Karam Khairullah | Energy | Best Researcher Award

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Dr. Karam Khairullah | Energy | Best Researcher Award

Dr. Karam Khairullah | University of Malaya | Malaysia

Dr. Karam Khairullah is a distinguished researcher in electrical engineering with a PhD from Universiti Teknologi Malaysia, a master’s degree in control systems from Universiti Teknikal Malaysia Melaka where he was recognized as the best postgraduate student, and a bachelor’s degree in power and machines engineering from the University of Mosul. His research interests span metaheuristic and bio-inspired algorithms, artificial intelligence, renewable energy, photovoltaic systems, maximum power point tracking, grid-connected solar systems, storage technologies, power system protection, wireless power transfer, fuel cells, electric vehicles, and energy efficiency. He has published extensively in high-impact Q1 and Q2 journals, contributing innovative methods for power optimization and renewable energy integration. Professionally, he has lectured at the University of Mosul and currently serves as a Postdoctoral Fellow at the University of Malaya. With prior industry experience in telecommunications and expertise in advanced simulation tools, he combines academic excellence with applied engineering innovation.

Publication Profile

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Educational Background

Dr. Karam Khairullah has built a strong academic foundation in the field of electrical engineering through his distinguished educational journey. He earned his Doctor of Philosophy in Electrical Engineering from Universiti Teknologi Malaysia, where he specialized in advanced research areas that align with renewable energy and intelligent control systems. Prior to this, he completed his Master’s degree in Electrical Engineering with a focus on control systems at Universiti Teknikal Malaysia Melaka, where he was honored as the best postgraduate student, reflecting both his academic excellence and dedication to innovation. His educational path began with a Bachelor’s degree in Electrical Engineering with a specialization in power and machines from the University of Mosul, which provided him with a solid technical base in electrical systems and energy applications. Collectively, his academic achievements highlight a consistent pursuit of excellence and a commitment to advancing knowledge in electrical engineering and renewable energy technologies.

Professional Experience

Dr. Karam Khairullah has gained extensive experience in both academia and industry, combining teaching, research, and practical engineering expertise. In the academic field, he served as a lecturer at the University of Mosul in the Faculty of Engineering, where he contributed to educating future engineers and advancing research initiatives. He is currently a Postdoctoral Fellow in the Department of Electrical Engineering at the University of Malaya, where his focus lies in renewable energy, control systems, and intelligent optimization techniques. Alongside his academic career, Dr. Karam has professional experience in the telecommunications sector, having worked with major companies. His expertise includes microwave implementation and operations with Huawei and Nokia Siemens systems, BTS installation, and hardware maintenance. He has also worked with various industry-standard software platforms, including Huawei U2000, NSN license manager, Ericsson OMT, and Putty-based systems, reflecting his strong technical background and multidisciplinary capabilities.

Research Focus

Dr. Karam Khairullah’s research focus lies in the advancement of renewable energy systems, intelligent optimization techniques, and power electronics. His work predominantly centers on the development of maximum power point tracking strategies for photovoltaic systems under varying and complex conditions, such as partial shading and load fluctuations. By integrating artificial intelligence, bio-inspired algorithms, and metaheuristic approaches, he has contributed significantly to enhancing the efficiency, reliability, and adaptability of solar energy technologies. His research also extends to hybrid energy management systems, including PV-battery storage-grid integration for electric vehicle charging, demonstrating his commitment to sustainable energy solutions. Moreover, his studies in intelligent fault detection and control mechanisms for power transmission systems highlight his broader contribution to power system protection and stability. Collectively, his research addresses critical challenges in renewable energy utilization and power optimization, positioning him as a leading contributor to the fields of energy efficiency, smart grid systems, and sustainable electrification.

Publication Top Notes

A modified perturb and observe MPPT for a fast and accurate tracking of MPP under varying weather conditions
Year: 2023
Citations: 45

Improved coot optimizer algorithm-based MPPT for PV systems under complex partial shading conditions and load variation
Year: 2024
Citations: 37

Improved rat swarm optimizer algorithm-based MPPT under partially shaded conditions and load variation for PV systems
Year: 2022
Citations: 37

Power management and optimized control of hybrid PV-BESS-grid integrated fast EV charging stations
Year: 2024
Citations: 35

Hybrid global maximum power tracking method with partial shading detection technique for PV systems
Year: 2021
Citations: 32

Conclusion

Dr. Karam Khairullah stands out as an innovative and prolific researcher whose contributions to renewable energy systems, control algorithms, and power electronics have significantly advanced the field. With his blend of academic excellence, impactful publications, and applied expertise, he is a highly suitable candidate for the Research for Best Researcher Award, reflecting strong merit and future potential.

