Electronic and System Laboratory National School of Applied Sciences, ENSA Mohammed first University, Morocco
Dr. Ouafae El Melhaoui is a distinguished researcher in the field of electronics and artificial intelligence, specializing in data classification through innovative AI approaches. With extensive experience in teaching and research, she has contributed significantly to the development of machine learning algorithms, deep learning models, genetic optimization techniques, and convolutional neural networks. Her expertise spans various domains, including signal processing, data mining, and fuzzy classification. Dr. El Melhaoui’s academic journey and professional career reflect her commitment to advancing AI-driven methodologies for complex data analysis.
Profile
Orcid
Education
Dr. El Melhaoui earned her Ph.D. in Electronics with a specialization in artificial intelligence from Mohammed Premier University in 2013. Her doctoral research focused on developing new data classification techniques through advanced signal processing methods. Prior to that, she obtained a Diploma of Advanced Studies (D.E.S.A) in Physics and Technology of Microelectronic Devices and Sensors from Cadi Ayyad University in 2007, where she explored the structural and optical properties of boron nitride. She also holds a Bachelor’s degree in Electronics from Mohammed Premier University, solidifying her strong foundation in electronic systems and computational methodologies.
Professional Experience
Dr. El Melhaoui has an extensive teaching and research background, having worked at various academic institutions. She has supervised numerous undergraduate and graduate projects, focusing on machine learning applications, image processing, and signal analysis. Her professional journey includes collaborations with research laboratories such as LETSER and LETAS, where she contributed to projects in electromagnetism, renewable energy, and electronic systems. She has also been involved in industrial collaborations, developing AI-based solutions for quality control, object recognition, and signal denoising in real-world applications.
Research Interests
Dr. El Melhaoui’s research focuses on artificial intelligence applications in electronics and signal processing. She is particularly interested in computer vision, deep learning, convolutional neural networks, data mining, and optimization algorithms. Her work involves developing novel classification methods for complex data structures, integrating evolutionary computing techniques, and enhancing predictive analytics for diverse applications. Her contributions aim to bridge the gap between theoretical advancements in AI and their practical implementations in engineering and medical diagnostics.
Awards and Recognitions
Dr. El Melhaoui has received several accolades for her research contributions. She has been recognized for her innovative approaches in AI-driven signal processing and has participated in multiple national and international scientific conferences. Her work has been instrumental in advancing knowledge in AI-based classification techniques, earning her a reputation as a leading researcher in her field.
Publications
Novel Classification Algorithm for Complex Class Structures, e-Prime – Advances in Electrical Engineering, Electronics and Energy (Under Review, 2024). Scopus Q1, SJR=0.65.
Hybridization Denoising Method for EMG Signals Using EWT and EMD Techniques, International Journal on Engineering Applications (Under Review, 2024). Scopus Q2, SJR=0.28.
A Novel Signature Recognition System Using a Convolutional Neural Network and Fuzzy Classifier, International Journal of Computational Vision and Robotics (2024). Scopus Q4, SJR=0.21.
Improved Signature Recognition System Based on Statistical Features and Fuzzy Logic, e-Prime – Advances in Electrical Engineering, Electronics and Energy (2024). Scopus Q1, SJR=0.65.
Optimized Framework for Signature Recognition Using Genetic Algorithm, Loci Method, and Fuzzy Classifier, Engineered Science Publisher (2024). Scopus Q1, SJR=0.87.
Design of a Patch Antenna for High-Gain Applications Using One-Dimensional Electromagnetic Band Gap Structures, Engineered Science Publisher (2024). Scopus Q1, SJR=0.87.
Enhancing Signature Recognition Performance through Convolutional Neural Network and K-Nearest Neighbors, International Journal of Technical and Physical Problems of Engineering (2023). Scopus Q3, SJR=0.23.
Conclusion
Dr. Ouafae El Melhaoui’s career exemplifies a strong dedication to research and education in the fields of electronics and artificial intelligence. Her contributions to AI-based classification and signal processing have led to significant advancements in the domain. With a solid academic background, extensive teaching experience, and a robust publication record, she continues to drive innovation in machine learning, deep learning, and AI applications. Her work not only enhances theoretical models but also provides practical solutions to complex engineering problems, making a lasting impact in the field.