Articles tagged with Algorithms
Comprehensive exploration of living organisms, biological systems, and life processes across all scales from molecules to ecosystems. Encompasses cutting-edge research in biology, genetics, molecular biology, ecology, biochemistry, microbiology, botany, zoology, evolutionary biology, genomics, and biotechnology. Investigates cellular mechanisms, organism development, genetic inheritance, biodiversity conservation, metabolic processes, protein synthesis, DNA sequencing, CRISPR gene editing, stem cell research, and the fundamental principles governing all forms of life on Earth.
Comprehensive medical research, clinical studies, and healthcare sciences focused on disease prevention, diagnosis, and treatment. Encompasses clinical medicine, public health, pharmacology, epidemiology, medical specialties, disease mechanisms, therapeutic interventions, healthcare innovation, precision medicine, telemedicine, medical devices, drug development, clinical trials, patient care, mental health, nutrition science, health policy, and the application of medical science to improve human health, wellbeing, and quality of life across diverse populations.
Comprehensive investigation of human society, behavior, relationships, and social structures through systematic research and analysis. Encompasses psychology, sociology, anthropology, economics, political science, linguistics, education, demography, communications, and social research methodologies. Examines human cognition, social interactions, cultural phenomena, economic systems, political institutions, language and communication, educational processes, population dynamics, and the complex social, cultural, economic, and political forces shaping human societies, communities, and civilizations throughout history and across the contemporary world.
Fundamental study of the non-living natural world, matter, energy, and physical phenomena governing the universe. Encompasses physics, chemistry, earth sciences, atmospheric sciences, oceanography, materials science, and the investigation of physical laws, chemical reactions, geological processes, climate systems, and planetary dynamics. Explores everything from subatomic particles and quantum mechanics to planetary systems and cosmic phenomena, including energy transformations, molecular interactions, elemental properties, weather patterns, tectonic activity, and the fundamental forces and principles underlying the physical nature of reality.
Practical application of scientific knowledge and engineering principles to solve real-world problems and develop innovative technologies. Encompasses all engineering disciplines, technology development, computer science, artificial intelligence, environmental sciences, agriculture, materials applications, energy systems, and industrial innovation. Bridges theoretical research with tangible solutions for infrastructure, manufacturing, computing, communications, transportation, construction, sustainable development, and emerging technologies that advance human capabilities, improve quality of life, and address societal challenges through scientific innovation and technological progress.
Study of the practice, culture, infrastructure, and social dimensions of science itself. Addresses how science is conducted, organized, communicated, and integrated into society. Encompasses research funding mechanisms, scientific publishing systems, peer review processes, academic ethics, science policy, research institutions, scientific collaboration networks, science education, career development, research programs, scientific methods, science communication, and the sociology of scientific discovery. Examines the human, institutional, and cultural aspects of scientific enterprise, knowledge production, and the translation of research into societal benefit.
Comprehensive study of the universe beyond Earth, encompassing celestial objects, cosmic phenomena, and space exploration. Includes astronomy, astrophysics, planetary science, cosmology, space physics, astrobiology, and space technology. Investigates stars, galaxies, planets, moons, asteroids, comets, black holes, nebulae, exoplanets, dark matter, dark energy, cosmic microwave background, stellar evolution, planetary formation, space weather, solar system dynamics, the search for extraterrestrial life, and humanity's efforts to explore, understand, and unlock the mysteries of the cosmos through observation, theory, and space missions.
Comprehensive examination of tools, techniques, methodologies, and approaches used across scientific disciplines to conduct research, collect data, and analyze results. Encompasses experimental procedures, analytical methods, measurement techniques, instrumentation, imaging technologies, spectroscopic methods, laboratory protocols, observational studies, statistical analysis, computational methods, data visualization, quality control, and methodological innovations. Addresses the practical techniques and theoretical frameworks enabling scientists to investigate phenomena, test hypotheses, gather evidence, ensure reproducibility, and generate reliable knowledge through systematic, rigorous investigation across all areas of scientific inquiry.
Study of abstract structures, patterns, quantities, relationships, and logical reasoning through pure and applied mathematical disciplines. Encompasses algebra, calculus, geometry, topology, number theory, analysis, discrete mathematics, mathematical logic, set theory, probability, statistics, and computational mathematics. Investigates mathematical structures, theorems, proofs, algorithms, functions, equations, and the rigorous logical frameworks underlying quantitative reasoning. Provides the foundational language and tools for all scientific fields, enabling precise description of natural phenomena, modeling of complex systems, and the development of technologies across physics, engineering, computer science, economics, and all quantitative sciences.
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.
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.
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 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.
Researchers developed a new, accurate method to detect North Atlantic Right Whale up-calls using Multimodal Deep Learning algorithms. The technology outperformed conventional methods in detecting up-calls, non-up-calls, and false alarms.
A team of scientists from Incheon National University developed a programmable DNA-based microfluidic chip that can perform complex mathematical calculations, such as Boolean logic operations. The chip uses a motor-operated valve system to execute a series of reactions in rapid and convenient manner.
Researchers from the University of Cambridge have created a real-time approach to predict drone flight paths and intentions, enabling safer use of drones. The solution uses statistical techniques and radar data to identify potential threats before they enter restricted airspace.
A novel mortality risk prediction method helps tailor treatment decisions and transplant needs for patients based on individual symptoms. The new tool uses a random survival forest algorithm to predict individual mortality risk curves, calculate mortality at any given time, and provide a 95% confidence interval.
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.
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 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.
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.