Metwally Rashad | Energy | Best Researcher Award

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Dr. Metwally Rashad | Energy | Best Researcher Award

Dr. Metwally Rashad, Prince Prince Sattam Bin Abdulaziz University, Saudi Arabia

Dr. Metwally Rashad is an Assistant Professor of Computer Science at the Faculty of Computers & Artificial Intelligence, Benha University, Egypt. He earned his Ph.D. in Computer Science from the University of Pannonia, Hungary, specializing in image processing and computer vision. With over 18 years of academic experience, he has taught a wide range of courses, including artificial intelligence, medical image processing, and computer graphics. Dr. Rashad has published extensively in high-impact journals and conferences, with notable work in object recognition, video summarization, and medical image retrieval. He has supervised numerous M.Sc. and Ph.D. students and actively contributes to research in AI, deep learning, and data mining. He holds leadership roles within his faculty, including executive director of the IT unit and faculty board member. A recipient of international publishing awards, Dr. Rashad is a committed researcher with global exposure and a strong background in both teaching and applied research.

Publication Profile

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🎓 Academic Qualifications

Dr. Metwally Rashad has pursued a progressive academic path rooted in mathematics and computer science. He earned his B.Sc. in Mathematics in 2002 from the Faculty of Science at Benha University, Egypt. In 2006, he completed a two-year Diploma in Computer Science from the Institute of Statistical Studies and Research (ISSR) at Cairo University, reflecting his growing interest in computational systems. Building on this foundation, he completed a Pre-master program in Computer Science at Benha University in 2008. His M.Sc., awarded in 2013 from the same institution, focused on string representation for efficient mining and searching over string databases. Dr. Rashad advanced his academic career further by earning a Ph.D. in Computer Science from the University of Pannonia, Hungary, in 2018. His doctoral research centered on video-based object retrieval and recognition using lightweight devices, aligning with cutting-edge developments in image processing and computer vision. His qualifications demonstrate a consistent dedication to both theoretical and applied computing sciences.

👨‍🏫 Professional Positions

Dr. Metwally Rashad has built a robust academic career through progressive roles at Benha University, Egypt. He began as a Demonstrator in the Department of Mathematics (Computer Science), Faculty of Science, from 2004 to 2009. In 2010, he transitioned to the Faculty of Computers & Informatics, continuing as a Demonstrator in the Information System Department until 2011. His dedication and academic growth led to his promotion as an Assistant Lecturer in the same department, a position he held from 2012 to 2017. In 2018, Dr. Rashad was appointed Assistant Professor in the Information System Department, Faculty of Computers & Artificial Intelligence. Since April 2020, he has been serving as an Assistant Professor in the Computer Science Department of the same faculty. Throughout his tenure, Dr. Rashad has demonstrated strong commitment to research, education, and leadership, contributing significantly to the development of the academic and research landscape at Benha University.

🔬 Research Interests

Dr. Metwally Rashad’s research interests encompass a wide range of cutting-edge topics in computer science and artificial intelligence. He specializes in exact and approximate matching, with a strong focus on similarity search and similarity join techniques, which are vital for efficient data comparison and retrieval. His work also explores data cleaning and integration methods, enhancing the quality and usability of large datasets. Dr. Rashad is deeply engaged in database systems and data mining, aiming to extract meaningful patterns from complex data structures. His research extends into the fields of cryptography, image processing, and computer vision, where he applies innovative techniques for object recognition and image analysis. Additionally, he is active in machine learning and deep learning, leveraging these technologies to solve real-world problems. A significant portion of his work involves video processing and artificial intelligence applications, making his contributions highly relevant in today’s data-driven and multimedia-intensive landscape

📘 Teaching

Dr. Metwally Rashad has taught a wide array of undergraduate and postgraduate courses in computer science and artificial intelligence, reflecting his deep expertise and versatility as an academic. His teaching portfolio includes foundational programming languages such as C++, Java, and Matlab, as well as critical subjects like Data Structures, Algorithms, and File Structures. He has also delivered advanced courses in Artificial Intelligence, Machine Learning, and Expert Systems, along with specialized topics like Image Processing, Medical Image Processing, and Computer Vision. Dr. Rashad’s instruction covers essential theoretical areas, including Theory of Computation, Logic Design, Discrete Mathematics, and Structural Programming. He is also experienced in teaching Information Systems, Database Design, Compiler Construction, and Security. His course offerings further extend to emerging and interdisciplinary areas like Information Visualization, Multimedia, Computer Graphics, and Operations Research. This comprehensive teaching background underscores his commitment to fostering technical excellence and innovation among his students.

