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 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.
Researchers have used ensemble machine learning models to predict mechanical properties of 3D-printed polylactic acid (PLA) specimens. Models like extremely randomized tree regression (ERTR) and random forest regression (RFR) excelled in predicting tensile strength and surface roughness, demonstrating the potential of ensemble methods in optimizing 3D printing parameters.
Researchers developed a method using UAV-based remote sensing and machine learning to evaluate soybean drought tolerance, tested on hundreds of genotypes across varying conditions. This high-throughput approach, validated against manual measurements, offers rapid and accurate drought assessment.
Researchers reviewed AI advancements in electric power systems, highlighting its transformative potential due to modern microprocessors and data storage. The study categorizes AI applications into four areas and presents detailed case studies in wind power forecasting, smart grid security, and fault detection.
Researchers have developed an advanced machine learning model utilizing long short-term memory (LSTM) to improve the accuracy of predicting extreme rainfall events in Rwanda. This model offers significant insights for climate adaptation and disaster management, especially amid escalating severe weather conditions.
In a study published in Scientific Reports, researchers used machine learning to predict upper secondary education dropout with high accuracy. By analyzing comprehensive data from kindergarten to Grade 9, the study identified key factors influencing dropout, enabling early intervention strategies to support at-risk students.
Researchers emphasized the need for comprehensive multi-variable models for tsunami disaster management, critiquing traditional univariate fragility functions. Using data from the 2011 Great East Japan tsunami, hydrodynamic modeling, and machine learning, they highlighted the significance of factors like water velocity, shielding, and debris impact, demonstrating improved damage assessment accuracy.
Researchers introduced the global climate change mitigation policy dataset (GCCMPD), created using a semi-supervised hybrid machine learning approach to classify 73,625 policies across 216 entities. This comprehensive dataset aims to aid policymakers and researchers by offering detailed insights into climate mitigation efforts, enhancing the understanding of global climate activities.
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