Articles tagged with Algorithms
Researchers create a new method for detecting diagnostic markers of epilepsy using EEG and MEG, enabling precise localisation of epileptogenic cortical structures. The biomimetic algorithm improves the effectiveness of neurosurgical interventions for epilepsy patients with resistant treatment.
Xie aims to develop algorithms that reflect human domain knowledge and reasoning patterns, increasing trust in machine learning models. He also explores algorithmic fairness, considering multiple perspectives on what's considered fair, to address concerns about biased decision-making.
A University of Auckland research team analyzed the legal documents of Spotify and Tinder to understand their algorithmic recommendations. The study found that both platforms collect and use personal data without transparency, influencing users' choices in various ways.
Scientists have developed a machine learning algorithm that can accurately predict the lifetimes of different battery chemistries using as little as a single cycle of experimental data. The technique could reduce costs and accelerate the development of new battery materials, enabling researchers to quickly evaluate and test multiple ma...
Researchers at Tokyo University of Science propose two new search strategies to reduce the computational cost of rebalancing in bicycle-sharing systems. The approaches focus on finding feasible solutions more efficiently and redefine the problem to minimize solving time.
Researchers have developed a method using nanomagnets to perform artificial intelligence, slashing energy costs and offering huge efficiency gains. The technology uses 'nanomagnetic states' to process and store data, cutting out the need for software simulation.
A new method reduces computational complexity of traffic models, making them operate more efficiently. The modified algorithm breaks down complex forecasting questions into smaller problems that can be solved in parallel, significantly reducing run time. This approach also allows for a good enough solution within an error bar, rather t...
Researchers developed a decision support program to reduce blood draws in pediatric intensive care units (PICU), leading to reduced antibiotic prescriptions without increasing sepsis risk. The program resulted in significant reductions in blood culture rates and unnecessary medication use.
James Anderson, assistant professor of electrical engineering at Columbia University, has received a National Science Foundation (NSF) CAREER award for his work on developing randomized algorithms for the analysis and control of large-scale cyber-physical systems. His goal is to provide robust, reliable, and secure autonomy to large-sc...
Researchers from Dartmouth College used artificial intelligence to draft wine and beer reviews, finding agreement between human and machine-generated reviews. The team also developed a system to write review syntheses, aggregating elements from existing reviews to provide limited but relevant information about products.
Researchers from Bar-Ilan University discovered that brain learning occurs mainly in dendritic trees, where the trunk and branches modify their strength. This finding paves the way for a new type of AI hardware and algorithms with comparable success rates to existing parallel GPUs.
Researchers have developed a novel image reconstruction method using Vision Transformer (ViT) architecture to overcome limitations of conventional methods. The proposed algorithm enables the acquisition of high-quality images in a short computing time, suitable for real-time capture and various applications.
A new algorithm developed by researchers at the University of Córdoba uses Artificial Intelligence to predict crops' water needs with greater accuracy and precision. The algorithm requires fewer meteorological variables than traditional methods, making it a more efficient tool for managing water resources.
Researchers at the University of Georgia developed a model to prioritize land conservation in the Upper Chattahoochee Watershed, which provides drinking water to 95 cities. The model considers ecosystem services like water, carbon, and habitat, and favors higher connectivity among parcels of land.
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.
Researchers develop a new AI agent that uses four strategies to solve single-agent stochastic puzzles like Minesweeper, achieving comparable performance to state-of-the-art studies. The approach defines solvability in this context and establishes the boundary between puzzle-solving and game-playing paradigms.
A new study from George Mason University researchers highlights the use of artificial intelligence and seasonality to screen patients and identify the probability of COVID-19. The algorithm takes into account symptom clusters and age/gender differences, suggesting that fever is a stronger predictor of COVID outside of flu season.
Researchers developed a novel algorithm, 'Joint Space and Frequency Reconstruction' (JSFR-SIM), to accelerate image reconstruction in optically sectioned superresolution structured illumination microscopy. The method achieves 80 times faster execution speed without compromising image quality.
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.
Researchers developed an AI model to predict side effects from new combination therapies by analyzing over 15 million records of adverse events. The model can recognize patterns between drugs and their side effects, enabling the prediction of adverse events for individual therapy profiles.
