Deep Learning is a subset of machine learning that uses artificial neural networks with multiple layers (hence "deep") to model and understand complex patterns in datasets. It's particularly effective for tasks like image and speech recognition, natural language processing, and translation, and it's the technology behind many advanced AI systems.
Researchers developed an automated system utilizing UAVs and deep learning to monitor and maintain remote gravel runways in Northern Canada. This system accurately detects defects and evaluates runway smoothness, proving more effective and reliable than traditional manual methods in harsh and isolated environments.
A novel approach integrates deep learning with geotechnical knowledge to predict the stochastic thermal regime of permafrost embankments. Validated against real data, this method enhances accuracy and reduces computational costs, proving effective for diverse environmental conditions.
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.
A comprehensive review highlights the evolution of object-tracking methods, sensors, and datasets in computer vision, guiding developers in selecting optimal tools for diverse applications.
Researchers reviewed the integration of NLP in software requirements engineering (SRE) from 1991 to 2023, highlighting advancements in machine learning and deep learning. The study found that AI technologies significantly enhance the accuracy and efficiency of SRE tasks, despite challenges in integrating these technologies into existing workflows.
Researchers evaluated deep learning models for waste classification in smart cities, with ResNeXt-101 emerging as the top performer. The study suggests a federated learning framework to enhance trash detection across diverse environments, leveraging multiple CNN models for improved efficiency in waste management.
Researchers introduced CIMNet, a novel network for crop disease image recognition, excelling in noisy environments. Featuring a non-local attention module and multi-scale critical information fusion, CIMNet outperformed traditional models in accuracy and applicability, significantly enhancing crop disease detection and improving agricultural productivity.
This article in Scientific Reports compares ML and DL methods for localizing PD sources within power transformer tanks using single-sensor electric field measurements. Various techniques including CNN, SVR, SVM, BPNN, KNN, MLP, and XGBoost were evaluated across multiple case studies, demonstrating the CNN model's superior accuracy and robustness.
Researchers developed an advanced automated system for early plant disease detection using an ensemble of deep-learning models, achieving superior accuracy on the PlantVillage dataset. The study introduced novel image processing and data balancing techniques, significantly enhancing model performance and demonstrating the system's potential for real-world agricultural applications.
Researchers introduced biSAMNet, a cutting-edge model integrating word embedding and deep neural networks, for classifying vessel trajectories. Tested in the Taiwan Strait, it significantly outperformed other models, enhancing maritime safety and traffic management.
Researchers highlight wearable optical sensors as an emerging technology for sweat monitoring. These sensors utilize advancements in materials and structural design to convert sweat chemical data into optical signals, employing methods like colorimetry and SERS to provide non-invasive, continuous health monitoring.
A recent article in "Artificial Intelligence in Agriculture" reviewed machine learning (ML) techniques for detecting plant diseases in apple, cassava, cotton, and potato crops. The study highlighted the superior accuracy of convolutional neural networks (CNNs) and emphasized ML's potential to enhance crop yield and quality, despite challenges related to data quality and ethical considerations.
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.
Researchers used eXplainable AI (XAI) to identify critical coherent structures in wall-bounded turbulence, improving predictions of flow states. This novel approach, applicable to complex and high Reynolds number flows, enhances understanding and control of turbulent phenomena in engineering and natural systems.
Researchers have introduced InsectSound1000, a dataset featuring over 169,000 labeled sound samples from 12 insect species. This dataset, recorded in an anechoic box with high precision, is primed for training deep-learning models to enhance pest and ecological monitoring systems.
Researchers developed advanced deep learning (DL)-based automatic feature recognition (AFR) methods that significantly enhance computer-aided design (CAD), process planning (CAPP), and manufacturing (CAM) integration. Their approach, using the multidimensional attributed face-edge graph (maFEG) and Sheet-metalNet, a graph neural network, improves recognition accuracy and adapts to evolving datasets, addressing limitations of traditional and voxelized representations.
Researchers developed a deep learning and particle swarm optimization (PSO) based system to enhance obstacle recognition and avoidance for inspection robots in power plants. This system, featuring a convolutional recurrent neural network (CRNN) for obstacle recognition and an artificial potential field method (APFM) based PSO algorithm for path planning, significantly improves accuracy and efficiency.
Researchers presented a novel dual-branch selective attention capsule network (DBSACaps) for detecting kiwifruit soft rot using hyperspectral images. This approach, detailed in Nature, separates spectral and spatial feature extraction, then fuses them with an attention mechanism, achieving a remarkable 97.08% accuracy.
Researchers introduce a novel electronic tongue (E-tongue), the multichannel triboelectric bioinspired E-tongue (TBIET), engineered with advanced triboelectric components on a single glass slide chip. Through comprehensive classification studies across medical, environmental, and beverage samples, the TBIET demonstrates exceptional taste classification accuracy, promising significant advancements in on-site liquid sample detection and analysis.
This study proposes an innovative method for detecting cracks in train rivets using fluorescent magnetic particle detection (FMPFD) and instance segmentation, achieving high accuracy and recall. By enhancing the YOLOv5 algorithm and developing a single coil non-contact magnetization device, the researchers achieved significant improvements in crack detection.
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