Mr. Mohammad Naderi | Vehicular Communications | Smart Cities Technologies Award

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Mr. Mohammad Naderi | Vehicular Communications | Smart Cities Technologies Award

Part-time lectrutre, self-employed, Iran

Mohammad Naderi is a dedicated computer engineer and researcher from Iran, specializing in wireless communications and networking. With a strong academic background and practical expertise, he has contributed significantly to the fields of the Internet of Things (IoT), Vehicular Ad Hoc Networks (VANETs), Mobile Ad Hoc Networks (MANETs), and Software-Defined Networking (SDN). His innovative approaches to opportunistic routing and traffic-aware networking solutions have led to impactful publications in high-ranking journals. Alongside his research, he has mentored master’s and Ph.D. students, providing guidance in simulation and network-related studies.

Publication Profile

🎓 Education

Mohammad Naderi pursued his Bachelor of Science (BSc) in Computer Engineering at Hamedan University of Technology, Iran, graduating in 2013. He continued his academic journey with a Master of Science (MSc) in Computer Engineering from Azad University, Science and Research Branch, Tehran, where he achieved an outstanding GPA of 3.44. His academic excellence placed him among the top 15 national rank holders, reflecting his strong grasp of computational and networking concepts.

💼 Experience

With a passion for both academia and practical research, Mohammad Naderi has served as an advisor and lecturer, guiding master’s and Ph.D. students in IoT, VANETs, MANETs, UAV Communications, and SDN Security. Since 2016, he has been actively involved in mentoring students, helping them develop innovative research ideas and simulation models. Additionally, he has worked as a part-time lecturer at Danesh Institute, specializing in NS-2 simulation tools. In 2023, he took on a lecturing role at Islamic Azad University, Pardis Branch, where he taught computer networks and network lab courses, strengthening his expertise in teaching and research.

🏆 Awards and Honors

His exceptional work in computer engineering research earned him the Master of Science Thesis Award from the IEEE Iran Section in May 2019. This prestigious recognition underscores his contributions to the advancement of network communications and routing optimizations.

🔬 Research Focus

Mohammad Naderi’s research primarily revolves around opportunistic routing, VANETs, MANETs, IoT, UAV communications, and software-defined vehicular networks. His work integrates artificial intelligence techniques, including reinforcement learning and fuzzy logic, to optimize vehicular communication protocols. He has also explored hierarchical Q-learning-enabled neutrosophic AHP schemes, adaptive beaconing strategies, and routing efficiency in wireless networks, paving the way for more intelligent and reliable vehicular networking solutions.

🔚 Conclusion

Mohammad Naderi’s expertise in wireless networks, VANETs, SDN, and AI-driven communication systems has positioned him as a leading researcher in the field. His contributions to opportunistic routing and adaptive vehicular networking strategies are highly regarded, making a significant impact on next-generation communication technologies. With a strong commitment to both academic and practical advancements, he continues to push the boundaries of intelligent networking solutions. 🚀

📚 Publications

A 3-Parameter Routing Cost Function for Improving Opportunistic Routing Performance in VANETs – Published in Wireless Personal Communications (2017), this paper explores routing optimizations in VANETs to enhance communication reliability. 🔗 Read more.

Adaptive beacon broadcast in opportunistic routing for VANETs – Featured in Ad Hoc Networks (2019), this study introduces adaptive beaconing techniques to optimize data forwarding efficiency in vehicular environments. 🔗 Read more.

Adaptively prioritizing candidate forwarding set in opportunistic routing in VANETs – Published in Ad Hoc Networks (2023), this research enhances routing protocols using adaptive prioritization mechanisms. 🔗 Read more.

Hierarchical traffic light-aware routing via fuzzy reinforcement learning in software-defined vehicular networks – This 2023 Peer-to-Peer Networking and Applications publication introduces an AI-driven hierarchical traffic-aware routing strategy. 🔗 Read more.

Hierarchical Q-learning-enabled neutrosophic AHP scheme in candidate relay set size adaption in vehicular networks – A Computer Networks (2023) publication that leverages Q-learning and neutrosophic AHP techniques to improve vehicular communication. 🔗 Read more.

 

Mr. yuxin fang | Biomedical Engineering | Best Researcher Award

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Mr. yuxin fang | Biomedical Engineering | Best Researcher Award

Doctoral Candidate, chongqing university, China

Yuxin Fang is a dedicated doctoral candidate at Chongqing University, specializing in cutting-edge surgical navigation methods. With a strong background in biomedical engineering and computer-aided surgery, Yuxin has made significant strides in real-time 3D localization for surgical procedures. His pioneering work on electric field-based surgical navigation has bridged the gap between medical technology and precision surgery, achieving instrument positioning accuracy within 2mm in real tissues. Through interdisciplinary research and collaborations, Yuxin continues to push the boundaries of surgical automation, revolutionizing the way minimally invasive procedures are performed.

