Articles tagged with Algorithms
Researchers designed a new AI model that accurately predicts treatment outcomes for stroke prevention in people with heart disease. The model, called CURE, was trained on a massive dataset of de-identified patient information and fine-tuned to focus on specific health conditions and treatments.
The American Heart Association has funded four new grants to evaluate the use of race in predicting heart disease risk and develop unbiased clinical algorithms. The research projects will test various risk models to remove racial bias from care decisions, improving patient outcomes.
Researchers at Aalto University have created a method to manipulate objects with wind, allowing for controlled movement in different directions. The technique uses an airflow field to move objects along desired trajectories, with potential applications in robotics and complex processing tasks.
Researchers developed an algorithm that uses stochastic differential equations to identify key data points in noisy data, improving early warning signal accuracy. The approach can be applied to various domains, including ecology and mental health, and has been validated through simulations.
Researchers from Drexel University have developed a machine-learning algorithm that can extract and recognize digital fingerprints of AI-generated video, including Stable Video Diffusion, Video-Crafter, and Cog-Video. The algorithm can be trained on just a few examples of new AI generators to detect them.
A Concordia PhD candidate argues that tech companies' opaque algorithms for storing and sharing personal content raise questions about selection and representation. An Apple update blocking Holocaust-themed photos in the iPhone Photos app's Memories sparked a debate among users, with some expressing annoyance at being told what to see.
A new framework mitigates bias in machine classification by evaluating under different fairness metrics and inferring specific bias terms from data. The framework substantially reduces bias in classification outcomes while preserving accuracy across seven datasets and 21 classifiers.
A team of researchers at MIT has developed a new method to model the irrational behavior of humans, which can be used to predict their future actions. By analyzing an agent's previous decisions, the technique infers its computational constraints and adapts to human collaborators' weaknesses.
Researchers at Bar-Ilan University developed a new AI confidence measure that distinguishes between high- and low-confidence decision making in deep learning architectures. This breakthrough enables the creation of safer and more reliable autonomous vehicles by prioritizing human intervention when confidence levels are lower.
The project, AI STORIES, aims to understand how artificial intelligence creates new narratives and challenges dominant American narratives. The research will test the hypothesis that AI-generated responses mimic sci-fi stories where AI gains human emotions.
A new machine-learning approach outperforms human testers in generating diverse prompts that trigger a wider range of undesirable responses from chatbots. The technique provides a faster and more effective way to ensure the safety of large language models.
A new study by Carey Morewedge and colleagues found that people are more likely to recognize bias in algorithmic decisions than their own. This is because algorithms can codify and amplify human bias, but also reveal structural biases in society. The research suggests ways to increase awareness of biases and correct them.
Researchers propose a hybrid data driven framework combining VMD, ISSA, and MKSVR to enhance battery SOH estimation and RUL prediction. The approach achieves accurate predictions with high stability.
Recent advancements in AI and IoT have improved earthquake prediction by identifying patterns in historical seismic data. However, limitations such as computational complexity, data quality, and interpretability remain, requiring a comprehensive approach to integrate diverse datasets.
Researchers from the University of Washington created an AI algorithm to analyze infant poses using limited training data. By leveraging generative AI, they were able to produce high-quality results, enabling parents to monitor their babies' daily activities and detect potential health issues early.
Researchers have developed a new open-source algorithm called Conditional Variational Diffusion Model (CVDM) that improves the quality of images by reconstructing them from randomness. The CVDM is computationally less expensive than established diffusion models and can be easily adapted for various applications.
A new MIT-derived algorithm corrects coarse climate model predictions by 'nudging' them toward more realistic patterns, leading to more accurate forecasts of extreme weather events. The approach uses machine learning and dynamical systems theory to improve the resolution of large-scale climate models.
A recent study published in JAMA found that Black patients are less likely to receive a heart transplant than white patients, with odds of acceptance being higher for white candidates. The study also revealed that the intersection of race and gender often worsens access for Black women.
Researchers have proposed an innovative quantum algorithm that effectively solves combinatorial optimization problems with constraints in a short time. The pVSQA algorithm uses a quantum device to generate a variational quantum state and transform infeasible solutions into feasible ones, achieving near-optimal performance.
A new algorithm combines deep learning and transfer learning to retrieve advanced geosynchronous radiation imager (AGRI) aerosol optical depth with high accuracy. The approach facilitates feature selection and offers applicability to other multispectral sensors.
