Articles tagged with Machine Learning
A new control allocation method using a neural network improves the performance of quadrotor controllers by considering aerodynamic effects. This approach reduces errors in command generation and delivers better thrust and torque signals.
A University of Arkansas professor is developing learning algorithms for building fair decision models in both offline and online learning settings. He will utilize Pearl's Structural Causal Model to analyze causal effects from observational data and propose universal formulations for measuring long-term fairness.
The new computational tool, AF2Complex, predicts the structure of protein complexes and their interactions, offering insights into biomolecular mechanisms. The model is based on AlphaFold 2 and performs well in predicting protein structures and complex formations.
A new KAUST study uses machine learning to predict disease spread with high accuracy, dynamically incorporating latest data without human bias. This approach offers a promising alternative to conventional models, providing a more accurate story of the underlying epidemic data.
A novel method developed by the University of Tsukuba uses drones and machine learning to estimate the amount of plastic litter in rivers. The approach combines high-resolution optical and thermal images, resulting in more accurate estimates than other methods.
A study found that trainee teachers who received AI-generated feedback improved their diagnostic reasoning, identifying potential learning difficulties in pupils more accurately. The AI system analyzed the trainees' work and provided clear, adaptive feedback.
Scientists develop models that complement simulations using reinforcement learning and numerical methods to predict climate change, turbulent flows, and morphogenesis. This approach enables faster and more energy-efficient predictions, solving complex problems in engineering and climate applications.
Researchers used AI to analyze videos of over 2,500 ASL signs and found that challenging signs are made closer to the signer's face, making them easier for perceivers to recognize. This suggests that ASL has evolved to be more recognizable, improving communication.
A machine learning model has been trained to accurately identify individuals with post-traumatic stress disorder (PTSD) by analyzing text data. The model achieved an 80% accuracy rate in distinguishing between those with and without PTSD. This breakthrough could lead to the development of a cost-effective screening tool for health prof...
A proof-of-concept study demonstrates that machine learning technology can enable humans to see in the dark with full-color night vision. The research uses deep learning to enhance color perception in low-light conditions.
Researchers have developed a new method called Shared Interest that enables users to aggregate, sort, and rank individual explanations of a machine-learning model's reasoning. This technique uses quantifiable metrics to compare how well the model's reasoning matches human thinking, helping to uncover concerning trends in decision-making.
A novel 'rational' neural network reveals underlying mathematical equations through Green's functions, enabling humans to understand machine-generated findings. This breakthrough in partial differential equation learning holds promise for advancing scientific exploration of weather systems, climate change, and more.
A new Stanford University-led study uses machine learning and human insights to map regions and ports most at risk for illicit practices, like forced labor or illegal catch. The results highlight two main risk factors: the vessel's flag state and type of fishing gear onboard.
A new e-nose prototype, NOS.E, can distinguish between six whiskies by brand names, regions, and styles in under four minutes, with 100% accuracy for region detection and 96.15% for brand name identification. The technology has applications beyond whisky, including counterfeiting detection in perfume and wine.
Researchers found that AI-enhanced diagnosis helps doctors accurately detect fetal congenital heart disease, with fellows making the most accurate diagnoses. The new system uses graphical charts to represent the AI's analysis of ultrasound videos, improving accuracy and trust among medical professionals.
Researchers at MIT developed a framework for robotic manipulation systems that can perform complex tasks using a two-stage learning process. This allows robots to learn abstract ideas about manipulating deformable objects, such as pizza dough, and execute skills to complete tasks.
Researchers integrated biological signals with gold-standard machine learning methods to create emotionally intelligent speech dialog systems. The study found that combining language information with biological signal information improved the AI's performance, making it comparable to human-like emotional recognition.
Researchers at MIT created a process called DualFair that can remove bias from data used to train machine-learning models. The method tackles both label bias and selection bias, significantly reducing discrimination in loan predictions while maintaining high accuracy.
The VIGICOVID system uses deep neural architectures to answer natural language questions about COVID-19 and SARS-CoV-2. Researchers have developed a prototype that provides good results and can be easily scaled up for marketability.
Recent advances in machine learning (ML) and artificial intelligence (AI) have revolutionized the field of metaphotonics. The integration of ML with photonics enables the creation of intelligent systems that can adapt to changing environmental conditions. Self-adapting systems, such as cloaks that adjust themselves to changes in freque...