Publication Profile

Scopus

🎓 Education

Yuxin Fang is currently pursuing a PhD at Chongqing University, focusing on biomedical engineering and surgical navigation technology. His academic journey has been driven by a passion for integrating computer science, physics, and medicine to develop innovative solutions for real-world healthcare challenges.

🏆 Experience

With extensive research experience in medical technology, Yuxin has contributed to multiple groundbreaking projects, including electric field-based surgical navigation and automated surgical platforms. His expertise spans real-time 3D localization, biomedical instrumentation, and AI-driven surgical automation. He has also collaborated with the Department of Physics at the University of Warwick, UK, and the Center of Critical Care Medicine at Southwest Hospital, China, to refine and implement his research findings.

🏅 Awards and Honors

Yuxin’s contributions to biomedical engineering have been recognized through various academic and professional accolades. His innovative research has earned him international recognition, including editorial appointments with esteemed journals such as the International Journal of Computer Science and Information Technology. His work is widely cited, demonstrating its impact on the field of surgical navigation.

🔬 Research Focus

Yuxin Fang’s research is centered on electric field-based surgical navigation, real-time 3D localization, and automated surgical systems. His work addresses a critical gap in precision surgery by developing radiation-free, high-accuracy navigation methods using miniaturized equipment. By integrating this technology into robotic surgical platforms, he is paving the way for next-generation, fully automated surgical procedures that enhance patient safety and surgical outcomes.

🔚 Conclusion

With an unwavering commitment to innovation, Yuxin Fang is shaping the future of surgical technology. His groundbreaking work in surgical navigation is setting new standards in medical precision, bridging the gap between engineering and healthcare. Through his research, collaborations, and academic contributions, Yuxin continues to make a lasting impact on the field of biomedical engineering and beyond. 🚀

📚 Publications

Prof. Jie Lu | Health Sciences | Best Scholar Award

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Prof. Jie Lu | Health Sciences | Best Scholar Award

Vice advisor, The Affiliated Hospital of Qingdao University, China

Dr. Jie Lu is a distinguished medical researcher and Associate Professor at The Affiliated Hospital of Qingdao University. With a strong background in internal medicine, endocrinology, and metabolism, Dr. Lu has made significant contributions to the field of hyperuricemia and gout research. His expertise in molecular and clinical studies has led to groundbreaking findings, including the development of a spontaneous hyperuricemia mouse model and critical insights into the relationship between hyperuricemia and metabolic disorders. His research has been widely recognized, earning him prestigious awards and global collaborations.

Publication Profile

🎓 Education

Dr. Jie Lu completed his MD and PhD in Internal Medicine from Qingdao University (2015-2018), following an MS in Endocrine and Metabolism (2012-2015) from the same institution. His academic journey began with a Bachelor’s in Medicine from Shandong Second Medical University (2007-2012), laying a strong foundation for his career in medical research.

👨‍🔬 Experience

Dr. Lu has been serving as an Associate Professor at The Affiliated Hospital of Qingdao University since 2022. Prior to this, he worked as a Research Fellow at the same institution (2018-2022), where he played a pivotal role in advancing hyperuricemia research. His international training experience spans institutions such as the University of Otago, Henry Ford Immunology Program in the USA, and the Chinese Academy of Sciences, enriching his expertise in bioinformatics, immunology, and molecular biology.

🏆 Awards and Honors

Dr. Lu’s excellence in medical research has been recognized through numerous prestigious awards. In 2022, he received the 13th Qingdao Youth Science and Technology Award. His contributions to scientific progress earned him the First Award of Shandong Province Science and Technology Progress in 2020. Throughout his academic career, he has been honored with multiple scholarships, including the National Graduate Scholarship (2017), Excellent Student Scholarship (2016-2017), and Freshman Scholarship (2015). His outstanding performance as a researcher and student has been acknowledged with multiple “Excellent Graduate” awards from Shandong Province and Qingdao University.

🔬 Research Focus

Dr. Lu’s research is dedicated to understanding the clinical and molecular mechanisms of hyperuricemia and gout. His pioneering work in constructing a spontaneous hyperuricemia mouse model has provided invaluable insights into disease progression and treatment strategies. His studies explore the relationship between hyperuricemia and renal function, diabetes, and arterial sclerosis, with high-impact publications in leading journals such as Nature Reviews Rheumatology, Kidney International, and Diabetes. He has also been instrumental in identifying the impact of COVID-19 vaccination on gout attacks, proposing colchicine as a preventive treatment.

🏁 Conclusion

Dr. Jie Lu is a leading researcher in hyperuricemia and gout, with a remarkable academic and research career. His extensive publications, international collaborations, and pioneering research have significantly contributed to the medical field. Recognized for his scientific achievements, Dr. Lu continues to make impactful discoveries that shape the future of metabolic disease treatment.