Researchers at Klick Labs developed an algorithm to detect deepfakes with 80% accuracy by analyzing speech pause patterns, offering a solution to the growing problem of AI-generated content. The study's findings suggest that vocal biomarkers can distinguish between real and fake voices, providing a novel approach to flagging deepfakes.
A recent study analyzed data from multiple social media platforms over three decades, finding that toxic interactions are a persistent aspect of online discussions. The study suggests user resilience to negativity and highlights the importance of understanding human behavior in digital environments.
Researchers developed a new training tool to help AI programs account for human dishonesty, especially in mortgage and insurance applications. The modified AI was better able to detect inaccurate information from users.
A recent study integrates domain knowledge into data-driven machine learning models for well logging reservoir parameter prediction. The model, PINN, uses petrophysics constraints to guide training and improve accuracy, especially in cases with small sample sizes.
A review published in Intelligent Computing outlines the strengths of automatic approaches to designing metaheuristics, which can lead to more successful outcomes and reduce redundant, metaphor-based algorithms. The authors encourage research that relies on automatic design, utilizing modular software frameworks and configuration tools.
Researchers at Bar-Ilan University discovered that each filter recognizes small clusters of images, with sharpened recognition as layers progress. This breakthrough can improve AI performance by reducing latency and memory usage while maintaining accuracy.
A 20-year analysis found that calcium and vitamin D supplements reduced cancer mortality by 7%, but increased cardiovascular disease mortality by 6% in postmenopausal women. The study suggested that cancer incidence depends on pre-supplementation status, while CVD mortality is higher among supplement users.
Researchers aim to address LVAD shortcomings, reducing blood damage and clotting risk through innovative designs and coatings. A novel flexible stented blood inlet and slippery hydrophilic coatings will be used to prevent flow stasis and clot formation.
Researchers found counterexamples to ADOPT's termination and optimality properties, potentially leading to suboptimal solutions or failure to terminate. They proposed a modified version that corrects these issues, ensuring the algorithm's termination and optimality.
Researchers developed a deep learning algorithm to remove probe effects from AFM images, enabling the resolution of material features smaller than the probe's tip. This breakthrough allows for accurate three-dimensional surface profiles, crucial for nanoelectronics development and scientific studies.
A study compared traditional logistic regression models and machine learning algorithms to predict the best surgical technique for patients with submucosal tumors. The traditional regression approach was found to outperform ML algorithms, with morphology ranking as the highest contributor to prediction accuracy.
Explainable AI methods have been developed to make audio models more interpretable and transparent. Researchers categorize existing audio XAI methods into two groups: general methods and audio-specific methods, offering new possibilities for improving the trustworthiness of AI decision-making in audio tasks.
Researchers have defined what a premature aging disease is and developed tools to diagnose progeria patients, allowing them to identify new syndromes. The study also identified correlations between progeroid syndromes and other conditions, providing a significant step forward in understanding premature aging.
Researchers at the University of South Florida have created an algorithm that can reconstruct a hidden scene in 3D using an ordinary digital camera, enabling users to see around obstacles. The technology has broad applications in various fields, including car safety, law enforcement, and military efforts.
A new study from the Computational Social Science Lab at the University of Pennsylvania found that YouTube's recommendation system has a moderating effect on user behavior. Contrary to popular narratives, users' own political interests and preferences play the primary role in what they choose to watch.
Researchers at North Carolina State University are developing a suite of performance metrics to standardize the evaluation of self-driving labs in chemistry and materials science. These metrics aim to compare different lab technologies and identify areas for improvement, ultimately advancing the field and accelerating discovery.
A new AI tool, DeepGO-SE, successfully predicts the molecular functions of unknown proteins with high accuracy. This breakthrough enables researchers to analyze uncharacterized proteins, facilitating tasks such as drug discovery, metabolic pathway analysis, and disease associations.
A comprehensive online tool, Metrics Reloaded, has been developed to select suitable metrics for AI-assisted image analysis. The tool helps physicians and scientists identify relevant variables for their specific task, increasing understanding of key problems in AI-assisted image analysis.
Researchers at UC Santa Cruz find that popular link prediction metrics are flawed and do not accurately measure algorithm performance. They recommend using a new metric, VCMPR, to benchmark link prediction tasks and highlight the importance of accurate metrics in machine learning decision-making.
A new study from MIT researchers found that doctors are less accurate in diagnosing skin diseases based on images of patients with darker skin. The researchers also discovered that an artificial intelligence algorithm can assist doctors in improving their diagnosis, although the improvements were more pronounced for lighter skin tones.
