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 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.
Researchers used machine learning (ML) to predict the compressive strength (CS) of graphene nanoplatelet (GrN)-reinforced cement composites. They employed CatBoost and other ML models on a dataset of 172 data points, highlighting GrN thickness as a critical predictor via SHAP analysis.
This paper introduces a method combining matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) with machine learning (ML) techniques to classify milk products efficiently. The study focused on differentiating organic milk (OM) from conventional milk (CM) using spectral data analysis
Published in Intelligent Systems with Applications, this study introduces SensorNet, a hybrid model combining deep learning (DL) with chemical sensor data to detect toxic additives in fruits like formaldehyde. SensorNet integrates convolutional layers for image analysis and sensor data preprocessing, achieving a high accuracy of 97.03% in distinguishing fresh from chemically treated fruits.
Researchers introduced an entropy-based uncertainty estimator to tackle false and unsubstantiated outputs in large language models (LLMs) like ChatGPT. This method detects confabulations by assessing meaning, improving LLM reliability in fields like law and medicine.
Researchers introduced QINCo, a novel vector quantization method that employs neural networks to dynamically generate codebooks, significantly improving data compression and vector search accuracy. Experimental results demonstrated QINCo's superiority over existing methods, achieving better nearest-neighbor search performance with more compact code sizes across multiple datasets.
Researchers explored 13 machine learning models to predict the efficacy of titanium dioxide (TiO2) in degrading air pollutants. Models like XG Boost, decision tree, and lasso regression demonstrated high accuracy, with XG Boost notably excelling with low mean absolute error and root mean squared error.
Researchers developed two physics-informed machine learning (PIML) models to predict the peak overpressure of ground-reflected explosion shockwaves, significantly improving accuracy over traditional methods. This innovation aids in structural design and explosion hazard assessment.
Researchers used AI models to analyze Flickr images from global protected areas, identifying cultural ecosystem services (CES) activities. Their study reveals distinct regional patterns and underscores the value of social media data for conservation management.
Researchers reviewed deep learning (DL) techniques for drought prediction, highlighting the dominance of the standardized precipitation index (SPI), standardized precipitation evapotranspiration index (SPEI), and normalized difference vegetation index (NDVI). The study emphasizes the need for more research in America and Africa, suggesting opportunities for developing countries.
Researchers demonstrated how reinforcement learning (RL) can improve guidance, navigation, and control (GNC) systems for unmanned aerial vehicles (UAVs), enhancing robustness and efficiency in tasks like dynamic target interception and waypoint tracking.
Researchers used a novel AI method combining RGB orthophotos and digital surface models to improve building footprint extraction from aerial and satellite images, achieving higher accuracy and efficiency.
A study in Applied Sciences utilized machine learning models to predict pedestrian compliance at crosswalks in Jordan, revealing significant influences of local infrastructure and traffic conditions. Among the models tested, the random forest (RF) model demonstrated the highest accuracy and precision, highlighting ML's potential to improve urban traffic management and pedestrian safety.
A study in Desalination and Water Treatment employed machine learning models to predict chemical oxygen demand (COD), biological oxygen demand (BOD), and suspended solids (SS) at the AlHayer wastewater treatment plant in Saudi Arabia.
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