Researchers developed an AI-driven image analysis pipeline that identified novel cellular hallmarks of Parkinson's disease from images of over a million skin cells. The platform can distinguish between patient cells and healthy controls, revealing new signatures for potential therapeutic targets.
Researchers developed an AI method to analyze electronic health record data and predict optimal drug regimens for type 2 diabetes patients with similar characteristics. The algorithm successfully supported medication selection for over 83% of patients, leading to better management of the disease and improved patient engagement.
Researchers developed a lightweight exoskeleton that uses machine learning to predict user intentions and provide assistance. The system successfully helped participants stand up, demonstrating potential for supporting individuals with mobility impairments.
Researchers warn of machine learning bias when data published for one task is used to train algorithms for a different one. This can lead to compromised integrity and 'overly optimistic' results in medical imaging applications.
Engineer Thomas Senftle at Rice University has won a prestigious NSF CAREER Award to improve catalysts through machine learning. He will develop open-source models to speed up the development of catalysts with optimized particle/support combinations, aiming to reduce unwanted molecules in water.
Researchers developed an AI-powered model to assess rare-earth compound stability, leveraging machine learning and high-throughput density-functional theory. This framework has far-reaching applications in materials science, including designing new compounds for clean energy technologies and optimizing magnetic properties.
A new study from St John's College, University of Cambridge suggests that robots can help produce solar fuels, accelerating the world's transition to green renewables. The 'cyber-leaf' concept uses AI and robots to create sustainable syngas, reducing reliance on fossil fuels.
A new AI method accurately estimates timetable robustness within milliseconds, enabling efficient optimization. This improves balance between passenger needs and economic conditions.
Scientists developed an AI approach to model and map the Earth's natural features in greater detail and accuracy. The new system can recognise intricate features and aspects of the terrain far beyond traditional methods, generating enhanced-quality environmental maps.
A team of researchers from McGill University and other institutions analyzed 6,850 testimonials from psychedelic users, creating a 3D map of brain receptors linked to subjective experiences. The study found associations between serotonin receptors and ego-dissolution, suggesting potential new treatments for psychiatric conditions.
Researchers at NC State University have developed a 'self-driving lab' that uses artificial intelligence and fluidic systems to advance our understanding of metal halide perovskite nanocrystals. The technology can autonomously dope MHP nanocrystals, adding manganese atoms on demand, allowing for faster control over properties.
A study published in Frontiers in Microbiology has found that machine learning analysis of microscopy images can be used to identify bacteria resistant to antibiotics. Researchers discovered that shape changes in bacterial cells can predict drug resistance, suggesting a new approach for detecting and predicting drug resistance.
Researchers at Duke University found a collection of coordinated brain regions that predict and direct social behavior in mice. By analyzing the electrical activity of these regions, they identified how social or solitary an individual mouse is and were able to prompt them to be more gregarious. This study may lead to better diagnostic...
Researchers using machine learning methods risk underestimating uncertainties in their final results due to decorrelation with imperfections in simulations. This could weaken or bias classifier algorithms' ability to identify fundamental particles.
A Bocconi University study used machine learning to analyze data on 2038 couples in Germany, finding that life satisfaction and housework are key predictors of union dissolution. The analysis also revealed complex interactions between variables, including the impact of personal traits like openness and extraversion.
Researchers at Texas A&M University developed a novel error estimator using transfer learning principles to evaluate machine-learning model performance. The technique enables fast screening of source data sets, improving the accuracy of diagnoses in complex medical issues like schizophrenia.
Researchers at West Virginia University are using machine learning and geographic information systems to identify areas with low COVID-vaccine uptake. They aim to pinpoint counties with increased risk of outbreaks, predict where testing is most crucial, and develop targeted interventions to increase testing rates. By acknowledging comm...
Researchers in Brazil developed a computer program that locates swimming pools and rooftop water tanks in aerial photographs, using artificial intelligence to identify socio-economically deprived urban areas at risk for diseases transmitted by Aedes aegypti. The innovation can be used as a public policy tool for dynamic socio-economic ...
A new mathematical model developed by UCI researchers combines human and algorithmic predictions and confidence scores to improve AI accuracy. The hybrid model outperforms individual human or machine predictions, demonstrating the potential of human-AI collaboration in building smarter AI systems.
Researchers have created a groundbreaking dataset of ultrasonography scans of three major arteries supplying blood to the brain in children. The dataset consists of 821 participants, allowing for the development of machine learning models that can accurately predict a child's age and cognitive abilities based on their ultrasounds.
