In multi-label data, a sample is associated with multiple labels at the same time, and the computational complexity is manifested in the high-dimensional feature space as well as the interdependence and unbalanced distribution of labels, which leads...
Machine learning models can automatically discover biomedical research trends and promote the dissemination of information and knowledge. Text feature representation is a critical and challenging task in natural language processing. Most methods of t...
Representation learning-based hyperspectral target detection (HTD) methods generally follow a learning paradigm of single-layer or one-step representation residual learning and the target detection on original full spectral bands, which, in some case...
Effective feature representations play an important role in remote sensing image analysis tasks. With the rapid progress of deep learning techniques, deep features have been widely applied to remote sensing image understanding in recent years and sho...
DNA-binding proteins play vital roles in cellular processes, such as DNA packaging, replication, transcription, regulation, and other DNA-associated activities. The current main prediction method is based on machine learning, and its accuracy mainly...
High-dimensional signals, such as image signals and audio signals, usually have a sparse or low-dimensional manifold structure, which can be projected into a low-dimensional subspace to improve the efficiency and effectiveness of data processing. In...
Network traffic classification is crucial for identifying network applications and defending against network threats. Traditional traffic classification approaches struggle to extract structural features and suffer from poor interpretability of featu...
The Latin Cuengh is a kind of language used in China’s minority areas. Due to its complex pronunciation and semantic system, it is difficult to spread widely. To deal with and protect this language further, this paper considers using the curren...
The hyperspectral anomaly detection algorithm based on collaborative representation does not fully utilize the two-dimensional spatial features in hyperspectral images. It also has the problem that anomalous pixels will pollute the background diction...
Text representation is one of the key tasks in the field of natural language processing (NLP). Traditional feature extraction and weighting methods often use the bag-of-words (BoW) model, which may lead to a lack of semantic information as well as th...
When it comes to feature retention in multi-scale representations of ocean flow fields, not all data points are equal. Therefore, this paper proposes a method of selecting data points based on their importance. First, an autocorrelation analysis is p...
Modern deep neural networks (DNNs) have shown promising results in brain studies involving multi-channel electroencephalogram (EEG) signals. The representations produced by the layers of a DNN trained on EEG signals remain, however, poorly understood...
With the rapid development of renewable energy, wind power forecasting has become increasingly important in power system scheduling and management. However, the forecasting of wind power is subject to the complex influence of multiple variable featur...
Scene text detection task aims to precisely localize text in natural environments. At present, the application scenarios of text detection topics have gradually shifted from plain document text to more complex natural scenarios. Objects with similar...
Arbitrary-oriented object detection (AOOD) is a crucial task in aerial image analysis but is also faced with significant challenges. In current AOOD detectors, commonly used multi-scale feature fusion modules fall short in spatial and semantic inform...
This paper presents a novel approach for detecting sensor failures using image-based feature representation and the Convolutional Variational Autoencoder (CVAE) model. Existing methods are limited when analyzing multiple failure modes simultaneously...
Vessel segmentation in fundus images is crucial for diagnosing eye diseases. The rapid development of deep learning has greatly improved segmentation accuracy. However, the scale of the retinal blood-vessel structure varies greatly, and there is a lo...
Trajectory clustering is of great significance for identifying behavioral patterns and vessel types of non-cooperative ships. However, existing trajectory clustering methods suffer from limitations in extracting cross-spatiotemporal scale features an...
Unsupervised learning-based approaches for training speech vector representations (SVR) have recently been widely applied. While pretrained SVR models excel in relatively clean automatic speech recognition (ASR) tasks, such as those recorded in labor...
Leaf shape is a crucial visual cue for plant recognition. However, distinguishing among plants with high inter-class shape similarity remains a significant challenge, especially among cultivars within the same species where shape differences can be e...
This study aims to improve the performance of multiclass classification of biomedical texts for cardiovascular diseases by combining two different feature representation methods, i.e., bag-of-words (BoW) and word embeddings (WE). To hybridize the two...
Due to the complexity of scene interference and the variability of ship scale and position, automatic ship detection in remote sensing images makes for challenging research. The existing deep networks rarely design receptive fields that fit the targe...
Qarhan Salt Lake, located in the Qaidam Basin of northwestern China, is a highland lake characterized by diverse surface features, including salt lakes, salt crusts, and saline-alkali lands. Investigating the distribution and dynamic variations of sa...
Scene classification is one of the bases for automatic remote sensing image interpretation. Recently, deep convolutional neural networks have presented promising performance in high-resolution remote sensing scene classification research. In general,...
Generative Adversarial Nets (GANs) are a kind of transformative deep learning framework that has been frequently applied to a large variety of applications related to the processing of images, video, speech, and text. However, GANs still suffer from...
