Dr. Fettah Amina, Echahid Cheikh Larbi Tebessi University, Algeria
👩💼 Dr. Fettah Amina is a dedicated PhD student in artificial intelligence at El Chahid Cheikh Laarbi Tébessi University in Tébessa, Algeria. With a Bachelor’s and Master’s degree in Computer Systems and Systems and Multimedia respectively, from the same university, she has fortified her academic journey. Holding a Cisco CCNA routing and switching certification from NSFP in Tébessa further solidifies her expertise. Amina’s practical experience encompasses roles at Algérie Télécom, focusing on communication channel multiplexing, and as a vaccataire at the Department of Social Sciences in Tébessa, where she taught computer science tutorials. Her passion lies in artificial intelligence, particularly in deep learning, machine learning, image compression, and medical imaging, as evidenced by her notable research contributions and publications. 🌟
Professional Profile:
🎓 Education:
Amina is currently a PhD student in artificial intelligence at El Chahid Cheikh Laarbi Tébessi University in Tébessa. She completed her Bachelor’s degree in Computer Systems and a Master’s degree in Systems and Multimedia at the same university. Additionally, she holds certifications in Cisco CCNA routing and switching from NSFP in Tébessa.
💼 Experience:
Amina has gained practical experience through various roles, including a practical training assignment at Algérie Télécom, where she focused on multiplexing between communication channels. She has also served as a vaccataire (temporary teaching assistant) at the Department of Social Sciences in Tébessa, teaching computer science tutorials.
🔬 Expertise:
Amina’s expertise lies in artificial intelligence, particularly in deep learning, machine learning, image compression, and medical imaging. She has conducted research in these areas, resulting in publications such as “Machine Learning for Medical Image Analysis: A Survey” presented at the International Conference on Advanced Intelligent Systems for Sustainable Development, and “Convolutional Autoencoder-Based Medical Image Compression using a Novel Annotated Medical X-ray Imaging Dataset” published in Biomedical Signal Processing and Control.
Publication Top Notes: