Articles tagged with Algorithms
A study using machine-learning models trained on over 1 million companies reveals that AI can accurately predict the success of startups. The tool, developed by researchers, has the potential to help investors make informed decisions and avoid significant losses.
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.
Researchers developed a new AI algorithm called 'basis profile curve identification' to simplify comparisons between effects of electrical stimulation on the brain. The algorithm may help understand which brain regions interact with each other, guiding placement of electrodes for treating network brain diseases.
Researchers developed an algorithm to rank apps based on their privacy scores, allowing users to easily find and install non-intrusive apps. The system considers two scores: permission and listener access, providing a ranking of apps from least intrusive to most private.
Researchers developed a method to overlay a virtual scale on acquired endoscope images in real-time, allowing accurate estimation of colorectal polyp sizes. The approach uses triangulation principles and minimal image processing, enabling cost-effective diagnosis without adding extra instrumentation.
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.
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.
Researchers developed an AI tool that can quickly and accurately identify suspicious proteins in the body by analyzing their movements. The method, known as diffusional fingerprinting, uses machine learning algorithms to predict protein behavior with over 90% accuracy.
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.
Researchers at Lawrence Berkeley National Laboratory have created a new mathematical algorithm to decipher the rotational dynamics of twisting particles in complex systems. By analyzing X-ray scattering patterns, they can gain insights into the function and properties of materials.
A team of researchers has created a new algorithm that determines the most efficient route for robots to navigate complex spaces. The RBF-Galerkin method combines two existing approaches to find the optimal solution, surpassing other methods in terms of cost and time efficiency.
Researchers at UC San Diego developed a system that splits a single millimeter wave beam into multiple paths to improve reliability and throughput. The technology achieved high speeds of up to 800 Mbps with 100% reliability, even in outdoor tests over distances of 262 feet.
Researchers used social media posts to predict COVID-19 case counts, achieving correlation rates of up to 0.98 and improving on existing Google Flu Trends algorithm results. The study provides a highly-adaptive approach for feature engineering in epidemic prediction.
Researchers at University of Michigan develop faster path planning approach for rubble-roving robots, enabling them to find stable paths in treacherous terrain more efficiently. The new algorithm outperformed traditional methods in success and total time to plan, with an 84% success rate in virtual experiments.
A new study uses machine-learning algorithms to predict the next phase of a traffic signal, giving bicyclists a smoother ride. The researchers achieved high accuracy with 85% prediction success rate, using LSTM and 1D CNN models.
Researchers at MIT have created an algorithm that enables drones to navigate complex obstacle courses at high speeds without crashing. The new approach combines simulations with real-world experiments, allowing drones to adapt to challenging aerodynamics and find the fastest routes.
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.
A new study developed a wearable technology-based method to assess myoclonus symptoms in the home environment. The method, which measures electrical neuromuscular function and movement, correlates well with assessments performed by experienced physicians.
Researchers developed a novel evidence-based material recommender system that predicts high entropy alloy formation without data descriptors, overcomes data bias and poor availability. The method recommends an FeMnCoNi alloy as the most probable HEA and successfully synthesizes it, confirming its validity.
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.
Lehigh University researcher Roberto Palmieri aims to make RDMA technology even faster by revisiting a long-held theory. His goal is to enable all machines to interact with local memory, reducing latency and improving performance.
Researchers from The University of Tokyo Institute of Industrial Science have identified the origin of a phenomenon that occurs when rubber materials under stress rapidly break. Their simplified step-loading model replicates the non-monotonic mechanical behavior observed in these materials, shedding light on the velocity jump phenomenon.
A machine learning model predicts new quasicrystals with nearly 71% accuracy, identifying key factors such as electron concentration and chemical composition. The discovery sheds light on the stabilization mechanism of quasicrystals, offering a step towards innovative materials like semiconductor and superconducting quasicrystals.
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 from Skoltech and their colleagues developed a neural network that can efficiently generate IUPAC names for organic compounds in accordance with the IUPAC nomenclature system. The network, trained using the Transformer architecture, achieved an accuracy of nearly 99%, outperforming traditional rule-based solutions.
A new algorithm has been developed that can guide a quadrotor drone through a series of waypoints on a circuit, beating the fastest lap of two world-class human pilots. The algorithm generates time-optimal trajectories that fully consider the drones' limitations, resulting in faster and more consistent flight times.
Researchers from Skoltech have developed a new augmentation technique called MixChannel to help train computer vision algorithms with limited data. This approach outperformed state-of-the-art solutions in testing with three neural networks and can be combined with other methods for even more training data.
A team of scientists from Michigan State University is using artificial intelligence to analyze plant genomes and predict the functions of unknown genes. With a $1.4 million NSF grant, they aim to help farmers grow crops that can withstand drought and disease.
Researchers have developed new algorithms to record and display color in digital images with greater realism. The methods improve color accuracy for electronic displays and create more natural LED lighting.
Researchers at Columbia University School of Engineering and Applied Science have developed a computer vision technique that enables machines to predict human behavior with higher accuracy. The algorithm leverages higher-level associations between people, animals, and objects to make more intuitive predictions about future actions, ope...
The University of Houston's Air Quality Forecasting and Modeling Lab has developed an artificial intelligence system that can accurately predict ozone levels up to two weeks in advance. This breakthrough could lead to improved ways to control high ozone problems and contribute to solutions for climate change issues.
A team of researchers developed an algorithm that can predict when narwhals hunt, opening up new insights into their life and behavior. The algorithm uses acoustic data from clicks and buzzing sounds to map the whales' movement patterns and identify hunting areas.
