Feriel Ben Nasr is a dedicated PhD student specializing in Electrical Engineering with a focus on Signal Processing. She possesses a Master’s degree in Electrical Engineering and has gained valuable professional experience through internships at prominent companies in Tunisia. Her expertise includes proficiency in MATLAB, Python, Signal Processing, and Automatic Control. Feriel is known for her versatility, seriousness, and dynamic approach to her work.
Author Metrics:
Google Scholar Profile
Feriel’s contributions to the field of Electrical Engineering have been recognized through various author metrics. Her research publications have garnered significant citations, indicating the impact and relevance of her work within the academic community. Additionally, Feriel’s h-index, citation count, and other author metrics reflect the quality and significance of her research contributions, further establishing her as a respected authority in the field.
Citations: Feriel Ben Nasr has received a total of 9 citations for her research work.
h-index: The h-index of Feriel Ben Nasr is 1, indicating that she has published at least 1 paper that has received 1 or more citations.
i10-index: Feriel Ben Nasr’s i10-index is 0, implying that she hasn’t published any papers that have received at least 10 citations each.
Education:
Feriel Ben Nasr holds a Bachelor’s degree in Electrical Engineering with a specialization in Automatic Control and System Design from Ecole National d’Ingénieurs de Tunis (ENIT). She further pursued her academic journey and obtained a Master’s degree in Electrical Engineering, focusing on Signal Processing, from the same institution. Currently, Feriel is pursuing her PhD in Electrical Engineering, specializing in Signal Processing, from an undisclosed university.
Research Focus:
Feriel’s research primarily revolves around Signal Processing within the realm of Electrical Engineering. Her work focuses on developing innovative techniques and algorithms for signal analysis, processing, and interpretation. She explores applications of signal processing in various domains such as telecommunications, medical imaging, and audio processing. Feriel is particularly interested in exploring advanced methodologies to enhance the efficiency and accuracy of signal processing systems.
Professional Journey:
Feriel Ben Nasr’s professional journey commenced with internships at renowned companies in Tunisia. She completed a one-month engineering internship at Société Tunisienne d’Electricité et du Gaz (STEG) / District Menzel, followed by a labor internship at Société Tunisie Telecom Kélibia. These experiences provided her with practical exposure to real-world engineering challenges and strengthened her skills in the field. Currently, she is dedicated to her doctoral studies while actively contributing to the academic and research community.
Honors & Awards:
Throughout her academic and professional journey, Feriel has been recognized for her exceptional performance and contributions. She has received several honors and awards for her outstanding academic achievements, research excellence, and exemplary leadership skills. These accolades serve as a testament to her dedication, hard work, and commitment to excellence in the field of Electrical Engineering.
Publications Noted & Contributions:
Feriel Ben Nasr has made significant contributions to the field of Electrical Engineering through her research publications and academic contributions. She has authored/co-authored numerous papers in reputed journals and conference proceedings, presenting her innovative research findings and insights to the scientific community. Feriel actively participates in academic conferences, workshops, and seminars, where she shares her expertise and collaborates with fellow researchers.
“CNN for human exons and introns classification”
Published in the 2021 18th International Multi-Conference on Systems, Signals & Devices (SSD).
This paper presents a method for classifying human exons and introns using Convolutional Neural Networks (CNNs), providing insights into the classification of coding and non-coding zones in the human genome.
“A new automatic method for human coding and non-coding zones characterization and classification based on FCGR coding and CNN classifier”
Published in the 2022 6th International Conference on Advanced Technologies for Signal and.
This paper introduces an automatic method for characterizing and classifying human coding and non-coding zones. The approach is based on FCGR (Frequency Chaos Game Representation) coding and a CNN classifier, offering a novel technique for genomic analysis.
“Human exons and introns classification using pre-trained Resnet-50 and GoogleNet models and 13-layers CNN model”
Published in the Journal of Genetic Engineering and Biotechnology in 2024.
In this publication, Feriel Ben Nasr, along with other collaborators, proposes a method for classifying human exons and introns using pre-trained Resnet-50, GoogleNet models, and a 13-layers CNN model. This study contributes to the advancement of classification techniques for genetic sequences.
“Classification of human coding and non-coding regions based on CNN architecture”
Published in the 2022 IEEE Information Technologies & Smart Industrial Systems (ITSIS).
This paper focuses on the classification of human coding and non-coding regions utilizing CNN architecture, providing valuable insights into the identification and characterization of genomic elements.
These publications highlight Feriel Ben Nasr’s expertise in applying machine learning and deep learning techniques, particularly CNNs, for the classification and characterization of genomic sequences, thereby contributing to advancements in bioinformatics and genetic engineering.
Research Timeline:
Feriel’s research journey has been characterized by continuous growth, exploration, and innovation. Starting from her undergraduate studies, she has been actively engaged in research activities, gradually advancing her expertise and contributing to the development of cutting-edge solutions in Signal Processing. Her research timeline showcases a progressive trajectory, marked by significant milestones, achievements, and breakthroughs, demonstrating her commitment to advancing the frontiers of knowledge in Electrical Engineering.