📚 Publications

Colchicine prophylaxis is associated with fewer gout flares after COVID-19 vaccination – Annals of the Rheumatic Diseases, 2022 🔗

Mouse models for human hyperuricaemia: a critical review – Nature Reviews Rheumatology, 2019 🔗

Knockout of the urate oxidase gene provides a stable mouse model of hyperuricemia associated with metabolic disorders – Kidney International, 2018 🔗

Hyperuricemia predisposes to the onset of diabetes via promoting pancreatic β-cell death in uricase-deficient male mice – Diabetes, 2020 🔗

Urate-lowering therapy alleviates atherosclerosis inflammatory response factors and neointimal lesions in a mouse model – The FEBS Journal, 2019 🔗

Superiority of low-dose benzbromarone to low-dose febuxostat in a prospective, randomized comparative effectiveness trial in gout patients with renal uric acid underexcretion – Arthritis Rheumatol, 2022 🔗

Metabolomics and Machine Learning Identify Metabolic Differences and Potential Biomarkers for Frequent Versus Infrequent Gout Flares – Arthritis Rheumatol, 2023 🔗

Profiling of Serum Oxylipins Identifies Distinct Spectrums and Potential Biomarkers in Young People with Very Early Onset Gout – Rheumatology (Oxford), 2023 🔗

Effects of fenofibrate therapy on renal function in primary gout patients – Rheumatology (Oxford), 2021 🔗

Trends in the manifestations of 9754 gout patients in a Chinese clinical center: A 10-year observational study – Joint Bone Spine, 2020 🔗

Dr. Hao Wang | Transformer vibration | Best Researcher Award

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Dr. Hao Wang | Transformer vibration | Best Researcher Award

ph.d, Shandong University, China

Wang Hao is a dedicated researcher in electrical engineering, specializing in vibration noise suppression technology for large oil-filled equipment and functional ceramic doping modification. Born in April 1995 in Jining City, Shandong Province, he is a member of the Communist Party of China. Currently pursuing his Ph.D. at Shandong University under the supervision of Prof. Zhang Li, he has made remarkable contributions to the field, particularly in the development of innovative noise suppression techniques. His research findings have been widely recognized in prestigious journals, and his work plays a crucial role in enhancing the reliability of high-end power equipment.

Publication Profile

Scopus

🎓 Education:

Wang Hao’s academic journey began with a Master’s degree in Electrical Engineering from Xinjiang University (2018-2021), where he conducted extensive research under Prof. Zhao Hongfeng. He then pursued his Ph.D. at Shandong University (2021-2025), deepening his expertise in the vibration noise mechanism and suppression of large converter transformers. His strong academic foundation has positioned him as a leading researcher in electrical power engineering.

💼 Experience:

With a rich research background, Wang Hao has been actively involved in numerous high-impact projects funded by the State Grid Corporation of China and the National Natural Science Foundation of China. His work on key technologies for suppressing converter transformer vibration and reducing noise has significantly influenced power transmission systems. As a main participant in multiple national-level projects, he has contributed to advancing ultra-high voltage equipment technology and optimizing operating environments for large-scale power systems. His research outcomes have been implemented in major industrial applications, benefiting power grid reliability and efficiency.

🏆 Awards and Honors:

Wang Hao’s groundbreaking research has earned him several accolades. His contributions to vibration and noise reduction in power transformers have been recognized in high-impact journals and conferences. His excellence in project execution has been acknowledged by the State Grid Corporation of China, where his research findings have been successfully applied to product upgrades. Additionally, he has authored numerous patents, further solidifying his role as an innovator in electrical engineering.

🔬 Research Focus:

Wang Hao’s primary research revolves around the vibration noise mechanism in converter transformers and the deep suppression of such disturbances. His work integrates finite element simulation, deep learning, and experimental validation to enhance the performance of large-scale oil-filled equipment. Additionally, his research on functional ceramic doping modification has led to advancements in ZnO varistors, improving their electrical properties. His studies bridge the gap between theoretical modeling and practical applications, contributing to the sustainable development of power systems.

🔚 Conclusion:

Wang Hao is a rising star in the field of electrical engineering, contributing groundbreaking research on vibration noise suppression and functional ceramic modifications. His extensive publication record, involvement in high-impact projects, and innovative approaches to power system reliability make him a valuable asset to the industry. With a strong academic and research background, he continues to drive advancements in large-scale power equipment technology, paving the way for a more efficient and resilient energy infrastructure.

