A5025442250- Network Security and Intrusion Detection
- Advanced Malware Detection Techniques
- Smart Agriculture and AI
- Spectroscopy and Chemometric Analyses
- Remote Sensing in Agriculture
- Anomaly Detection Techniques and Applications
- Asthma and respiratory diseases
- Sugarcane Cultivation and Processing
- Allergic Rhinitis and Sensitization
- IL-33, ST2, and ILC Pathways
- Bone and Dental Protein Studies
- dental development and anomalies
- Methane Hydrates and Related Phenomena
- Birth, Development, and Health
- Receptor Mechanisms and Signaling
- Animal Behavior and Welfare Studies
- Team Dynamics and Performance
- Plant Pathogenic Bacteria Studies
- Vehicle License Plate Recognition
- Water Quality Monitoring Technologies
- Adipose Tissue and Metabolism
- Advanced Decision-Making Techniques
- Ocean Acidification Effects and Responses
- Water Quality Monitoring and Analysis
- Software Engineering Techniques and Practices
Inner Mongolia Agricultural University
2024-2025
Beijing Normal University
2025
National Institute of Environmental Health Sciences
2008-2024
Beijing Jiaotong University
2024
National Institutes of Health
2008-2018
National Institute of Dental and Craniofacial Research
2018
Saga University
2009
Potato, a vital food and cash crop, necessitates precise identification area estimation for effective planting planning, market regulation, yield forecasting. However, extracting large-scale crop areas using satellite remote sensing is fraught with challenges, such as low spatial resolution, cloud interference, revisit cycle limitations, impeding the creation of high-quality time–series datasets. In this study, we developed high-resolution vegetation index by calculating coordination...
As 5G technology becomes more widespread, the significant improvement in network speed and connection density has introduced challenges to security. In particular, distributed denial of service (DDoS) attacks have become frequent complex software-defined (SDN) environments. The complexity diversity networks result a great deal unnecessary features, which may introduce noise into detection process an intrusion system (IDS) reduce generalization ability model. This paper aims improve...
Potato is a major food crop in China. Its development and nutritional state can be inferred by the content of chlorophyll its canopy. However, existing study on applying feature extraction optimization algorithms to determine canopy SPAD (Soil–Plant Analytical Development) values potatoes at various fertility stages inadequate not very reliable. Using Pearson selection algorithm Competitive Adaptive Reweighted Sampling (CARS) method, Vegetation Index (VI) with highest correlation was...
Vulnerability detection in software source code is crucial ensuring security. Existing models face challenges with dataset class imbalance and long training times. To address these issues, this paper introduces a multi-feature screening integrated sampling model (MFISM) to enhance vulnerability efficiency accuracy. The key innovations include (i) utilizing abstract syntax tree (AST) representation of extract potential vulnerability-related features through multiple feature techniques; (ii)...
This paper proposes an innovative video behaviour classification method based on pyramid pooling and a variable-scale training strategy, which aims to improve the behaviour-recognition performance of 3D convolutional neural network (3D-CNN) dual-stream C3D network. By introducing secondary operations, number layers is optimised, parameters model significantly reduced, recognition accuracy effectively improved. In improved network, early fusion strategy adopted better combine spatio-temporal...
Individual recognition of Holstein cows is the basis for realizing precision dairy farming. Current machine vision individual systems usually rely on fixed vertical illumination and top-view camera perspectives or require complex systems, these requirements limit their promotion in practical applications. To solve this problem, a lightweight cow feature extraction network named CowBackNet designed paper. This not affected by angle lighting changes suitable farm environments. Secondly, fusion...
Basic-helix-loop-helix (bHLH) transcription factors play an important role in various organs’ development; however, a tooth-specific bHLH factor has not been reported. In this study, we identified novel factor, which named AmeloD, by screening tooth germ complementary DNA (cDNA) library using yeast 2-hybrid system. AmeloD was mapped onto the mouse chromosome 1q32. Phylogenetic analysis showed that belongs to achaete-scute complex-like ( ASCL) gene family and is homologue of ASCL5. uniquely...
With the increasingly severe challenge of Software Supply Chain (SSC) security, rising trend in guarding against security risks has attracted widespread attention. Existing techniques still face challenges both accuracy and efficiency when detecting malware SSC. To meet this challenge, paper introduces two novel models, named Bayesian Optimization-based Support Vector Machine (BO-SVM) Long Short-Term Memory–BO-SVM (LSTM-BO-SVM). The BO-SVM model is constructed on an SVM foundation, with its...
The fast growth of the Internet has made network security problems more noticeable, so intrusion detection systems (IDSs) have become a crucial tool for maintaining security. IDSs guarantee normal operation by tracking traffic and spotting possible assaults, thereby safeguarding data However, traditional methods encounter several issues such as low efficiency prolonged time when dealing with massive high-dimensional data. Therefore, feature selection (FS) is particularly important in IDSs....
Early blight and ladybug beetle infestation are important factors threatening potato yields. The current research on disease classification using the spectral differences between healthy disease-stressed leaves of plants has achieved good progress in a variety crops, but less been conducted early potato. This paper proposes CARS-SPA-GA feature selection method. First, raw data visible/near-infrared light region were preprocessed. Then, wavelengths selected via competitive adaptive reweighted...
In precision feeding, non-contact and pressure-free monitoring of sheep feeding behavior is crucial for health optimizing production management. The experimental conditions real-world environments differ when using acoustic sensors to identify behaviors, leading discrepancies consequently posing challenges achieving high-accuracy classification in complex environments. This study enhances the performance by integrating deep spectrogram features characteristics associated with behavior. We...
Chlorophyll-a (Chl-a) concentration is one of the important indicators in water bodies for assessing ecological health quality. In this paper, an OGolden-DBO-XGBoost Chl-a inversion model proposed using Wuliangsu Lake as study area, and by combining Sentinel-2 remote-sensing satellite images measured data Lake, XGBoost optimized hybrid-strategy-improved dung beetle optimization algorithm (OGolden-DBO), model. The model’s coefficients determination (R2s) were 0.8936 0.8850 on training set...
With the increasing popularity of Android smartphones, malware targeting platform is showing explosive growth. Currently, mainstream detection methods use static analysis to extract features software and apply machine learning algorithms for detection. However, can be less effective when faced with that employs sophisticated obfuscation techniques such as altering code structure. In order effectively detect improve accuracy, this paper proposes a dynamic model based on combination an...
Sugarcane is the primary crop in global sugar industry, yet it remains highly susceptible to a wide range of diseases that significantly impact its yield and quality. An effective solution required solve issues given by manual identification plant diseases, which time-consuming wasteful, as well low detection accuracy. This paper proposes development robust deep ensemble convolutional neural network (DECNN) model for accurate sugarcane leaf diseases. Initially, several transfer learning (TL)...
Sugarcane is the primary crop in global sugar industry, yet it remains highly susceptible to a wide range of diseases that significantly impact its yield and quality. An effective solution required address issues caused by manual identification plant diseases, which time-consuming has low detection accuracy. This paper proposes development robust Deep Ensemble Convolutional Neural Network (DECNN) model for accurate sugarcane leaf diseases. Initially, several transfer learning (TL) models,...