Due to the superior spatial–spectral extraction capability of the convolutional neural network (CNN), CNN shows great potential in dimensionality reduction (DR) of hyperspectral images (HSIs). However, most CNN-based methods are supervised while the...
With the rapid development of artificial intelligence, how to take advantage of deep learning and big data to classify polarimetric synthetic aperture radar (PolSAR) imagery is a hot topic in the field of remote sensing. As a key step for PolSAR imag...
Ship detection and recognition are important for smart monitoring of ships in order to manage port resources effectively. However, this is challenging due to complex ship profiles, ship background, object occlusion, variations of weather and light co...
The convolutional neural networks (CNNs) are a computation and memory demanding class of deep neural networks. The field-programmable gate arrays (FPGAs) are often used to accelerate the networks deployed in embedded platforms due to the high computa...
Accurate and reliable estimation of the pelvic tilt is one of the essential pre-planning factors for total hip arthroplasty to prevent common post-operative complications such as implant impingement and dislocation. Inspired by the latest advances in...
Instrument recognition is a critical task in the field of music information retrieval and deep neural networks have become the dominant models for this task due to their effectiveness. Recently, incorporating data augmentation methods into deep neura...
For achieving seamless global coverage and real-time communications while providing intelligent applications with increased quality of service (QoS), AI-enabled space–air–ground integrated networks (SAGINs) have attracted widespread atten...
Geoacoustic inversion is a challenging task in marine research due to the complex environment and acoustic propagation mechanisms. With the rapid development of deep learning, various designs of neural networks have been proposed to solve this issue...
U-Net, based on a deep convolutional network (CNN), has been clinically used to auto-segment normal organs, while still being limited to the planning target volume (PTV) segmentation. This work aims to address the problems in two aspects: 1) apply on...
Steganalysis is a method to detect whether the objects contain secret messages. With the popularity of deep learning, using convolutional neural networks (CNNs), steganalytic schemes have become the chief method of combating steganography in recent y...
High-performance deep learning models require extensive computational resources and datasets, making their ownership protection a pressing concern. To address this challenge, we focus on advancing model security through robust watermarking mechanisms...
Manually crafted features often suffer from being subjective, having an inadequate accuracy, or lacking in robustness in recognition. Meanwhile, existing deep learning methods often overlook the structural and dynamic characteristics of the human han...
Currently, the target detection based on convolutional neural network plays an important role in image recognition, speech recognition and other fields. However, the current network model features a complex structure, a huge number of parameters and...
Achieving fast and accurate recognition of garlic clove bud orientation is necessary for high-speed garlic seed righting operation and precision sowing. However, disturbances from actual field sowing conditions, such as garlic skin, vibration, and ra...
Fully convolutional networks (FCN) such as UNet and DeepLabv3+ are highly competitive when being applied in the detection of earthquake-damaged buildings in very high-resolution (VHR) remote sensing images. However, existing methods show some drawbac...
Neuromorphic hardware systems have been gaining ever-increasing focus in many embedded applications as they use a brain-inspired, energy-efficient spiking neural network (SNN) model that closely mimics the human cortex mechanism by communicating and...
Deep learning methods have been shown to significantly improve the performance of building extraction from optical remote sensing imagery. However, keeping the morphological characteristics, especially the boundaries, is still a challenge that requir...
In this study, the authors implemented an intelligent long-term care system based on deep learning techniques, using an AI model that can be integrated with the Lab’s Virtual Instrumentation Engineering Workbench (LabVIEW) application for senti...
This paper presents a novel deep neural network for real-time emotion recognition based on facial expression measurement, optimized for low computational complexity, called Lightweight Expression Recognition Network (LiExNet). The LiExNet architectur...
Aiming to address insufficient feature extraction, vanishing gradients, and low prediction accuracy in tool wear prediction, this paper proposes a hybrid deep neural network based on a Convolutional Neural Network (CNN), Residual Network (ResNet) res...
Neural networks and deep learning have been successfully applied to tackle problems in drug discovery with increasing accuracy over time. There are still many challenges and opportunities to improve molecular property predictions with satisfactory ac...
In recent years, Environmental Sound Recognition (ESR) has become a relevant capability for urban monitoring applications. The techniques for automated sound recognition often rely on machine learning approaches, which have increased in complexity in...
Recently, deep learning has become the most innovative trend for a variety of high-spatial-resolution remote sensing imaging applications. However, large-scale land cover classification via traditional convolutional neural networks (CNNs) with slidin...
Based on the openness and accessibility of user data, personality recognition is widely used in personalized recommendation, intelligent medicine, natural language processing, and so on. Existing approaches usually adopt a single deep learning mechan...
To address the problem that traditional deep learning algorithms cannot fully utilize the correlation properties between spectral sequence information and the feature differences between different spectra, this paper proposes a parallel network archi...
