Tag: AI Innovation Award

Mini Han Wang | Artificial Intelligence | Young Scientist Award

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Dr. Mini Han Wang | Artificial Intelligence | Young Scientist Award

Chinese University of Hong Kong, Hong Kong

Dr. Mini Han Wang is a distinguished senior researcher specializing in ophthalmology, artificial intelligence (AI) in medical imaging, and biomolecular pathways in ocular diseases. She holds dual Ph.D.s in Ophthalmology & Visual Sciences from The Chinese University of Hong Kong and Data Science from the City University of Macau, demonstrating her expertise in integrating medical research with AI-driven analytical techniques. Dr. Wang has made significant contributions to age-related macular degeneration (AMD) research, AI-based disease diagnostics, and precision medicine. She currently serves as a Senior Researcher at Zhuhai People’s Hospital, affiliated with the Beijing Institute of Technology and Jinan University, and Director of the Frontier Science Computing Center at the Chinese Academy of Sciences. Beyond research, she is an experienced lecturer, delivering courses on intelligent data mining, evidence-based medicine, and AI applications in healthcare. Her work is widely published in peer-reviewed journals, and she actively collaborates with leading academic and medical institutions. With a commitment to advancing medical AI technologies and personalized healthcare solutions, Dr. Wang stands out as a leading expert at the intersection of medicine and data science.

Professional Profile

Education

Dr. Mini Han Wang has pursued a multidisciplinary academic journey, combining medical sciences, engineering, and data science. She earned a Ph.D. in Ophthalmology & Visual Sciences from The Chinese University of Hong Kong (2022-2025), where her research focuses on AI-driven diagnostics and molecular mechanisms of retinal diseases. In parallel, she completed a Ph.D. in Data Science at the Institute of Data Science, City University of Macau (2020-2023), further enhancing her ability to develop AI-integrated solutions for medical applications. Before her doctoral studies, Dr. Wang completed an M.Sc. in Management (2016-2018) at City University of Macau, gaining insights into research administration and healthcare management. She also holds dual bachelor’s degrees from Jiangxi Science & Technology Normal University (2012-2016) in Internet of Things (IoT) Engineering and English Literature, showcasing her strong foundation in technology and global scientific communication. As an Outstanding Graduate Representative, her diverse educational background enables her to bridge the gap between medical research, AI innovation, and healthcare management, making her a pioneering figure in modern ophthalmic research.

Professional Experience

Dr. Wang’s professional journey is marked by leadership in research, teaching, and AI-driven medical advancements. She currently serves as a Senior Researcher at Zhuhai People’s Hospital, affiliated with Beijing Institute of Technology and Jinan University, where she leads projects on AI-based ophthalmic disease diagnosis and retinal molecular research. Additionally, she holds the position of Director of the Frontier Science Computing Center at the Chinese Academy of Sciences, overseeing cutting-edge AI applications in medicine and multi-omics data integration. Since 2018, Dr. Wang has collaborated with Shenzhen Institute of Advanced Technology and Zhuhai Institute of Advanced Technology, conducting research on medical imaging, knowledge graphs, and AI-driven predictive modeling. Her academic contributions include guest lectures at Beijing Institute of Technology, Jinan University, and Zhuhai Science & Technology Institute, focusing on intelligent data mining, evidence-based medicine, and AI in disease diagnosis. With her interdisciplinary expertise, Dr. Wang has played a key role in bridging fundamental research with clinical applications, contributing significantly to medical AI advancements and personalized treatment strategies.

Research Interest

Dr. Wang’s research revolves around three core areas: ophthalmology, AI in medical imaging, and biomolecular pathways in ocular diseases. Her primary focus is age-related macular degeneration (AMD) and retinal diseases, where she investigates molecular mechanisms, genetic variations, and metabolic dysregulation. She is also deeply involved in AI-driven predictive modeling to enhance early disease detection and precision therapeutics. In the field of medical imaging, she integrates multi-modal imaging techniques (OCT, UWF Fundus) with AI algorithms to improve retinal disease diagnostics and prognosis. Furthermore, her research extends to biomolecular analysis, where she studies oxidative stress, mitochondrial dysfunction, and complement system activation in ocular diseases. By combining multi-omics data, AI-driven drug discovery, and knowledge graph-driven ophthalmic AI systems, Dr. Wang aims to revolutionize personalized medicine and enhance treatment strategies for degenerative eye diseases.

Research Skills

Dr. Wang possesses a diverse and advanced skill set, allowing her to lead high-impact research in medical AI and ophthalmology. She specializes in AI-based predictive modeling, machine learning for medical imaging, and deep learning for disease classification. Her expertise in biomolecular analysis includes multi-omics data integration, pathway analysis, and molecular crosstalk identification for precision medicine applications. Dr. Wang is also proficient in data mining, statistical modeling, and computational biology, which are essential for her research on retinal diseases and AI-driven diagnostics. Additionally, she has hands-on experience with multi-modal imaging techniques (OCT, UWF, fundus photography) and their integration with AI-based disease detection frameworks. She is well-versed in academic writing, research methodology, and project management, with an extensive record of peer-reviewed publications and collaborative research projects. With these skills, Dr. Wang is able to bridge the gap between clinical research and AI-powered healthcare solutions, making her a leading figure in medical innovation.

Awards and Honors

Dr. Wang has received multiple recognitions for her outstanding research contributions and academic achievements. As an Outstanding Graduate Representative, she was acknowledged for her exceptional performance in data science and medical research. She has been the recipient of research grants and funding awards for her work in ophthalmic AI, biomolecular studies, and precision medicine. Her research on AMD and AI-driven diagnostics has earned recognition from international conferences and peer-reviewed journals. She has been invited as a keynote speaker and panelist at various scientific conferences, where she has shared insights on AI applications in medicine, multi-omics integration, and retinal disease research. Additionally, her collaborations with leading universities and medical institutions have led to numerous institutional awards for excellence in research and innovation. With a strong academic and professional track record, Dr. Wang continues to be recognized as a pioneering researcher at the forefront of AI-driven medical advancements.

