A5075612401- Privacy-Preserving Technologies in Data
- Topic Modeling
- Semantic Web and Ontologies
- Sentiment Analysis and Opinion Mining
- Stochastic Gradient Optimization Techniques
- Advanced Neural Network Applications
- Advanced Wireless Communication Techniques
- Advanced Text Analysis Techniques
- Streptococcal Infections and Treatments
- Cryptography and Data Security
- Power Transformer Diagnostics and Insulation
- Diabetic Foot Ulcer Assessment and Management
- Advanced Wireless Network Optimization
- Mangiferin and Mango Extracts
- Phytochemicals and Antioxidant Activities
- Blockchain Technology Applications and Security
- Natural Antidiabetic Agents Studies
- Microfluidic and Capillary Electrophoresis Applications
- Data Quality and Management
- Adversarial Robustness in Machine Learning
- Peer-to-Peer Network Technologies
- Distributed and Parallel Computing Systems
- Advanced Graph Neural Networks
- Oral microbiology and periodontitis research
- Heat and Mass Transfer in Porous Media
Dalian University of Technology
2024-2025
University of Electronic Science and Technology of China
2024
Objectives: Teacher’s evaluation in education system is quite important to improve the learning experience ininstitutions. For this purpose, sentiment analysis model developedto identify student sentiments from piece of text. Methods/ Statistical Analysis: Long Short-Term Memory Model (LSTM) used for analyzing expressed by students through textual feedback. dataset has been built student’s feedback and then divided into 70% 30% training testing. The proposed trained using softmax adam along...
ABSTRACT: Background: Diabetes mellitus is a known risk factor for certain infectious diseases such as skin, mucus membrane, soft tissue, urinary tract, respiratory tract and surgical or hospital associated infections. In elderly patients initial antibiotic therapy diabetic infection empirical. To study the efficacy of empirical significant to ensure potency given therapy. Objective: The was aimed determine effectiveness in infective Mellitus patients. Method: A Prospective observational...
Entity matching in Customer 360 is the task of determining if multiple records represent same real world entity. Entities are typically people, organizations, locations, and events represented as attributed nodes a graph, though they can also be relational data. While probabilistic engines artificial neural network models exist for this task, explaining entity has received less attention. In demo, we present our Explainable Matching (xEM) system discuss different AI/ML considerations that...