Articles tagged with Algorithms
A team of researchers from UMass Amherst debunks the idea that Facebook's algorithms successfully filtered out untrustworthy news during the 2020 election. The study found that temporary changes to the algorithm were not accounted for, leading to misperceptions about the platform's reliability.
Researchers found that large language models used in home surveillance can make inconsistent decisions about calling the police, even when videos show no crime. Models often disagreed with each other and exhibited inherent biases influenced by neighborhood demographics.
Researchers at Klick Labs developed an AI technique using vocal biomarkers to predict chronic high blood pressure with up to 84% accuracy. The study used machine learning to analyze hundreds of indiscernible vocal biomarkers, including pitch variability and speech energy distribution patterns.
A breakthrough technology allows for touchless infrared imaging to monitor changes in pupil size and gaze direction behind closed eyes. This innovation can help identify wakefulness, awareness, and pain in sleep, anesthesia, and intensive care, enabling more accurate clinical decision-making.
Researchers Maria Eichlseder and Fariba Karimi will study keyless encryption and AI's impact on online social networks to promote fair algorithms. Their projects aim to address open problems in cryptographic systems and quantify intersectional inequality.
A new algorithm, inspired by the nervous system's matchmaker, pairs drivers with riders in a way that maximizes everyone's happiness. The algorithm creates near-optimal pairings while preserving privacy, making it suitable for everyday applications.
Researchers questioned the Cascadia subduction zone's earthquake record, finding that turbidite layers showed no better correlation than random chance. The study suggests a need for further research on turbidite layers and their connection to past earthquakes.
Researchers at Boston University created an AI tool that can determine the cause of dementia using commonly collected patient data, boosting doctor accuracy by 26%. The algorithm identifies 10 types of dementia, including vascular and frontotemporal dementia, to help doctors manage patients more effectively.
The researchers will develop new algorithms to identify clusters within large datasets, enabling better community detection. They plan to test the method in various applications, including single-cell genomics and scientometrics.
A recent study found that approximately half of FDA-approved AI medical devices are not trained on real patient data, sparking concerns about device accuracy. The researchers analyzed 500+ medical AI devices and discovered that many lacked clinical validation data, which is essential for ensuring the credibility of these technologies.
A new AI-based digital platform has been developed to analyze tissue sections from lung cancer patients, making diagnosis faster and more accurate. The platform uses algorithms that enable fully automated analysis of digitized tissue samples, allowing for personalized therapy based on molecularly specific genetic changes.
Researchers at Washington State University developed an AI algorithm that optimizes 3D printing settings, reducing time and cost for engineers. The algorithm improved the accuracy and quality of printed models, particularly for complex biomedical devices like kidneys and prostates.
A new algorithm developed at Washington State University improves safety and efficiency in robots working with humans by accounting for human carelessness. The tool has shown a maximum improvement of 80% in safety and 38% in efficiency compared to existing methods, and the researchers plan to test it in real-world settings.
A computer algorithm has achieved a 98% accuracy in predicting different diseases by analyzing the color of the human tongue. The proposed imaging system can diagnose various health conditions, including diabetes, stroke, and COVID-19, using a simple and affordable method.
Researchers have discovered a non-memory-based mechanism for animals to cache and retrieve food, challenging long-held beliefs about animal cognition. The proposed mechanism uses neural networks similar to hash functions, allowing for efficient storage and retrieval of cache locations.
Researchers develop an unsupervised deep learning-based method to reconstruct particle distribution in Tomographic PIV, achieving superior performance over traditional methods. The new technique demonstrates potential for practical applications in high-density particle fields and high-velocity flow fields.
A new study led by CU Boulder computer scientist Theodora Chaspari found that AI algorithms can be confused by natural variations in speech patterns between different genders and races. This can lead to underdiagnosis or misdiagnosis of mental health concerns like depression.
Researchers have introduced a new AI calibration method called Thermometer, which enables efficient calibration of large language models for various tasks. This technique leverages temperature scaling to adjust a model's confidence and can generalize to new tasks without requiring additional labeled data.
Researchers at Pohang University of Science & Technology have developed a novel analog hardware using ECRAM devices that maximizes AI computational performance. Their technique, which uses a three-terminal structure with separate paths for reading and writing data, demonstrates excellent electrical and switching characteristics.
