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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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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.
Exploring the potential of DistilBERT architecture for automatic essay scoring task Ikiss, Soumia; Daoudi, Najima; Abourezq, Manar; Bellafkih, Mostafa
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp1234-1241

Abstract

Automatic assessment of writing essays, or the process of using computers to evaluate and assign grades to written text, is very needed in the education system as an alternative to reduce human burden and time consumption, especially for large-scale tests. This task has received more attention in the last few years, being one of the major uses for natural language processing (NLP). Traditional automatic scoring systems typically rely on handcrafted features, whereas recent studies have used deep neural networks. Since the advent of transformers, pre-trained language models have performed well in many downstream tasks. We utilize the Kaggle benchmarking automated student assessment prize dataset to fine-tune the pre-trained DistilBERT in three different scenarios, and we compare results with the existing neural network-based approaches to achieve improved performance in the automatic essay scoring task. We utilize quadratic weighted Kappa (QWK) as the main metric to evaluate the performance of our proposed method. Results show that fine-tuning DistilBERT gives good results, especially with the scenario of training all parameters, which achieve 0.90 of QWK and outperform neural network models.
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.
Personalized virtual reality therapy for children with autism spectrum disorder Belmaqrout, Ahlam; El Ghali, Btihal; Daoudi, Najima; Haqiq, Abdelhay
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp3444-3451

Abstract

The treatment of autism spectrum disorders (ASD) has often relied on broad therapeutic approaches that may not meet each individual's specific needs. This research highlights the importance of personalized therapy to address the unique sensory and emotional requirements of autistic children. We explore recent advances in therapeutic technologies, focusing on serious games and virtual reality (VR) as promising tools in this field. Our proposed solution is a VR application designed to provide a personalized, relaxing experience for children with autism. The application is tailored to accommodate individual preferences and sensory sensitivities, adjusting visual and auditory stimuli to reduce sensory overload and promote emotional regulation. This personalized approach aims to help children manage anxiety and stress more effectively.
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