🔗 Publications:

Finite element simulation and experimental study on vibration characteristics of converter transformer under DC bias. Protection and Control of Modern Power Systems. (2024) [Cited by X articles] [🔗Link]

Research on Novel Noise Reduction of Converter Transformers Based on Dipole and Multipole Boundary. International Journal of Electrical Power and Energy Systems. (2023) [Cited by X articles] [🔗Link]

Impact of Component Structure on Vibration and Noise of Converter Transformers under Harmonic Excitation. High Voltage. (2023) [Cited by X articles] [🔗Link]

A vibration similarity model of converter transformer and its verification method. Symmetry. (2023) [Cited by X articles] [🔗Link]

An economical dopant for improving the comprehensive electrical properties of ZnO varistor ceramics. Materials Letters. (2022) [Cited by X articles] [🔗Link]

A Convolutional Neural Network Based on Attention Mechanism for Designing Vibration Similarity Models of Converter Transformers. Machines. (2022) [Cited by X articles] [🔗Link]

Effect of Sintering Process on the Electrical Properties and Microstructure of Ca-doped ZnO Varistor Ceramics. Materials Science in Semiconductor Processing. (2022) [Cited by X articles] [🔗Link]

Research on comprehensive deep suppression technology for vibration and noise of converter transformers. IEEE Transactions on Magnetics. (Submitted)

Yulin Yang | Algorithm optimization | Best Researcher Award

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Mr. Yulin Yang | Algorithm optimization | Best Researcher Award

Shenyang University, China

Yulin Yang is a dedicated graduate student at Shenyang University, specializing in logistics engineering and management. His research interests lie in swarm intelligence algorithm optimization and path planning, with a focus on improving computational efficiency and solving complex optimization problems. Passionate about advancing artificial intelligence techniques, he has contributed to algorithmic enhancements that improve convergence speed and search accuracy.

Publication Profile

ORCID

🎓 Education:

Yulin Yang is currently pursuing a master’s degree in logistics engineering and management at Shenyang University. His academic journey is centered around algorithm optimization, particularly in swarm intelligence applications for logistics and transportation systems.

💼 Experience:

As a researcher, Yulin Yang has actively explored novel computational techniques to enhance optimization algorithms. His recent work focuses on developing hybrid whale optimization algorithms to address challenges in search precision and problem-solving capabilities. His expertise extends to route optimization and intelligent decision-making models in logistics.

🏆 Awards and Honors:

While early in his academic career, Yulin Yang’s innovative research contributions have gained recognition, leading to the publication of his work in reputed international journals. His advancements in algorithmic optimization showcase his potential as a rising researcher in the field.

🔬 Research Focus:

Yulin Yang specializes in swarm intelligence algorithm optimization, particularly in improving the performance of metaheuristic techniques. His research emphasizes solving real-world computational problems in logistics through intelligent algorithmic design, enhancing efficiency in route planning and decision-making. His notable contribution includes a multi-strategy hybrid whale optimization algorithm aimed at overcoming limitations in search accuracy and convergence speed.

🔚 Conclusion:

With a strong foundation in optimization algorithms and artificial intelligence applications in logistics, Yulin Yang is poised to make significant contributions to computational research. His commitment to innovation and problem-solving drives his ongoing research, paving the way for impactful advancements in AI-driven optimization.

📄 Publication:

Multi-Strategy Hybrid Whale Optimization Algorithm Improvement. Applied Sciences, 15(4), 2224. DOI: 10.3390/app15042224. This study presents an advanced hybrid optimization approach to address challenges in convergence speed and search efficiency.

Caie Hu | Intelligence Optimization | Best Researcher Award

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Ms. Caie Hu | Intelligence Optimization | Best Researcher Award

lecture, Xi’an University of Technology, China

Hu Caie is a dedicated researcher in Control Science and Engineering, specializing in data-driven optimization, evolutionary optimization, transfer learning, and machine learning 🤖. With a strong academic foundation and extensive experience in computational intelligence, Hu has made significant contributions to antenna design, surrogate modeling, and expensive optimization problems 📡. Passionate about leveraging artificial intelligence for real-world applications, Hu continues to push the boundaries of research in optimization and machine learning.

Publication Profile

🎓 Education

Hu Caie pursued a Ph.D. in Control Science and Engineering at China University of Geosciences (Wuhan) 🎓. As part of a Successive Postgraduate and Doctoral Program of Study and Research, Hu specialized in data-driven optimization, machine learning, and evolutionary optimization from September 2017 to June 2023. This academic journey laid the foundation for advanced research in optimization techniques and computational intelligence.

💼 Experience

Hu has been actively involved in intelligent optimization and electromagnetic simulation projects 🤖. As the Superintendent of the Data-driven Ultra-wideband Antenna Intelligent Optimization Technology Project (2019-2021), Hu played a key role in enhancing antenna design using AI. Additionally, as the Principal Responsible Person for the Electromagnetic Simulation Study of Non-uniform Array with Intelligent Optimization Based on Deep Learning (2020-2022), Hu contributed significantly to advanced simulation techniques 📊. Apart from leading projects, Hu also participated in State Key Laboratory research on antenna arrays and signal processing.

🏆 Awards and Honors

Hu Caie’s academic excellence has been recognized with multiple prestigious scholarships and accolades 🏅. Notable honors include the National Academic Scholarship and the CSC Scholarship for outstanding research contributions. Additionally, Hu received an Academic Report Award, highlighting expertise in presenting complex optimization techniques effectively.

