Real name: 
GGALIWANGO MARVIN

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First Name: 
GGALIWANGO
Last Name: 
MARVIN
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Trustworthy Medical Sentiment Detection for Maternal and Neonatal Healthcare

Datasets & Competitions

Responsible Medical Corpus for Maternal Sexual and Reproductive Health

Maternal, sexual and reproductive healthcare (MSRH) are sensitive urgent public health issues that require timely trustworthy authentic medical responses. Unfortunately, curative healthcare systems of Low Middle-Income Countries (LMICs) are insufficiently responsive to such healthcare needs. Such needs vary among social groups often founded on social inequalities like income, gender and education.

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Artificial Intelligence
Machine Learning
Biomedical and Health Sciences
Computational Intelligence
Health
Submitted On: 
Sun, 06/30/2024 - 13:45
Last Updated On: 
Sun, 06/30/2024 - 14:01

Responsible Software Systems with Emotional Intelligence

In the era of advanced artificial intelligence, the integration of emotional intelligence into AI systems has become crucial for developing Responsible Software Systems that are not only functional but also emotionally perceptive. The Microe dataset, a pioneering compilation focusing on micro-expressions, aims to revolutionize AI systems by enhancing their capability to recognize and interpret subtle emotional cues. This dataset encompasses over eight classes of common emotions, meticulously captured and categorized to aid in the synthesis and recognition of micro-expressions.

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Artificial Intelligence
Education and Learning Technologies
Machine Learning
Image Fusion
Image Processing
Submitted On: 
Sun, 06/30/2024 - 11:23
Last Updated On: 
Tue, 07/16/2024 - 11:30

Maternal Health Medical Advice

The dataset analyzed in this study is the result of a systematic literature review and a crowdsourced mini-project that aimed to identify and validate metrics relevant to maternal and neonatal healthcare examinations. The study involved a diverse group of participants, including 193 registered medical personnel from reputable institutions and 161 non-medical individuals who were active on various social media platforms related to maternal and neonatal healthcare.

Categories:
Artificial Intelligence
Machine Learning
Social Sciences
Biomedical and Health Sciences
Computational Intelligence
Demographic
Health
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