Khalifa Aliyu Ibrahim | Artificial Intelligence | Best Researcher Award

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Mr. Khalifa Aliyu Ibrahim | Artificial Intelligence | Best Researcher Award

PhD Researcher at Cranfield University, United Kingdom

Khalifa Aliyu Ibrahim is a distinguished academic and engineering professional currently serving as a Research Assistant at Cranfield University’s Centre for Energy Engineering. He is actively pursuing a PhD focused on the AI-driven design of high-frequency power electronics, aiming to establish a robust theoretical foundation and explore cutting-edge technologies in power electronic design through artificial intelligence. His career is marked by a seamless transition from theoretical and experimental physics to engineering, underscoring his versatility and dedication to advancing energy systems, power electronics, and thermal management. ​

Profile

Google Scholar

Education

Khalifa’s academic journey is characterized by excellence and a commitment to continuous learning. He earned a Master of Science in Energy Systems and Thermal Processes with Distinction from Cranfield University, UK, in 2021. Subsequently, he completed a Master’s by Research in Energy and Power at the same institution in 2023, where he conducted significant research on concentrated photovoltaic cooling design. Prior to his postgraduate studies, Khalifa graduated as the only first-class student in his cohort with a Bachelor of Science in Physics from Kaduna State University, Nigeria, in 2016, achieving a CGPA of 4.56/5.00.

Experience

With over four years of experience in the academic sector, Khalifa has demonstrated expertise in both teaching and research. At Cranfield University, he has been instrumental in building and testing prototypes for small-scale green hydrogen plants and has supervised laboratory activities for MSc students. His previous roles include lecturing positions at Kaduna State University and Nuhu Bamalli Polytechnic in Nigeria, where he taught theoretical and experimental physics to large student cohorts. Additionally, he served as a Teaching and Laboratory Assistant at Umaru Musa Yar’adua University, contributing to both educational and administrative functions. ​

Research Interests

Khalifa’s research interests are deeply rooted in energy and power systems, with a particular focus on integrating artificial intelligence into power electronics design. He is also engaged in exploring sustainable hydrogen production methods, advanced cooling techniques for photovoltaic cells, and the development of scalable optical meta-surface designs for agricultural applications. His work aims to address contemporary challenges in energy efficiency and sustainability, reflecting a commitment to innovative solutions in the field. ​

Awards

Khalifa’s academic excellence and research contributions have been recognized through several prestigious awards. In 2021, he received the Petroleum Technology Development Fund Scholarship worth £31,000. The previous year, he was awarded a merit-based foreign scholarship by the Kaduna State Scholarship and Loan Board, Nigeria, valued at £27,000. Earlier in his academic journey, he secured a cash prize and a Certificate of Participation in the Nigeria Centenary Quiz Show in 2014, highlighting his longstanding dedication to academic and intellectual pursuits. ​

Publications

Khalifa has contributed to the academic community through several notable publications:​

“Harnessing Energy for Wearables: A Review of Radio Frequency Energy Harvesting Technologies” (2023, Energies). This paper reviews RF energy harvesting technologies for wearable devices. ​

“Cooling of Concentrated Photovoltaic Cells—A Review and the Perspective of Pulsating Flow Cooling” (2023, Energies). This article examines cooling methods for concentrated photovoltaic cells, emphasizing pulsating flow cooling. ​

“High-Performance Green Hydrogen Generation System” (2021, IEEE 20th International Conference on Micro and Nanotechnology for Power Generation and Energy Conversion Applications). This conference paper presents a high-performance system for green hydrogen generation.

“The Effect of Solar Irradiation on Solar Cells” (2019, Science World Journal). This study investigates how solar irradiation impacts the performance of solar cells.

“Use of Azimuthal Square-Array Direct-Current Resistivity Method to Determine Geological Fractures” (2019, Journal of the Nigerian Association of Mathematical Physics). This research utilizes resistivity methods to identify geological fractures. ​

“Advancing Hydrogen: A Closer Look at Implementation Factors, Current Status and Future Potential” (2023, Energies). This paper explores the current status and future potential of hydrogen implementation. ​

“A Scalable Optical Meta-Surface Glazing Design for Agricultural Greenhouses” (2024, Physica Scripta). This article discusses a scalable design for optical meta-surface glazing in agricultural greenhouses. ​

Conclusion

Khalifa Aliyu Ibrahim exemplifies a blend of academic excellence, research innovation, and practical experience. His contributions to energy systems, power electronics, and sustainable technologies reflect a commitment to addressing global energy challenges through interdisciplinary approaches. As he continues his PhD research, his work is poised to make significant impacts in the fields of artificial intelligence and power electronics, further solidifying his role as a leading figure in contemporary energy research.