Conclusion

Dr. Mini Han Wang is a leading researcher at the intersection of ophthalmology, AI, and biomolecular analysis, making groundbreaking contributions to AMD research, AI-driven diagnostics, and precision medicine. Her multidisciplinary expertise in medical science, data analytics, and computational biology allows her to develop innovative solutions for early disease detection and personalized treatment strategies. As a senior researcher, director, and academic lecturer, she has demonstrated leadership in both research and education, mentoring young scientists and collaborating with top-tier institutions. Her work in AI-integrated ophthalmology and molecular disease modeling is shaping the future of medical research and healthcare technology. While further global collaborations, large-scale clinical applications, and expanded research beyond AMD

Publications Top Notes

  • Title: Place attachment to pseudo establishments: An application of the stimulus-organism-response paradigm to themed hotels
    Authors: J. Sun, P.J. Chen, L. Ren, E.H.W. Shih, C. Ma, H. Wang, N.H. Ha
    Year: 2021
    Citations: 86

  • Title: The effect of online investor sentiment on stock movements: an LSTM approach
    Authors: G. Wang, G. Yu, X. Shen
    Year: 2020
    Citations: 43

  • Title: Big data and predictive analytics for business intelligence: A bibliographic study (2000–2021)
    Authors: Y. Chen, C. Li, H. Wang
    Year: 2022
    Citations: 33

  • Title: AI-based advanced approaches and dry eye disease detection based on multi-source evidence: Cases, applications, issues, and future directions
    Authors: M.H. Wang, L. Xing, Y. Pan, F. Gu, J. Fang, X. Yu, C.P. Pang, K.K.L. Chong
    Year: 2024
    Citations: 32

  • Title: Artificial intelligence in ophthalmopathy and ultra-wide field image: A survey
    Authors: J. Yang, S. Fong, H. Wang, Q. Hu, C. Lin, S. Huang, J. Shi, K. Lan, R. Tang
    Year: 2021
    Citations: 29

  • Title: Research on data security in big data cloud computing environment
    Authors: F. Wang, H. Wang, L. Xue
    Year: 2021
    Citations: 27

  • Title: An explainable artificial intelligence-based robustness optimization approach for age-related macular degeneration detection based on medical IoT systems
    Authors: M.H. Wang, K.K. Chong, Z. Lin, X. Yu, Y. Pan
    Year: 2023
    Citations: 26

  • Title: Applications of explainable artificial intelligent algorithms to age-related macular degeneration diagnosis: A case study based on CNN, attention, and CAM mechanism
    Authors: M. Wang, Z. Lin, J. Zhou, L. Xing, P. Zeng
    Year: 2023
    Citations: 13

  • Title: Metamaterials design method based on deep learning database
    Authors: X. Zhou, Q. Xiao, H. Wang
    Year: 2022
    Citations: 10

  • Title: A YOLO-based method for improper behavior predictions
    Authors: M. Wang, Y. Zhao, Q. Wu, G. Chen
    Year: 2023
    Citations: 9

Qichuan Tian | Computer Science | Best Researcher Award

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Prof. Dr. Qichuan Tian | Computer Science | Best Researcher Award

Prof. Dr. at  Beijing University of Civil Engineering and Architecture, China

Qichuan Tian, born in 1971, is a distinguished professor and technical expert specializing in artificial intelligence, pattern recognition, and computer vision. He holds a Ph.D. in Engineering from Northwestern Polytechnical University (2006) and currently serves as a professor and master’s supervisor at Beijing University of Civil Engineering and Architecture (BUCEA). As the Director of the Department of Artificial Intelligence at the School of Intelligent Science and Technology, he leads research in biometrics, human-computer interaction, and deep learning. He is a member of multiple prestigious organizations, including the National Information Technology Standardization Technical Committee and the Chinese Society of Biomedical Engineering. His career spans academia and industry, with significant contributions in developing national standards, publishing books, and mentoring graduate students. Tian has also played a key role in over 20 research projects funded by national and provincial foundations, solidifying his reputation as a thought leader in AI and computational sciences.

Professional Profile

Education

Qichuan Tian has an extensive academic background in engineering. He obtained his Bachelor of Engineering (1993) and Master of Engineering (1996) from Taiyuan University of Science and Technology. In 2006, he completed his Doctor of Engineering at Northwestern Polytechnical University, specializing in artificial intelligence and computer vision. His academic training laid a strong foundation for his later contributions to AI, biometrics, and deep learning. His studies focused on integrating computational intelligence into practical applications, a theme that continues to define his research and professional endeavors.

Professional Experience

Tian has a diverse career in academia and research. Since 2012, he has served as the Head of the Department of Artificial Intelligence at BUCEA, where he spearheads innovative AI programs. From 2009 to 2010, he was a Visiting Scholar at Auburn University, USA, gaining international exposure in computer science. Between 2006 and 2008, he conducted postdoctoral research at Tianjin University. Previously, he held various roles at Taiyuan University of Science and Technology (1993–2012), where he advanced from Assistant Professor to Associate Professor and later became the Chief Leader of Circuits and Systems. His leadership has been instrumental in shaping AI research and education in China.

Research Interests

Tian’s research interests focus on artificial intelligence, pattern recognition, image processing, and deep learning. He specializes in biometric recognition, computer vision, and human-computer natural interaction. His work extends to security authentication, big data analysis, and IoT-based embedded systems. Tian has published over 100 journal and conference papers, authored six books, and contributed significantly to national standards in AI applications. His interdisciplinary research bridges theoretical advancements with practical AI implementations, making substantial contributions to the field.

Research Skills

With expertise in artificial intelligence and computer vision, Tian possesses strong research skills in deep learning algorithms, biometric recognition systems, and real-time image processing. He has successfully led projects in autonomous driving, green building AI integration, and complex object detection. His experience includes handling large-scale datasets, implementing machine learning frameworks, and designing AI-driven applications. Additionally, he has obtained over 50 invention patents and software copyrights, showcasing his ability to translate theoretical research into impactful technological innovations.

Awards and Honors

Tian’s contributions to academia and AI research have earned him multiple accolades. In 2024, he was recognized among CNKI’s Highly Cited Scholars (Top 5). He received the First Prize for Teaching Achievements at BUCEA in 2021 and was honored for developing a National First-Class Blended Online and Offline Course in 2020. Additionally, he was awarded the Outstanding Master’s Thesis Advisor Award in 2012. His accolades highlight his commitment to education, research, and AI-driven innovations, reinforcing his influence in the field of intelligent science and technology.

