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We report on a series of experiments with convolutional neural networks (CNN) trained on top of pre-trained word vectors for sentence-level classification tasks. We show that a simple CNN with little hyperparameter tunin...

This paper explores the application of convolutional neural networks (CNNs) in predictive modelling for seismic events and flood risks, with a particular focus on forecasting extreme quantile events that exceed historica...

The rapid development of information technology has fueled an ever-increasing demand for ultrafast and ultralow-energy-consumption computing. Existing computing instruments are pre-dominantly electronic processors, which...

In recent years, convolutional neural networks and deep neural networks have been used extensively in various fields of analytical chemistry. The use of these models for calibration tasks has been highly effective; howev...

In this research, we address a critical problem for strawberry growers in Lima: the management of phytosanitary diseases. Traditionally, these farmers relied on time-consuming, imprecise, and error-prone visual observati...

Abstract Roller bearing failure is one of the most common faults in rotating machines. Various techniques for bearing fault diagnosis based on faults feature extraction have been proposed. But feature extraction from fau...

IntroductionReliable classification of lung nodules from computed tomography (CT) images remains a challenging problem due to variations in image intensity, noise, and unstable feature representations during deep network...

Recent advances in computer vision and deep learning, particularly Convolutional Neural Networks (CNNs), have significantly increased road safety. CNNs were used in this work to automatically detect and categorise traffi...

Natural disasters are events caused by nature such as earthquakes, tornadoes, tsunamis, forest fires, and others. The impacts of natural disasters are significant and varied across various sectors, including the economy,...

Wildfires pose a grave danger and threat to both human health and the environment, which is why early detection of wildfires is crucial. In this study, a convolutional neural network, which is a deep learning technique f...

Abstract Underwater imaging suffers from various challenges such as color distortion, low contrast, and reduced visibility caused by light attenuation. Addressing these issues is crucial for improving the performance of ...

Abstract The co-frequency vibration fault is one of the common faults in the operation of rotating equipment, and realizing the real-time diagnosis of the co-frequency vibration fault is of great significance for monitor...

We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 e...

Remarkable progress has been made in image recognition, primarily due to the availability of large-scale annotated datasets and deep convolutional neural networks (CNNs). CNNs enable learning data-driven, highly represen...

Recently, channel attention mechanism has demonstrated to offer great potential in improving the performance of deep convolutional neural networks (CNNs). However, most existing methods dedicate to developing more sophis...

Subjects / keywords: Convolutional neural network; Computer science; Deep learning; Artificial intelligence; Activation function; Artificial neural network; Computation; Machine learning; Graphics; Pattern recognition (psychology); Algorithm...

En este libro se introducen los conceptos fundamentales del aprendizaje profundo (Deep learning, DL) mediante el uso de redes neuronales artificiales (Artificial neural networks, ANN). El lector podrá encontrar una revi...

Automático (Machine Learning). El objetivo del machine learning es que los sistemas informáticos sean capaces de aprender a partir de los datos, emulando de esta manera las capacidades humanas. El Aprendizaje Profundo ...

Convolutional neural networks (CNNs) and visual Transformers have become the dominant approaches for hyperspectral image (HSI) classification due to their powerful feature representation capabilities. However, CNNs are l...

Convolutional neural networks (CNNs) are inherently limited to model geometric transformations due to the fixed geometric structures in their building modules. In this work, we introduce two new modules to enhance the tr...

Convolutional neural networks are built upon the convolution operation, which extracts informative features by fusing spatial and channel-wise information together within local receptive fields. In order to boost the rep...

The management and operation of the power system would be significantly enhanced if accurate prior knowledge of power exchanges were provided, which could provide valuable insights into the system and help predict the re...

Abstract Marine habitat maps are essential tools for marine spatial planning, providing information for decision‐making in conservation and resource management. Accurate classification of benthic habitats supports thei...

Biotic and abiotic stresses reduce crop yields, yet existing image‑based diagnostic models are often crop‑specific, computationally intensive, and poorly interpretable. This study present: (i) a multi-crop diagnostic...