Articles tagged with Machine Learning
A team of scientists from Osaka University developed a machine learning method for classifying the type of building and its primary façade color using deep learning models applied to street-level images. This work may assist in fostering neighborhood cohesion and support urban renewal by providing tailored street-view datasets.
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.
A multi-site, multi-disorder resting-state magnetic resonance image database was compiled to harmonize data from patients with various diseases, measured at 14 sites. The dataset comprises over 2,400 samples and includes 'traveling-subject' data to minimize inter-site differences.
Researchers offer standards for machine learning studies in life sciences to promote reproducibility and advance knowledge. The bronze standard requires data, code, and models to be publicly available, while the silver and gold standards add more information to facilitate duplication of training processes.
Researchers developed a model to predict the risk of death and unplanned cardiac hospitalization among patients awaiting cardiac surgery. The tool, based on an ethnically diverse group of 62,375 patients, identifies factors such as gender, urban residency, and cardiac symptoms that increase the risk of adverse events.
Researchers have developed an approach that predicts accurate structures computationally, overcoming the problem of determining molecular shapes. The algorithm succeeds even when learning from only a few known structures, making it applicable to difficult-to-determine molecules.
The team used machine learning technique generative adversarial networks to digitally remove clouds from aerial images, generating accurate datasets of building image masks. This work may help automate computer vision jobs critical to civil engineering, enabling the detection of buildings in areas without labeled training data.
Scientists have created a system dubbed "NanoporeTERs" allowing cells to express themselves in a whole new light. These new reporter proteins can detect multiple protein expression levels and shed new light on biological systems, enabling deeper analysis than before.
A recent study has uncovered the evolutionary forces at play in the aging of the blood system and identified individuals at increased risk of blood cancer. The research provides a robust indicator for classifying patients with ARCH mutations, allowing for more frequent screening and early treatment.
A new Yale University study reveals that social media platforms like Twitter amplify expressions of moral outrage over time, encouraging users to express more outrage with increased likes and shares. This finding has significant implications for leaders and policymakers who use these platforms.
Researchers have created an AI software that uses Minecraft to test its ability to plan for future events and solve complex tasks. The software, developed by Penn State researchers, aims to advance artificial intelligence in areas such as robotics, logistics management, and drone flight.
A machine learning analysis has identified 50 genes strongly associated with neurological aging in both Drosophila fruit flies and humans. The study suggests that fruit flies could be used as a model organism to further investigate aging-related processes.
A new machine learning tool developed by Stanford researchers can accurately predict extreme precipitation events in the Midwest, accounting for over half of all major US flood disasters. The approach uses atmospheric circulation patterns to identify factors responsible for recent increases in Midwest extreme precipitation.
Scientists at CiTIUS have developed a new fast support vector classifier (FSVC) that significantly improves data classification using Machine Learning techniques. The FSVC is much faster and operates with less memory than traditional approaches, making it suitable for large-scale classification problems.
Researchers at C-Crete Technologies have developed a method that utilizes deep learning to quickly predict and design novel hybrid organic-inorganic materials, offering improved materials design for various industries. By feeding quantum mechanics calculations to layered machine learning based on artificial neural networks, they can un...
Researchers from Nara Institute of Science and Technology developed a machine learning program that accurately predicts the location of proteins related to actin in cells. The program achieved a high degree of similarity with actual images, showing promise for future applications in cell analysis and artificial cell staining.
A new study by AKASA finds that its Read, Attend and Code machine learning model exceeds the performance of current state-of-the-art models for automatic coding of inpatient clinical notes. The technology surpasses human coders in terms of both efficiency and accuracy.
A team of computer scientists has developed an assembly selection process that balances representation and fairness in citizens' assemblies. By using a machine learning-based algorithm, the researchers ensure that all volunteers have an equal chance of being chosen, regardless of demographic quotas or education level.
A new study from Washington University in St. Louis shows that guided by sparsity, silicon neurons learn to pick the most energy-efficient perturbations and wave patterns, enabling an emergent phenomenon of efficient communication between neurons. This research has significant implications for designing neuromorphic AI systems.
The University of Washington is leading a new NSF institute focused on using artificial intelligence to understand dynamic systems, which describe chaotic situations where conditions are constantly shifting and hard to predict. The institute aims to integrate fundamental AI theory with applications in critical technological areas.