Conclusion

Qichuan Tian is a prominent scholar and AI expert dedicated to advancing artificial intelligence and biometric research. His leadership in academia, combined with his extensive research portfolio, underscores his impact on technological advancements in pattern recognition, computer vision, and human-computer interaction. With a career spanning over two decades, Tian has played a pivotal role in shaping AI education, national standards, and industry collaborations. His legacy continues to influence emerging AI technologies and inspire the next generation of researchers in intelligent computing.

Publications Top Notes

  • Title: An improved framework for breast ultrasound image segmentation with multiple branches depth perception and layer compression residual module

    • Authors: K. Cui, Qichuan Tian, Haoji Wang, Chuan Ma
    • Year: 2025

  • Title: Mobile Robot Path Planning Algorithm Based on NSGA-II

    • Authors: Sitong Liu, Qichuan Tian, Chaolin Tang
    • Year: 2024
    • Citations: 1

  • Title: OcularSeg: Accurate and Efficient Multi-Modal Ocular Segmentation in Non-Constrained Scenarios

    • Authors: Yixin Zhang, Caiyong Wang, Haiqing Li, Qichuan Tian, Guangzhe Zhao
    • Year: 2024

  • Title: Convolutional Neural Network–Bidirectional Gated Recurrent Unit Facial Expression Recognition Method Fused with Attention Mechanism

    • Authors: Chaolin Tang, Dong Zhang, Qichuan Tian
    • Year: 2023
    • Citations: 4

 

 

 

Yunxiang Lu | neural network dynamics | Best Researcher Award

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Dr. Yunxiang Lu | neural network dynamics | Best Researcher Award 

at Nanjing University of Posts and Telecommunications, China.

Dr. Yunxiang Lu is an accomplished scholar in Control Science and Engineering, currently pursuing a combined Master and Ph.D. program at the College of Automation and Artificial Intelligence at Nanjing University of Posts and Telecommunications, China. His research focuses on nonlinear dynamic systems, bifurcation theory, and the application of control systems in ecological and biological networks. Throughout his academic career, Yunxiang has demonstrated his proficiency through numerous publications in high-impact journals and participation in prestigious conferences. His work contributes significantly to the understanding of neural networks, eco-epidemiological systems, and cyber-physical systems. In addition, Yunxiang has industry experience as a technical engineer, applying advanced control theories in real-world projects like smart factories powered by 5G technology.

Profile

Scopus

ORCID

Education 

Yunxiang Lu is currently pursuing a combined Master and Ph.D. degree in Control Science and Engineering at Nanjing University of Posts and Telecommunications. His studies cover diverse areas such as matrix theory, bifurcation of nonlinear dynamic systems, and adaptive control. Throughout his education, Yunxiang has excelled in courses like Image Analysis and Understanding, Nonlinear Systems and Chaos Control, and Optimization Methods, reflecting his deep understanding of advanced control theories. His exceptional academic performance includes top grades in Matrix Theory (100), Linear System Theory (95), and Image Analysis and Understanding (95), indicating his strong analytical and mathematical capabilities. His educational background equips him to analyze complex networks and systems, which are fundamental to his research in ecological competition networks and neural systems.

Experience 

Yunxiang Lu has gained practical experience through his role as an IT Technical Engineer at China Telecom Corporation’s Nanjing Branch. In this position, he contributed to the 5G+MEC smart factory project, where he applied his knowledge in telecommunications and control systems to enhance smart factory operations. Yunxiang participated in developing a 5G+MEC virtual private network, integrating 5G wireless scanning guns and machine vision systems, which underscores his ability to apply cutting-edge technologies in real-world environments. In academia, Yunxiang presided over the Postgraduate Research and Practice Innovation Program of Jiangsu Province, leading research on bifurcation control in fractional-order ecological networks. His ability to balance academic research with practical engineering projects reflects his diverse expertise and versatility.

Research Interests 

Yunxiang Lu’s research is primarily focused on control theory, bifurcation dynamics, and ecological and biological systems. He is particularly interested in the dynamical behavior of complex networks, such as ecological competition networks and neural networks, under various influences like fractional orders and time delays. His work explores how network topology and control strategies affect the stability and evolution of these systems. Yunxiang has also ventured into cyber-physical systems, investigating tipping points and bifurcation mechanisms in networks. His research aims to develop optimized control strategies for managing the dynamics of anomalous diffusion systems, which include neural networks and ecological competition networks, contributing to both theoretical advancements and practical applications in system stability and control.

Awards 

Yunxiang Lu has received multiple prestigious awards for his academic excellence. In 2022, he was honored as an Excellent Graduate by Nanjing University of Posts and Telecommunications, a reflection of his outstanding performance throughout his Ph.D. program. He was also recognized as an Excellent Postgraduate in both 2021 and 2020, receiving second prizes in the university’s Postgraduate Academic Scholarship competition during those years. These accolades underscore his dedication to academic success and research excellence. Yunxiang’s continuous recognition over the years highlights his consistency and high academic standards, making him a standout student in the College of Automation and Artificial Intelligence.

Publications 

Dr. Yunxiang Lu has contributed extensively to high-impact research in nonlinear systems and control theory. His key publications include:

 

  1. “Stability and bifurcation exploration of delayed neural networks with radial-ring configuration and bidirectional coupling”, IEEE Transactions on Neural Networks and Learning Systems, 2023, in press.
    • Cited by: 10
  2. “A delayed eco-epidemiological competition network with reaction-diffusion terms: Tipping anticipation”, Applied and Computational Mathematics, 2023, accepted.
    • Cited by: 7
  3. “Hybrid control synthesis for Turing instability and Hopf bifurcation of marine planktonic ecosystems with diffusion”, IEEE Access, 2021, 9: 111326-111335.
    • Cited by: 15
  4. “Hopf bifurcation of biological competition network with independent non-cross propagation characteristics”, Complex System and Complexity Science, 2022, 19(1): 1-11.
    • Cited by: 5

Conclusion

Yunxiang Lu, Ph.D., is a strong candidate for the Best Researcher Award, given his extensive contributions to the fields of control science, nonlinear systems, and neural network modeling. His technical expertise, research leadership, and publication record in high-impact journals demonstrate his commitment to advancing scientific knowledge. With a focus on expanding his research’s practical and interdisciplinary impact, he would be a highly deserving recipient of this award.

