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Research 24
- Ensemble of pre-learned deep learning model and an optimized LSTM for Alopecia Areata classification
- Predicting Hair Loss with AI: A Deep Learning Framework Combining Genetic and Scalp Health Data
- HairSentinel: a time-aware anomaly detection framework for forecasting hairfall trends using temporal fusion transformers
- AB-MTEDeep Classifier Trained with AAGAN for the Identification and Classification of Alopecia Areata
- Aspect-Level Drug Reviews Sentiment Analysis Based on Double BiGRU and Knowledge Transfer
- A machine learning approach for non-invasive PCOS diagnosis from ultrasound and clinical features
- Quantitative analysis and development of alopecia areata classification frameworks
- AI-driven biomaterial design: an intelligent closed loop from reverse design to biological response
- Generative adversarial networks for anonymous acneic face dataset generation
- Scalp Disorder Imaging: How Deep Learning and Explainable Artificial Intelligence are Revolutionizing Diagnosis and Treatment
- BroadAMP-GPT: AI-Driven generation of broad-spectrum antimicrobial peptides for combating multidrug-resistant ESKAPE pathogens
- Data-Driven Machine-Learning Methods for Diabetes Risk Prediction
- Drug Repositioning with GraphSAGE and Clustering Constraints Based on Drug and Disease Networks
- Exosome Revolution or Marketing Mirage? AI-Based Multi-domain Evaluation of Claims, Scientific Evidence, Transparency, Public Sentiment, and Media Narratives
- Skin image analysis for detection and quantitative assessment of dermatitis, vitiligo and alopecia areata lesions: a systematic literature review
- Exploring machine learning strategies for single-cell transcriptomic analysis in wound healing
- A Quantitative Measurement Method for Nuclear-Pleomorphism Scoring in Breast Cancer
- Leveraging deep neural networks to uncover unprecedented levels of precision in the diagnosis of hair and scalp disorders
- Post-COVID Clinical Manifestations in Non-Hospitalized Patients with COVID-19: A Retrospective Cohort Study
- KRDQN: An Interpretable Prediction Framework for Adverse Drug Reactions via Knowledge–Graph Reinforced Deep Q-Learning
- Microalgal Biofactories: Sustainable Solutions for Nutrition and Cosmetics
- An Early Hair Loss Detection and Prediction Method Based on Machine Learning
- Rapid Screening of Anticoagulation Compounds for Biological Target-Associated Adverse Effects Using a Deep-Learning Framework in the Management of Atrial Fibrillation
- LEVERAGING MACHINE LEARNING METHODS IN PREDICTING AND ANALYZING THE ASSOCIATION BETWEEN DIETARY INFLAMMATORY INDEX AND ALOPECIA