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.

Petcharaporn Yodjai | Computer Science | Best Researcher Award

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Ms. Petcharaporn Yodjai | Computer Science | Best Researcher Award

Ph.D. Student at King Mongkut’s University of Technology Thonburi, Thailand

Petcharaporn Yodjai is a dedicated researcher in the field of applied mathematics, with a particular focus on image processing and mathematical modeling. Currently a Ph.D. candidate at King Mongkut’s University of Technology Thonburi (KMUTT), Bangkok, Thailand, she has made significant contributions to the development of advanced techniques in image inpainting and completion. Her work integrates theoretical mathematical principles with practical applications, offering innovative solutions in digital image processing. Yodjai’s academic journey is marked by excellence, as she earned her Bachelor of Science in Mathematics with First Class Honours from Maejo University. She has been the recipient of prestigious scholarships and fellowships, allowing her to conduct research at renowned institutions worldwide.

Profile

Scopus

Education

Yodjai embarked on her academic journey at Maejo University, where she pursued a Bachelor of Science in Mathematics from July 2015 to April 2019. Her outstanding academic performance earned her First Class Honours. Continuing her passion for applied mathematics, she enrolled in the Ph.D. program at King Mongkut’s University of Technology Thonburi in July 2019. Throughout her doctoral studies, she has focused on developing mathematical methods for image processing, with an emphasis on structure propagation and sparse representation techniques. Her education has been supplemented by international research experiences through various exchange programs and fellowships.

Experience

Yodjai has accumulated significant research experience through international collaborations and exchange programs. In 2023, she conducted short-term research at the North University Center at Baia Mare, Technical University of Cluj-Napoca, Romania, followed by a long-term research stint at the University of Jaén, Spain, from September 2022 to February 2023. Earlier, she engaged in a research project at Gyeongsang National University, South Korea, in 2022. Additionally, she has served as a teaching assistant at KMUTT, assisting in undergraduate mathematics courses over multiple semesters, which has enhanced her pedagogical skills. Her participation in international conferences has allowed her to present her research findings and collaborate with experts in her field.

Research Interests

Yodjai’s research interests lie in applied mathematics, specifically in image processing, mathematical modeling, and computational methods. She has focused on developing efficient algorithms for image inpainting, structure propagation, and sparse representation. Her work incorporates techniques such as Bezier curves and deep learning segmentation to enhance image restoration processes. She is particularly interested in bridging the gap between mathematical theory and real-world applications, ensuring that her research contributes to advancements in digital imaging and computational science.

Awards and Scholarships

Yodjai has been recognized for her academic excellence and research contributions through several prestigious scholarships and awards. She is a recipient of the Royal Golden Jubilee Ph.D. Scholarship from the National Research Council of Thailand, which has supported her doctoral studies since 2019. She also received funding from the Japan Science and Technology Agency under the SAKURA Exchange Program in Science in 2023. Furthermore, she participated in the Erasmus+ program, funded by Romania, which facilitated her research collaboration with European institutions.

Publications

Yodjai, P., Kumam, P., & Martínez-Moreno, J. (2025). Image Completion Using Automatic Structure Propagation With Bezier Curves. Mathematical Methods in the Applied Sciences.

Jirakipuwapat, W., Sombut, K., Yodjai, P., & Seangwattana, T. (2025). Enhancing Image Inpainting With Deep Learning Segmentation and Exemplar-Based Inpainting. Mathematical Methods in the Applied Sciences.

Yodjai, P., Kumam, P., Martínez-Moreno, J., & Jirakitpuwapat, W. (2024). Image inpainting via modified exemplar-based inpainting with two-stage structure tensor and image sparse representation. Mathematical Methods in the Applied Sciences, 47(11), 9027-9045.

Awwal, A. M., Wang, L., Kumam, P., Sulaiman, M. I., Salisu, S., Salihu, N., & Yodjai, P. (2023). Generalized RMIL conjugate gradient method under the strong Wolfe line search with application in image processing. Mathematical Methods in the Applied Sciences, 46(16), 17544-17556.

Yodjai, P., Kumam, P., Kitkuan, D., Jirakitpuwapat, W., & Plubtieng, S. (2019). The Halpern approximation of three operators splitting method for convex minimization problems with an application to image inpainting. Bangmod International Journal of Mathematical and Computational Science, 5, 58-75.