Abid Iqbal | Artificial Intelligence | Best Researcher Award

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Assist Prof Dr. Abid Iqbal | Artificial Intelligence | Best Researcher Award

Assistant Professor at King Faisal University, Saudi Arabia

Dr. Abid Iqbal is an accomplished Assistant Professor at the University of Engineering and Technology Peshawar, specializing in Electrical Engineering and artificial intelligence. He earned his Ph.D. from Griffith University, Australia, where he researched piezoelectric energy harvesters. With a strong academic background, he ranked first in his Master’s program at Ghulam Ishaq Khan Institute, Pakistan. Dr. Iqbal has a diverse professional experience, including roles as an Electrical Design Engineer and Research Assistant. His expertise encompasses developing embedded devices and innovative teaching methodologies, mentoring students, and conducting impactful research. He has successfully secured funding for multiple projects in AI applications for health and agriculture. Dr. Iqbal’s publication record includes numerous papers in reputable journals, reflecting his commitment to advancing knowledge in his field. His technical skills in programming and software further enhance his research capabilities, making him a valuable asset to academia and industry.

Profile

Education

Dr. Abid Iqbal is a highly accomplished academic with a solid educational foundation in electrical and electronics engineering. He earned his Ph.D. from the Queensland Micro- and Nanotechnology Centre at Griffith University, Australia, from April 2013 to February 2017. His doctoral research focused on the design, fabrication, and analysis of aluminum nitride (AlN)/silicon carbide (SiC)-based piezoelectric energy harvesters, contributing significantly to renewable energy technologies. Prior to his Ph.D., Dr. Iqbal completed his Master’s degree in Electronics Engineering at the Ghulam Ishaq Khan Institute in Topi, Swabi, Pakistan, graduating with a remarkable GPA of 3.88/4 and securing the top position in his class. His academic journey began with a Bachelor’s degree in Electrical Engineering from the University of Engineering & Technology in Peshawar, Pakistan, where he was recognized as an outstanding student. Dr. Iqbal’s educational background reflects his dedication and expertise in his field, laying a strong foundation for his professional career.

Professional Experience

Dr. Abid Iqbal is an accomplished electrical engineer currently serving as an Assistant Professor at the University of Engineering and Technology Peshawar since August 2019. In this role, he has been instrumental in teaching undergraduate courses in Electrical Engineering, developing innovative teaching methods, and mentoring students on research projects. Prior to this position, he worked as an Electrical Design Engineer at Alliance Power and Data in Australia, focusing on ERGON and NBN projects. He also contributed to the development of embedded systems for individuals with disabilities while employed as an Electronic Engineer at Community Lifestyle Support. His research experience includes a significant role as a Research Assistant at Griffith University, where he worked on piezoelectric devices for harsh environments and gained expertise in various semiconductor fabrication processes. Additionally, he has served as a lecturer at Comsat Institute of Information Technology and worked as a research associate at the City University of Hong Kong, demonstrating a robust and diverse professional background in academia and industry.

Research Interest

Dr. Abid Iqbal’s research interests lie at the intersection of electrical engineering and artificial intelligence, focusing on the development of innovative technologies that enhance energy efficiency and improve healthcare outcomes. His work includes designing and fabricating advanced piezoelectric energy harvesters using AlN/SiC materials, aimed at harnessing renewable energy sources. Additionally, Dr. Iqbal is deeply involved in projects utilizing artificial intelligence for agricultural applications, such as real-time disease detection in crops, and developing telehealth systems that leverage IoT technology to monitor patient health remotely. He has a keen interest in embedded systems and the design of hardware for assistive technologies, including portable ventilators and muscle sensors for individuals with disabilities. Through his research, Dr. Iqbal aims to contribute to sustainable energy solutions and advancements in healthcare technology, fostering a multidisciplinary approach that integrates engineering principles with artificial intelligence for practical applications.

Research Skills

Dr. Abid Iqbal possesses a robust set of research skills that underscore his expertise in Electrical Engineering and artificial intelligence. His extensive experience in designing and fabricating piezoelectric energy harvesters highlights his proficiency in materials science and device characterization. Dr. Iqbal is adept at using advanced simulation tools such as COMSOL, Ansys, and Coventorware, which facilitate in-depth analysis and optimization of microelectromechanical systems (MEMS). His work on artificial intelligence applications in telehealth and agricultural systems showcases his ability to integrate machine learning techniques with practical engineering solutions. Additionally, Dr. Iqbal has a strong background in programming languages such as Python and MATLAB, enhancing his capability to develop innovative software solutions for complex engineering problems. His involvement in funded projects and numerous publications further illustrates his commitment to advancing research and contributing to knowledge in his field. Overall, Dr. Iqbal’s diverse skills position him as a valuable asset to any research team.

Award and Recognition

Dr. Abid Iqbal is a distinguished electrical engineer and academic known for his significant contributions to the field of electrical and electronics engineering. He has received multiple accolades for his research and academic excellence, including the IGNITE funding for four innovative projects focused on machine learning applications in health and agriculture. Dr. Iqbal was awarded publication scholarships and prestigious Griffith University PhD scholarships, recognizing his outstanding academic performance during his doctoral studies. Additionally, he ranked first among his peers in the Master’s program at Ghulam Ishaq Khan Institute, further demonstrating his commitment to excellence in engineering. His dedication to teaching and mentoring future engineers is evident in his role as an Assistant Professor at the University of Engineering and Technology Peshawar, where he has developed innovative curricula and guided numerous student research projects. Dr. Iqbal’s work has been widely published, contributing significantly to advancements in artificial intelligence, embedded systems, and renewable energy technologies.

Conclusion

Dr. Abid Iqbal is a highly qualified candidate for the Best Researcher Award, demonstrating exceptional expertise in Electrical Engineering and a strong commitment to research and education. His accomplishments in renewable energy research, successful project management, and dedication to mentoring future engineers make him a standout choice. While he has areas for growth, particularly in expanding collaborative networks and enhancing commercialization efforts, his current achievements and potential for future contributions position him as an inspiring figure in his field. This award would not only recognize his past efforts but also encourage his continued pursuit of excellence in research and education.

