Speech Emotion Recognition Using openSMILE and GPT 3.5 Transformer.

In recent years, artificial intelligence, and machine learning (ML) models have advanced significantly, offering transformative solutions across diverse sectors. Emotion recognition in speech has particularly benefited from ML techniques, revolutionizing its accuracy and applicability. This article proposes a method for emotion detection in Romanian speech analysis by combining two distinct approaches: semantic analysis using GPT Transformer and acoustic analysis using openSMILE. The results showed an accuracy of 74% and a precision of almost 82%. Several system limitations were observed due to the limited and low-quality dataset. However, it also opened a new horizon in our research by analyzing emotions to identify mental health disorders.

Real-Time Detection of Emotions Based on Facial Expression for Mental Health.

When it comes to health, a very widespread problem nowadays is mental health, where almost 18% of the world's population suffers from certain mental illnesses. Artificial intelligence (AI) is a concept that evolves strongly and is expected in the near future to bring improvements and help to humans in various fields. The field of mental health is not excluded, so AI can help in the performance of medical services, either by helping the medical staff or patients. In terms of mental health, it has been observed that by recognizing facial expressions, depression, schizophrenia, or other similar conditions can be detected. However, for automatic learning, a very large data set is required for good accuracy. In this paper, we present a facial expression recognition method using only a few training data, which could be used remotely through a mobile or web application.

Artificial Intelligence in Primary Care: An Overview.

With the increase in computing power and the development of numerous technological devices that facilitate remote work, the involvement of artificial intelligence in medicine has seen a significant increase to help the doctor make decisions and intervene in the medical process and telemedicine. In this paper, we gave an overview of the practical involvement of artificial intelligence through different support systems used in primary medicine or telemedicine and also identified the possibilities and opportunities for the development of new support systems for family medicine. Thus, we identified systems used for primary diagnosis, diagnosis of hypertension, early detection of heart abnormalities, detection of diabetes, support in the prescription process, helping clinicians in the daily workflow by providing certain answers to questions, treatment guidance, determining patient priority for treatment for SARS-CoV-2 infection or early detection of disease, support of artificial ventilation in medical emergency centers, remote support for treatments and medication.