Articles tagged with Algorithms
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.
A new AI algorithm, APOLLO, accurately predicts microprocessor power consumption by analyzing just 100 signals out of millions, offering potential to improve efficiency and develop new processors. The technique has been validated on high-performance microprocessors and could help designers inform future chip design.
A doctoral student at Texas A&M University has discovered blood outgrowth endothelial cells (BOECs) as an alternative to induced pluripotent stem cells (IPSCs) for organs-on-chips, offering a cheaper and more accessible option for patient-specific research. The new cells can be isolated from just 50-100 milliliters of blood and have sh...
A recent study used computer vision algorithms to analyze nearly 9,400 Flickr photos taken along Colorado's Front Range, identifying preferred outdoor landscapes with moderate accuracy. The algorithm performed well for images of water, structures, and agricultural lands, but struggled with forests. Combining social media data with on-s...
A new machine learning-based algorithm can predict stable material compounds much faster than traditional methods, opening up new avenues for research and discovery. The researchers identified several thousand potential new compounds using the computer, offering a promising breakthrough in materials science.
Researchers developed a communication-effective, divide and conquer algorithm to address computational challenges in large-scale data analysis. The algorithm combines summary statistics from subsystems using confidence distributions, balancing statistical accuracy and computational efficiency.
A research team developed an AI framework that analyzes protein interactions to predict effective and low-toxicity cancer drug combinations. The framework, GraphSynergy, outperforms conventional models in identifying synergistic combinations.
A Michigan Tech-developed machine learning model uses probability to classify breast cancer shown in histopathology images and evaluate the uncertainty of its predictions. The model outperforms similar models and can measure uncertainty, promising time savings and referrals to human experts.
Researchers at Osaka City University developed a new quantum algorithm that calculates potential energy curves of molecules without controlled time evolutions. This addresses issues with conventional quantum phase estimation algorithms, enabling parallel processing and efficient full-CI calculations.
Researchers developed an algorithm predicting COVID-19 patient outcomes based on individual data, achieving high accuracy (over 90%) for up to ten days. This innovation enables hospitals to allocate staff and resources efficiently, potentially saving lives during future pandemic waves.
A KAUST team developed an improved method for detecting malicious intrusions using deep learning, achieving accuracy rates of up to 99% in simulations of different kinds of attacks. This stacked deep learning approach promises an effective defense against cyberattacks and could prevent outages in critical infrastructure.
A recent study published in The Journal of Finance and Data Science suggests that long-term returns are stable and easier to predict than short-term returns, which suffer from noise and 'look-ahead' bias. The study also found that larger training samples are required for optimal model performance
A wearable device has been developed to detect and reverse opioid overdoses by injecting naloxone, a lifesaving antidote. The device, which senses when a person stops breathing and moving, has shown promising results in clinical trials.
Machine learning enables better understanding of climate-induced hazards, predicting floods and landslides with high accuracy. The technology combines diverse data sources to assess risk extent, considering both triggering hazards and socio-economic vulnerability.
Researchers have developed a new method that uses deep neural networks to predict extreme heat waves with unprecedented accuracy, up to two weeks before they occur. This breakthrough has significant implications for risk management, planning, and warning systems, which will greatly improve public safety and support public policies.
Researchers developed an algorithm that predicts suicidal thoughts and behavior among adolescents with 91% accuracy, analyzing data from 179,384 students. The study reveals online harassment and bullying as leading predictors of suicidal ideation and behavior, with females more likely to experience suicidal thoughts.
A new study explores the problem of shortcuts in a popular machine learning method and proposes a solution that can prevent shortcuts by forcing the model to use more data. By removing simpler characteristics and asking the model to solve the task two ways, researchers reduce the tendency for shortcut solutions and boost performance.
Scientists have developed a software that adds missing sugar components to protein models created with AlphaFold, enabling more accurate structural predictions. This breakthrough has the potential to revolutionize workflows in biology, allowing scientists to understand proteins and their mutations faster than ever.