Publication Top Notes

  1. Enhanced TumorNet: Leveraging YOLOv8s and U-net for superior brain tumor detection and segmentation utilizing MRI scans
    • Authors: Zafar, W., Husnain, G., Iqbal, A., AL-Zahrani, M.S., Naidu, R.S.
    • Year: 2024
    • Journal: Results in Engineering
    • Volume/Page: 24, 102994
  2. Novel dual absorber configuration for eco-friendly perovskite solar cells: design, numerical investigations and performance of ITO-C60-MASnI3-RbGeI3-Cu2O-Au
    • Authors: Hasnain, S.M., Qasim, I., Iqbal, A., Amin Mir, M., Abu-Libdeh, N.
    • Year: 2024
    • Journal: Solar Energy
    • Volume/Page: 278, 112788

 

 

 

Dr. Cong Guo | Computer Science | Best Researcher Award

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Dr. Cong Guo | Computer Science | Best Researcher Award

Nurse Practitioner at UNC Blue Ridge, United States.

Cong Guo, who earned his master’s degree in 2024 from the School of Computer and Information Engineering at Henan University, is currently pursuing a PhD in Computer Science and Technology at Zhejiang Normal University. His research specializes in machine learning and pattern recognition, fields that are increasingly relevant in today’s data-driven landscape. Guo has made significant contributions to the field, as evidenced by his publications, including a novel feature selection framework for incomplete data and a method for iterative missing value imputation based on feature importance. These works demonstrate his innovative approach to addressing common challenges in data science. While his academic background and publication record are impressive, expanding his publication scope and enhancing networking opportunities could further elevate his research impact. With his solid foundation and commitment to advancing knowledge in machine learning, Cong Guo is a promising candidate for recognition as a leading researcher.

Profile:

Education

Cong Guo received his master’s degree in 2024 from the School of Computer and Information Engineering at Henan University, where he laid a strong foundation in computer science principles and research methodologies. His academic journey has been characterized by a focus on machine learning and pattern recognition, reflecting his passion for harnessing data to solve complex problems. Currently, Cong is pursuing his Ph.D. at the School of Computer Science and Technology at Zhejiang Normal University, further enhancing his expertise in these cutting-edge fields. His educational experiences have equipped him with essential skills in data analysis, algorithm development, and statistical modeling, which are critical for his research. Throughout his studies, Cong has demonstrated a commitment to academic excellence and innovation, making significant strides in understanding and improving feature selection and data imputation techniques. His educational background positions him as a promising researcher in the rapidly evolving landscape of computer science.

Professional Experiences 

Cong Guo has demonstrated significant commitment to his academic and professional development in the field of computer science. He obtained his master’s degree from the School of Computer and Information Engineering at Henan University in 2024, where he developed a solid foundation in computer science principles and applications. Currently, he is pursuing his PhD at the School of Computer Science and Technology at Zhejiang Normal University, focusing on machine learning and pattern recognition. During his studies, Guo has engaged in research projects that involve innovative approaches to data analysis, particularly in handling incomplete datasets and missing value imputation. His publications in reputable journals reflect his dedication to advancing knowledge in his field. Additionally, his collaborative work with fellow researchers highlights his ability to contribute effectively to team-oriented projects, enhancing his experience and understanding of complex computational problems. This combination of academic rigor and research experience positions Guo as a promising researcher in computer science.

Research Interests

Cong Guo’s research interests lie primarily in the fields of machine learning and pattern recognition, where he aims to develop innovative algorithms and frameworks to address real-world challenges in data analysis. His work focuses on enhancing feature selection and imputation techniques, particularly in the context of incomplete datasets, which are common in many applications. By investigating novel approaches to handle missing data, Cong seeks to improve the accuracy and efficiency of machine learning models. Additionally, he is interested in exploring the broader implications of machine learning across various domains, such as healthcare, finance, and environmental science. Cong’s passion for advancing knowledge in these areas drives his commitment to research that not only contributes to theoretical advancements but also has practical applications that can benefit society. Through his ongoing doctoral studies and collaborative projects, he aims to further explore the intersections of machine learning and real-world problem-solving.

Research Skills 

Cong Guo possesses a robust set of research skills that enhance his capabilities in machine learning and pattern recognition. His proficiency in feature selection and data imputation techniques demonstrates a strong analytical mindset, enabling him to address complex challenges in handling incomplete datasets effectively. Guo is adept at employing various machine learning algorithms and tools, which allows him to develop innovative frameworks that optimize data analysis processes. His experience in collaborative research, evidenced by his co-authored publications, showcases his ability to work effectively in teams, share ideas, and contribute to collective goals. Additionally, Guo’s familiarity with statistical methods and computational techniques underpins his research, ensuring that his findings are both rigorous and applicable. His commitment to continuous learning and adaptation to emerging trends in technology further solidifies his expertise, making him a valuable asset in advancing the field of computer science and information engineering.

Award and Recognition 

Cong Guo has distinguished himself in the field of machine learning and pattern recognition, earning recognition for his innovative research contributions. He completed his master’s degree in 2024 at the School of Computer and Information Engineering, Henan University, where he developed a strong foundation in computational methodologies. Currently pursuing his PhD at Zhejiang Normal University, Cong has co-authored impactful publications, including “A novel feature selection framework for incomplete data” and “Iterative missing value imputation based on feature importance,” which have been well-received in reputable journals. His research not only addresses critical challenges in data science but also demonstrates his potential to influence future advancements in the field. Cong’s commitment to academic excellence and his collaborative spirit have garnered him respect among peers and mentors alike, positioning him as a promising candidate for the Best Researcher Award. His ongoing efforts are indicative of a bright future in research and innovation.

Conclusion

Cong Guo exhibits a promising trajectory in research, with a strong academic foundation and relevant publications in machine learning and pattern recognition. His commitment to advancing the field is evident in his current work. By broadening his publication efforts and enhancing his professional network, he can significantly improve his contributions to research. Given his strengths and potential for growth, Cong Guo is a suitable candidate for the Best Researcher Award.

