Articles tagged with Machine Learning
Max Planck scientists explore the possibilities of artificial intelligence in materials science, discussing how combining physics-based modeling with AI can unlock complex material designs. The research focuses on overcoming limitations of traditional methods and handling sparse, noisy data.
A new study published in The European Heart Journal – Digital Health found that smart watch data can predict a higher risk of developing heart failure and irregular heart rhythms. Researchers used machine learning to analyze ECG recordings from wearable devices and identified extra beats as indicators of increased cardiovascular risk.
Researchers developed a high-speed prediction model combining physical simulations and machine learning, achieving high accuracy without compromising computation time. The technology uses correspondence between input physical conditions and abstract data space handled by machine learning algorithms.
A team of experts at the University of Manchester has received €2.5million funding to design smarter robots that can understand humans' inner feelings through language. The project aims to develop robots that can engage meaningfully with older generations, helping them in everyday life.
MIT researchers discovered unconventional activation functions that enable optimal neural network performance, leading to better classification on various datasets. The findings suggest that selecting the correct activation function can significantly improve data accuracy in machine learning applications.
A recent study found that genetic risk scores are more predictive of Alzheimer's disease in adults over 65 than age. The study used machine learning models to rank risk factors, including household income, which emerged as an important risk factor. The findings suggest considering genetic information when working on Alzheimer's disease.
Researchers at the University of Washington developed GlucoScreen, a new system that leverages smartphone capacitive touch sensing to measure blood glucose levels. The system's accuracy is comparable to standard glucometer testing, making it potentially less costly and more accessible for widespread screening.
A new AI tool, CLEAN, can predict enzyme functions based on amino acid sequences, outperforming leading state-of-the-art tools in accuracy and reliability. The tool was developed using contrastive learning and verified experimentally with both computational and in vitro experiments.
A recent study suggests that cardiac sphericity, or the roundness of the heart, may be a useful indicator for diagnosing cardiovascular conditions. The research used machine learning to analyze medical images and found a link between increased sphericity and future heart troubles.
A machine learning model developed by Colorado State University researchers has been tested with forecasters at the Storm Prediction Center to improve medium-range severe weather forecasts. The tool provides a probabilistic measure of hazardous weather events, such as tornadoes and hail, four to eight days in advance.
Researchers used explainable AI to analyze gut microbiomes for CRC biomarkers, discovering four distinct subgroups with unique bacterial profiles. This approach shows promise for a more personalized microbiome exploration and potential disease subgroup identification.
Researchers developed a deep learning approach to recognize and predict motion using vector-based relative change in position. The method, VecNet+LSTM, scored higher than other frameworks in recognizing motion and predicting future movements. This study has implications for machine learning in video analysis and artificial intelligence.
Researchers at NIST have developed a new method of digitally simulating hurricanes using AI techniques, accurately representing the trajectory and wind speeds of real storms. The simulations can help develop improved guidelines for building design in hurricane-prone regions.
A team led by Professor Yoshihiro Yamazaki from Kyushu University discovered the chemical innerworkings of a perovskite-based electrolyte developed for solid oxide fuel cells. By combining synchrotron radiation analysis, large-scale simulations, machine learning, and thermogravimetric analysis, they found that protons are introduced at...
Researchers developed a VR imaging system to measure neural activity in mouse brains during behavior, revealing abnormalities in cortical functional network dynamics associated with autism. The system successfully distinguished between autism model mice and wild-type mice based on their brain network patterns.
A new technique combines machine learning with short-wave infrared fluorescence imaging to detect precise tumor boundaries with higher accuracy than traditional methods. The approach achieved a remarkable per-pixel classification accuracy of 97.5 percent and demonstrated robustness against changes in imaging conditions.
Scientists have identified distinct mechanisms by which people remove information from their working memory and found that forgetting requires much effort. They also discovered four brain networks that activate when memories are maintained or purged through different mechanisms.
A team of researchers used a genetic algorithm to discover an organic catalyst for the Morita–Baylis–Hillman reaction, which outperformed traditional catalysts. The computational method suggested new molecular structures that were not present in the initial population, leading to a novel discovery.
A new study using ChatGPT highlights the challenges of AI in academia, including concerns over academic dishonesty. However, researchers believe that AI can also be leveraged to improve education by automating administrative tasks and adapting assessments.