Researchers from the University of Cambridge have developed a robotic sensor that reads Braille at twice the speed of humans, achieving 87% accuracy. The breakthrough uses machine learning algorithms to 'deblur' images and recognize letters, paving the way for potential applications in robotics and prosthetics.
Researchers at UTSA's Department of Physics and Astronomy have used deconvolution algorithms to enhance images of galaxy NGC 5728 obtained by the James Webb Space Telescope. The study reveals a faint extended feature that could be part of an outflow from a supermassive black hole interacting with the host galaxy.
Researchers have developed a novel optical neural network architecture that achieves nonlinear optical computation by precisely controlling ultrashort pulse propagation in multimode fibers. This approach streamlines the need for energy-intensive digital processes, achieving comparable accuracy with significantly reduced parameters.
Researchers have invented a simple yet effective algorithm called SIEVE to optimize cache management. By labeling objects as 'zero' or 'one', the system efficiently evicts the least recently used items, reducing computational complexity and bugs.
Researchers developed an AI-driven algorithm that accurately predicts death and complications after percutaneous coronary intervention (PCI). The tool uses patient feedback to provide a patient-centered, individualized risk prediction score.
Researchers at UB have developed a new deepfake detection algorithm that reduces biases in facial recognition, with one method classifying videos based on demographics and the other relying on features not visible to the human eye. The algorithms improved fairness metrics and reduced disparities in accuracy across races and genders.
The Lehigh University Plasma Control Group is working on advanced controls and machine learning to improve plasma dynamics simulation capabilities and stabilize superheated gases in future reactors. The goal is to address technological issues with ITER and FPP, ensuring safe and controllable operation.
Researchers from Bar-Ilan University discover a possible mechanism underlying the brain's efficient shallow learning, enabling it to perform complex classification tasks with similar accuracy as deep learning. The study proposes a wider and higher architecture as a complementary mechanism to deep architectures.
Boleslaw Szymanski and his team developed a clustering method called SpeakEasy2: Champagne to group molecular data, which showed consistent performance across diverse applications. The method was tested on bulk gene expression, single-cell data, protein interaction networks, and large-scale human networks data, revealing its effectiven...
Gurdip Singh's project aims to develop a unified model to study cyber-physical systems and design distributed algorithms for fundamental problems in pervasive systems. The proposed model will be integrated into existing simulation tools to evaluate scenarios involving different types of physical entities and phenomena.
A computer vision system developed by University of Malaga engineers estimates vehicle speeds in real time using a single camera, reducing complexity and costs. The algorithm, published in Neurocomputing, aims to improve vehicle safety and has potential applications in autonomous driving and driver assistance.
Researchers developed a new solver algorithm for the MPAS-Ocean ocean circulation model, reducing run time by 45% and enabling semi-implicit stability. This allows for faster climate predictions and energy efficiency, as well as reduced computational power consumption.
A new commentary in JAMA Pediatrics recommends improving adolescents' media information literacy skills and promoting inclusive content to combat health misinformation spread on social media. This approach aims to harness the potential of social media as a tool for positive connections and mental well-being.
Researchers have developed an algorithm to train an analog neural network just as accurately as a digital one, decreasing energy consumption and eliminating the need for a digital twin. This approach is more biologically plausible and shows improved speed, robustness, and reduced power consumption compared to other methods.
New research from the Max Planck Institute challenges previous claims of giant exomoons around Kepler-1625b and Kepler-1708b. The study uses a computer algorithm to analyze observations, finding that 'planet-only' interpretations are more conclusive than initially thought.
A new software algorithm intelligently combines vocals and instrumentals from different songs, mimicking professional music composition techniques. The result is a unique blend of pleasing lyrics and exciting instrumentals that appeal to listeners, particularly hip-hop fans.
Researchers developed an adaptable machine learning algorithm to measure driver 'workload' using driving performance signals, enabling real-time adjustments to in-vehicle systems for enhanced safety and user experience. The system can respond to changes in the driver's behavior, status, road conditions, or characteristics.
Researchers from MIT and ETH Zurich developed a filtering technique to simplify a key intermediate step in MILP solvers, speeding up the process by 30-70% without compromising accuracy. A machine-learning model is then used to pick the best combination of algorithms for a specific optimization problem.
A team of researchers at North Carolina State University has developed a new experiment to better understand human moral judgments in traffic scenarios. The study aims to collect data for training autonomous vehicles to make
Researchers have developed an AI algorithm that uses people's flavor impressions to make accurate predictions of individual wine preferences. The algorithm combines data from wine labels, user reviews, and sensory tastings to provide personalized recommendations.