🔎 Research Focus

Hu Caie’s research revolves around data-driven optimization, evolutionary optimization, transfer learning, and machine learning applications in antenna design 🤖. The work primarily focuses on uncertainty modeling, surrogate-assisted optimization, and scalable Gaussian processes for solving expensive optimization problems. By integrating AI and computational intelligence, Hu aims to develop more efficient and adaptive optimization techniques for complex engineering challenges.

🔚 Conclusion

Hu Caie is a passionate researcher at the intersection of AI-driven optimization and computational intelligence 💡. With a strong background in machine learning, evolutionary algorithms, and antenna design, Hu’s contributions continue to shape advancements in data-driven engineering solutions. Through groundbreaking publications and leadership in research projects, Hu remains committed to developing more efficient, scalable, and intelligent optimization techniques 🚀.

📚 Publications

A Robust Technique without Additional Computational Cost in Evolutionary Antenna Optimization – IEEE Transactions on Antennas & Propagation (2019) [Cited by: X articles] 🔗 Read here

On Nonstationary Gaussian Process Model for Solving Data-driven Optimization Problems – IEEE Transactions on Cybernetics (2021) [Cited by: X articles] 🔗 Read here

An Uncertainty Measure for Prediction of Non-Kriging Surrogates – Evolutionary Computation (2021, under revision) 🔗 Read here

A Framework of Global Exploration and Local Exploitation using Surrogates for Expensive Optimization – Knowledge-Based Systems (2023) [Cited by: X articles] 🔗 Read here

Scalable GP with Hyperparameters Sharing Based on Transfer Learning for Solving Expensive Optimization Problems – Applied Soft Computing (2023) [Cited by: X articles] 🔗 Read here

Hyperparameters Adaptive Sharing Based on Transfer Learning for Scalable GPs – IEEE Congress on Evolutionary Computation (2022) 🔗 Read here

An Adaptive Model Management Strategy: Balancing Exploration and Exploitation – IEEE Symposium Series on Computational Intelligence (2021) 🔗 Read here

Elliptical Wide Slot Microstrip Patch Antenna Design Using Dynamic Constrained Multiobjective Optimization Evolutionary Algorithm – Artificial Intelligence Algorithms and Applications (2020) 🔗 Read here

 

MUHAMMAD ANWAR | Biotechnology | Best Researcher Award

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Assoc. Prof. Dr. MUHAMMAD ANWAR | Biotechnology | Best Researcher Award

Associate Professor, Hainan University, China

Dr. Muhammad Anwar is a renowned Molecular Biologist and Genetic Engineer specializing in metabolic engineering, biosynthesis, and plant biotechnology 🌱🧬. With extensive expertise in molecular biology, metabolic pathways, and bioinformatics, he has significantly contributed to understanding flavonoid and terpenoid biosynthesis, plant defense mechanisms, and genetic transformation techniques. Currently serving as an Associate Professor at Hainan University, China, Dr. Anwar has a robust academic and research background spanning over a decade in leading international institutions. His impactful research has led to several high-impact publications in molecular genetics, secondary metabolite biosynthesis, and plant breeding, making him a prominent figure in plant sciences and metabolic engineering.

Publication Profile

🎓 Education 

Dr. Anwar holds a Ph.D. in Agriculture/Horticulture (2014-2018) from Fujian Agriculture and Forestry University, China, where he specialized in genetic and metabolic engineering of plants. He pursued a Postdoctoral Research Fellowship (2019-2023) at Shenzhen University, China, focusing on biosynthesis and functional genomics of medicinal compounds. His academic journey began with an M.Sc. (Hons) in Agriculture (2006-2008) from PMAS Arid Agriculture University, Pakistan, where he worked on salicylic acid’s impact on plant growth. His undergraduate degree, B.Sc. (Hons) in Agriculture/Horticulture (2002-2006), was completed at Bahauddin Zakariya University, Pakistan, laying the foundation for his illustrious career.

👨‍🏫 Experience 

With a stellar career spanning academia and industry, Dr. Anwar is currently an Associate Professor at Hainan University, China (2023–Present), where he leads research in molecular genetics, metabolic pathways, and plant biotechnology. Prior to this, he served as a Postdoctoral Research Fellow at Shenzhen University (2019-2023), focusing on genetic transformation and biosynthetic pathways. His earlier roles include Assistant Professor at the University College of Management Sciences (2018-2019) and research positions at WWF Pakistan, Pakistan Agriculture Research Council, and The Bank of Punjab, contributing to agricultural biotechnology and finance. His expertise in genetic transformation, bioinformatics analysis, and plant defense mechanisms has paved the way for groundbreaking discoveries in plant sciences.

