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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Computer Science
Daoudi, Najima
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Journal : International Journal of Electrical and Computer Engineering

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Deep learning approaches for recognizing facial emotions on autistic patients El Rhatassi, Fatima Ezzahrae; Ghali, Btihal El; Daoudi, Najima
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4034-4045

Abstract

Autistic people need continuous assistance in order to improve their quality of life, and chatbots are one of the technologies that can provide this today. Chatbots can help with this task by providing assistance while accompanying the autist. The chatbot we plan to develop gives to autistic people an immediate personalized recommendation by determining the autist’s state, intervene with him and build a profil of the individual that will assist medical professionals in getting to know their patients better so they can provide an individualized care. We attempted to identify the emotion from the image's face in order to gain an understanding of emotions. Deep learning methods like Convolutional neural networks and Vision Transformers could be compared using the FER2013. After optimization, CNN achieved 74% accuracy, whereas the VIT achieved 69%. Given that there is not a massive dataset of autistic individuals accessible, we combined a dataset of photos of autistic people from two distinct sources and used the CNN model to identify the relevant emotion. Our accuracy rate for identifying emotions on the face is 65%. The model still has some identification limitations, such as misinterpreting some emotions, particularly "neutral," "surprised," and "angry," because these emotions and facial traits are poorly expressed by autistic people, and because the model is trained with imbalanced emotion categories.
Integration of web scraping, fine-tuning, and data enrichment in a continuous monitoring context via large language model operations Bodor, Anas; Hnida, Meriem; Daoudi, Najima
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp1027-1037

Abstract

This paper presents and discusses a framework that leverages large-scale language models (LLMs) for data enrichment and continuous monitoring emphasizing its essential role in optimizing the performance of deployed models. It introduces a comprehensive large language model operations (LLMOps) methodology based on continuous monitoring and continuous improvement of the data, the primary determinant of the model, in order to optimize the prediction of a given phenomenon. To this end, first we examine the use of real-time web scraping using tools such as Kafka and Spark Streaming for data acquisition and processing. In addition, we explore the integration of LLMOps for complete lifecycle management of machine learning models. Focusing on continuous monitoring and improvement, we highlight the importance of this approach for ensuring optimal performance of deployed models based on data and machine learning (ML) model monitoring. We also illustrate this methodology through a case study based on real data from several real estate listing sites, demonstrating how MLflow can be integrated into an LLMOps pipeline to guarantee complete development traceability, proactive detection of performance degradations and effective model lifecycle management.
Co-Authors Abourezq, Manar Bellafkih, Mostafa Belmaqrout, Ahlam Bodor, Anas El Ghali, Btihal El Rhatassi, Fatima Ezzahrae Ghali, Btihal El Haqiq, Abdelhay Hnida, Meriem Ikiss, Soumia