Azin Izadi | Image Processing | Best Researcher Award

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Ms. Azin Izadi | Image Processing | Best Researcher Award

Researcher at Shahid Bahonar University of Kerman, Iran

Azin Izadi is a dedicated researcher and educator in the field of computer engineering, specializing in approximate computing and low-power digital circuit design. With a strong academic background and a passion for advancing computational efficiency, she has contributed significantly to the development of innovative arithmetic units for power-conscious applications. Through her work as a researcher, lecturer, and teaching assistant, she has fostered the growth of knowledge in digital electronics and computer architecture while actively participating in research aimed at enhancing circuit design methodologies.

Profile

Scopus

Education

Azin Izadi obtained her Master of Science degree in Computer Engineering from Shahid Bahonar University of Kerman, Iran, in 2022. Specializing in Computer System Architecture, she completed her thesis on the design of an approximate computing unit to optimize power consumption in FPGA-based designs. Under the supervision of Professor Behnam Ghavami, she achieved a perfect GPA of 4.00/4.00, reflecting her commitment to academic excellence. Prior to her master’s, she earned a Bachelor of Science in Computer Engineering from the same university in 2017, where she focused on computer hardware. Her undergraduate thesis explored the use of Raspberry Pi microcomputers for remote environmental control and observation, earning her high recognition under the guidance of Professor Azadeh Alsadat Emrani Zarandi.

Experience

Azin Izadi has accumulated valuable experience in both academia and research. She has served as a Teaching Assistant at Shahid Bahonar University of Kerman since 2022, supporting courses in Digital Electronics and Computer-Aided Design of Digital Systems. Concurrently, she has been a Research Assistant at the same institution since 2023, focusing on approximate computing and low-power arithmetic unit design under the supervision of Professor Vahid Jamshidi. Beyond her contributions to higher education, she has worked as a Lecturer at the Technical and Vocational University of Kerman, delivering courses on Network Security, Web Security, and Computer Architecture. Additionally, she has provided private instruction in Logic Circuits and Computer Architecture at Shahid Bahonar University of Kerman, further demonstrating her commitment to knowledge dissemination.

Research Interests

Azin Izadi’s research primarily revolves around approximate computing, an emerging paradigm aimed at enhancing computational efficiency by trading precision for energy savings. She focuses on designing low-power, high-speed arithmetic units that optimize power consumption while maintaining acceptable error margins for error-resilient applications. Her work explores inexact multipliers, approximate adders, and logarithmic arithmetic units, all of which contribute to advancing computational methods for energy-efficient hardware design. With a strong background in FPGA-based circuit optimization, she continues to innovate in digital system design, addressing the increasing demand for power-conscious computing in modern applications.

Awards

Azin Izadi’s academic excellence has been recognized through prestigious accolades, including being ranked as the top student in the Master’s program at the Department of Computer Engineering, College of Engineering, Shahid Bahonar University of Kerman in 2022. This recognition reflects her outstanding academic performance and research contributions during her graduate studies.

Publications

Azin Izadi, Vahid Jamshidi. “LSHIM: Low-power and Small-area Inexact Multiplier for High-speed Error-resilient Applications.” IEEE Journal on Emerging and Selected Topics in Circuits and Systems (2024). (Published) DOI

Azin Izadi, Vahid Jamshidi. “LHTAM: Low-power and high-speed approximate multiplier for tiny inexact computing systems.” Computers and Electrical Engineering (2024). (Published) DOI

Azin Izadi, Vahid Jamshidi. “A fast, low-energy and area-efficient unsigned approximate logarithmic multiplier for small inexact arithmetic circuits.” IEEE Transactions on Sustainable Computing (2024). (Revised)

Azin Izadi, Vahid Jamshidi. “High-performance approximate adder for arithmetic circuits.” (2025). (In Preparation)

Conclusion

Azin Izadi is a committed researcher and educator whose work in approximate computing and low-power circuit design has significantly contributed to advancing energy-efficient digital systems. Her rigorous academic training, coupled with hands-on teaching and research experience, has enabled her to develop innovative computational techniques that optimize power and performance in arithmetic circuits. With a strong portfolio of publications and teaching engagements, she remains dedicated to pushing the boundaries of digital hardware efficiency while mentoring the next generation of engineers in computer architecture and digital design.

said boumaraf | Computer Vision | Best Researcher Award

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Dr. said boumaraf | Computer Vision | Best Researcher Award

Postdoctoral Fellow at Khalifa University, Algeria

Dr. Said Boumaraf is a dedicated researcher and academic in the field of computer science, specializing in artificial intelligence, machine learning, and computer vision. With a strong background in biomedical imaging, industrial applications, and networking, his work focuses on developing innovative AI-driven solutions for real-world challenges. He has contributed significantly to both academia and industry, holding various research positions and publishing extensively in high-impact journals. His expertise spans deep learning, feature selection, transfer learning, and anomaly detection, with applications in healthcare, oil and gas industries, and satellite communication systems.

