, argues that employees are key to overcoming transformational hurdles. By uncovering unique competencies and empowering them, organizations can break through challenges and succeed in a rapidly evolving space. The book offers a story-driven approach to meeting the needs of the current technological ...
Researchers developed a machine learning model that accounts for biological variability to identify optimal formulations for serum-free culture media. The model achieved approximately 1.6-fold higher cell density compared to commercially available products.
The new model can predict how well any given molecule will dissolve in an organic solvent, helping chemists choose the right solvent for reactions. The researchers trained two models on a comprehensive dataset and found that their predictions were two to three times more accurate than the previous best model.
The grant aims to support novel applications of data visualization technology that benefit various stakeholders in the research ecosystem. Successful applicants will receive up to £25,000 for their innovative tech ideas.
The study highlights how machine learning offers adaptive, data-driven alternatives for precise control and accurate characterization of quantum systems. Tools like neural networks and attention-based architectures have shown promise for quantum tomography.
MIT researchers have developed a method to reveal the inner workings of protein language models, which can accurately predict proteins suitable for drug or vaccine targets. By analyzing sparse representations of proteins, they identified key features that drive these predictions.
A new study introduces a machine learning tool that combines satellite imagery and weather data to monitor chickpea crop health. The system accurately estimates Leaf Area Index (LAI) and Leaf Water Potential (LWP), enabling farmers to make smarter irrigation decisions and improve yields.
Researchers developed an AI framework to map interactions between content and algorithms on digital platforms, reducing the spread of misinformation. The system would allow users and platform operators to pinpoint sources of potential misinformation and promote diverse information sources.
Researchers trained GAMES to generate accurate SMILES combinations, enhancing computer-aided drug design and accelerating discovery. The model combines LoRA and QLoRA techniques to reduce hardware and energy needed.
Researchers developed a novel approach called R3DG that analyzes representations at varying granularities to capture nuanced emotional fluctuations and reduce computational complexity. This framework demonstrates superior performance in multiple multimodal tasks, including sentiment analysis, emotion recognition, and humor detection.
A research team developed a new method to precisely edit DNA by combining genetic engineering with artificial intelligence. The technique enables accurate modeling of human diseases and lays the groundwork for next-generation gene therapies.
Math experts at Aberystwyth University are using AI to optimize kiln packing, aiming to increase production capacity and reduce greenhouse gas emissions. The project aims to create customised algorithms for dense packing complex-shaped objects, leading to a significant reduction in the ceramic industry's carbon footprint.
Researchers have developed an AI system that can recognize the early warning stages of laryngeal cancer from voice recordings. By analyzing variations in tone, pitch, and volume, the AI can distinguish between voices with benign vocal fold lesions and those with cancer. The study's findings offer a promising breakthrough for non-invasi...
Researchers are combining machine learning algorithms with neuromorphic hardware to build brain-like devices that can learn from data and adapt in real-time. These devices have the potential to revolutionize industries such as manufacturing by enabling machines to sense their environment, adapt to new tasks, and make decisions without ...
The Intelligent Data Exploring Assistant (IDEA) framework combines large language models with scientific data to analyze complex geoscience data. Researchers can ask IDEA to retrieve data, run analyses, and generate plots using plain-language questions.
Researchers from Shibaura Institute of Technology used machine learning algorithms to predict bearing layer depth and assess liquefaction risk. The study found that random forest models outperformed others, especially with increasing spatial data density.
Researchers developed a machine learning model to predict liquid crystalline polyimides with high thermal conductivity, achieving 96% accuracy. The model identified six promising candidates, which demonstrated up to 1.26 W/mK thermal conductivities, accelerating the development of efficient thermal materials.
A large-scale modeling study led by MIT researchers reveals that dynamically adjusting vehicle speeds can cut annual city-wide intersection carbon emissions by 11-22%. Implementing eco-driving measures could also result in a 25-50% reduction in CO2 emissions if only 10% of vehicles adopt the technology.
Researchers led by Francesco Fedele analyzed 27,500 wave records to debunk the conventional explanation of rogue wave formation. Instead, they found that linear focusing and second-order bound nonlinearities are the primary drivers behind these massive waves.
A new study suggests that an AI-driven surgical education model can significantly improve the quality of resident training, reducing errors and costs. The model uses artificial intelligence algorithms and extended-reality headsets to simulate complex procedures without instructor presence.
Researchers found that AI chatbots can easily repeat and elaborate on false medical information, but a simple warning prompt can significantly reduce this risk. The study suggests that stronger safeguards are needed before these tools can be trusted in healthcare.
A large-scale study found that Green AI significantly improves operational and environmental performance in Pakistani SMEs, but only when leaders invest, and institutions are in place. Adoption works under the right conditions, with perceived ease of use and usefulness driving behavior.