Machine learning is a subfield of artificial intelligence that focuses on developing algorithms and models capable of automatically learning and making predictions or decisions from data without being explicitly programmed. It involves training models on labeled datasets to recognize patterns and make accurate predictions or classifications in new, unseen data.
Research led by Oregon State University and the U.S. Forest Service indicates that artificial intelligence can effectively analyze acoustic data to monitor the elusive marbled murrelet, offering a promising tool for tracking this threatened seabird's population.
Researchers introduce a novel method for edge detection in color images by integrating Support Vector Machine (SVM) with Social Spider Optimization (SSO) algorithms. The two-stage approach demonstrates superior accuracy and quality compared to existing methods, offering potential applications in various domains such as object detection and medical image analysis.
Researchers propose a solution for the Flexible Double Shop Scheduling Problem (FDSSP) by integrating a reinforcement learning (RL) algorithm with a Deep Temporal Difference Network (DTDN), achieving superior performance in minimizing makespan.
Researchers utilized machine learning algorithms to predict anemia prevalence among young girls in Ethiopia, analyzing data from the 2016 Ethiopian Demographic and Health Survey. The study identified socioeconomic and demographic predictors of anemia and highlighted the efficacy of advanced ML techniques, such as random forest and support vector machine, in forecasting anemia status.
Researchers discussed the integration of machine learning (ML) algorithms, particularly convolutional neural networks (CNNs), to automate cell quantification and lineage classification in microscopy images. Despite challenges like misclassifications for certain cell strains, the approach showed promising accuracy exceeding 86% for five strains.
Researchers introduced Deep5HMC, a machine learning model combining advanced feature extraction techniques and deep neural networks to accurately detect 5-hydroxymethylcytosine (5HMC) in RNA samples. Deep5HMC surpassed previous methods, offering promise for early disease diagnosis, particularly in conditions like cancer and cardiovascular disease, by efficiently identifying RNA modifications.
Researchers combined X-ray tomography with machine learning (ML) to analyze degradation in Pb-free solder balls, revealing intergranular fatigue cracking as the primary failure mode during thermal cycling. Their study investigated the effect of bismuth (Bi) content on solder properties, enhancing fatigue resistance and delaying recrystallization. The findings advance the development of sustainable solder alloys and offer insights for optimizing microelectronics reliability.
Researchers introduce the regularized recurrent inference machine (rRIM), a novel ML method integrating physical principles for extracting pairing glue functions from optical spectra in superconductivity research. The rRIM offers robustness to noise, flexibility with out-of-distribution data, and reduced data requirements, bridging gaps in understanding complex physical phenomena.
Researchers investigate jumbo drill rate prediction in underground mining using regression and machine learning methods, highlighting the effectiveness of support vector regression (SVR) with rock mass drillability index (RDi) for accuracy. ML outperforms regression, offering insights into drilling efficiency and rock mass characteristics.
Researchers introduced a deep convolutional neural network (DCNN) model for accurately detecting and classifying grape leaf diseases. Leveraging a dataset of grape leaf images, the DCNN model outperformed conventional CNN models, demonstrating superior accuracy and reliability in identifying black rot, ESCA, leaf blight, and healthy specimens.
Researchers demonstrated the effectiveness of integrating Sentinel-2 satellite imagery and machine learning algorithms to estimate key parameters in tropical pastures, crucial for sustainable grazing management. Models incorporating support vector regression (SVR) achieved promising accuracy in predicting pasture leaf forage mass (FM), crude protein (CP), and fiber content, highlighting the potential for improving precision feeding technologies and decision support tools in tropical regions.
Researchers investigated the performance of recurrent neural networks (RNNs) in predicting time-series data, employing complexity-calibrated datasets to evaluate various RNN architectures. Despite LSTM showing the best performance, none of the models achieved optimal accuracy on highly non-Markovian processes.
Researchers developed an explainable machine learning (ML) model using NHANES data to predict high-risk metabolic dysfunction-associated steatohepatitis (MASH). Their ensemble-based XGBoost model outperformed traditional biomarkers, offering a promising tool for early identification of high-risk MASH patients.
Researchers conducted a noise audit on human-labeled benchmarks for machine commonsense reasoning (CSR), revealing significant levels of noise across different experimental conditions and datasets. The study emphasized the impact of noise on performance estimates of CSR systems, challenging the reliance on single ground truths in AI benchmarking practices and advocating for more nuanced evaluation methodologies.
This review explores the critical role of image-processing technologies in structural health monitoring (SHM) for civil infrastructures. It highlights the integration of artificial intelligence (AI) and machine learning (ML) to enhance SHM automation and accuracy. Various imaging modalities, including drones, thermography, LiDAR, and satellite imagery, are discussed for damage detection, crack identification, and deformation monitoring.
Using advanced imaging techniques and machine learning algorithms, researchers investigated the suprachiasmatic nucleus (SCN), the central circadian pacemaker in mammals. They discovered that collective calcium signals within the SCN correlated strongly with time of day, with distinct functional subtypes of neurons forming spatially organized modules. These modules exhibited ripple-like patterns representing specific time features, offering new insights into the systemic design principles of the biological clock.
Scholars utilized machine learning techniques to analyze instances of sexual harassment in Middle Eastern literature, employing lexicon-based sentiment analysis and deep learning architectures. The study identified physical and non-physical harassment occurrences, highlighting their prevalence in Anglophone novels set in the region.
Researchers evaluated 13 machine learning models to forecast compressive strength in preplaced aggregate concrete. Extreme gradient boosting (XGBoost) emerged as the most accurate, with sensitivity and SHAP analyses highlighting crucial factors like gravel and water-to-binder ratio.
Researchers employed machine learning techniques to analyze residential water consumption patterns in Adama City, Ethiopia, highlighting factors influencing water usage. Findings emphasized low per capita usage rates and the need for infrastructure improvements. ML models identified key determinants and suggested targeted interventions for promoting water conservation and sustainability.
Researchers introduced two novel predictive models employing metaheuristic algorithms, Backtracking Search Algorithm (BSA) and Equilibrium Optimizer (EO), combined with artificial neural networks (ANNs) to assess the bearing capacity of footings on two-layered soil masses. Both BSA-ANN and EO-ANN models demonstrated improved prediction accuracy over conventional ANN models, with EO exhibiting superior performance.
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