Due to the influence of complex factors such as atmospheric dynamic processes, physical processes and local topography and geomorphology, the prediction of near-surface meteorological elements in the numerical weather model often has deviation. The d...
Deep neural network (DNN) watermarking is a potential approach for protecting the intellectual property rights of DNN models. Similar to classical watermarking techniques for multimedia content, the requirements for DNN watermarking include capacity,...
Due to the limitations of deep learning models in processing one-dimensional signal feature extraction, and high model complexity leading to low training accuracy and large consumption of computing resources, this paper innovatively proposes a rollin...
This study focuses on driver-behavior identification and its application to finding embedded solutions in a connected car environment. We present a lightweight, end-to-end deep-learning framework for performing driver-behavior identification using in...
Deep learning (DL)-based architectures have shown a strong capacity to identify changes. However, existing change detection (CD) networks still suffer from limited applicability when it comes to multi-scale targets and spatially misaligned objects. F...
The prediction and modeling of stock price movements have been shown to possess considerable economic significance within the finance sector. Recently, a range of artificial intelligence methodologies, encompassing both traditional machine learning a...
Change detection (CD) methods work on the basis of co-registered multi-temporal images with equivalent resolutions. Due to the limitation of sensor imaging conditions and revisit period, it is difficult to acquire the desired images, especially in em...
Deforestation remains one of the key concerning activities around the world due to commodity-driven extraction, agricultural land expansion, and urbanization. The effective and efficient monitoring of national forests using remote sensing technology...
Inspired by enormous success of fully convolutional network (FCN) in semantic segmentation, as well as the similarity between semantic segmentation and pixel-by-pixel polarimetric synthetic aperture radar (PolSAR) image classification, exploring how...
Machine learning based predictions of protein–protein interactions (PPIs) could provide valuable insights into protein functions, disease occurrence, and therapy design on a large scale. The intensive feature engineering in most of these methods make...
High-precision automatic identification and mapping of forest tree species composition is an important content of forest resource survey and monitoring. The airborne hyperspectral image contains rich spectral and spatial information, which provides t...
Deep neural networks, often equipped with powerful auto-optimization tools, find widespread use in diverse domains like NLP and computer vision. However, traditional neural architectures come with specific inductive biases, designed to reduce paramet...
Hyperspectral anomaly detection (HAD) is of great interest for unknown exploration. Existing methods only focus on local similarity, which may show limitations in detection performance. To cope with this problem, we propose a relationship attention-g...
With the development of remote sensing technology, classification has become a meaningful way to explore the rich information in hyperspectral images (HSIs). However, various environmental factors may cause noise and shadow areas in HSIs, resulting i...
Partial discharge (PD) is not only an important symptom for monitoring the imperfections in the insulation system of a gas-insulated switchgear (GIS), but also the factor that accelerates the degradation. At present, monitoring ultra-high-frequency (...
LiDAR point clouds are rich in spatial information and can effectively express the size, shape, position, and direction of objects; thus, they have the advantage of high spatial utilization. The point cloud focuses on describing the shape of the exte...
Recent advances in time series classification (TSC) have exploited deep neural networks (DNN) to improve the performance. One promising approach encodes time series as recurrence plot (RP) images for the sake of leveraging the state-of-the-art DNN to...
The exponential growth of multi-modal data in the real world poses significant challenges to efficient retrieval, and traditional single-modal methods are no longer suitable for the growth of multi-modal data. To address this issue, hashing retrieval...
Oblique photogrammetry-based three-dimensional (3D) urban models are widely used for smart cities. In 3D urban models, road signs are small but provide valuable information for navigation. However, due to the problems of sliced shape features, blurre...
Recommender systems (RS) have been widely deployed in many real-world applications, but usually suffer from the long-standing user/item cold-start problem. As a promising approach, cross-domain recommendation (CDR), which has attracted a surge of int...
Fine-grained feature extraction and affective semantic mapping remain significant challenges in product form analysis. To address these issues, this study proposes a contrastive learning-based cross-modal fusion approach for product form imagery reco...
Gastrointestinal (GI) diseases constitute a leading problem in the human digestive system. Consequently, several studies have explored automatic classification of GI diseases as a means of minimizing the burden on clinicians and improving patient out...
With the increasing demand for efficient deep learning models in resource-constrained environments, Binary Neural Networks (BNNs) have emerged as a promising solution due to their ability to significantly reduce computational complexity while maintai...
Traditional force plate-based systems offer high measurement precision but are limited to laboratory settings, restricting their use in real-world environments. To address this, we propose a method for estimating a three-axis ground reaction force (G...
With the continuous development of surface observation methods and technologies, we can acquire multiple sources of data more effectively in the same geographic area. The quality and availability of these data have also significantly improved. Conseq...
of 2