Ouafae El Melhaoui | Machine Learning | Best Researcher Award

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Dr. Ouafae El Melhaoui | Machine Learning | Best Researcher Award

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.

Qizhi He | Reinforcement Learning | Best Researcher Award

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Dr. Qizhi He | Reinforcement Learning | Best Researcher Award

Associate Researcher | DJI Innovation Technology Co., Ltd. | China

Dr. Qizhi He is an accomplished engineer and researcher specializing in navigation, guidance, and control systems. His academic and professional journey has been characterized by excellence and innovation, contributing significantly to the fields of multi-sensor information fusion, aircraft damage reconstruction, and autonomous vehicle localization. With a Doctor of Engineering degree from Northwestern Polytechnical University and a Master’s with Distinction from the University of Leicester, Dr. He has consistently demonstrated expertise in both theoretical research and practical application. His work spans prominent roles in academia, industry-leading companies, and national projects, underscoring his versatility and dedication to advancing technological solutions.

Profile

Scholar

Education

Dr. He’s academic journey began with a Bachelor of Engineering degree at Northwestern Polytechnical University, where he participated in an integrated undergraduate, master’s, and doctoral program. He later pursued a Master of Science in Advanced Engineering at the University of Leicester, achieving a distinction and excelling in dynamics of mechanical systems. His doctoral research at Northwestern Polytechnical University focused on multi-sensor information fusion and aircraft damage reconstruction, culminating in groundbreaking contributions to Shaanxi Key Laboratory of Aircraft Control and Simulation. Throughout his education, Dr. He earned numerous scholarships and accolades, reflecting his exceptional academic performance.

Experience

Dr. He’s professional experience spans both academia and industry. At DJI Innovation Technology Co., Ltd., he led localization modules for agricultural drones, logistics drones, and automatic parachutes, optimizing sensor fusion algorithms to enhance system performance. He also contributed to autonomous vehicle localization at XPENG Motors and developed advanced robotics algorithms during his tenure at Limx Dynamics. His current role as an assistant researcher at the Yangtze River Delta Research Institute focuses on unmanned systems, leveraging his expertise to innovate in multi-sensor fusion and localization technologies.

Research Interests

Dr. He’s research interests lie at the intersection of multi-sensor information fusion, robust control systems, and autonomous navigation technologies. He has contributed to advancing the understanding of information fusion through Kalman filters, observer-based methods, and manifold theory, with applications in unmanned aerial vehicles (UAVs), autonomous driving, and robotics. His work emphasizes the development of vibration-resistant and interference-free algorithms, pushing the boundaries of GPS-denied localization and fault-tolerant systems for aircraft and underwater vehicles.

Awards

Dr. He’s achievements have earned him prestigious recognitions, including the “Belt and Road” Special Scholarship, Outstanding Talent Scholarship, and several academic excellence awards. His exceptional performance in circuit experiments and his distinction at the University of Leicester further attest to his technical and intellectual prowess.

Publications

Dr. Qizhi He has authored over 20 SCI/EI papers, including influential articles in top-tier journals. Below are a selection of his publications:

“Robust Adaptive Flight Control for Faulty Aircraft” (2020) – Published in Aerospace Science and Technology, cited by 15 articles.

“Multi-Sensor Information Fusion for UAV Localization” (2021) – Published in Journal of Navigation, cited by 12 articles.

“Dynamic Modeling of Aircraft Wing Damage Control” (2019) – Published in Control Engineering Practice, cited by 10 articles.

“Innovations in AHRS Algorithm Design” (2022) – Published in IEEE Transactions on Aerospace and Electronic Systems, cited by 20 articles.

“Error State Kalman Filter on SO(3) for Robotics” (2023) – Published in Robotics and Autonomous Systems, cited by 8 articles.

“Reconfigurable Control Systems for Civil Aircraft” (2021) – Published in Aerospace Systems Design, cited by 6 articles.

“Vision-Based Localization in GPS-Denied Environments” (2022) – Published in Sensors, cited by 18 articles.

Conclusion

Dr. Qizhi He embodies the fusion of rigorous academic research with practical engineering applications. His expertise in navigation and control systems, combined with his dedication to innovation, has made him a valuable contributor to both industrial advancements and scholarly research. As he continues his journey, Dr. He remains committed to addressing critical challenges in unmanned systems and autonomous technologies, advancing the state of the art in multi-sensor information fusion and robust control systems.