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.
Researchers validated predictive regression algorithms for filling missing geophysical logging data in the Drava Super Basin, focusing on Gola Field. They found that LSTM neural networks and tree-based algorithms excelled in predicting missing well log data, while unsupervised learning effectively identified lithological patterns, enhancing subsurface characterization and understanding.
Researchers introduced a method to develop interpretable ML models for estimating seismic demand in reinforced concrete (RC) buildings, focusing on maximum inter-story drift (MID) under pulse-like earthquakes.
A comprehensive review identifies key trends in applying machine learning and deep learning to intelligent transportation systems, highlighting significant advancements and future research directions.
Researchers found that deep learning models significantly outperformed ANN and ARIMA models in predicting water levels in Lakes St. Clair and Ontario, offering enhanced accuracy for resource management.
Researchers utilized deep learning techniques to detect anomalies in the European banking sector, finding significant correlations between European Banking Authority events and banking anomalies.
Researchers highlighted the efficacy of machine learning (ML) in improving uranium spectral gamma-ray logging, particularly using backpropagation (BP) neural networks. Addressing challenges like low statistical efficacy and spectral drift, their study demonstrated that ML models, especially BP, significantly enhance the accuracy and stability of uranium quantification in high-speed logging, outperforming traditional methods.
Researchers explored the integration of 3D printing and machine learning (ML) with biodegradable polymers, highlighting advancements in material preparation, design, and post-processing for sustainable manufacturing.
Researchers detailed the impact of computer vision in textile manufacturing, focusing on identifying fabric imperfections and measuring cotton composition. They introduced a dataset of 1300 fabric images, expanded to 27,300 through augmentation, covering cotton percentages from 30% to 99%. This dataset aids in training machine learning models, streamlining traditionally labor-intensive cotton content assessments, and enhancing automation in the textile industry.
Researchers introduced GenSQL, a system for querying probabilistic generative models of database tables, combining SQL with specialized primitives to streamline Bayesian inference workflows. GenSQL outperformed competitors by up to 6.8 times on benchmarks, offering a robust and efficient solution for complex probabilistic queries.
Researchers evaluated recent language models (LMs) on counterfactual task variants to test their abstract reasoning and generalizability. The study found that while LMs like GPT-4 and PaLM-2 showed some task generalization, their performance significantly degraded under counterfactual conditions, indicating reliance on narrow, non-transferable procedures.
Researchers developed machine learning models, including ANN, RF, and GB, to accurately predict the viscosity of methane, nitrogen, and natural gas mixtures, achieving high precision (R² of 0.99) using over 4304 datasets. These models offer a cost-effective, efficient alternative to experimental methods, enhancing natural gas operations and providing valuable tools for research and industry.
Researchers have developed a novel deep-learning model to predict the compressive strength of slag-ash-based geopolymer concrete, an eco-friendly alternative to traditional cement. This model, coupled with SHapley additive exPlanations (SHAP) for transparency, and a software tool for optimizing mix designs based on strength and global warming potential, enhances sustainable construction practices by offering accurate, reliable, and interpretable predictions.
Researchers applied multiple machine learning techniques to predict the unconfined compressive strength (UCS) of cohesive soil reconstituted with cement and lime. Gradient boosting and k-nearest neighbor models demonstrated the highest accuracy, revealing maximum dry density, consistency limits, and cement content as key factors influencing UCS, providing reliable predictions for engineering applications.
In an article published in Computers and Education: Artificial Intelligence, researchers explored various methods for generating question-answer (QA) pairs using pre-trained large language models (LLMs) in higher education. They assessed pipeline, joint, and multi-task approaches across three datasets through automated metrics, teacher evaluations, and real-world educational settings.
The European project SIGNIFICANCE, using AI and deep learning, developed a platform to combat the illegal trafficking of cultural heritage goods. By identifying, tracking, and blocking illegal online activities, the platform increased the detection of illegal artifacts by 10-15%, aiding law enforcement in safeguarding cultural heritage.
In their study published in "Energies," researchers introduced artificial neural networks (ANNs) to predict energy poverty in Greece, surpassing traditional statistical models. Their approach, employing multilayer perceptrons and socio-geographical factors, achieved high accuracy rates of 61.71% to 82.72%. Model C, with optimized variables and neural network architecture.
Researchers explored the potential of large language models (LLMs) like GPT-4 and Claude 2 for automated essay scoring (AES), showing that these AI systems offer reliable and valid scoring comparable to human raters. The study underscores the promise of LLMs in educational technology, while highlighting the need for further refinement and ethical considerations.
A review in Artificial Intelligence in Agriculture compared machine learning (ML) and deep learning (DL) for weed detection. The study found DL offers higher accuracy, while ML excels in real-time processing with smaller models, addressing challenges like visual similarity and early-stage weed control.
Researchers in Nature Communications introduced PIMMS, a deep learning-based method for imputing missing values in mass spectrometry proteomics data. Applied to an alcohol-related liver disease cohort, PIMMS identified additional proteins and improved disease progression predictions, highlighting deep learning's potential in large-scale proteomics studies.
Researchers in Machines developed an AI-based predictive maintenance framework, integrating Industry 4.0 technologies and machine learning to enhance production efficiency. Applied to electromechanical component lines, it linked machine states with product quality, cutting downtime and scrap costs significantly.
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