The University of Leicester is developing a method to shrink artificial intelligence algorithms for smarter spacecraft. The REALM project aims to demonstrate streamlined machine learning algorithms suitable for limited spacecraft power and computing performance.
A machine learning algorithm was trained to predict individuals with functional neurological disorder (FND) by analyzing their brain structure. The algorithm achieved significant above-chance accuracy in classifying FND participants against healthy controls and psychiatric samples, highlighting the importance of considering both brain ...
Researchers found that large language models perform poorly in high-stakes situations despite being better than smaller models, due to misalignment with human generalization function. Human generalization, which involves forming beliefs about others' abilities, plays a significant role in LLM performance and deployment.
A study found that large language models, despite accuracy in medical exams, fail to consistently request necessary examinations and often deviate from treatment guidelines. In comparison to human doctors, AI diagnoses achieved lower accuracy rates, highlighting concerns about their suitability for everyday clinical practice.
A new position paper argues that single race-agnostic FRAX models would unfairly discriminate against Black, Asian, and Hispanic communities. The authors recommend retention of ethnic and race-specific FRAX models for the US with updated data on fracture and death hazards.
Researchers at USC developed a new method to accurately predict wildfire spread using satellite data and artificial intelligence. The model offers a potential breakthrough in wildfire management and emergency response, providing more precise and timely data for firefighters and evacuation teams battling wildfires.
A team of UCSF specialists predicts 24-hour seizure risk using brain activity patterns that foreshadow seizures. The discovery may improve quality of life for 2.9 million Americans living with epilepsy.
A new study reveals significant discrepancies in the effectiveness of political ads on Facebook and Instagram, favoring more extremist groups. Over 70% of parties used user profiling in their ads, and the far-right AfD proved to be the most effective, with ads almost six times more efficient than competitors.
Researchers developed a method to assess the reliability of foundation models, enabling users to choose the best model for their task without testing it on real-world data. The approach measures consensus among multiple models and aligns representations to compare consistency.
Scientists from Trinity College Dublin created a computer program that visualizes molecular structure in the style of Piet Mondrian. The program uses blocks of color to represent symmetry and shape, making it easier to understand complex molecular interactions.
GenSQL integrates a tabular dataset and a generative probabilistic AI model to analyze complex tabular data. It can detect anomalies, predict outcomes, and generate synthetic data with just a few keystrokes.
Researchers developed an AI model that can estimate lung function from chest radiographs with high accuracy, potentially expanding options for pulmonary function assessment in patients who have difficulty performing spirometry. The study found a remarkably high agreement rate between the AI model's estimates and actual spirometric data.
A new machine learning-based method uses 3D structure of protein backbone with large language models to predict molecular changes that lead to better antibody drugs. The approach resulted in a 25-fold improvement against a virus, outperforming traditional methods that rely on generating huge amounts of data about protein sequences.
The SDC-DeepLabv3+ algorithm achieved a mean pixel accuracy of 95.84% and mean intersection over union of 96.87%, reducing background interference and enhancing filament visibility. The method shows potential for improved harvesting robot performance and precise filament harvesting.
Researchers developed an AI model that accurately predicts metal yield strength by combining physical theory with machine learning. The model outperforms traditional methods, which often rely on extensive experimentation.
A study found that large language models (LLMs) like ChatGPT underperform state-of-the-art detectors but can explain their analysis in plain language. LLMs' semantic knowledge makes them well-suited for detecting deepfakes, providing a common sense understanding of reality.
Researchers at NTNU have developed a way to reconstruct 3D models of the colon using single images taken by capsule endoscopy cameras. These models can aid in detecting abnormalities and signs of disease, enabling specialists to make diagnoses faster.
A new study found that animals use a wide range of strategies to accomplish tasks, many of which are just as effective as the optimal solution but require less brain power. The research provides a theoretical framework for understanding these 'good enough' strategies and their potential applications in animal behavior.
Researchers at Bar-Ilan University have discovered a new scaling law that governs how artificial neural networks handle an increasing number of categories for identification. This law reveals how the identification error rate increases with the number of required recognizable objects, impacting AI latency and efficiency.
A team of researchers created an advanced method for automatic microfossil detection and analysis using AI. The method has shown great potential in utilizing AI to analyze vast amounts of microfossil data, potentially helping geologists better utilize wellbore samples.
Researchers developed two clinical ageing clocks, PCAge and LinAge, that use blood tests, a urine test, and a health questionnaire to estimate future mortality risk. These clocks show significant predictive efficacy in characterising individual future ageing trajectories.