Publication Top Notes
  1. A novel feature selection framework for incomplete data
  2. Iterative missing value imputation based on feature importance
  3. KNCFS: Feature selection for high-dimensional datasets based on improved random multi-subspace learning

 

 

Karimeh Ata | Artificial Intelligence | Best Researcher Award

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Dr. Karimeh Ata | Artificial Intelligence | Best Researcher Award

Researcher at UPM, Jordan

Dr. Karimeh Ata is a Computer and Artificial Intelligence Engineering Ph.D. candidate at Universiti Putra Malaysia (UPM), specializing in deep learning and big data analytics for urban mobility and vehicle flow optimization. With a strong academic foundation, she holds a Master’s degree in Computer Engineering and Embedded Systems from UPM and a Bachelor’s degree in Computer Engineering from Fahad Bin Sultan University, Saudi Arabia, where she graduated with first-class honors. Dr. Ata’s research focuses on solving complex problems using advanced algorithms like Dijkstra’s and Ant Colony Optimization, contributing to various high-impact projects. In addition to her academic achievements, she has experience as an AI trainer and lecturer, and her work is highlighted by numerous publications in top-tier journals and conferences. Proficient in technologies like Microsoft Azure, GIS, Python, and Raspberry Pi, Dr. Ata is committed to driving innovation in the fields of artificial intelligence and computer engineering.

Profile

Education

Dr. Karimeh Ata is currently pursuing her Ph.D. in Computer Engineering and Artificial Intelligence at Universiti Putra Malaysia (UPM), with an expected completion in June 2024. Her doctoral research focuses on traffic flow prediction using deep learning and big data analysis, and she has maintained an outstanding GPA of 4.00 throughout her studies. Prior to this, she earned a Master of Computer Engineering and Embedded Systems from UPM in 2019, where she addressed challenges in vehicle navigation and parking optimization using algorithms like Dijkstra’s and Ant Colony Optimization, achieving a GPA of 3.57. Dr. Ata holds a Bachelor of Computer Engineering from Fahad Bin Sultan University (FBSU) in Saudi Arabia, where she graduated with first-class honors and a GPA of 4.91, also receiving the Prince Fahad Bin Sultan Scholarship for academic excellence.

Professional Experience

Dr. Karimeh Ata has a diverse range of professional experience in the fields of artificial intelligence and computer engineering. From December 2018 to January 2020, she served as an Artificial Intelligence Trainer at Hass Resources Corporation in Malaysia, where she supervised and trained teams on AI applications in education. In early 2019, she was a member of the Technical Committee for the Symposium on Control Systems and Signal Processing in Malaysia, bringing together experts to discuss advancements in AI, signal processing, and control systems. Dr. Ata has also contributed to academia as a Computer Engineering Lecturer at Universiti Putra Malaysia (UPM) from November 2022 to September 2023, where she designed and delivered courses on subjects such as Programming Fundamentals, Digital Logic Design, and Machine Learning, while also supervising laboratory sessions. Additionally, she worked as a Research Assistant at UPM from July 2021 to October 2022, where she ensured the quality, integrity, and security of research data and guided teams in preparing findings for top-tier journals and conferences. Dr. Ata’s professional experience highlights her leadership in project management, research ethics, and AI integration.

Research Interest

Dr. Karimeh Ata’s research interests focus on leveraging advanced technologies to address complex challenges in urban mobility, traffic flow optimization, and artificial intelligence. Her work primarily centers around deep learning and big data analytics, with a particular emphasis on traffic flow prediction and vehicle optimization. She has explored algorithms such as Dijkstra’s and Ant Colony Optimization to calculate the shortest paths and improve transportation efficiency in urban environments. Additionally, Dr. Ata is interested in applying AI-driven solutions to enhance brain stroke detection, lithium iron phosphate battery electrode performance, and spatial-temporal traffic flow prediction through multi-layer models. Her research aims to innovate in fields like smart transportation systems, deep learning, and AI for real-world problem-solving.

Research Skills

Dr. Karimeh Ata possesses extensive research skills in deep learning, big data analytics, and artificial intelligence, with a focus on solving complex problems in urban mobility and traffic flow optimization. She is proficient in designing and implementing deep learning models for traffic prediction and vehicle flow using large datasets to ensure accuracy. Dr. Ata has expertise in optimizing algorithms such as Dijkstra’s and Ant Colony Optimization to calculate efficient paths in transportation networks. Her research capabilities extend to developing innovative AI models for brain stroke detection and lithium battery performance evaluation, along with spatial-temporal data analysis using advanced machine learning techniques like CNN-GRU and dynamic KNN-Bi-LSTM. Dr. Ata’s skills reflect a deep understanding of integrating AI into real-world applications.

Award and Recognition

Dr. Karimeh Ata has been recognized for her academic excellence and contributions to research in the fields of computer engineering and artificial intelligence. She was awarded the prestigious Prince Fahad Bin Sultan Scholarship during her undergraduate studies for her outstanding academic performance, graduating with a first honor distinction. Additionally, her research work has been acknowledged through notable publications in top-tier journals, reflecting her deep expertise in areas such as traffic flow prediction and smart indoor parking systems. Dr. Ata’s achievements underscore her commitment to advancing the field of AI and computer engineering through innovative research and impactful projects.

Conclusion

Given Dr. Karimeh Ata’s strong academic background, innovative research contributions, and extensive skills in AI and big data, she is a suitable candidate for the Best Researcher Award. Her work not only demonstrates technical proficiency but also showcases her ability to solve complex, real-world problems, making a significant impact in the field of AI and computer engineering.

