A5016030951- Mental Health via Writing
- Sentiment Analysis and Opinion Mining
- Digital Mental Health Interventions
- Water Quality Monitoring Technologies
- Brain Tumor Detection and Classification
- Hate Speech and Cyberbullying Detection
- Advanced Malware Detection Techniques
- Artificial Intelligence in Healthcare
- Machine Learning and ELM
- Misinformation and Its Impacts
- Machine Learning in Healthcare
- Underwater Vehicles and Communication Systems
University of California, Berkeley
2025
Nanjing University of Information Science and Technology
2020
Marine unmanned vehicle is a novel robot widely used in ocean observation, and its accurate control of significance to their path planning.We want find method predict the velocity course this robot, which can help us realize it.The paper proposed promising type hybrid robotic fish (HRF), two kinds motion modes on sea surface.Firstly, configuration dynamic model HRF were analyzed elaborately.Then, prediction under HRF, influence factors are presented complex marine environment.Based...
Social media has become an important source for understanding mental health, providing researchers with a way to detect conditions like depression from user-generated posts. This tutorial provides practical guidance address common challenges in applying machine learning and deep methods health detection on these platforms. It focuses strategies working diverse datasets, improving text preprocessing, addressing issues such as imbalanced data model evaluation. Real-world examples step-by-step...
Social media platforms provide valuable insights into mental health trends by capturing user-generated discussions on conditions such as depression, anxiety, and suicidal ideation. Machine learning (ML) deep (DL) models have been increasingly applied to classify from textual data, but selecting the most effective model involves trade-offs in accuracy, interpretability, computational efficiency. This study evaluates multiple ML models, including logistic regression, random forest, LightGBM,...
The global rise in depression necessitates innovative detection methods for early intervention. Social media provides a unique opportunity to identify through user-generated posts. This systematic review evaluates machine learning (ML) models on social media, focusing biases and methodological challenges throughout the ML lifecycle. A search of PubMed, IEEE Xplore, Google Scholar identified 47 relevant studies published after 2010. Prediction model Risk Of Bias ASsessment Tool (PROBAST) was...