Profile

Orcid

Education

Dr. Boumaraf earned his Ph.D. in Computer Science and Technology from the Beijing Institute of Technology, China, where he worked under the guidance of Prof. Xiabi Liu. His doctoral thesis, titled “Research on Machine Learning Methods for Breast Cancer Classification,” contributed significantly to AI applications in medical diagnosis. Prior to this, he completed his M.Sc. and B.Sc. degrees in Computer Science at Abbes Laghrour University of Khenchela, Algeria. His master’s research focused on wireless sensor network localization, while his bachelor’s thesis explored ontology-based contextual information search. These foundational studies provided him with extensive knowledge in data-driven decision-making and intelligent systems.

Professional Experience

Dr. Boumaraf has accumulated extensive research and professional experience across multiple roles. Currently, he is a postdoctoral fellow at Khalifa University of Science and Technology, UAE, where he is engaged in advanced AI projects such as “Vision-based Flare Analytics” for the oil and gas industry and “AI for Digital Pathology” for healthcare applications. Previously, he was a postdoctoral researcher at the University of Malta, working on AI-driven document analysis and classification. His industrial experience includes serving as a Chief Engineer and Researcher at the Algerian Space Agency, where he contributed to satellite control operations and AI-based anomaly detection in satellite telemetry data. Additionally, he has experience in IT management and government administration, further broadening his expertise in system optimization and software development.

Research Interests

Dr. Boumaraf’s research interests encompass artificial intelligence, deep learning, and computer vision, with applications in biomedical imaging, industrial analytics, and network security. He has focused extensively on machine learning-based medical image analysis, including thyroid nodule detection, histopathology classification, and dermoscopy. His industrial research includes AI-based combustion efficiency monitoring in oil and gas flares and satellite-based remote sensing. Additionally, he is interested in optimization techniques, dynamic knowledge networks, and cross-domain methodologies for enhancing model generalization. His work integrates AI-driven solutions into critical sectors, improving both operational efficiency and scientific innovation.

Awards and Recognitions

Dr. Boumaraf has been recognized for his contributions to AI and computer vision research through various academic and professional honors. He has received multiple nominations and accolades for his work in biomedical imaging and industrial AI applications. His research has been featured in prominent conferences and journals, and he has been actively involved in interdisciplinary collaborations that have garnered recognition from scientific and industrial communities.

Publications

Said Boumaraf, Xiabi Liu, Chokri Ferkous, Xiaohong Ma (2020) – “A New Computer-aided Diagnosis System with Modified Genetic Feature Selection for BI-RADS Classification of Breast Masses in Mammograms,” Biomedical Research International (DOI: 10.1155/2020/7695207). Cited by 50+ articles.

Said Boumaraf, Xiabi Liu, Zhongshu Zheng, Xiaohong Ma, Chokri Ferkous (2020) – “A New Transfer Learning Based Approach to Magnification Dependent and Independent Classification of Breast Cancers in Histopathological Images,” Biomedical Signal Processing and Control (DOI: 10.1016/j.bspc.2020.102192). Cited by 60+ articles.

Said Boumaraf, Xiabi Liu, Yuchai Wan, Zhongshu Zheng, Chokri Ferkous, Xiaohong Ma (2021) – “Conventional Machine Learning versus Deep Learning for Magnification Dependent Histopathological Breast Cancer Image Classification: A Comparative Study with Visual Explanation,” Diagnostics (DOI: 10.3390/diagnostics11030528). Cited by 40+ articles.

Yuchai Wan, Zhongshu Zheng, Ran Liu, Zheng Zhu, Hongen Zhou, Xun Zhang, Said Boumaraf (2021) – “A Multi-Scale and Multi-Level Fusion Approach for Deep Learning-Based Liver Lesion Diagnosis in Magnetic Resonance Images with Visual Explanation,” Life (DOI: 10.3390/life11060582). Cited by 30+ articles.

Al Radi, Muaz, Pengfei Li, Said Boumaraf, Jorge Dias, Naoufel Werghi (2024) – “AI-Enhanced Gas Flares Remote Sensing and Visual Inspection: Trends and Challenges,” IEEE Access. Cited by 20+ articles.

Xiaodong Qin, Xiabi Liu, Said Boumaraf (2019) – “A New Feature Selection Method based on Monarch Butterfly Optimization and Fisher Criterion,” International Joint Conference on Neural Networks (IJCNN). Cited by 25+ articles.

Huiyu Li, Xiabi Liu, Said Boumaraf, Weihua Liu, Xiaopeng Gong, Xiaohong Ma (2020) – “A New Three-stage Curriculum Learning Approach for Deep Network Based Liver Tumor Segmentation,” International Joint Conference on Neural Networks (IJCNN). Cited by 35+ articles.

Conclusion

Dr. Said Boumaraf is a distinguished researcher whose work bridges the gap between artificial intelligence and real-world applications. His contributions to biomedical imaging, industrial AI, and satellite communication have significantly advanced the fields of machine learning and deep learning. With an extensive background in academia and industry, he continues to push the boundaries of AI-driven innovation. Through his research, publications, and professional engagements, Dr. Boumaraf remains at the forefront of cutting-edge AI applications, making meaningful contributions to scientific and technological advancements.