🏆 Awards & Honors

Dr. Anwar has been the recipient of numerous prestigious awards 🎖️, including the International Young Researcher Fund from the National Science Foundation of China (2020-2022). He was awarded the China Government Scholarship (CSC) (2014-2018) for his Ph.D. and the USA Need-Based Scholarship (2006-2008) for his Master’s degree. His excellence extends beyond academics, having received multiple medals and certificates in sports and extracurricular activities. Additionally, he has played an active role in various international training programs, including the Advanced Training Program on Rice Agriculture under the Belt and Road Initiative.

🔬 Research Focus 

Dr. Anwar’s research is centered on molecular biology, genetic engineering, and biosynthetic pathways 🧪. His key focus areas include flavonoid and terpenoid biosynthesis, gene cloning, transcriptomics, bioinformatics, and secondary metabolite regulation in plants. He has led several projects in Agrobacterium-mediated genetic transformation, transcriptomics, and metabolomics of medicinal plants. His contributions to the identification and functional characterization of R2R3-MYB transcription factors in flower pigmentation and plant defense mechanisms have been widely recognized. His interdisciplinary approach combines computational biology, metabolic engineering, and synthetic biology to enhance crop resilience and secondary metabolite production.

🔚 Conclusion

Dr. Muhammad Anwar stands as a pioneering scientist in molecular biology, genetic engineering, and metabolic pathways 🏆. His commitment to advancing plant biotechnology, biosynthetic pathways, and functional genomics has led to groundbreaking discoveries that bridge the gap between fundamental plant sciences and applied agricultural research. His multi-disciplinary expertise, international collaborations, and dedication to scientific innovation continue to make a lasting impact on genetic transformation, plant defense mechanisms, and bioinformatics-driven molecular breeding. 🌱🔬👨‍🔬

📚 Publications 

NtMYB3, an R2R3-MYB from Narcissus, Regulates Flavonoid Biosynthesis – International Journal of Molecular Sciences, 2019 (6.2 IF) 🧬 🔗 Read More

Gene pyramiding improved cell membrane stability under heat stress in cotton – BMC Plant Biology, 2024 (4.3 IF) 🌿 🔗 Read More

Computational Identification and Characterization of Glycosyltransferase 47 Gene Family in Sorghum bicolor – Processes, 2025 🏗️ 🔗 Read More

Recent advancement and strategy on bio-hydrogen production from photosynthetic microalgae – Bioresource Technology, 2019 (11.88 IF) 💡 🔗 Read More

Metabolic and Genetic Engineering of Phenylpropanoid and Terpenoid Biosynthetic Pathways – International Journal of Molecular Sciences, 2021 (6.2 IF) 🌱 🔗 Read More

Mitigating chromium stress in tomato plants using green-silicone nanoparticles – Scientia Horticulturae, 2024 (4.3 IF) 🍅 🔗 Read More

The chloroplast genome of Chrozophora sabulosa and its evolutionary position in Chrozophora – BMC Genomics, 2024 (4.3 IF) 🧬 🔗 Read More

Melatonin foliar application promotes anthocyanin accumulation and oxidative stress management – Scientia Horticulturae, 2024 (4.3 IF) 🌿 🔗 Read More

A Decade of Progress in Rhizoengineering for Salinity Amelioration – Plant Stress, 2023 (5.0 IF) 🌍 🔗 Read More

Multi-epitopes vaccine design for SARS-CoV-2 using an immunoinformatic approach – Heliyon, 2024 (4.0 IF) 🦠 🔗 Read More

Mr. Mingi Kwon | Computer Engineering | Best Researcher Award

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Mr. Mingi Kwon | Computer Engineering | Best Researcher Award

University of California San Diego, United States

Mingi Kwon is an aspiring computer engineer with a strong foundation in VLSI design, computer architecture, and hardware acceleration. 🎓 Currently pursuing an MS in Electrical and Computer Engineering at the University of California, San Diego, he previously earned his BS in Electrical Engineering from Hanyang University, South Korea. With a deep interest in optimizing hardware for AI acceleration, he has worked on advanced projects involving reconfigurable systolic arrays, low-power circuit design, and RISC-V processor architectures. His dedication to high-performance computing and low-power hardware systems is evident through his research contributions and hands-on experience with industry-standard tools. 🚀

Publication Profile

ORCID

🎓 Education:

Mingi Kwon is currently pursuing his Master of Science in Electrical and Computer Engineering at the University of California, San Diego (2024–2026), specializing in computer engineering. He completed his Bachelor of Science in Electrical Engineering from Hanyang University, South Korea (2019–2024), graduating with an impressive GPA of 3.97/4.5. 📚 His academic journey has been focused on advanced coursework, including computer architecture, low-power VLSI design, and deep learning accelerators, equipping him with a strong foundation in hardware and system design.

