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Ihababdelbasset Annaki

Assistant Professor @ESEFO - UMP

About Me

Ihababdelbasset Annaki, Ph.D., and Assistant Professor at ESEF - Mohammed First University, specializes in artificial intelligence and virtual reality. His research focuses on their applications in neuroscience and educational technologies. Passionate about mentorship and interdisciplinary collaboration, he aims to inspire and train future innovators in AI while delivering impactful and practical solutions.

Interests

Artificial Intelligence | Machine and Deep Learning
Virtual Reality
Neuroscience
Education | Digitalization, Didactics, and Pedagogy

Education

Ph.D. in Science | Specialization in Artificial Intelligence (ML&DL) and Virtual Reality
Mohammed First University (UMP)
Computer Engineering | Specialization in Business Intelligence and Data Engineering
National Institute of Statistics and Applied Economics (INSEA)
Mathematics and Physics
CPGE - Preparatory Classes for Grandes Écoles

📚 My Research

I develop AI-based approaches, including machine and deep learning for time series, to address real-world challenges in neuroscience and education. By exploring the integration of advanced technologies, my goal is to create practical and innovative solutions to improve educational practices and understanding of cognitive mechanisms.

I firmly believe that innovation is born from interdisciplinary collaboration. If my work resonates with you, let’s connect to exchange ideas and create new solutions together!

Featured Publications

Timeseriesdataaugmentation

Overview of Data Augmentation Techniques in Time Series Analysis

This paper explores the transformative impact of data augmentation on time series analysis, highlighting classic methods such as interpolation and advanced techniques like GANs and VAEs. It examines the challenges, including computational costs and risks of overfitting, while showcasing the advantages of these methods. Offering a balanced perspective, the paper serves as a comprehensive guide for researchers and practitioners to address challenges and drive advancements in machine learning and deep learning applications for time series data.

1 Jan 2024

Deeptimeseriesclustering

Spatiotemporal Clustering of Human Locomotion Neuropsychological Assessment in Virtual Reality Using Multi-step Model

This paper introduces a spatiotemporal clustering model to analyze neuropsychological assessments conducted in a virtual reality environment. The study focuses on clustering human navigation behaviors using a deep learning framework that separates feature extraction and clustering stages. By applying a 1D-DCAE autoencoder for feature extraction and a soft temporal clustering mechanism, the method identifies five distinct clusters corresponding to clinical groups. This research contributes to understanding navigation patterns in cognitive impairments and advances VR-based neuropsychological assessments.

1 Jan 2023

Recent Publications

Ihababdelbasset Annaki, Mohammed Rahmoune, Mohammed Bourhaleb (2024). Overview of Data Augmentation Techniques in Time Series Analysis. International Journal of Advanced Computer Science and Applications.

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Ihababdelbasset Annaki, Mohammed Rahmoune, Mohammed Bourhaleb (2023). Computational Analysis of Human Navigation Trajectories in the VR Magic Carpet ™ Using K-Means. Lecture Notes in Electrical Engineering.

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Ihababdelbasset Annaki, Mohammed Rahmoune, Mohammed Bourhaleb, Mohamed Zaoui, Alexander Castilla, Alain Berthoz, Bernard Cohen (2023). Evaluating the Efficiency of Multilayer Perceptron Neural Network Architecture in Classifying Cognitive Impairments Related to Human Bipedal Spatial Navigation. Lecture Notes in Networks and Systems.

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Ihababdelbasset Annaki, Mohammed Rahmoune, Mohammed Bourhaleb, Noureddine Rahmoun, Mohamed Zaoui, Alexander Castilla, Alain Berthoz, Bernard Cohen (2023). Joint Unsupervised Deep Temporal Clustering for Modeling Human Behavior in Vestibular Dysfunction: A Study of Navigation Pattern. Lecture Notes in Networks and Systems.

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Ihababdelbasset Annaki, Mohammed Rahmoune, Mohammed Bourhaleb, Mohamed Zaoui, Alexander Castilla, Alain Berthoz, Bernard Cohen (2023). Residual Neural Network Architecture for Identifying Vestibular Disease Based on Head Kinematic Characteristics (Velocity). Lecture Notes in Networks and Systems.

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Recent & Upcoming Talks

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1 Jun 2030

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27 Oct 2023

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27 Oct 2023

🧠 Sharpen your thinking with a second brain

26 Oct 2023

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26 Oct 2023

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25 Oct 2023

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25 Oct 2023

👩🏼‍🏫 Teach academic courses

24 Oct 2023

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24 Oct 2023

✅ Manage your projects

23 Oct 2023

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23 Oct 2023

Collaborate with Me

Are you interested in collaborating on cutting-edge projects in AI, neuroscience, or educational technologies? Contact me, and together, let’s make a difference!

Contact Me