🏅 Grants and Awards

Dr. Metwally Rashad has been recognized for his impactful research through multiple prestigious awards and grants. He was honored with the International Publishing Prize and Citations from Benha University on two separate occasions—first in January 2018 and again in July 2020—in acknowledgment of his significant contributions to high-impact scientific publications. In addition to these accolades, Dr. Rashad secured a post-doctoral research grant from the Tempus Public Foundation (TPF), supporting his research stay at the Department of Electrical Engineering and Information Systems, Faculty of Information Technology, University of Pannonia, Hungary, from February to May 2020. These recognitions not only highlight his excellence in publishing and international collaboration but also reflect his sustained commitment to advancing knowledge in computer science and artificial intelligence. His achievements stand as a testament to his academic leadership and contribution to global research innovation.

🔍 Research Focus

Dr. Metwally Rashad’s research primarily centers around Artificial Intelligence (AI), Computer Vision, and Data-Driven Healthcare Systems, with a strong interdisciplinary approach linking image processing, machine learning, and information retrieval. His most cited works involve irony detection in Arabic tweets, medical image retrieval, video object recognition, and string similarity join algorithms—reflecting his deep engagement with natural language processing (NLP), deep learning, and visual data analytics. A recurring theme in his publications is the development of lightweight, efficient models for video summarization, emotion recognition, and 3D object retrieval, optimized for low-resource environments. Additionally, he contributes to healthcare technologies through AI-assisted diagnosis, content-based retrieval in medical imaging, and melanoma detection using deep neural networks. His research aligns with the growing global emphasis on AI for smart healthcare, secure cloud computing, and intelligent multimedia processing, making him highly suitable for recognition in advanced computational and AI-powered research categories

Publication Top Notes

📘 BENHA@ IDAT: Improving Irony Detection in Arabic Tweets using Ensemble Approach – Cited by 31 (2019) 🧠🗣️📊
📘 RbQE: An Efficient Method for Content-Based Medical Image Retrieval Based on Query Expansion – Cited by 17 (2023) 🖼️🧬📥
📘 PreJoin: An Efficient Trie-Based String Similarity Join Algorithm – Cited by 9 (2012) 🧵📚🔍
📘 View Centered Video-Based Object Recognition for Lightweight Devices – Cited by 8 (2016) 🎥🔎📱
📘 CCNN-SVM: Emotion Recognition with Custom CNNs and SVM – Cited by 7 (2024) 😊📈🧠
📘 Content-Based Medical Image Retrieval Based on Deep Features Expansion – Cited by 7 (2022) 🏥🧠🔎
📘 Use of IMUs for Video Object Retrieval in Lightweight Devices – Cited by 7 (2017) 🎥📡🤖
📘 Lightweight Active Object Retrieval with Weak Classifiers – Cited by 6 (2018) 📽️📊🧠
📘 Lightweight Video Object Recognition Based on Sensor Fusion – Cited by 5 (2015) 📹📡🔀
📘 Modern Techniques in Content-Based Medical Image Retrieval: A Survey – Cited by 4 (2022) 🏥📚📈
📘 Active Multiview Recognition with Hidden Markov Temporal Support – Cited by 4 (2021) 🧠📽️🔁
📘 Efficient String Edit Similarity Join Algorithm – Cited by 4 (2017) 🧵📝⚙️
📘 Fusion of Optical and Orientation Info in Markovian Framework for 3D Object Retrieval – Cited by 4 (2017) 🎥📐📊
📘 Automatic Melanoma Detection Using Deep Neural Networks – Cited by 2 (2024) 🧬🧠⚕️

Jixiang Dai | Clean Energy | Excellence in Innovation

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Mr. Jixiang Dai | Clean Energy | Excellence in Innovation

Mr. Jixiang Dai, Wuhan University of Technology, China

Mr. Jixiang Dai is an accomplished researcher at the Wuhan University of Technology, specializing in hydrogen-sensitive materials and optical fiber sensing technology. With a Ph.D. in hydrogen-sensitive films and over a decade of research experience, he has developed advanced WO₃- and Pd-based nanocomposite films for fiber optic hydrogen, temperature, and humidity sensors. Mr. Dai possesses extensive expertise in magnetron sputtering, e-beam deposition, FBG writing, ellipsometry, and advanced material characterization methods. He has led multiple nationally funded projects, including those under China’s National Key R&D Program, focusing on intrinsically safe hydrogen sensing systems. His outstanding work has earned him prestigious honors, such as the Second Prize of the China Instrument and Control Society for Technical Invention and the Hubei Province Natural Science Award. Mr. Dai has authored over 15 high-impact journal papers in Nanomaterials, Optics Letters, and Sensors and Actuators B, significantly advancing fiber optic sensor technology for safety and energy applications.

