Articles tagged with Algorithms
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 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 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.
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.
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.
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.
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.
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 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.
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.
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.
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...
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.
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 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.
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.
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 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 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...
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 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.
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.
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.
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 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.
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
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.
A study by University of Minnesota Medical School researchers shows that merging AI with electrical brain stimulation can enhance specific brain functions related to self-control and mental flexibility. The method improved cognitive control in patients undergoing brain surgery for epilepsy, reducing anxiety and depression symptoms.
A new study improves AI diagnoses by penalizing algorithms for false negatives, which can be more urgent than accuracy. Researchers achieved significant improvements in precision and recall for chronic kidney disease and other conditions using cost sensitivity techniques.
A new algorithm has been developed to train spiking neural networks, mimicking the human brain's structure and function. This approach enables these powerful, fast, and energy-efficient systems to solve complex tasks like image classification with high precision.
A new study at Columbia University Mailman School of Public Health uses machine learning to predict successful opioid dispensing models in U.S. counties. The analysis reveals that prescription drug monitoring program access provisions are the most consistent predictors of high-dispensing and high-dose dispensing counties.
Researchers develop an algorithm to find optimal or near-optimal solutions in the space of 'infeasible solutions' to speed up search, alleviating traffic congestion and improving city living. A novel solution to a combinatorial optimization problem in bicycle sharing systems is proposed.
A new project aims to empower people who are blind to independently review and protect their personal visual content from accidental privacy leaks. Researchers have developed novel computer vision algorithms that can detect sensitive information in images and videos, allowing users to blur or remove private content before sharing.
A team of researchers has developed a novel machine learning model that can identify medication orders requiring pharmacy intervention using provider behavior and contextual features. This approach reduces the risk of exposing sensitive patient data, while alleviating the workload of pharmacists and increasing patient safety.
Assistant Professor Kang Hao Cheong and his team discovered that chaotic switching for quantum coin Parrondo's games has similar underlying ideas to encryption. They found that using pre-generated chaotic sequences enhances the work, making it easier to invert the encrypted message to obtain the original state.
Researchers at MIT develop a data-driven process using machine learning to optimize new 3D printing materials with multiple characteristics. The system lowers costs and lessens environmental impact by reducing chemical waste and suggesting unique chemical formulations that human intuition might miss.
A new AI-powered algorithm, GEM, has been developed to quickly identify genetic causes of serious disease in newborns. The technology leverages machine learning and natural language processing to analyze vast amounts of genomic data and clinical records, achieving an accuracy rate of 92% compared to existing tools.
A new study by MIT researchers has found that blind and sighted readers have sharply different takes on what content is most useful to include in a chart caption. The study created a four-level framework for evaluating charts, which could help develop more effective tools for automatically generating captions and alternative text.
Researchers developed an algorithm that leverages medical informatics to predict autism spectrum disorder (ASD) diagnoses in young children. The new approach uses diagnostic codes from past doctor's visits to calculate a risk score, identifying which patients are at risk of receiving a confirmed ASD diagnosis.
Researchers at Tokyo University of Agriculture and Technology developed a simple and rapid method to detect amyloid protein in bovine livers using fluorescence fingerprint analysis. This approach allows for quick processing and accurate detection of AA amyloidosis, potentially leading to more efficient diagnostic tools for this disease.
A blockchain-based system allows leader robots to signal movements and add transactions to a chain, while malicious leaders forfeit tokens when caught in a lie. This limits the spread of incorrect information and enables follower robots to eventually reach their destination.
Researchers propose a solution using tethered unmanned aerial vehicles (TUAVs) to receive signals while minimizing uplink exposure. The system uses low-power 'green antennas' that only receive signals and do not radiate EMF, offering increased data transfer speeds.
By analyzing pitch, length, octaves, chords, dynamics, and main theme of four pieces from the mid-1800s Romantic era of classical music, researchers created protein songs with improved musicality. The study found that using a specific music style guided the structure of proteins to produce more pleasant melodies and harmonies.
Researchers developed a feature selection algorithm that uses boosting to select relevant features from high-dimensional data sets. The algorithm outperforms other methods in terms of accuracy and number of features used, making it more scalable and explainable.
A team of researchers, led by University of Houston associate professor Ryan Kennedy, has received a $750,000 NSF grant to create an algorithm-accountability benchmark. The project aims to establish general ways of analyzing algorithms and studying their impact on public policy decisions.
SUTD researchers develop sensor that assigns dirt score to areas based on visual and tactile analysis, allowing for more efficient exploration of complex spaces. The sensor is integrated with a smart algorithm that directs the robot to focus on areas with high dirt probability.
A multidisciplinary organization has reached consensus on guidelines for performing, interpreting, and reporting MR defecography. The consensus templates aim to standardize care for patients with evacuation disorders of the pelvic floor.