Due to the high dimensionality and high data redundancy of hyperspectral remote sensing images, it is difficult to maintain the nonlinear structural relationship in the dimensionality reduction representation of hyperspectral data. In this paper, a f...
Modern Multi-Function Radars (MFRs) are sophisticated sensors that are capable of flexibly adapting their control parameters in transmitted pulse sequences. In complex electromagnetic environments, efficiently and accurately recognizing the inter-pul...
Most existing studies on an unsupervised intrusion detection system (IDS) preprocessing ignore the relationship among packets. According to the homophily hypothesis, the local proximity structure in the similarity relational graph has similar embeddi...
Most hyperspectral anomaly detection methods directly utilize all the original spectra to recognize anomalies. However, the inherent characteristics of high spectral dimension and complex spectral correlation commonly make their detection performance...
Parcel-based crop classification using multi-temporal satellite optical images plays a vital role in precision agriculture. However, optical image sequences may be incomplete due to the occlusion of clouds and shadows. Thus, exploring inherent time-s...
Ineffective protein feature representation poses problems in protein classification in hierarchical structures. Discrete wavelet transform (DWT) is a feature representation method which generates global and local features based on different wavelet f...
Umami ingredients have been identified as important factors in food seasoning and production. Traditional experimental methods for characterizing peptides exhibiting umami sensory properties (umami peptides) are time-consuming, laborious, and costly....
Local feature descriptors are a critical problem in computer vision; the majority of current approaches find it difficult to achieve a balance between descriptiveness, robustness, compactness, and efficiency. This paper proposes the local discrete fe...
When confronted with limited labelled samples, most studies adopt an unsupervised feature learning scheme and incorporate the extracted features into a traditional classifier (e.g., support vector machine, SVM) to deal with hyperspectral imagery clas...
Wearable inertial measurement unit (IMU) sensors are powerful enablers for acquisition of motion data. Specifically, in human activity recognition (HAR), IMU sensor data collected from human motion are categorically combined to formulate datasets tha...
The precise tie-points (TPs) on synthetic aperture radar (SAR) images are a critical cornerstone in the global digital elevation model (DEM) and digital ortho map (DOM) production process. While there are abundant studies on SAR TPs matching, improve...
The results of aerial scene classification can provide valuable information for urban planning and land monitoring. In this specific field, there are always a number of object-level semantic classes in big remote-sensing pictures. Complex label-space...
Impervious surfaces have been widely recognized as an indicator for urbanization and environment monitoring. Plenty of methods have been proposed to extract impervious surfaces using remote sensing images. However, accurately extracting impervious su...
In an era where large-scale data are produced and collected rapidly, great interest is attributed to symbolic data analysis in order to explore connotative and significant information from massive data. Recently, novel statistical techniques for hist...
Collaborative representation-based detector (CRD), as the most representative anomaly detection method, has been widely applied in the field of hyperspectral anomaly detection (HAD). However, the sliding dual window of the original CRD introduces hig...
Socialized services for rural characteristic industries are becoming a key support for promoting rural industries’ transformation and upgrading. They are permeating the development process of modern agricultural service technologies, achieving...
Users pay increasing attention to their data privacy in online social networks, resulting in hiding personal information, such as profile attributes and social connections. While network representation learning (NRL) is widely effective in social net...
With the development of deep neural networks, many object detection frameworks have shown great success in the fields of smart surveillance, self-driving cars, and facial recognition. However, the data sources are usually videos, and the object detec...
Cascaded H-bridge Multilevel Inverter (CHMI) is widely used in industrial applications thanks to its many advantages. However, the reliability of a CHMI is decreased with the increase of its levels. Fault diagnosis techniques play a key role in ensur...
Remote sensing object detection is a critical task in Earth observation. Despite the remarkable progress made in general object detection, existing detectors struggle with remote sensing scenarios due to the prevalence of numerous small objects with...
In recent years, deep learning has been widely studied for remote sensing image analysis. In this paper, we propose a method for remotely-sensed image classification by using sparse representation of deep learning features. Specifically, we use convo...
This study investigated the structure of sensorimotor representations during goal-directed grasping actions and explored their relationship with object features. Sixteen 3D-printed spheres that varied in size (i.e., a diameter of 20 mm, 40 mm, 60 mm,...
The detection of leaks in water distribution systems (WDS) has always been a major concern for urban water supply companies. However, the performance of traditional leak detection classifiers highly depends on the effectiveness of handcrafted feature...
This paper investigates the role of symmetry and asymmetry in the learning process of modern machine learning models, with a specific focus on feature representation and optimization. We introduce a novel symmetry-aware learning framework that identi...
A bitter taste often identifies hazardous compounds and it is generally avoided by most animals and humans. Bitterness of hydrolyzed proteins is caused by the presence of bitter peptides. To improve palatability, bitter peptides need to be identified...
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