Researchers at NYU Tandon School of Engineering develop a holistic view for machine learning in healthcare, incorporating data about communities and environments. They introduce a novel approach to understanding fairness relationships using causal inference, synthesizing a means to assess effects of sensitive macro attributes.
Researchers at Texas A&M University have developed a method to cool steam turbines using phase change materials, potentially reducing fresh water usage. By leveraging machine learning techniques, they created a system that can predict when and how much of the PCM will melt and freeze, maximizing cooling power and capacity.
A study from McGill University and France uses AI to identify factors predicting suicidal behavior in students, finding self-esteem as a major predictor. Approximately 17% of students exhibited suicidal behaviors, highlighting the need for large-scale screening tools.
Researchers developed a new framework using deep learning techniques to create 3D visualizations from X-ray data. This method is hundreds of times faster than traditional methods, enabling scientists to analyze large amounts of 3D data more efficiently.
Researchers applied supervised machine learning to lidar data from fishery surveys, automating the identification process in regions with a strong possibility of harboring fish. The technique decreased manual inspection in datasets from Yellowstone Lake and Gulf of Mexico studies by 61.14% and 26.8%, respectively.
Researchers predict two nicotine biomarkers, NMR and TNE, in smokers of multiple ethnicities using machine learning approaches. These models can estimate nicotine biomarker levels from DNA data or existing genomic information.
A new machine learning-based model predicts ICU patients' mortality risk based on characteristics such as demographics, comorbidities, and APACHE II score. The model overcomes traditional approaches' weak points, offering a better alternative for personalized medical predictions.
Researchers have created an AI-powered method to automate the identification of promising lunar landing and exploration areas. The technique uses machine learning and deep learning frameworks to accurately detect craters and rilles with precision rates as high as 83.7%, outperforming existing state-of-the-art methods.
A team of researchers developed a method to study how parents adjust their language to match their child's speech development. They found that caregivers have an incredibly precise knowledge of their child's language and use this information to fine-tune the linguistic input they provide.
The 'SynRap' project aims to accelerate the production of large amounts of synthetic data by a factor of one thousand using machine learning algorithms. The project will assess the quality of generated data sets in high energy density physics and high energy physics research areas.
Researchers developed resource-efficient federated learning to train analytic models on local data, enabling coalition partners to learn similar tasks without sharing sensitive data. The new technology provides cutting-edge capability over adversaries and is crucial for defense applications.
A new AI-based tool has been developed to predict genomic subtypes of pancreatic cancer using histology slides, offering a potential solution for patient molecular stratification. The tool, trained and validated on machine learning models, can be used in clinical practice worldwide.
Researchers from KIT and universities in Göttingen and Toronto develop machine learning methods to simulate material behavior, achieving high accuracy and speed. Hybrid methods combining machine learning and molecular mechanics are also suggested to accelerate simulations of large biomolecules.
Lehigh University engineers use Frontera supercomputer to simulate photovoltaic fabrication and train AI to optimize energy production. Their 'physics-informed machine learning' approach reduces time required to reach optimal process by 40%.
Researchers aim to identify when individuals are falling out of the flow state by detecting physiological cues with off-the-shelf sensors. AI will be used to introduce interactive stimuli that nudge subjects back into a higher cognitive state.
The Shadow Figment technology uses AI-powered deception to keep attackers engaged in a pretend world, rewarding them with false signals of success while defenders learn about the attackers' methods. This creates a distraction that allows defenders to take action and protect real systems.
Researchers developed P-Flash, an AI-powered tool predicting flashover in burning buildings. It uses temperature data from heat detectors and shows promise in anticipating simulated flashovers, identifying unmodeled physical phenomena that can improve forecasting in real fires.
Researchers from UTSA, UCF, AFRL, and SRI International have developed a new method that improves how artificial intelligence learns to see. By adding noise to multiple layers of a neural network, the team creates more robust representations of images recognized by AI, leading to better explanations for AI decisions.
A team of UK scientists aims to improve understanding of Deep Learning algorithms' decision-making process, making them more trustworthy. The project will combine theory, modeling, data, and computation to unlock the next generation of deep learning.
Researchers have developed a novel AI technology, Swarm Learning, to analyze big data in decentralized fashion, enabling private and collaborative analysis of scientific data. The approach combines machine learning with blockchain technology, allowing for secure information exchange and optimized parameters.
