Articles tagged with Machine Learning
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 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.
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 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.
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.
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.
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.
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 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...
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.
The University of Houston is part of a $50 million NIH-funded consortium to increase AI diversity and address health inequities. The AIM-AHEAD program will bring together experts in AI, data science, and health equity research to develop more inclusive AI tools.
A new machine learning algorithm has enabled researchers to automatically identify and map the inner structures of cells, including organelles, with unprecedented precision. By processing tens of thousands of high-resolution images, scientists have gained insights into how these structures interact and are arranged within the cell.
Researchers at MIT develop RFusion, a robotic system that uses data from a camera and radio frequency antenna to locate and retrieve lost items. The system relies on RFID tags and machine learning algorithms to optimize the robot's trajectory and grasp the object.
Researchers at GlaxoSmithKline and CCDC combined proprietary and published datasets to train machine learning models for predicting stable polymorphs in new drug candidates. The approach leverages the large volume and variety of data in the Cambridge Structural Database, resulting in more confident predictions and improved model accuracy.
Researchers developed a dynamic respirator that modulates pore size in response to changing conditions like exercise and air pollution. The device features an AI-powered system that adjusts filtration characteristics wirelessly, providing improved breathability and comfort.
A study published in PLOS Biology suggests that machine learning models using viral genomes can predict the likelihood of an animal-infecting virus infecting humans. The researchers identified generalizable features in viral genomes that are independent of taxonomic relationships and developed models to identify candidate zoonoses.
A new study using machine learning uncovers 'genes of importance' in plants that help them grow more efficiently with less fertilizer, reducing economic and environmental costs. The approach also predicts additional traits in plants and disease outcomes in animals.
Researchers at the University of Rochester have developed a time-domain single-pixel imaging technique that detects ultrafast light pulses with high accuracy and speed. The new method can capture 5 femtojoule pulses with temporal sampling sizes as low as 16 femtoseconds, outperforming existing methods.
Researchers developed a method to apply deep learning to polarization-sensitive optical coherence tomography, enhancing cancer diagnosis. The technique enables OCT systems to detect abnormalities on a deeper level, differentiating microstructural features such as collagen fiber orientations.
Researchers developed an AI model to classify patients with localized breast cancer as high or low risk of metastatic relapse. The study shows that AI can accurately assess relapse risk with an AUC of 81%, providing a valuable aid for therapeutic decisions and avoiding unnecessary chemotherapy.
Researchers at Osaka University used machine learning to analyze locomotion data from diverse species, revealing common features associated with dopamine deficiency. The study found that worms, mice, and humans exhibit similar movement disorders when lacking dopamine, despite their evolutionary differences.
A set of guidelines published in Nature Methods provide recommendations for better reporting standards in AI methods used to classify biomedical data. The guidelines aim to ensure the quality and reproducibility of predictive methods, addressing issues such as accuracy, bias, and reproducibility.
Researchers developed DamageMap, an AI system that uses machine learning to identify building damage from post-wildfire images, achieving 92% accuracy. The system can analyze satellite and aerial photos to pinpoint damaged buildings, providing immediate results for first responders and fire victims.
A recent study employs machine learning to guide the design of novel materials for CO2 capture, identifying elemental composition and textural properties as key factors. The research team's findings suggest prioritizing adsorption parameters and surface area optimization for high CO2 adsorption efficiency.
Researchers develop crowd-assisted deep learning system to analyze disasters, integrating human intelligence with AI models for better results. The project aims to improve AI's interpretability and accuracy in disaster assessment applications.
Researchers developed a new, accurate method to detect North Atlantic Right Whale up-calls using Multimodal Deep Learning algorithms. The technology outperformed conventional methods in detecting up-calls, non-up-calls, and false alarms.
The Ariel Machine Learning Data Challenge has announced its winners, ML Analytics and TU Dortmund University, who developed highly accurate solutions for observing exoplanet atmospheres despite instrument noise. The winning teams will receive a cash prize of €500 and present their research at various conferences.
A team of NTU Singapore scientists developed a predictive computer model called NSGA-II, which proposed strategies to reduce COVID-19 infections and deaths by an average of 72%. The model recommended timely and country-specific advice on interventions such as home quarantines and social distancing measures.
Researchers developed an AI tool using data from hospitals across four continents to predict oxygen needs of hospital Covid patients anywhere in the world. The study achieved high-quality predictions with a sensitivity of 95% and specificity of over 88%, demonstrating the transformative power of federated learning in healthcare.
Researchers developed a machine learning model that identified promising compounds for treating yellow fever. The team synthesized five of the most active molecules and found one with a half-maximal effective concentration of 3.2 uM, suggesting a potential new antiviral drug.
Researchers developed a machine learning model that produced fewer decision-making errors than human behavior analysts. The AI system showed improved consistency and predictability in treatment decisions, with potential applications in diagnosing and treating autism, ADHD, anxiety, and depression.
The NYU-led Learning the Earth with Artificial Intelligence and Physics (LEAP) center will combine artificial intelligence and climate modeling to better predict climate change impacts. The center aims to provide more accurate climate predictions by analyzing satellite images, large-scale observational data, and developing new algorithms.
The RIT workshop series on sustainable computing aims to create computers with environmental consciousness from raw materials to recycling. Keynote speakers will discuss trends in computing and its environmental footprint.
Researchers developed a robust, deep neural network model to analyze automobile traffic impacts of construction zones. The model estimates hourly traffic volumes without adjustment factors, helping transportation agencies plan for efficient work zone operations.
A team of researchers from the Beckman Institute for Advanced Science and Technology has developed a fast, accurate, and cost-effective COVID-19 test. Using label-free microscopic imaging combined with artificial intelligence, they can detect and classify SARS-CoV-2 in under one minute.
The article explores the potential of AI in improving climate modeling by proposing a hybrid approach combining classical and machine learning methods. Researchers found that AI can help improve forecasts, but limitations include computational resource requirements and uncertainties in predictions.
A new platform called LEARNER allows researchers to share private patient data from multiple institutions while protecting privacy. This enables single institutions to access advanced predictive tools and improve patient outcomes.
Researchers at The Hebrew University of Jerusalem have developed a new deep learning artificial infrastructure inspired by individual neurons. Their approach uses complex mathematical modeling to replicate the brain's electrical processes and create more intelligent AI systems.
A new electronic 'nose' has been developed to detect when a lung transplant is beginning to fail, with 86% accuracy. The device uses machine learning algorithms to analyze exhaled breath patterns and identify lung diseases, offering new hope for patients diagnosed with chronic allograft dysfunction.
A team of researchers at Osaka University created a custom dataset to train an AI algorithm to digitally remove unwanted objects from building façade images. The algorithm achieved high accuracy in inpainting occluded regions with digital inpainting.
A new algorithm, Phe2vec, accurately identified patients with certain diseases, outperforming traditional methods in classifying diagnoses. The study suggests that this automation will facilitate further research in clinical informatics.
A new machine learning technology can rapidly screen for genetic syndromes in children, considering facial variability related to sex, age, racial and ethnic background. This innovation has the potential to reduce health inequality in under-resourced areas worldwide.
Researchers at Technical University of Munich have developed a new machine learning algorithm that can analyze complex markets and their equilibrium strategies. This breakthrough has potential applications in auction theory, wireless spectrum auctions, and more.
A new unsupervised machine learning algorithm, B-SOiD, developed by Carnegie Mellon University researchers makes studying animal behavior more accurate and efficient. The algorithm identifies patterns in an animal's body position to discover behaviors, removing human error and bias.