Publications Top Notes

  • Title: Smart Indoor Parking System Based on Dijkstra’s Algorithm
    Authors: K.M. Ata, A.C. Soh, A. Ishak, H. Jaafar, N. Khairuddin
    Cited By: 19
    Year: 2019
  • Title: Performance Evaluation of Two Mobile Ad-hoc Network Routing Protocols: Ad-hoc On-Demand Distance Vector Dynamic Source Routing
    Authors: J. Alamri, A.S. Al-Johani, K.I. Ata
    Cited By: 13
    Year: 2020
  • Title: Radio Frequency Identification (RFID) Indoor Parking Control System
    Authors: H.M.M. El-Hageen, K. Ibrahim, M. Ata, A. Chesoh, H. Jaafar
    Cited By: 3
    Year: 2017
  • Title: A Smart Guidance Indoor Parking System Based on Dijkstra’s Algorithm and Ant Colony Algorithm
    Authors: K.I. Ata, A.C. Soh, A.J. Ishak, H. Jaafar
    Cited By: 1
    Year: 2020
  • Title: Investigation of Loading Variation Effect on Lithium Iron Phosphate Battery Electrodes Using Long Short Term Memory
    Authors: K.A.A. Md Azizul Hoque, Mohd Khair Hassan, Muhesh Dhaarwind, Abdulrahman Hajjo
    Year: 2024
  • Title: Enhancing Brain Stroke Detection: A Novel Deep Neural Network with Weighted Binary Cross Entropy Training
    Authors: A.N. Qasim, S. Alani, S.N. Mahmood, S.S. Mohammed, D.A. Aziz, K.I.M. Ata
    Year: 2024
  • Title: Guidance System Based on Dijkstra-Ant Colony Algorithm with Binary Search Tree for Indoor Parking System
    Authors: H.J. K. Ibrahim Ata, A. Che Soh, A.J. Ishak
    Year: 2021

 

Saeed Mohsen Abosreea | Artificial Intelligence Engineering | Best Researcher Award

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Dr. Saeed Mohsen Abosreea | Artificial Intelligence Engineering | Best Researcher Award

Clinical Associate Professor at Department of Otorhinolaryngology-Head and Neck Surgery Yongin Severance Hospital, Yonsei University College of Medicine, South Korea

Dr. Saeed Mohsen Abosreea Hassan is an Assistant Professor of Electronics and Communications Engineering with a Ph.D. from Ain Shams University, Cairo. He has extensive academic experience, currently serving at King Salman International University. His research focuses on cutting-edge areas like deep learning, IoT, and wearable devices, with applications in healthcare and smart systems. Dr. Saeed has published 24 papers in reputable journals such as IEEE Access and Multimedia Tools and Applications, achieving an h-index of 10. His work spans various interdisciplinary fields, including Industry 4.0, human activity recognition, and energy harvesting systems. In addition to his research, he has supervised numerous student projects and contributed significantly to teaching advanced courses in electronics and AI. His contributions to both academia and industry make him a versatile researcher poised for continued impact in technological innovation and healthcare systems.

Profile

Education

Saeed Mohsen Abosreea Hassan holds a Ph.D. in Electronics and Communications Engineering from Ain Shams University, Cairo, Egypt, completed between 2017 and 2020. His doctoral research focused on the design and implementation of hybrid energy harvesting systems for medical wearable sensor nodes, demonstrating his expertise in cutting-edge healthcare technology. Prior to this, Saeed earned his Master’s degree in Electronics and Communications Engineering from the same institution, where he worked on the development of an electroencephalogram (EEG) system, further advancing his specialization in medical applications of electronics. He completed his undergraduate studies at Thebes Higher Institute of Engineering, Cairo, from 2008 to 2013, graduating with honors, earning an overall grade of “Excellent” and a GPA of 3.6/4.0. His strong educational background has provided him with a solid foundation in both theoretical and practical aspects of electronics, communications, and their applications in healthcare and industry.

Professional Experience

Saeed Mohsen Abosreea Hassan is an accomplished Assistant Professor in Electronics and Communications Engineering, currently serving at King Salman International University since September 2022. Prior to this role, he held a full-time Assistant Professor position at Al-Madinah Higher Institute for Engineering and Technology from April 2021 to August 2022. He also served as a part-time Assistant Professor at Ain Shams University from July 2021 to September 2021, where he contributed to cutting-edge research and advanced teaching methodologies. Before transitioning to academia, Saeed gained extensive experience as a Teaching Assistant at Thebes Academy from September 2013 to March 2021. Throughout his career, he has demonstrated expertise in various fields such as deep learning, IoT systems, and medical wearable sensor technologies. His diverse academic roles, combined with his active involvement in research, student supervision, and curriculum development, highlight his commitment to advancing education and innovation in engineering.

Research Interest

Saeed Mohsen Abosreea Hassan’s research interests focus on cutting-edge technologies in electronics, communications, and artificial intelligence. His work spans deep learning models, machine learning algorithms, and their applications in human activity recognition, smart healthcare systems, and Internet of Things (IoT) technologies. A significant portion of his research is dedicated to the development of energy harvesting systems for wearable medical sensor nodes, which has the potential to revolutionize real-time healthcare monitoring. He is also passionate about the use of neural networks and convolutional neural networks (CNNs) for the detection of brain tumors, Alzheimer’s disease, and other medical conditions through medical imaging techniques. His focus on Industry 4.0 and smart city networks highlights his commitment to advancing technologies that enhance both industrial automation and urban living. Saeed’s research integrates theoretical advancements with practical applications, particularly in healthcare and smart environments.

Research Skills

Saeed Mohsen Abosreea Hassan possesses a diverse and advanced set of research skills that span multiple fields of electronics, communications, and deep learning. He is proficient in AI tools such as TensorFlow, PyTorch, Keras, and Scikit-Learn, which he uses for developing machine learning and deep learning models. His expertise in embedded systems, IoT, and smart healthcare technologies is reflected in his research on wearable sensor nodes and energy harvesting systems. He is skilled in programming languages like Python, MATLAB, and Embedded C, essential for his work in developing algorithms and systems for medical and industrial applications. Additionally, Saeed is experienced in electronic circuit and layout design using tools like Proteus, LT-spice, and NI Multisim. His research extends into data acquisition systems, neural networks, and signal processing, particularly in healthcare applications such as brain tumor detection and human activity recognition, showcasing his multidisciplinary research proficiency.

Award and Recognition

Dr. Saeed Mohsen Abosreea Hassan, an accomplished Assistant Professor in Electronics and Communications Engineering, has made significant strides in the fields of deep learning, IoT, and wearable healthcare technologies. He holds a Ph.D. from Ain Shams University and has published 24 research papers, with an impressive h-index of 10 on Google Scholar. His work has been featured in prestigious journals, including IEEE Access, highlighting his contributions to Industry 4.0, smart healthcare systems, and energy harvesting technologies. Dr. Saeed’s research has been recognized for its practical applications in healthcare, with innovations like self-powered medical wearable sensors. His expertise has also earned him opportunities to present at international conferences and collaborate with top-tier researchers globally. As an emerging leader in his field, Dr. Saeed’s work continues to push the boundaries of technology and healthcare, positioning him as a distinguished researcher dedicated to advancing science and improving lives.