According to a review article in Science Robotics, researchers are making progress in learned robot manipulation, which enables robots to adapt to changing stimuli. The authors propose nine promising areas for future exploration, including representation learning, modular design, and task/skill customization.
Researchers developed a machine learning model that accurately predicted cardiac arrest risk by combining timing and weather data. The results showed that Sundays, Mondays, public holidays, winter, and low temperatures were associated with higher risks of cardiac arrest.
Researchers developed a new multi-modal image fusion method based on supervised deep learning to enhance image clarity, reduce redundant features, and support batch processing. The method achieves state-of-the-art performance in visual quality and quantitative evaluation metrics, improving medical diagnosis accuracy.
Researchers developed a new method to test AI algorithms' decision-making processes by presenting them with carefully designed synthetic data. The technique, called Global Importance Analysis, revealed that AI models consider more factors beyond just sequence length, such as RNA folding and motif proximity.
Researchers at TU Darmstadt created an interactive typeface, AdaptiFont, that adjusts font styles to increase reading speed. The system uses machine learning to generate personalized fonts based on individual users' preferences.
A new AI model precisely replicates human touchscreen typing by simulating eye and finger movements, making it easier to optimize keyboard designs for better typing. The model can also account for different user types, including those with motor impairments, to develop personalized typing aids.
Researchers use aerodynamic levitation and laser heating to suspend small samples of refractory oxides in mid-air, allowing for precise data collection. Machine learning algorithms are then used to predict structural changes and interactions between atoms at high temperatures.
Researchers developed a modular robot that autonomously adapts to its environment, achieving optimal behavior without a central controller. The robot learned to navigate and maintain behavior even with damage, paving the way for miniaturized robotic materials for various applications.
A study found that deep neural networks can accurately predict lung cancer type from CT scans, identifying new associations between genes and imaging features. This approach increases radiologists' confidence in assessing tumor types, informing individualized treatment planning.
Federated learning, a new approach to training AI models, is found to have a significantly greener impact than traditional methods. By distributing training across multiple devices, the energy consumption and CO2 emissions are reduced. This method has important privacy benefits as well, keeping data local and secure.
A team of researchers developed an AI technique to accurately detect sarcasm in social media text. The model identifies patterns and cue words that indicate sarcasm, enabling more effective customer feedback analysis.
A team of scientists at IBEC and Stanford University reveals that the brain's ability to autonomously learn reflects nature more closely than previously thought. This discovery has implications for improving memory deficits in humans and building new AI systems with advanced memory capabilities.
A team from the University of Bristol's QETLabs developed an algorithm that uses machine learning to reverse engineer Hamiltonian models and formulate approximate models for quantum systems. This breakthrough enables the automated characterization of new devices, such as quantum sensors.
Researchers developed DeepShake, a deep spatiotemporal neural network trained on over 36,000 earthquakes, which analyzes seismic signals in real time and provides advanced warnings of strong shaking. The model was tested using the 2019 Ridgecrest earthquake, sending simulated alerts up to 13 seconds prior to high-intensity ground shaking.
Active learning is used to identify promising organic molecules for efficient solar cells by iteratively deciding which data to learn from. This approach allows the algorithm to efficiently explore a vast molecular space and continuously improve its performance.
A research group from Tohoku University has developed an AI model that can accurately identify flooded buildings using news media photos within 24 hours of a disaster. The model achieved an 80% estimation accuracy, showcasing the potential for rapid damage mapping and accelerated disaster response.
A neural network developed by Skoltech researchers improves credit scoring using transactional banking data, surpassing existing models. The EWS-GCN model processes large-scale temporal graphs directly and aggregates information to predict target client credit ratings.
Researchers at KAUST developed a new technology that increases machine learning speed on parallelized computing systems by five-fold. This 'in-network aggregation' method uses readily available programmable network hardware to provide dramatic speed improvements.
Researchers at Rice University have optimized artificial intelligence software to run on commodity processors and train deep neural networks up to 15 times faster than top GPU trainers. The 'sub-linear deep learning engine' (SLIDE) uses hash tables to solve the search problem of matrix multiplication, reducing training time for AI models.
A new system enables robots to recognize human workers and predict their poses, providing a safer and more efficient working environment. This allows robots to work side-by-side with humans on assembly lines without unnecessary interruptions.