Karel Gutiérrez-Batista
Biographic Data
| ID | 8548456 |
|---|---|
| NAME | Karel Gutiérrez-Batista |
| GIVEN NAMES | Karel |
| FAMILY NAME | Gutiérrez-Batista |
| SIGNATURE | GUTIÉRREZ-BATISTA K |
| VERIFIED | No |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 2 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2024 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Forecasting user perceptions of mHealth apps
User perceptions of mHealth apps are critical for forecasting adoption trends, optimizing app design, and evaluating their broader societal implications for public health and digital inclusion. Understanding how users engage with these applications is essential for their sustained use. This research incorporates AI-driven methodologies to systematically analyze large-scale user-generated content (UGC), providing predictive insights into consumer …
Improving automatic cyberbullying detection in social network environments by fine-tuning a pre-trained sentence transformer language model
The internet use among children and adolescents has increased massively recently. This situation has promoted harmful situations such as cyberbullying, which is becoming a worldwide problem that entails serious consequences for well-being. The detection of these attitudes is essential to prevent and act accordingly. Groundbreaking techniques based on deep learning, like pre-trained language models, have achieved state-of-the-art results in many d…
No prominent works on this page.
Improving automatic cyberbullying detection in social network environments by fine-tuning a pre-trained sentence transformer language model
The internet use among children and adolescents has increased massively recently. This situation has promoted harmful situations such as cyberbullying, which is becoming a worldwide problem that entails serious consequences for well-being. The detection of these attitudes is essential to prevent and act accordingly. Groundbreaking techniques based on deep learning, like pre-trained language models, have achieved state-of-the-art results in many d…
Forecasting user perceptions of mHealth apps
User perceptions of mHealth apps are critical for forecasting adoption trends, optimizing app design, and evaluating their broader societal implications for public health and digital inclusion. Understanding how users engage with these applications is essential for their sustained use. This research incorporates AI-driven methodologies to systematically analyze large-scale user-generated content (UGC), providing predictive insights into consumer …
Advanced Malware Detection Techniques (1 work) · Analytics (1 work) · Artificial Intelligence (1 work) · Bullying, Victimization, and Aggression (1 work) · Computer Science (1 work) · Digital content (1 work) · Digital health (1 work) · Digital Mental Health Interventions (1 work) · Engineering (1 work) · Hate Speech and Cyberbullying Detection (1 work)