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 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.
Researchers in Scientific Reports introduced an AI-based approach to predict rice production in China using multi-source data. Hybrid models, particularly RF-XGB, outperformed single models in accuracy, emphasizing the importance of soil properties and sown area over climate variables in determining rice yields.
A study in Sensors introduces the RECPO method for safe, robust autonomous highway driving using reinforcement learning (RL). Tested in CARLA simulations, RECPO outperformed traditional methods, achieving zero collisions and improved decision-making stability by transforming the problem into a constrained Markov decision process (CMDP).
A recent study in Scientific Reports introduces the Fourier–Helmholtz–Maxwell neural operator (FoHM-NO) method for electrodynamics, leveraging Fourier transformations of Maxwell's equations to predict electromagnetic fields without gauge ambiguity. Utilizing a U-Net architecture, this approach demonstrated superior accuracy and generalization in electron beam simulations, significantly enhancing computational efficiency.
A review in Materials & Design explores how big data, machine learning (ML), and digital twin technologies enhance additive manufacturing (AM). These technologies improve AM by optimizing design, material properties, and process efficiency, offering significant advancements in quality, efficiency, and sustainability.
Researchers developed robust deep learning models to predict CO2 solubility in ionic liquids (ILs), crucial for CO2 capture. The artificial neural network (ANN) model proved more computationally efficient than the long short-term memory (LSTM) network, demonstrating high accuracy and utility in IL screening for CO2 capture applications.
Researchers demonstrated that machine learning (ML) models significantly enhance seismic vulnerability assessments within rapid visual screening (RVS) frameworks. By training binary classifiers and applying ML feature attribution techniques, the study showed that ML models outperform traditional engineering practices, offering a more accurate method for ranking structures by seismic vulnerability.
Researchers introduced a semi-supervised concept bottleneck model (SSCBM) to improve the accuracy and interpretability of concept bottleneck models by generating pseudo labels and alignment loss with both labeled and unlabeled data. Experiments showed SSCBM achieved high prediction accuracy with only 20% labeled data, making it a promising solution for image analysis tasks requiring minimal annotation efforts.
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