Publication Profile

Scopus

Expertise

Mr. Jixiang Dai is a highly skilled researcher with deep expertise in the synthesis of hydrogen-sensitive materials, particularly WO₃-based and Pd-based nanocomposite films. His work focuses on developing advanced fiber optic sensors capable of detecting hydrogen, temperature, and humidity with high sensitivity and precision. He has significant experience in designing complete sensing systems tailored for safety-critical environments. Mr. Dai is proficient in operating sophisticated deposition systems such as magnetron sputtering and e-beam assisted techniques, and is adept at fabricating fiber Bragg gratings (FBGs) using the phase mask method. He is also skilled in measuring optical constants using ellipsometry and simulating optical properties with RSoft and TFC software. Additionally, he is well-versed in a wide range of material characterization tools, including XRD, SEM, EDAX, TEM, XPS, and AFM. His multidisciplinary skill set enables him to bridge material science and optical engineering for innovative sensor development.

Work and Education Background

Mr. Jixiang Dai has a strong academic and professional background in optical fiber sensing and materials science. Since July 2013, he has been serving as an associate researcher at the National Engineering Laboratory for Fiber Optic Sensing Technology, where he initially worked as an assistant researcher until December 2017. His research focuses on advanced optical fiber sensing technologies. He earned his Ph.D. from 2010 to 2013, concentrating on hydrogen-sensitive films under the mentorship of Professor Minghong Yang, a recipient of the National Distinguished Young Scholars of China. Prior to that, from 2008 to 2011, he completed his master’s degree focusing on fiber Bragg grating hydrogen sensors based on WO₃-Pd composite films, guided by Professor Dengsheng Jiang, an academician of the Chinese Academy of Engineering. Mr. Dai began his academic journey with a bachelor’s degree from 2004 to 2008, studying dielectric thin films under Professor Jing Zhou, laying the foundation for his research career.

Awards and Recognitions

Mr. Jixiang Dai has received notable awards in recognition of his significant contributions to the field of optical fiber sensing and materials science. He was honored with the Second Prize for Technical Invention by the China Instrument and Control Society, where he ranked second for his pioneering work in the development of advanced hydrogen sensing systems using nano-composite films. This award highlights his role in creating impactful technological innovations that enhance safety and performance in sensor applications. Additionally, Mr. Dai received the Second Prize in Natural Science from Hubei Province, also ranking second, in acknowledgment of his high-level scientific research in materials engineering and sensor development. These prestigious awards not only reflect the originality and value of his research but also demonstrate the national and regional recognition of his innovative achievements in advancing sensor technologies and their real-world applications in energy, safety, and environmental monitoring systems

Research Focus

Mr. Jixiang Dai’s Research Focus centers on optical fiber hydrogen sensing technologies with an emphasis on advanced nanomaterials like WO₃-based, Pd/Ta alloys, and graphene quantum dots. His work targets the design, enhancement, and integration of fiber Bragg grating (FBG)-based sensors, leveraging π-phase-shifted structures, optical heating, and ZIF-8 frameworks for heightened sensitivity. His innovations address hydrogen leakage detection, quasi-distributed sensor networks, and environmental sensing applications. The research spans sensor fabrication, performance optimization, and material characterization. 🚀 His impactful contributions are well-cited in journals like Optics Letters, IEEE, and International Journal of Hydrogen Energy.

Publication Top Notes

📄 Improved performance of a fiber-optic hydrogen sensor based on a controllable optical heating technology – Optics Letters, 2024 | 🔁 Cited by: 4 📅
📄 Pd–Ta alloy films hydrogen sensors based on partially coated π-phase-shifted FBG – Optical Materials, 2024 | 🔁 Cited by: 2 📅
📄 Quasi-distributed optical fiber hydrogen leakage detecting system based on bus chain topology structure – Optics Express, 2024 | 🔁 Cited by: 1 📅
📄 Improved performance of fiber-optic hydrogen sensor of porous Pt/WO₃ based on ZIF-8 – Int. J. of Hydrogen Energy, 2024 | 🔁 Cited by: 16 📅
📄 Performance of fiber hydrogen sensor with Pd/Ta composite films – Conference Paper, Year Not Specified | 🔁 Cited by: 0 📅
📄 Performance of fiber-optic hydrogen sensor based on locally coated π-shifted FBG – IEEE Sensors Journal, 2022 | 🔁 Cited by: 11 📅
📄 Ultra-high sensitive fiber optic hydrogen sensor in air – Journal of Lightwave Technology, 2022 | 🔁 Cited by: 12 📅
📄 TBAOH intercalated WO₃ for high-performance optical fiber hydrogen sensor – Int. J. of Hydrogen Energy, 2022 | 🔁 Cited by: 11 📅
📄 Advanced fiber-optic humidity sensor using graphene quantum dots doped polyimide – IEEE Photonics Tech. Lett., 2022 | 🔁 Cited by: 18 📅
📄 Novel optical fiber sensing with tantalum-based hydrogen sensing film – Acta Photonica Sinica, 2022