Scholars developed an AI program using symbolic regression to improve mass inferences of galaxy clusters. The new equations extract jelly at the center and concentrate on doughy outskirts for reliable mass inferences.
An international team developed a novel method for evaluating AI interpretability methods to decipher the basis of AI reasoning and possible biases. The approach helps users understand what influences AI results and whether they can be trusted, especially in medical applications where AI-powered decisions can impact health and lives.
Researchers used artificial intelligence to analyze electrocardiogram results from 1.6 million patients, predicting mortality risk with 85% accuracy. The study aims to improve individual care and create a learning health-care system that feeds data back into the healthcare system.
Researchers developed an image analysis algorithm that can automatically measure Arabidopsis thaliana stomatal aperture with high accuracy and speed. The technology also includes a portable imaging device for non-destructive observation using intact plants, allowing for rapid measurement of subtle changes in stomatal aperture.
Researchers develop unsupervised machine learning algorithm to classify osteosarcoma at diagnosis based on gene expression modules. This approach enables personalized treatment strategies for osteosarcoma patients.
Researchers developed a machine learning model that maps graphene-gas molecule van der Waals complex bonding evolution for selective gas detection. The model achieved 100% accuracy in distinguishing between different atmospheric environments, showcasing its potential for environmental monitoring and non-invasive medical diagnosis.
According to Li, machine intelligence is based on a combination of matter, energy, structure, and time, which he calls
A low-cost, open-source device called Flatburn can measure air pollution using 3D printing or inexpensive parts. Researchers have tested and calibrated it against existing state-of-the-art machines, making it suitable for community groups and individual citizens to track local air quality.
The TOAST project, led by Aarhus University, aims to develop the Tactile Internet technology and upscale the talent pool for this emerging field. Researchers will focus on edge intelligence, haptic communication, and machine learning to create immersive user experiences.
A University of Illinois project uses AI-powered object recognition to quantify kernel damage in wheat, enabling faster disease analysis and improved resistance. The technology has shown promising results, with potential for an online portal to automate scoring and support breeders in their efforts to eliminate fusarium head blight.
Researchers developed a self-driven lab, AlphaFlow, that uses AI to optimize complex chemical reactions and discover new materials. The system significantly reduces the time needed to develop new chemistries from months to hours.
Researchers developed classification models using machine learning to accurately sort different types of compostable and biodegradable plastics from conventional ones. The technique achieved perfect accuracy for larger samples but showed some variability with smaller ones, highlighting its potential for industrial-scale implementation.
Researchers have created a transformer model for Metal-Organic Frameworks (MOFs), allowing for faster results and less data. The MOFTransformer model predicts key properties such as hydrogen storage capacity with improved accuracy.
Researchers developed a deep learning-based AI model to automate cirrhosis identification using large amounts of data from EHRs. The model successfully identified patients with cirrhosis with a precision of 97%, offering potential for early diagnosis and improved management of the disease.
A cross-disciplinary team developed a convolutional neural network to analyze microscopy images of chromosomes with cohesion defects. The algorithm achieved 73.1% accuracy in classifying new images, streamlining experiments with chromosome analysis.
A University of California, Riverside study found that people's memories for celebrity faces are sharper but less accurate than those for non-celebrity faces. This 'variance-bias tradeoff' suggests that humans prioritize precision over accuracy to accommodate the natural variability in face recognition.
Researchers at the University of Rochester have created a nitrogen-doped lutetium hydride that exhibits superconductivity at 69 degrees Fahrenheit and 10 kilobars of pressure. This breakthrough material has the potential to enable practical applications, as it reduces the required pressure for superconductivity to occur.
A new AI study suggests dementia prevalence rates in India could be up to 8.44%, affecting 10.08 million older adults, higher than previous estimates and comparable to countries like the US and UK
The UTSA ScooterLab will collect data on riders' mobility, context and environment to improve sustainable transportation solutions. The project aims to transform the way we think about micro-mobility.
Machine learning models can automatically detect individual heads on grain crops in images taken from drones, providing a simpler alternative to manual counting. The study provides a detailed pipeline outlining the use of these models, covering data preparation, model validation, inference, and yield-specific metrics.
A new study led by Yale School of the Environment researchers examines the application of machine learning in the sustainable development of biomass. The study found that machine learning has not been applied across the entire life cycle of biomass-derived materials (BDM), limiting its ability to advance research and development.