💼 Experience:

Mingi has gained significant hands-on experience through various projects and his military service. During his undergraduate studies, he developed a Cyclone IV GX-Based Reconfigurable 2D Systolic Array for AI Acceleration, optimizing power consumption and chip area. He also worked on a RISC-V 5-stage Pipeline Processor with an advanced branch predictor, significantly improving execution efficiency. 🔧 Additionally, he served as a cybersecurity specialist and squad leader in the Republic of Korea Army (2020–2022), where he managed encrypted communications and network security while leading a team of 20 soldiers, earning a Distinguished Service Award. 🏅

🏆 Awards and Honors:

Mingi’s excellence in academics and research has been recognized through multiple awards. He was named to the Dean’s List (2022) with a perfect GPA of 4.5/4.5. 🎖️ He also received the National Logic Chip Design Track Scholarship (2023–2024), awarded by the South Korean government for outstanding achievements in electrical engineering. His leadership and dedication in the military earned him a Distinguished Service Award (2021–2022) for enhancing work efficiency and team collaboration.

🔬 Research Focus:

Mingi’s research is centered around hardware acceleration for AI, low-power VLSI design, and computer architecture. 🖥️ His work on systolic arrays focuses on optimizing deep learning computations with reconfigurable architectures, improving efficiency in sparse neural networks. He has also explored low-power circuit design, reducing leakage power and optimizing combinational logic for improved energy efficiency. His expertise extends to processor architecture, particularly RISC-V pipeline design and branch prediction, enhancing execution speed and minimizing stalls.

🔚 Conclusion:

Mingi Kwon is a highly motivated researcher and engineer passionate about bridging the gap between hardware and AI acceleration. 🚀 With extensive experience in VLSI design, digital systems, and processor architecture, he is committed to advancing high-performance, energy-efficient computing systems. His technical expertise, research achievements, and leadership skills position him as a promising innovator in the field of computer engineering. 💡

📄 Publication:

Circuit-Centric Genetic Algorithm for the Optimization of a Radio-Frequency ReceiverElectronics

DOI: 10.3390/electronics14040770

Prof. Dr. Hamid Arabnia | Data Science | Best Researcher Award

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Prof. Dr. Hamid Arabnia | Data Science | Best Researcher Award

Professor Emeritus, University of Georgia, United States

Dr. Hamid R. Arabnia is a distinguished Professor Emeritus of Computer Science at the University of Georgia, USA 🎓. With a Ph.D. in Computer Science from the University of Kent, England (1987), he has made substantial contributions to the fields of Artificial Intelligence, Data Science, Machine Learning, HPC, and STEM education 🤖📊. Over his career, he has mentored 23 Ph.D. students and played a vital role in advancing computational science and intelligence. He has been an active advocate against cyber-harassment and cyberbullying, winning a landmark lawsuit in 2017–2018, securing a $3 million ruling ⚖️. Prof. Arabnia has an extensive publication record with 300+ peer-reviewed papers and 200+ edited research books, establishing himself among the top 2% most impactful scientists, as recognized by Stanford University 🌍📚.

Publication Profile

🎓 Education

Dr. Arabnia earned his Ph.D. in Computer Science from the University of Kent, England (1987) 🏛️. His research during his doctoral studies laid the foundation for his pioneering contributions in supercomputing and artificial intelligence 🤖💡.

💼 Experience

Dr. Arabnia has been with the University of Georgia since 1987, contributing as a Professor, Graduate Coordinator, and Research Director 🏫. He has served as Editor-in-Chief of The Journal of Supercomputing (Springer) and is the book series editor for Transactions of Computational Science and Computational Intelligence (Springer) 📖. His leadership has also extended to roles as a senior adviser for global corporations and National Science Foundation (NSF) committees for over 10 years 🏆.

🏅 Awards and Honors

Prof. Arabnia has received numerous prestigious awards, including recognitions from IEEE BIBE, ACM SIGAPP, and IMCOM 🏅. His legal victory against cyber-harassment was a landmark case, setting an important precedent in the U.S. legal system ⚖️. His contributions to STEM education and securing $12 million in funding for graduate research at UGA have also been widely recognized 💰📚.

🔬 Research Focus

Dr. Arabnia’s research spans Data Science, AI, HPC, Machine Learning, Imaging Science, and Compute-Intensive Problems 🤖📊. He has been actively involved in cybersecurity legislation advocacy, focusing on cyberstalking and online harassment 🔒. His latest work integrates deep learning, upsampling techniques, and AI-driven smart city applications 🌍.

🔚 Conclusion

Dr. Hamid R. Arabnia is a highly influential researcher, educator, and advocate for ethical AI and cybersecurity 🏆. With over 500 publications and millions in research funding, his contributions have shaped modern supercomputing, artificial intelligence, and digital security 🔬. Recognized among the top 2% impactful scientists globally, his work continues to inspire the next generation of AI and computer science researchers 🚀.

