Articles tagged with Algorithms
Researchers developed an AI-based early warning system that combines acoustic technology with artificial intelligence to classify earthquakes and determine potential tsunami risk. The system uses underwater microphones to measure acoustic radiation, which travels faster than tsunami waves and carries information about the tectonic event.
Researchers developed an algorithm to accurately extract rapeseed silique morphology from 3D imaging data, achieving high accuracy in yield estimation and phenotyping. The new approach can differentiate between various branching patterns and has the potential to support global oilseed rape breeding operations.
Computational heuristics like Grey Wolf Optimizer and Whale Optimization Algorithm offer fast solutions to complex supply chain problems, providing accurate results within a shorter time frame. The research suggests these tools can be a valuable addition to supply chain management, especially in responding to unexpected disruptions.
Researchers at Bar-Ilan University have discovered that efficient learning on artificial shallow architectures can achieve the same classification success rates as deep learning architectures, but with less computational complexity. This breakthrough has significant implications for the development of unique hardware and advanced GPU t...
Researchers developed machine-learning algorithms to generate proteins with specific structural features, enabling the creation of biologically inspired materials. The models can produce millions of new protein ideas in a few days, allowing scientists to explore unique applications.
A University of Minnesota study found that TikTok can serve as both a haven and a hindrance for users struggling with mental health. The platform offers a sense of self-discovery and community, but its algorithm can repeatedly expose users to harmful content, leading to distressing spirals.
Researchers developed an AI algorithm that allows electronic devices to express uncertainty when faced with unexpected data, improving cough detection technology. The new approach enables more precise detection with fewer sound samples per second, reducing computing power and addressing privacy concerns.
A new study published in Neurology shows that younger brain age is associated with superior post-stroke outcomes, suggesting a potential biomarker for stroke rehabilitation. The research team used multi-site data sets and 3D brain structural MRIs to analyze the relationship between brain age and stroke recovery.
Researchers developed a robotic finger with high-resolution sensors that capture data along the entire length of each finger. The three-fingered robotic hand can identify objects after just one grasp, with 85% accuracy, using tactile sensing and machine-learning algorithms.
Researchers at Pusan National University have created a new algorithm that can accurately predict ice resistance and fracture points for ships navigating through the Arctic shipping routes. The model uses an elastic material approach, allowing it to study continuous ice-breaking processes, which is essential for efficient navigation.
A new AI tool, CLEAN, can predict enzyme functions based on amino acid sequences, outperforming leading state-of-the-art tools in accuracy and reliability. The tool was developed using contrastive learning and verified experimentally with both computational and in vitro experiments.
Researchers have developed an AI algorithm that can remove atmospheric blur from astronomical images, resulting in more accurate scientific measurements and clearer data. The tool produces faster and more realistic images than current methods, producing 38.6% less error compared to classic methods.
Researchers developed a robust multiagent trajectory-planner that enables drones to generate collision-free trajectories even with delayed communications. The system achieved a 100% success rate in simulations and flight experiments, guaranteeing safe group operations.
Researchers at the University of Illinois Grainger College of Engineering have successfully integrated arrays of electrochemical random-access memory (ECRAM) onto silicon transistors, creating a practical AI accelerator. This innovation eliminates energy costs associated with data transfer and enables efficient deep learning operations.
Researchers found giant gas giants orbiting low-mass stars, challenging the widely accepted planet formation model. The study used NASA's TESS satellite data and identified 15 potential giant planets, with five confirmed as actual planets.
Researchers from Dartmouth and University Medicine Essen found that strong links between brain measures and traits can be obtained when machine learning algorithms are utilized. This approach allows for high-powered results from moderate sample sizes, opening up studies of many traits and clinical conditions previously inaccessible.
Researchers at the Complexity Science Hub have developed an algorithm that can be applied to different languages and expand word lists significantly better than others. The new method, called LEXpander, outperforms previous algorithms in German and English, especially in sentiment analysis tasks.
A remote hypertension program, operated by Mass General Brigham, successfully supported patients through the COVID-19 pandemic in achieving their blood pressure goals. Participants who enrolled during the pandemic reached and maintained their goal blood pressures an average of two months earlier than in the pre-pandemic period.
The Meta-Semi algorithm achieves competitive performance on challenging semi-supervised learning tasks, such as CIFAR-100 and STL-10, with only one additional hyper-parameter to tune. This approach naturally yields a meta-learning formulation where the correctly pseudo-labeled data has a similar distribution to labeled data.