Conclusion

Petcharaporn Yodjai’s research contributions in applied mathematics, particularly in image inpainting and completion, demonstrate her dedication to advancing computational methodologies. Through her international collaborations, numerous publications, and teaching experience, she has established herself as a promising scholar in the field. Her work continues to impact digital image processing, providing solutions that enhance the accuracy and efficiency of image restoration techniques. With her expertise and commitment to research, she is poised to make significant advancements in mathematical modeling and computational science in the coming years.

Deepak Parashar | Deep Learning | Best Researcher Award

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Dr. Deepak Parashar | Deep Learning | Best Researcher Award

Associate Professor | GSFC University Vadodara Gujarat | India

Dr. Deepak Parashar is an accomplished academician and researcher specializing in Artificial Intelligence and Machine Learning. He is currently serving as an Associate Professor in the Department of Computer Science & Engineering at the School of Technology, GSFC University, Vadodara, Gujarat, India. With over 14 years of academic and research experience, Dr. Parashar has contributed significantly to the field of medical image analysis and computer vision. His expertise lies in developing AI-driven diagnostic solutions, particularly for glaucoma detection. Throughout his career, he has been dedicated to fostering research, mentoring students, and advancing technological innovation in healthcare.

Profile

Scopus

Education

Dr. Parashar holds a Ph.D. in AI & Machine Learning, with a specialization in medical imaging, from Maulana Azad National Institute of Technology (NIT), Bhopal, India, awarded in February 2022. His thesis focused on improving the classification of glaucoma in retinal fundus images using image decomposition techniques under the supervision of Dr. D. K. Agrawal. He completed his M.Tech. from SGSITS Indore in 2011 and earned his B.E. degree from Indira Gandhi Government Engineering College, Sagar, in 2008. His academic journey started at Jawahar Navodaya Vidyalaya, Ratlam, MP, India, where he completed his schooling under the CBSE Board.

Experience

Dr. Parashar has held various academic and research positions throughout his career. Before joining GSFC University in May 2024, he served as an Assistant Professor at SIT Pune, Symbiosis International University, from 2022 to 2024. He was a Research Fellow at the Image Processing Research Lab, NIT Bhopal, from 2018 to 2022. Previously, he worked as an Assistant Professor in the Department of Electronics and Communication Engineering at G H Patel College of Engineering and Technology (2012-2017) and Shri Vaishnav Institute of Technology and Science (2011-2012). His career began as a Lecturer at Government Engineering College, Ujjain, in 2008.

Research Interests

Dr. Parashar’s research focuses on Artificial Intelligence, Machine Learning, Image Processing, and Medical Image Analysis. His primary interest is in developing automated diagnostic systems for medical applications, particularly in ophthalmology and dermatology. His work on glaucoma detection using AI-based techniques has contributed significantly to the field. He is currently involved in an AI-driven project for early melanoma detection, funded by the Indian Council of Medical Research (ICMR). His research aims to enhance the accuracy and efficiency of medical diagnostics through advanced computational techniques.

Awards and Achievements

Dr. Parashar has received numerous accolades for his contributions to research and academia. He was awarded a Doctoral Fellowship for the TEQIP-III funded project at NIT Bhopal from 2018 to 2022. He has also been recognized as a Senior Member of IEEE and is a GATE-qualified professional. Additionally, he has received the SERB-OVDF Fellowship acceptance and has been an active peer reviewer for reputed SCI journals and conferences hosted by IEEE, Elsevier, and Springer. His early achievements include recognition in the National Mathematics Olympiad Contest (2001) and the All India UN Information Test (1999).

Publications

Dr. Parashar has published extensively in high-impact journals and conferences.

“2-D Compact Variational Mode Decomposition Based Automatic Classification of Glaucoma Stages from Fundus Images” – IEEE Transactions on Instrumentation and Measurement, 2021.

“Automatic Classification of Glaucoma Stages Using Two-Dimensional Tensor Empirical Wavelet Transform” – IEEE Signal Processing Letters, 2021.

“Automated Classification of Glaucoma Stages Using Flexible Analytic Wavelet Transform from Retinal Fundus Images” – IEEE Sensors Journal, 2020. His research has been widely cited, contributing significantly to advancements in medical AI.

Conclusion

Dr. Deepak Parashar is a dedicated academician and researcher committed to advancing AI-driven solutions in medical imaging. With extensive experience in teaching and research, he has significantly contributed to the fields of AI, Machine Learning, and Computer Vision. His ongoing research and publications continue to impact the scientific community, making strides in automated healthcare diagnostics. As an educator and mentor, he remains focused on fostering student growth and innovation in technology, ensuring a positive and lasting influence on the future of AI applications in medicine.