📚 Publications

Comprehensive Analysis of Random Forest and XGBoost Performance with SMOTE, ADASYN, and GNUS Upsampling under Varying Imbalance Levels (2025) – Preprint

A New Efficient Hybrid Technique for Human Action Recognition Using 2D Conv-RBM and LSTM with Optimized Frame Selection (2025) – Technologies | DOI 📑

SWAG: A Novel Neural Network Architecture Leveraging Polynomial Activation Functions for Enhanced Deep Learning Efficiency (2024) – IEEE Access | DOI 📖

Hyperparameter Optimization and Combined Data Sampling Techniques in Machine Learning for Customer Churn Prediction: A Comparative Analysis (2023) – Technologies | DOI 📜

A Review of Deep Transfer Learning and Recent Advancements (2023) – Technologies | DOI 📘

Embodied AI-Driven Operation of Smart Cities: A Concise Review (2021) – TechRxiv | DOI 🌍

Mr. Ali Beikmohammadi | Machine Learning | Best Researcher Award

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Mr. Ali Beikmohammadi | Machine Learning | Best Researcher Award

PhD Researcher, Stockholm University, Sweden

👨‍💻 Ali Beikmohammadi is a dedicated researcher in Reinforcement Learning, Deep Learning, and Federated Learning. Currently pursuing his Ph.D. in Computer and Systems Sciences at Stockholm University, Sweden, he has made remarkable contributions to AI research, publishing 15+ papers in top-tier conferences and journals. With a strong foundation in stochastic optimization, telecommunications, and cyber-physical systems, Ali has worked on various industry projects and supervised 30+ Master’s students. His expertise extends to high-performance computing, AI applications in healthcare, and distributed learning, making him a highly influential figure in AI research. 🚀

Publication Profile

Education

🎓 Ali holds a Ph.D. in Computer and Systems Sciences (2021–Present) from Stockholm University, Sweden, where he focuses on sample-efficient reinforcement learning and AI-driven optimization. He earned an M.Sc. in Electrical Engineering (Digital Electronic Systems) (2017–2019) from Amirkabir University of Technology, Iran, specializing in deep learning for plant classification. His B.Sc. in Electrical Engineering (Electronics) (2013–2017) from Bu-Ali Sina University, Iran, involved research on license plate recognition using computer vision. 📚

Experience

💡 With extensive research and industry collaborations, Ali has supervised 30+ Master’s students at Stockholm University and Karolinska Institutet, applying AI to healthcare, recommendation systems, forecasting, and network optimization. He has also instructed 91 students in Health Informatics courses, focusing on time-series analysis, deep learning, and reinforcement learning. His industry collaborations include Scania CV AB, Hitachi Energy, and the University of California, where he played key roles in algorithm design, pipeline development, and AI-driven performance optimization. 🤖

Awards and Honors

🏆 Ali’s exceptional contributions to AI and engineering have earned him prestigious scholarships such as the Lars Hierta Memorial Foundation Scholarship (2025) and the Rhodins, Elisabeth, and Herman Memory Scholarship (2024). He is a member of the Iran National Elites Foundation and has received the Outstanding Paper Award at the 5th ICSPIS’19 Conference. His academic excellence is further highlighted by ranking 1st in GPA during his B.Sc. and M.Sc. studies. 🌟

Research Focus

🔬 Ali’s research revolves around Reinforcement Learning, Deep Learning, and Federated Learning, with a strong emphasis on stochastic optimization, telecommunications, and cyber-physical systems. His recent work explores teacher-assisted reinforcement learning, federated learning without data similarity constraints, and cost-sensitive AI models for industrial applications. His contributions aim to enhance AI’s efficiency, scalability, and applicability across domains like healthcare, robotics, and automation. ⚙️

Conclusion

🌍 Ali Beikmohammadi is an accomplished AI researcher, educator, and industry collaborator pushing the frontiers of Reinforcement Learning, Deep Learning, and Federated Learning. With multiple high-impact publications, prestigious awards, and hands-on experience in AI-driven solutions, he continues to bridge the gap between academic research and real-world AI applications. His passion for cutting-edge AI innovations positions him as a leading voice in modern AI research. 🚀✨

Publications

Comprehensive Analysis of Random Forest and XGBoost Performance with SMOTE, ADASYN, and GNUS Upsampling under Varying Imbalance Levels

TA-Explore: Teacher-assisted exploration for facilitating fast reinforcement learning – Published at International Conference on Autonomous Agents and Multiagent Systems (AAMAS) (2023).  Paper Link

Comparing NARS and Reinforcement Learning: An Analysis of ONA and Q-Learning Algorithms – Artificial General Intelligence Conference (2023).  Paper Link

Human-inspired framework to accelerate reinforcement learning – arXiv (2023).  Paper Link

Compressed federated reinforcement learning with a generative model – ECML-PKDD (2024).  Paper Link

On the Convergence of Federated Learning Algorithms without Data Similarity – IEEE Transactions on Big Data (2024).  Paper Link

Parallel Momentum Methods Under Biased Gradient Estimations – IEEE Transactions on Control of Network Systems (2025).  Paper Link

A Cost-Sensitive Transformer Model for Prognostics Under Highly Imbalanced Industrial Data – arXiv (2024).  Paper Link