Overfitting in AI refers to a situation where a machine learning model performs well on the training data but fails to generalize to new, unseen data. It occurs when the model learns to fit the training data too closely, capturing noise or irrelevant patterns, leading to poor performance on unseen data.
Researchers showed that using minimal satellite data with machine learning can accurately predict pasture biomass, comparable to traditional methods. This study emphasizes the potential of remote sensing and minimal data for efficient pasture management, revolutionizing grazing practices in dairy farming.
Researchers developed a 1D-CNN model to accurately predict global copper prices using data from 1991-2023. This CNN outperforms traditional methods, offering dependable forecasts until 2027, proving valuable for policymakers in managing price volatility and strategic decision-making.
Researchers developed a machine-learning model to predict concrete compressive strength using 228 samples and six algorithms. The XGBoost model delivered the highest accuracy, aligning predictions with conventional theory and demonstrating the potential of ML in concrete strength forecasting.
Researchers developed an automated method for recommending sublayer and form layer thicknesses in railway tracks using cone penetration test (CPT) data. Leveraging machine learning algorithms, the study achieved high accuracy with a random forest classifier fine-tuned via Bayesian optimization.
Researchers used feature selection-based artificial neural networks (ANN) to predict the optimal tilt angle (OTA) for photovoltaic (PV) systems, improving accuracy from 38.59% to 90.72%. The study, which focused on 37 sites across India, demonstrated that the Elman neural network (ELM) achieved the highest accuracy, significantly enhancing PV system efficiency for solar energy capture.
A study published in Applied Sciences explored integrating IoT with machine learning to distinguish pure gases in various applications. Researchers networked gas sensors for real-time monitoring, generating data for models using supervised algorithms like random forests.
Researchers used machine learning models to predict the impact behavior of Kevlar and carbon fabric composites. Low-velocity impact tests provided data for models, accurately forecasting impact force, absorbed energy, and displacement. The study highlighted the composites' performance variations and the efficacy of different models, enhancing understanding and application of these materials.
Researchers confirmed that partition-based sampling significantly improves landslide prediction models in Henan Province. The II-BPNN model, which utilized partition-based random sampling, outperformed other models in accuracy, recall, and specificity, showcasing the benefits of this approach for enhanced landslide susceptibility mapping.
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 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 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 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.
Researchers presented a study demonstrating how advanced alignment methods, such as identity preference optimization (IPO) and Kahneman-Tversky optimization (KTO), outperform traditional training techniques in ensuring conversational agents adhere to predefined rules.
Researchers developed a deep learning (DL) approach for non-destructive crop moisture assessment using thermal imagery, focusing on five DL models. Among them, MobilenetV3 excelled in accuracy and speed, demonstrating the potential for real-time water stress monitoring in cotton agriculture, enhancing precision irrigation strategies.
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 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 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.
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 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.
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