The University of Texas at Arlington has awarded over $130,000 to support new research projects through its Research Enhancement Program grants. The funding is intended to provide seed money for researchers to initiate creative endeavors and make a positive impact on society.
A new computer vision technique developed by MIT engineers significantly speeds up the characterization of newly synthesized electronic materials. The technique automatically analyzes images of printed semiconducting samples and quickly estimates two key electronic properties: band gap and stability.
Researchers developed a reliable iris recognition method by applying statistical limits to the spatial domain zero crossing technique, reducing errors to 0.022%. The algorithm uses a neural network to recognize unique features of each person's iris, achieving over 99.78% accuracy.
Researchers from Mass General Brigham created an algorithm to generate personalized DBS treatment plans based on four major PD symptoms. The algorithm improved patients' symptoms in four of five cases, demonstrating its potential to improve treatment beyond standard-of-care approaches.
A new study from Chalmers University of Technology shows that AI-controlled charging stations can offer personalized prices to electric vehicle users, minimizing both price and waiting time. However, the researchers highlight the importance of addressing ethical issues related to data exploitation by motorists.
Researchers Dr. Samson Zhou and Dr. David P. Woodruff aim to create secure algorithms for big data models using mathematical connections and cryptography ideas. They focus on streaming models, which process data in real-time, and address challenges such as randomness and different types of attacks.
Researchers at Texas A&M University have developed a more accurate method for tracking reservoir evaporation rates, accounting for factors not considered by current methods. The new algorithm reveals a clear geographic distribution and strong seasonality of evaporation throughout Texas.
A study found that thumbnails on a popular video-sharing platform used by children contained attention-capturing designs with violent and stereotyped themes. The researchers aim to understand how children respond to such thumbnail designs and their impact on the quality of consumed content.
A new algorithm called adaptive intersection maximization (AIM) removes high-frequency noise from super-resolution optical microscope data in real time, achieving sub-nanometer precision. This allows scientists to study chemical and biological systems far more easily and precisely than before.
Researchers have developed a system combining bio-inspired cameras with AI to quickly detect obstacles around cars, using less computational power. The hybrid system detects objects up to one hundred times faster than current systems while reducing data transmission and processing needs.
A breakthrough in trauma care has nearly quadrupled the 'golden hour' for treating large animals with internal bleeding during emergency ground and air transport. Researchers used a closed-loop, autonomous intervention system to resuscitate pigs with traumatic injuries, extending their survival time by several hours.
A new computer algorithm has been developed to enhance the management of invasive species globally, optimizing resource allocation and reducing costs. The innovative tool is adaptable to various population dynamical models and treatment methods, improving the effectiveness of environmental conservation efforts.
The team aims to create a system that can deliver items without human contact, using cables, knots, and multiple robots. They will focus on scaling up the transport of small objects like a basketball and solar panel.
Researchers have developed an AI algorithm that can track protein clumping under the microscope in real-time, revolutionizing the study of neurodegenerative disorders. The tool helps identify key characteristics of clumped proteins, which can lead to new therapies.
Engineers developed a material that mimics human bone for orthopedic femur restoration, providing optimized support and protection from external forces. This innovative approach uses machine learning, optimization, and 3D printing to create a fully controllable computational framework.
A UCF researcher is developing algorithms to track space objects and predict their orbits, which will also aid in maritime domain awareness. The computational framework will enable spacecraft to operate autonomously without intervention from Earth.
Researchers used AI to detect localized versus advanced cancer stages in oropharyngeal squamous cell carcinoma patients. The study suggests improved patient care and clinical decision-making through AI-assisted health record extraction.
Researchers developed a multimodal algorithm for improved sarcasm detection, examining multiple aspects of audio recordings for increased accuracy. The approach combines sentiment analysis using text and emotion recognition using audio for a comprehensive analysis.
A new approach uses neural networks to automatically determine polynomial coefficients for digital pre-distortion (DPD) in RF-PAs, reducing hardware complexity and power efficiency. This method can correct non-linearities and support emerging standards without extensive real-time processing.
Researchers created a digital twin model that predicts and controls complex systems, achieving higher accuracy than traditional methods. The algorithm is compact, energy-efficient, and easy to implement, making it suitable for self-driving vehicles and other dynamic systems.