Researchers aim to improve search results by accounting for users' systematic biases, which can lead to misleading information and poor decision-making. The project seeks to build bias-aware prediction models and promote informed, unbiased decision-making through proactively capturing bias-related search problems.
Using a computer-aided, multi-material 3D-printing method, researchers have created custom healthcare devices with tailored shapes and functions to enhance patient wellbeing. The technique offers a solution for mass-produced medical devices by providing bespoke designs that can improve comfort and durability.
Researchers developed an AI algorithm using 20,000 nights of sleep data to improve diagnoses and treatments for sleep disorders. The algorithm can analyze patient sleep patterns in seconds, freeing up medical hours that can be used to assess and diagnose more patients.
A team of bioengineers defined ten overarching classes of constraints on early metabolic networks based on basic chemistry and physics. These abiotic constraints dictate fundamental aspects of chemical processes, influencing the evolution of alternative transport systems and stress response mechanisms.
Researchers at Stanford University have developed a new prototype software to facilitate pattern-making in foundation paper piecing quilting. The algorithm is based on a graph structure called a hypergraph and allows users to design patterns without having to consider the geometry, ordering, and constraints of the technique.
A new algorithm allows solving applied problems like electrodynamics and hydrodynamics with a significant time gain of up to 50%. The method uses asynchronous processes and non-overlapping subdomains, providing accurate results with less computation time.
Researchers developed algorithms to solve curvature-dependent image processing problems using convex optimization, inspired by Euler's elastic curves and Gestalt laws of perception. The new models perform as well as current deep-learning algorithms but provide a deeper understanding of the structures learned.
Researchers at Brown University have developed an algorithm that can remove electrical artifacts from brain signals, enabling simultaneous recording and stimulation in deep brain stimulation devices. This breakthrough could lead to more effective DBS therapy with fewer side effects.
A RUDN mathematician has developed an algorithm that allows for the optimal distribution of computing tasks between IoT devices and the cloud, resulting in a reduction of power and time costs by about three times. The algorithm also protects devices from malware during data transmission.
Researchers developed a neuromorphic chip that can detect high-frequency oscillations and recognize complex biosignals in real time. The technology has the potential to improve neurosurgical interventions and monitor epilepsy outside of hospitals.
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.
A new AI algorithm developed by TUM and Imperial College London can detect pneumonia in pediatric x-ray images while maintaining patient privacy. The algorithm uses federated learning, secure aggregation, and differential privacy to keep data onsite and prevent individual identification.
Scientists at JAIST explored the connection between game-playing search tree algorithms and human decision-making, proposing two indicators - PPN and SCN - to analyze potential future states. These findings have applications in AI, optimization problems, and entertainment analysis.
Researchers developed a machine learning technique that measures topological traits of cell clusters, accurately categorizing them and inferring cellular behavior. The algorithm uses persistent homology to examine microscope images and identifies persistent topological objects that can be used to classify cells.
Research shows that removing 'race correction' from pulmonary function test results reveals a higher prevalence and severity of lung disease among Black patients. The study found an increase in the percentage of patients with any pulmonary defect from 59.5% to 81.7%, indicating a significant difference.
A research team from Iowa State University has developed a way to control hard-to-predict systems. The technique uses quotienting to identify the least fixed-point operator, which can result in a new model that acts as a supervisor of the system.
A new computational method enables researchers to design functional magnonic devices in a shorter time, overcoming traditional limitations. The approach uses inverse design and intelligent algorithms to create devices with multiple functionalities.
Researchers have created a software that can decode brain signals to translate mental handwriting into text on a computer screen. The technology, called 'mindwriting,' has been tested on a man with full-body paralysis and achieved speeds of over 18 words per minute, rivaling those of able-bodied peers.
A new brain-computer interface (BCI) system has been developed to restore communication in people with spinal cord injuries and neurological disorders. The system uses mental handwriting to record brain activity, allowing participants to compose sentences and communicate at a speed comparable to typing on a smartphone.
A scientist from HSE University has developed an image recognition algorithm that speeds up real-time processing of video-based image recognition systems by up to 40%. The algorithm uses a sequence of convolutional layers and fine-tuning to achieve accurate results while controlling loss in accuracy.
Researchers developed AI algorithms that assess unconsciousness in patients based on brain activity with high accuracy and reliability. The algorithms outperformed existing methods in real-world testing, distinguishing between conscious and unconscious states in surgery patients.
Eliminating race-based adjustments in equations to estimate kidney function may lead to significant changes in medication prescribing practices for Black adults, according to a new study. The findings suggest that this change could result in lower eGFR values and potentially impact treatment outcomes for diabetic patients.
A KAIST research team creates T-GPS, enabling the processing of large-scale graphs without storing them in main memory or on disks. This allows for faster computation and reduced costs compared to traditional approaches.
A new real-time 3D motion tracking system combines transparent light detectors with advanced neural network methods to enable fast tracking speed, compact hardware, and lower cost compared to existing solutions. The technology has promising applications in automated manufacturing, biomedical imaging, and autonomous driving.
Researchers adapted an algorithm to identify similarities in escort ads, highlighting common parts and indicating potential human trafficking activity. The InfoShield algorithm outperformed other algorithms at identifying trafficking ads with 85% precision, flagging them without false positives.
Researchers developed an algorithm to screen for pulmonary arterial hypertension in scleroderma patients, outperforming standard methods. The algorithm correctly identified all 10 patients with the condition in a study of 68 subjects.