A5116568402- Radiomics and Machine Learning in Medical Imaging
- Computational Drug Discovery Methods
- AI in cancer detection
- Collaboration in agile enterprises
- Innovative Microfluidic and Catalytic Techniques Innovation
- Protein Structure and Dynamics
- Economic and Technological Systems Analysis
- Machine Learning in Materials Science
- Risk Management in Financial Firms
- Rough Sets and Fuzzy Logic
- Evaluation and Optimization Models
- Lung Cancer Diagnosis and Treatment
- COVID-19 diagnosis using AI
- Machine Learning in Bioinformatics
- Digital Imaging for Blood Diseases
United International University
2024-2025
Wuhan University of Technology
2016
Lung cancer is a predominant cause of related deaths globally, with early detection for improving patient prognosis being essential. Deep learning models, particularly those attention mechanisms, have shown promising accuracy in detecting lung from medical imaging data. However, privacy concerns and data scarcity present significant challenges developing robust generalizable models. This paper proposes novel approach utilizing federated mechanisms ensemble to address these challenges....
Breast cancer is a complicated and diverse ailment that requires thorough understanding at the cellular level to provide more accurate diagnoses customized treatments. This work explores categorization division of breast cells using imaging technology, computational algorithms, histopathology examination. The classification result was derived by building hybrid model combined RNN EfficientNetV2S with an accuracy 99.99%, demonstrated promising improvement over baseline, even though it...
Proteins play a crucial role in various biochemical activities, making accurate protein class and function prediction critical. Accurate is fundamental computational biology for biological understanding. Relying solely on one modality limits the model's ability to capture protein's complete picture, potentially leading inaccurate predictions. This paper addresses these challenges presents machine-learning model precise prediction, leveraging sequence, secondary structure, interaction...
Abstract Motivation Structure-based drug design (SBDD) holds promising potential to ligands with high-binding affinity and rationalize their interaction targets. By utilizing geometric knowledge of the three-dimensional (3D) structures target binding sites, SBDD enhances efficacy selectivity therapeutic agents by optimizing interactions at molecular level. Here, we present CoDNet, a novel approach that combines conditioning capabilities ControlNet potency diffusion model create generative...
Rough set theory is relativly new to area of soft computing handle the uncertain big data efficiently. It also provides a powerful way calculate importance degree vague and help in decision making. Risk assessment very important for safe reliable investment. management involves assessing risk sources designing strategies procedures mitigate those risks an acceptable level. In this paper, we emphasize on classification different types factors find simple effective exposure.. The study uses...