Researchers developed a procedure-specific risk calculator to encourage changes in patient behaviors before surgery. The machine learning models accurately predicted which patients were most likely to experience complications or hernia recurrence after ventral hernia repair.
Researchers from Tokyo University of Science analyzed usage patterns of bicycles in four major US cities to improve bicycle sharing system efficiency. They found temporal patterns and similarities between weekdays and weekends, contradicting previous studies.
Researchers from the University of Cambridge and Oslo identify a century-old mathematical paradox as the Achilles' heel of modern AI. The paradox limits the existence of stable and accurate neural networks, making many AI systems untrustworthy in high-risk areas.
Sandia researchers have demonstrated that neuromorphic computers can solve more complex problems than artificial intelligence and may earn a place in high-performance computing. The findings show that neuromorphic simulations can track X-rays, disease spreading, information flowing through social networks, and financial markets.
A team of international researchers has developed an algorithm to translate pig grunts into actual emotions, with 92% accuracy. The study provides insight into animal welfare and could lead to improved living conditions for livestock.
MIT engineers mapped airplane contrails over the US in 2020 and found a 20% drop in coverage compared to prepandemic years. The team's computer-vision technique can help predict where contrails form, allowing airlines to reroute planes and reduce aviation's climate impact.
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.
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 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.
Researchers at the University of Copenhagen have developed an AI method to recognize and detect insect species based on their wingbeats, enabling easier monitoring of biodiversity. The method uses infrared sensors to measure wingbeats and group insects into different species without human input.
A recent study found that Google's image search for occupations like CEO still displays significant gender bias, even after the company claimed to have fixed the issue. The researchers proposed three algorithms to address this problem and tested them on four major search engines.
Researchers developed a novel method of measuring aortic growth, called vascular deformation mapping, which outperforms standard manual rating methods. The technique uses high-resolution CT imaging to calculate three-dimensional changes in the aortic wall, achieving an accuracy of less than 1 millimeter.
A study published in The Journal of Finance and Data Science found that using machine learning to estimate reward and risk improves portfolio performance. Machine learning helps provide accurate estimates by taking advantage of complex non-linear relationships between market variables, outperforming linear methods.
A team of researchers from Skoltech and universities developed a neural network-based solution for automated recognition of chemical formulas on research paper scans. The algorithm combines molecules, functional groups, fonts, styles, and printing defects to mimic existing molecular template depiction styles.
A new study reveals that online social network algorithms can distort and amplify biases against minority groups, leading to unequal visibility and ranking. Researchers found that homophily and the proportion of minorities in a network can contribute to this effect.
Anastasios Kyrillidis has won a National Science Foundation CAREER Award to explore the theory and design of non-convex optimization algorithms. His research aims to devise algorithmic foundations and theory that will accelerate problem-solving in machine learning, information processing, and optimization.
A recent study found that protracted inflammation following COVID-19 is strongly linked to long-term changes in lung structure and function. Monitoring patients for signs of inflammation after infection may help identify those at risk of long-term lung problems.
Researchers developed an algorithm that incorporates customer behavior into recommendation systems, making more accurate and personalized suggestions. The technique, known as tensor decomposition, analyzes data in multiple dimensions to capture complex patterns and relationships.
Researchers developed an algorithm that prioritizes reliable news sources based on audience diversity, reducing the spread of misinformation. The new strategy incorporates data on web traffic and self-reported partisanship to promote diverse news sources.
Researchers have developed a system that can capture the information in speech signals similar to how humans perceive speech. The method uses a matching pursuit algorithm and psychoacoustic principles to produce high-quality resynthesized speech signals with natural accents.
Researchers developed an AI system that can analyze retinal scans to identify patients at high risk of a heart attack over the next year. The system uses deep learning techniques and achieves an accuracy of 70-80%, revolutionizing the way patients are screened for signs of heart disease.
A new software uses pose estimation to track human motion with high accuracy, providing an objective assessment of motor function. The technology has the potential to revolutionize neurological care by enabling patients to record video that can be analyzed by their physicians remotely.