Researchers developed AI models based on UNet and MobileNet architectures to analyze standardized abnormalities in CT images, accurately identifying object presence and confidence. These models achieved an absolute percentage error of less than 5 percent, comparable to human professionals.
A new algorithm predicts female patients are at higher risk for kidney damage after aneurysm repair, with those having reduced glomerular filtration rate facing a 4.7 times increased risk. The algorithm could help identify patients at risk and optimize them before surgery.
Researchers developed an algorithm that uses computer vision techniques to estimate tree diameter from a single image in realistic field conditions. The app sped up the process significantly, being about four and a half times faster than manual measurement techniques.
A new process combining human evaluation and AI optimization produces better designs than fully automated systems or manual approaches. The 'Human-Informed Topology Optimization' method reduces material usage while maintaining strength, and can be applied to various scales and applications.
Researchers created a statistical model to predict who is resistant to COVID-19, using health characteristics from electronic records. The model identified patterns in patients with similar features, suggesting that genetic and environmental differences influence their defense against the virus.
Computer scientists designed a reconstruction attack that proves US Census data can be exposed and stolen with current privacy measures. The study demonstrates risks to individual respondents' privacy, highlighting the need for differential privacy techniques to protect sensitive information.
A new algorithm uses patient-reported outcomes to identify patients with cancer who are at increased odds of having unplanned emergency department visits. The algorithm, which calculates a symptom complexity score, has shown that patients with high complexity scores are three times more likely to use the ED.
A recent study published in Human Reproduction found a significant association between prenatal alcohol exposure and changes in children's facial features. The research used AI to analyze three-dimensional images of children's faces at ages nine and thirteen, revealing that mothers' alcohol consumption before and during pregnancy can h...
Researchers at MIT developed a technique to improve machine-learning models' reliability without requiring additional data or extensive computing resources. The method uses a simpler companion model to estimate uncertainty, enabling more effective uncertainty quantification.
Researchers at Brigham Young University have developed an AI algorithm that can accurately locate players and determine formations in football game footage. The system has achieved over 90% accuracy on player detection and 85% accuracy on formation identification, potentially eliminating the need for manual annotation and analysis.
Researchers tested three common techniques to make algorithms fairer and found that one approach didn't reduce social norm bias at all. They proposed a new technique: a formula to directly measure social norm bias in an algorithm so it can be corrected. This bias can persist even after overt discrimination is removed.
Researchers at Osaka Metropolitan University developed a new method to evaluate X-ray microbeam diameter using mathematical analysis, outperforming conventional methods. The uniform evaluation method is expected to be widely adopted as an international standard.
Researchers utilized the Chemistry42 platform to generate novel molecular structures and identified a hit molecule for CDK20, a promising target for hepatocellular carcinoma. The platform's customizable reward function and generative models enabled efficient design and optimization of molecules.
Researchers developed an ethical algorithm to distribute risk fairly in autonomous vehicles, considering multiple factors such as responsibility and traffic regulations. The new approach enables more nuanced decision-making in critical situations, weighing risks and avoiding aggressive maneuvers.
Researchers at Bar-Ilan University have developed a new type of artificial neural network that outperforms traditional deep learning architectures. By using tree architecture with single routes to output units, they achieve better classification success rates, paving the way for efficient and biologically-inspired AI hardware.
Scientists at Göttingen University have created a novel approach to produce X-ray images in color from a single exposure, eliminating the need for focusing and scanning. This breakthrough method uses an X-ray color camera and a specially structured plate to capture the intensity pattern of fluorescing atoms in a sample.
Lithuanian researchers have developed an AI system to detect early symptoms of Parkinson's disease using voice data. The study found that patients with early-stage Parkinson's often speak in a quieter, more monotonous tone, which can be difficult to notice by ear.
A new Swedish quantum computer is being made available to the industry, accompanied by a test bed and a quantum helpdesk. The test bed will allow companies and researchers to solve problems using quantum technology at a significantly lower cost than existing commercial options.
University of Illinois scientists developed a way to accurately map tilled land in real-time using ground, airborne, and satellite imagery. The method increased mapping accuracy by 67% compared to upscaling directly from the ground to the satellite.
Researchers use coherent correlation imaging to image the evolution of magnetic domains in time and space without prior knowledge. The study reveals thermal motion and pinning effects on domain boundaries, unlocking new insights into magnetism's microcosm.
Researchers use drone technology to detect changes in building facades and assess technical condition, enabling accurate classification and identification. The system can also be used for urban heritage site monitoring and planning urban development.
