A5101899176- Advanced Graph Neural Networks
- Complex Network Analysis Techniques
- Machine Learning and Data Classification
- Recommender Systems and Techniques
- Imbalanced Data Classification Techniques
- Caching and Content Delivery
- Text and Document Classification Technologies
- Machine Learning and Algorithms
- Anomaly Detection Techniques and Applications
- IoT and Edge/Fog Computing
- Domain Adaptation and Few-Shot Learning
- Network Security and Intrusion Detection
- Face and Expression Recognition
- Context-Aware Activity Recognition Systems
- Underwater Acoustics Research
- Diet and metabolism studies
- Gut microbiota and health
- Energy Efficient Wireless Sensor Networks
- Opinion Dynamics and Social Influence
- Advanced Computing and Algorithms
- Access Control and Trust
- Advanced Clustering Algorithms Research
- Bioinformatics and Genomic Networks
- Sentiment Analysis and Opinion Mining
- Advanced Neural Network Applications
University of Electronic Science and Technology of China
2025
Nanchang University
2020-2024
Nanjing University of Aeronautics and Astronautics
2014-2024
Northwest A&F University
2024
Gansu Agricultural University
2023-2024
Jiangnan University
2020-2023
Jiangsu Vocational College of Medicine
2023
State Key Laboratory of Food Science and Technology
2020-2023
University of Shanghai for Science and Technology
2019
The Synergetic Innovation Center for Advanced Materials
2019
A number of studies have confirmed the relationship between constipation and gut microbiota. Additionally, many human animal experiments identified probiotics as effectors for relief symptoms. In this study, probiotic compounds, including Lactobacillus acidophilus LA11-Onlly, Lacticaseibacillus rhamnosus LR22, Limosilactobacillus reuteri LE16, Lactiplantibacillus plantarum LP-Onlly, Bifidobacterium animalis subsp. lactis BI516, were administered to mice with loperamide-induced constipation,...
Activity recognition is a hot topic in context-aware computing. In activity recognition, machine learning techniques have been widely applied to learn the models from labeled samples. Since labeling samples requires human's efforts, most existing research focus on refining utilize costly as effectively possible. However, few of them consider using costless unlabeled boost performance. this work, we propose novel semi-supervised algorithm named En-Co-training make use Our extends co- training...
Sensor nodes usually have limited energy supply and they are impractical to recharge. How balance traffic load in sensors order increase network lifetime is a very challenging research issue. Many clustering algorithms been proposed recently for wireless sensor networks (WSNs). However, with one fixed sink node often suffer from hot spots problem since near sinks more burden forward during multi-hop transmission process. The use of mobile has shown be an effective technique enhance...
In recent years, SAR ship detection technology based on deep learning has developed vigorously. At present, most of the mainstream models rely supervised learning, and unsupervised are rare. images have following characteristics: 1) Similarity: under same intensity noise interference, similarity. 2) Difference: different image difference is obvious. First, similarity features, we propose a Queue-based Representative feature Contrastive method (QRC). QRC which significantly enhances...
Graph contrastive learning (GCL) has drawn much research attention for its ability to learn node representations in a self-supervised manner. However, the homophily assumption inherent GNN encoders limits direction (macro-level) and process (micro-level) of message passing current GCL frameworks, impairing expressive power non-homophilous graphs. This paper presents novel framework that employs Macro Micro Message Passing (M3P-GCL) overcome these limitations advance performance both...
This paper studies a practically meaningful ship detection problem from synthetic aperture radar (SAR) images by the neural network. We broadly extract different types of SAR image features and raise intriguing question that whether these extracted are beneficial to (1) suppress data variations (e.g., complex land-sea backgrounds, scattered noise) real-world images, (2) enhance ships small objects have aspect (length-width) ratios, therefore resulting in improvement detection. To answer this...
This study aimed to investigate the effects of a hypocaloric balanced diet (HBD) on anthropometric measures and gut microbiota 43 people with obesity. Fecal samples were collected from subjects at weeks 0 12, detailed analysis was performed using 16S rRNA gene sequencing. By comparing changes in before after HBD intervention, we revealed potential weight loss microbiota. Our results indicated that resulted significant decrease body mass index (BMI), most physiological indicators decreased...
The occurrence of obesity and related metabolic disorders is rising, necessitating effective long-term weight management strategies. With growing interest in the potential role gut microbes due to their association with responses different loss diets, understanding mechanisms underlying interactions between diet, microbiota, remains a challenge. This study aimed investigate impact multiphase dietary protocol, incorporating an improved ketogenic diet (MDP-i-KD), on microbiota. Using...
Introduction: The increasing global population and aging have made Parkinson's disease (PD) a significant public health concern. Comprehensive evaluations of PD burden trends in Asian subregions countries are lacking. This study investigated Asia from 1990 to 2021, categorized by age, sex, region. Methods: Data the Global Burden Diseases, Injuries, Risk Factors Study (GBD) 2021 were analyzed assess incidence, prevalence, mortality, disability-adjusted life years (DALYs) across five 34...
The crucial issue in many classification applications is how to achieve the best possible classifier with a limited number of labeled training data. Active learning one method which addresses this by selecting most informative data for training. In work, we argue that performance active could be improved through carefully initial samples. To confirm our argument, propose three selection mechanisms based on fuzzy clustering method: center-based selection, border-based and hybrid selection....
AbstractPattern classification is an important part of machine learning. To use it, a classifier trained on the training data and then predicts label for future unseen data. obtain with good performance, quality plays role. Unfortunately in many areas, it difficult to provide absolutely clean This paper focuses mislabeled data, which one main types noisy A number detection techniques have been proposed; however, there no survey work summarize those techniques. reviews existing studies...
The data in industrial informatics may be high-dimensional and mislabeled. Irrelevant or noisy features pose a significant challenge to the detection of mislabeling. traditional method usually adopts two-step solution, first finding relevant subspace then using it for mislabeling detection. This struggles provide optimal performance, since separates procedures feature selection label error To solve this problem, article, we integrate two steps propose sequential ensemble noise filter (SENF)....