The American Society for Biochemistry and Molecular Biology annual meeting will explore cutting-edge approaches in biochemistry and molecular biology. Leading experts will discuss the latest discoveries and advancements in fields such as AI, RNA regulation, and carbohydrates in health and disease.
Researchers at Columbia University are developing algorithms that enable robots to understand object permanence and learn from 3D information. This allows robots to track objects and humans as they move around, improving their perception capabilities in indoor environments.
Researchers used AI to map sparse life hidden in salt domes, rocks, and crystals at Salar de Pajonales, a Martian analog. The study found that microbial life is concentrated in patchy biological hotspots linked to water availability, and AI can detect biosignatures up to 87.5% of the time.
A new machine learning model developed by NYU researchers can predict food crises up to 12 months in advance by analyzing news articles and their frequency. The model shows a high correlation between news coverage and on-the-ground occurrences of risk factors, indicating its potential as an early-warning system.
A POSTECH research team developed a machine learning model that accurately predicts cancer type-specific driver mutations, shedding light on distinct pathological mechanisms across various tumors. The model's performance outperformed current leading methods of detection, with better accuracy and sensitivity.
Researchers at Massachusetts General Hospital developed an accurate method for detecting Alzheimer's disease using routinely collected clinical brain images. The AI model detected Alzheimer's disease risk with 90.2% accuracy across five datasets, regardless of age or scanner manufacturer.
A new study published in PLOS One demonstrates the potential of machine learning and artificial intelligence to aid in the development of new treatments for Rett syndrome. The researchers used a wearable electronic chest patch to monitor cardiac activity and movement, and developed an algorithm that identified patterns specific to seve...
GPT-3 performs nearly on par with humans in decision-making but struggles with causal reasoning and information search. The language model's limitations may be due to its passive information-gathering approach, highlighting the need for active interaction with the world to achieve human-like intelligence.
Bielefeld University researchers developed an AI method using Capsule Networks to analyze genotype profiles of 3,000 ALS patients, achieving 87% accuracy in predicting whether or not people will develop ALS. The study reveals over 900 genes that play a role in identifying the disease.
A team of researchers from Rensselaer Polytechnic Institute has developed a system to optimize TV ad scheduling, resulting in a 3-5% revenue increase for networks. The model combines mathematical programming and machine learning to assign ads to specific breaks and positions.
Researchers create lab-grown brain organoids as biological hardware to drive computing forward. These three-dimensional cultures can process information faster and more efficiently than current silicon computers.
A new machine learning model can predict city traffic activity in different zones of cities, enabling targeted responses from policymakers. Understanding people's mobility patterns is crucial for improving urban traffic flow, and the model provides insights into urban interactions.
Researchers at Sandia National Laboratories and Purdue University have developed a technique called moving target defense to protect against hackers taking control of military jets. This approach involves shuffling network addresses to make it difficult for attackers to access critical systems.
The K-EMERGE project aims to capture data from textbooks and human experts, combining them using deep-learning AI-based Natural Language Processing (NLP) and cognitive architecture modeling. The system learns continuously to evaluate and enhance the integrity of the data.
A novel ensemble learning model has achieved an accuracy of 96 percent in predicting mild cognitive impairment and dementia in older drivers. The two most influential driving variables are the right to left turn ratio and the number of hard braking events.
A new cybersecurity framework uses digital twin technology, machine learning, and human expertise to detect cyberattacks in manufacturing processes. The framework analyzes continuous data streams from physical machines and their digital twins to identify irregularities and flag potential threats.
Researchers found that individuals who scored high on questionnaires assessing mystical and insightful nature of their psychedelic experiences reported improvements in anxiety and depression symptoms. A challenging experience while on these substances was also beneficial, especially in the context of mystical and insightful experiences.
The University of Minnesota Medical School has received $1.4 million in funding to develop methods and risk management processes for clinical artificial intelligence and machine learning to optimize patient safety. The team will focus on understanding the performance of clinical AI at the individual patient level.
Researchers used machine-learning algorithms to design new light-emitting enzymes called luciferases that can efficiently recognize specific chemicals and emit light. This breakthrough could lead to custom enzymes for a wide range of applications in biotechnology, medicine, environmental remediation, and manufacturing.
Scientists at IISc develop neuromorphic camera that uses machine learning to pinpoint objects smaller than 50 nanometers in size, enabling nanoscale precision in biological processes, chemistry, and physics. The technique combines optical microscopy with the neuromorphic camera and machine learning algorithms.