Researchers at Brookhaven National Laboratory have successfully discovered new materials using artificial intelligence and self-assembly. The AI-driven technique led to the discovery of three new nanostructures, expanding the scope of self-assembly's applications in microelectronics and catalysis.
Researchers developed machine learning models to accurately calculate fine particulate matter in urban air pollution using AI and traffic data. The models provide a high-resolution estimation of city street pollution surface, enabling transportation and epidemiology studies to assess health impacts.
Scientists have developed AI techniques to track crevasses on the Thwaites Glacier, which could impact global sea levels by up to 60cm. The study found a complex interplay between crevasse formation and ice flow speed changes.
A team of researchers from Xi'an Jiaotong-Liverpool University and other institutions has identified a flexible and user-friendly model for predicting flood frequency in a changing environment. The fractional polynomial-based regression method is more effective than existing models, which often fail to account for factors like climate ...
Researchers used machine learning to analyze parole data from New York and found that the New York State Parole Board could safely grant parole to more inmates, potentially doubling the release rate. The study suggests eliminating racial disparities in release rates while maintaining public safety.
Researchers developed a new method using Bayesian estimation to reduce X-ray fluorescence analysis time from 7 seconds to 3 seconds per measurement point, saving up to 11 hours for large-scale elemental distributions.
Researchers at Drexel University used GPT-3 to spot early signs of Alzheimer's in spontaneous speech, achieving 80% accuracy. The program analyzed word-use, sentence structure and meaning from transcripts to identify characteristic profiles of Alzheimer's speech.
A recent study has identified common and unique cellular processes in six neurodegenerative diseases, providing new insights into the underlying causes of these conditions. The research used machine learning analysis to compare RNA markers in whole blood samples from patients with distinct diseases, revealing eight shared themes across...
A new algorithm developed by researchers at Charité improves motor symptoms comparable to standard of care treatment, increasing efficiency in deep brain stimulation for Parkinson's disease. The study suggests a promising result for imaging-based algorithms to simplify clinical practice and improve therapeutic outcomes.
New signal-processing algorithms have been shown to help mitigate the impact of turbulence in free-space optical experiments. The researchers achieved record results using commercially available photonic lanterns and a spatial light modulator to emulate turbulence.
Researchers from Brown and MIT developed a new framework that uses machine learning and sequential sampling to predict rare disasters like earthquakes and pandemics with less data. The framework, called DeepOnet, has been shown to outperform traditional modeling efforts in predicting scenarios, probabilities and timelines of rare events.
A team of researchers from Tokyo University of Science developed a super-hierarchical and explanatory analysis method for magnetic reversal processes, enabling the detection of subtle microscopic changes. The new algorithm can predict stable/metastable states in advance and improve the reliability of spintronics devices.
A new, non-invasive malaria detection tool developed by a University of Queensland-led team can quickly identify entire villages or towns suffering from the disease. The device uses infrared-light and is chemical-free, needle-free, and detects malaria through the skin.
A University of Houston researcher has developed a method to describe complex systems using the least number of variables possible, reducing complexity from millions to just one. This advancement speeds up science with efficiency and ability to understand and predict natural system behavior.
Researchers develop an algorithm to automate feature extraction from HD maps and point cloud data. The model achieved high precision in detecting road signs and traffic lights, with zero false detections.
Researchers at MIT have developed a scheme for private information retrieval that is about 30 times faster than other comparable methods. The technique enables users to search an online database without revealing their query to the server, with potential applications in private communication and targeted advertising.
A team of researchers proposes an intelligent routing scheme to optimize link load balancing in software-defined networks, achieving significant performance improvements. Their algorithms outperform traditional methods, reducing maximum bandwidth by 24.6 percent in real-world topologies.
A team of researchers from the University of Pennsylvania has developed a new algorithm, metadynamics, that can navigate high-dimensional energy landscapes to find low-energy configurations. This breakthrough has the potential to revolutionize fields such as protein folding and machine learning.
A new study reveals that the US Midwest has seen a significant increase in cover crop adoption, with 7.2% of cropland being planted with cover crops in 2021. This is attributed to government programs and funding initiatives, which have been shown to strongly correlate with the onset of cover crop assistance.
Scientists used AI-driven PandaOmics platform to analyze gene expression datasets from DNA repair diseases, identifying biomarkers associated with treatment response. The study focused on genes that stratify cancer patients by survival outcomes, providing potential targets for personalized therapies.