Physicists have detected X particles in quark-gluon plasma produced in the Large Hadron Collider, a phenomenon that could reveal the particles' unknown structure. The discovery uses machine-learning techniques to sift through massive datasets and identify decay patterns characteristic of X particles.
Researchers developed a machine learning approach enabling robots to separate, recognize, and grasp individual objects with high accuracy. The method achieved 97% success rate in real-world experiments, paving the way for industrial parts sorting and residential waste sorting applications.
A new approach uses reinforcement learning algorithm to help robotic knee mimic intact human knee in walking, achieving 100% success rate on even ground. The technology also adapts to uneven terrain and changes in walking pace, promising a more comfortable experience for prosthetic users.
MIT researchers develop teaching phase that guides humans in understanding AI strengths and weaknesses, enabling more accurate decisions and faster conclusions. The technique helps humans build a mental model of the AI agent, reducing reliance on biased assumptions.
The NIH is awarding $170 million to clinics and centers for a precision nutrition study that will develop algorithms to predict individual responses to food and dietary routines. The Nutrition for Precision Health study will recruit 10,000 participants from the All of Us Research Program.
A new study suggests there are likely to be rare individuals in the general population who possess a natural talent for visual comparison, comparable to expert forensic scientists. These 'super-matchers' may not even realise they have this skill, but researchers aim to identify and recruit them for future studies.
The UniSA-designed algorithm helps robots navigate paths without collisions, outperforming existing algorithms in simulations. It can direct robots to stop, turn, or reverse direction to avoid obstacles, with potential applications in industrial warehouses, agriculture, and more.
Researchers found that three algorithms - Multilayer Perceptron, Fuzzy Cognitive Map, and Deep Neural Network - outperformed others in diagnosing COVID-19 at early stages. These findings can guide software development to create intelligence-based tools for early diagnosis.
Researchers from Singapore-MIT Alliance for Research and Technology (SMART) have discovered a way to perform 'general inverse design' with high accuracy. This breakthrough enables the creation of materials with specific characteristics and properties, paving the way for revolutionizing materials science and industrial applications.
New experiments challenge conventional wisdom on neuronal refractory periods, discovering durations exceeding 20 milliseconds and sensitivity to input signal origin. These findings may hold the key to understanding degenerative diseases and advancing artificial intelligence-based applications.
Researchers developed a new hand gesture recognition algorithm that surpasses current methods in accuracy, complexity, and applicability. The algorithm combines adaptive hand type classification and a shortcut feature for efficient real-time recognition.
Researchers analyzed hundreds of thousands of secure email messages between doctors and patients to find that most doctors use language too complex for their patients' low health literacy. Effective communication can improve patient outcomes by tailoring electronic messages to match the complexity of the patient's language.
A long-term epidemiologic study found that a race-based formula for diagnosing lung disease is no better than a race-neutral equation, which could lead to more accurate diagnoses and treatments. The study used data from thousands of patients and compared the two formulas, finding that the race-neutral equation yielded better predictions.
A study published in PLOS ONE found that meditation can affect individuals in distinct ways, with experienced meditators exhibiting different physiological responses. While some practitioners displayed signs of relaxation, others showed mental concentration, highlighting the need for tailored approaches to assisted meditation.
Scientists have made a breakthrough in controlling the formation of vacancies in silicon carbide, a semiconductor material. The team's simulations tracked the pairing of individual vacancies into a divacancy and discovered the optimal temperatures for creating stable divacancies. This discovery could lead to highly sensitive sensors an...
Researchers from Pusan National University have developed an algorithm to restore missing data in event logs, improving restoration accuracy by 10-30% compared to existing algorithms. The high accuracy of the new algorithm ensures its widespread application in industries and potential improvements in AI technologies.
Researchers developed an algorithm to differentiate life-threatening gunshot events from non-life-threatening plastic bag explosion events. The study found that 75% of plastic bag pop sounds were misclassified as gunshot sounds, highlighting the need for a diverse dataset of similar sounds.
Researchers at Chalmers University of Technology have developed an algorithm that learns optimal energy usage for electric delivery-vehicles. By focusing on overall energy usage instead of just distance travelled, the vehicles can reduce their energy consumption by up to 20% and minimize battery usage.