A Convolutional Neural Network (CNN) is a type of deep learning algorithm primarily used for image processing, video analysis, and natural language processing. It uses convolutional layers with sliding windows to process data, and is particularly effective at identifying spatial hierarchies or patterns within data, making it excellent for tasks like image and speech recognition.
AI reconstructs historical climate extremes in Europe from 1901 to 2018, filling data gaps and enhancing climate risk assessment.
Researchers developed a CNN-based deep learning model to enhance intrusion detection in virtual networks, addressing the unique challenges of encapsulation and dynamic environments.
Researchers developed an automated system that animates children's drawings by addressing unique artistic styles, supported by a large, annotated dataset of over 178,000 images.
Researchers integrated a convolutional neural network with broadband dielectric spectroscopy to predict the electrical equivalent circuit (EEC) topology of polymer membranes. This method reduces user bias, enhancing the accuracy and efficiency of polymer analysis in renewable energy applications.
Researchers combined deep reinforcement learning with a CNN-based model to optimize flow control around square cylinders, reducing training time and improving accuracy. This method significantly enhances flow stability and offers promising applications in ocean engineering and aerodynamics.
The study compared various machine-learning models for predicting wind-solar tower power output. While linear regression was inadequate, polynomial regression and deep neural networks (DNN) showed improved accuracy. The DNN model outperformed others, demonstrating high prediction accuracy and efficiency for renewable energy forecasting.
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 and compared convolutional neural network (CNN) and support vector machine (SVM) models to predict damage intensity in masonry buildings on mining terrains. Both models achieved high accuracy, with the CNN model outperforming in precision and F1 score. The study highlights CNN's effectiveness despite its higher data preparation needs, suggesting its potential for automated damage prediction.
Researchers introduced RMS-DETR, a multi-scale feature enhanced detection transformer, to identify weeds in rice fields using UAV imagery. This innovative approach, designed to detect small, occluded, and densely distributed weeds, outperforms existing methods, offering precision agriculture solutions for better weed management and optimized rice production.
Researchers developed a novel deep learning approach using kinetic data from rolling stock to predict rail corrugation. This method employs a one-dimensional convolutional neural network (CNN-1D) to accurately forecast rail defects, offering a powerful tool for proactive maintenance and improved railway performance.
Researchers developed an automated system using computer vision and machine learning to detect early-stage lameness in sows. The system, trained on video data and evaluated by experts, accurately tracked key points on sows' bodies, providing a precise livestock farming tool to assess locomotion and enhance animal welfare.
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 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.
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.
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.
Researchers have introduced Decomposed-DIG, a set of metrics to evaluate geographic biases in text-to-image generative models by separately assessing objects and backgrounds in generated images. The study reveals significant regional disparities, particularly in Africa, and proposes a new prompting strategy to improve background diversity.
Researchers compared traditional feature-based computer vision methods with CNN-based deep learning for weed classification in precision farming, emphasizing the former's effectiveness with smaller datasets
A systematic review in the journal Sensors analyzed 77 studies on facial and pose emotion recognition using deep learning, highlighting methods like CNNs and Vision Transformers. The review examined trends, datasets, and applications, providing insights into state-of-the-art techniques and their effectiveness in psychology, healthcare, and entertainment.
Researchers from China have integrated computer vision (CV) and LiDAR technologies to improve the safety and efficiency of autonomous navigation in port channels. This innovative approach utilizes advanced path-planning and collision prediction algorithms to create a comprehensive perception of the port environment, significantly enhancing navigation safety and reducing collision risks.
Researchers in Nature explore the application of deep learning to analyze plasma plume dynamics in pulsed laser deposition (PLD). Using ICCD image sequences, a (2 + 1)D convolutional neural network correlates plume behavior with deposition conditions, enabling real-time monitoring and predictive insights for optimizing thin film growth.
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