A team of researchers has developed a DNA-based data storage platform with an expanded molecular alphabet, enabling the storage of vast amounts of digital information. The new system uses nanopores to distinguish between natural and chemically modified nucleotides, increasing storage density and sustainability.
A new framework for portfolio management uses deep reinforcement learning to predict price trends and make strategic decisions, overcoming limitations of existing systems. The system consists of evolving agent modules and strategic agent modules, allowing for modular design and scalability.
Researchers at Mainz University will examine algorithm decisions on transparency, fairness, and data protection while optimizing resource use. The project aims to create workable trade-offs for applications and integrates young researcher promotion programs.
A machine learning model has been developed to predict cognitive performance based on reaction time, eye movement, and difficulty level. The model achieved an accuracy of 82.8% in predicting participant success in visual attention tasks.
Researchers at University of Illinois develop new method to accurately estimate soil organic carbon using airborne and satellite hyperspectral sensing. The study leverages machine learning algorithms with a comprehensive soil spectral library, enabling large-scale monitoring of surface soil organic carbon.
Researchers propose various approaches using AI, deep learning, and machine learning to improve the accuracy and predictive power of biomarkers for cancer and other diseases. The tools have shown promising applications in identifying early-stage cancers, inferring the site of specific cancers, and predicting response to immunotherapy.
Researchers found that ant colonies use an algorithm similar to the internet's data optimization, which senses and stabilizes behavior. This principle is also used in cells and neurons. Nature's algorithms may inspire new cybersecurity strategies or alternative approaches to gene regulation.
Researchers developed a machine learning model that provides good predictions for human speech recognition in noisy environments, benefiting hearing-impaired listeners. The model outperformed expectations and showed strong correlations with measured data.
The end-Permian mass extinction was characterized by a 10-degree climate warming, with 75% of organisms going extinct on land and 90% in oceans. Machine learning analysis reveals that declining oxygen levels, rising water temperatures, and ocean acidification were the key factors in organism survival or extinction.
The SynGAP Research Fund has developed a pre-screening tool to identify potential SYNGAP1 patients through a free online survey. The partnership with Probably Genetic aims to screen undiagnosed patients and provide them with genetic testing resources, ultimately advancing treatment development for SYNGAP1.
A study using machine learning identifies climatic thresholds driving vegetation distribution, highlighting the importance of extreme climate conditions for savannas and deciduous forests. The findings provide valuable insights for improving process-based vegetation models and their coupling with Earth System Models.
Researchers developed a machine-learning technique that can pinpoint anomalies in large datasets, such as power grid failures and traffic bottlenecks. The model uses advanced probability distributions to identify low-density values, allowing for faster and more accurate anomaly detection.
Scientists have gained a new understanding of the atomic level interactions in complex catalysis, enabling more efficient and sustainable chemical production. Researchers used x-ray spectroscopy, machine learning analysis, and first principles calculations to model reactions and identify active site structures.
A new AI model combines multiple machine learning methods to accurately detect thyroid cancer and predict treatment outcomes from routine ultrasound images. The multimodal platform achieved high accuracy rates in detecting malignancies and predicting pathological stage and genomic mutations.
Researchers created a comprehensive genomic regulatory map of a 24-hour-old zebrafish embryo, identifying millions of regulatory segments that control gene transcription. The study used single-cell technologies and machine learning algorithms to analyze genome data from over 23,000 nuclei.
Researchers developed a new deep learning algorithm that allows for real-time reconstruction of images combining optical and magnetic resonance imaging data. The algorithm, Z-Net, enables faster image generation and can be trained with simulated data, improving breast cancer detection.
A team of scientists developed a soft haptic sensor that can accurately estimate contact points and forces using computer vision and deep neural networks. The sensor is sensitive enough to detect even tiny forces and detailed object shapes.
Researchers at the University of Illinois have developed a new type of water filtration membrane that mimics the natural process of morphogenesis. The membranes, made from soft polymers, exhibit complex 3D structures that allow them to efficiently separate pollutants from water.
Pharmaceutical firms are working towards using machine learning to analyze vast stores of data, developing models that evolve and improve as the data are processed. However, experts agree that a fully functional end-to-end approach is still a ways off due to biology's complexity.
Researchers used AI to predict flood damage in the US, finding a high probability of flood damage for more than 1.01 million square miles across the country. The study suggests that recent FEMA maps do not capture the full extent of flood risk, with 84.5% of reported damage not within high-risk flood areas.