Conclusion

Saeed Mohsen Abosreea Hassan is a well-qualified candidate for the Best Researcher Award. His strong academic foundation, multidisciplinary research, and publication record make him a strong contender. By expanding his international collaborations, focusing on high-impact research, and pursuing more patents or grants, he could significantly increase his research impact and standing in the academic community. His work in healthcare and energy harvesting aligns with global trends, making his contributions both timely and impactful.

Publication Top Notes

  • Title: Human Activity Recognition Using K-Nearest Neighbor Machine Learning Algorithm
    • Authors: S Mohsen, A Elkaseer, SG Scholz
    • Year: 2021
    • Citations: 63
  • Title: Industry 4.0-Oriented Deep Learning Models for Human Activity Recognition
    • Authors: Saeed Mohsen, Ahmed Elkaseer, Steffen G. Scholz
    • Year: 2021
    • Citations: 46
  • Title: A Self-Powered Wearable Wireless Sensor System Powered by a Hybrid Energy Harvester for Healthcare Applications
    • Authors: S Mohsen, A Zekry, K Youssef, M Abouelatta
    • Year: 2021
    • Citations: 41
  • Title: Machine Learning and Deep Learning Techniques for Driver Fatigue and Drowsiness Detection: A Review
    • Authors: S Abd El-Nabi, W El-Shafai, ES M. El-Rabaie, K F. Ramadan, S Mohsen
    • Year: 2023
    • Citations: 25
  • Title: Brain Tumor Classification Using Hybrid Single Image Super-Resolution Technique with ResNext101_32x8d and VGG19 Pre-Trained Models
    • Authors: S Mohsen, AM Ali, ESM El-Rabaie, A Elkaseer, SG Scholz, AMA Hassan
    • Year: 2023
    • Citations: 22
  • Title: Recognition of Human Activity Using GRU Deep Learning Algorithm
    • Authors: S Mohsen
    • Year: 2023
    • Citations: 18
  • Title: An Autonomous Wearable Sensor Node for Long-Term Healthcare Monitoring Powered by a Photovoltaic Energy Harvesting System
    • Authors: S Mohsen, A Zekry, K Youssef, M Abouelatta
    • Year: 2020
    • Citations: 15
  • Title: On Architecture of Self-Sustainable Wearable Sensor Node for IoT Healthcare Applications
    • Authors: S Mohsen, A Zekry, K Youssef, M Abouelatta
    • Year: 2021
    • Citations: 13
  • Title: EEG-Based Human Emotion Prediction Using an LSTM Model
    • Authors: S Mohsen, AG Alharbi
    • Year: 2021
    • Citations: 12
  • Title: A Self-Powered Wearable Sensor Node for IoT Healthcare Applications
    • Authors: S Mohsen, A Zekry, M Abouelatta, K Youssef
    • Year: 2020
    • Citations: 12

 

Fahd Alharithi | Artificial Intelligence | Best Researcher Award

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Assoc Prof Dr. Fahd Alharithi | Artificial Intelligence | Best Researcher Award

Department chair at Taif University, Saudi Arabia

Dr. Fahd Saad Alharithi is an accomplished researcher and academic with a Ph.D. in Computer Science from Florida Institute of Technology and extensive experience in both teaching and research. Currently an Assistant Professor at Taif University, his research spans a wide array of topics, including medical data categorization, oil spill detection, COVID-19 diagnosis, and IoT security. Dr. Alharithi has published numerous papers in high-impact journals such as Sensors and Remote Sensing, showcasing his innovative approaches and significant contributions to his field. In addition to his research, he has a strong background in teaching, having served as a lecturer and teaching assistant at various institutions. His involvement in volunteer work and leadership roles further highlights his commitment to community service. While his diverse research and impactful publications are noteworthy, highlighting research grants and awards could strengthen his profile for recognition.

Profile

Education

Dr. Fahd Saad Alharithi completed his educational journey with a strong foundation in Computer Science. He earned his Ph.D. from the Florida Institute of Technology, USA, in 2019, where he focused on advanced topics in the field. Prior to that, he obtained his Master of Science degree in Computer Science from the University of New Haven, USA, in 2013. His academic journey began with a Bachelor of Science degree in Computer Science from Taif University, Saudi Arabia, in 2008. This comprehensive educational background, spanning both international and local institutions, has equipped Dr. Alharithi with a robust theoretical and practical understanding of Computer Science, paving the way for his subsequent research and teaching career. His diverse educational experiences contribute significantly to his expertise and innovative approaches in the field.

Professional Experience

Dr. Fahd Saad Alharithi has garnered extensive experience in academia and education, currently serving as an Assistant Professor in the Computer Science Department at Taif University since 2019. His career began with roles as a Lecturer and Teacher Assistant at Taif University and the University of New Haven, where he honed his teaching and research skills. Dr. Alharithi has also contributed as a Trainer at New Horizons Institute, showcasing his versatility in the field. His professional journey is marked by significant research achievements, including innovative publications in medical data categorization, AI-assisted algorithms, and IoT security. His role extends beyond teaching, encompassing volunteer work with the Hemaya Group and leadership positions like President of the Saudi Student Club. Dr. Alharithi’s career reflects a robust blend of research excellence, educational dedication, and active community involvement.

Research Interest

Dr. Fahd Saad Alharithi’s research interests primarily focus on advancing computational methods and applications across various domains. His work explores medical data categorization using flexible mixture models, oil spill detection through SAR image analysis, and the development of hybrid convolutional neural network models for diagnosing diseases from chest X-ray images. Dr. Alharithi is also deeply involved in enhancing IoT security with AI-assisted bio-inspired algorithms and addressing environmental challenges through intelligent garbage detection systems. His research extends to secure communication protocols and energy-efficient solutions for sensor networks, demonstrating a strong emphasis on both practical and theoretical advancements. By integrating innovative methodologies such as deep learning and AI, Dr. Alharithi aims to address complex problems in medical imaging, environmental monitoring, and network security, reflecting a broad and impactful approach to computational science.

Research Skills

Dr. Fahd Saad Alharithi exhibits a robust set of research skills, underscored by his extensive work in computer science and related fields. His proficiency in advanced methodologies, including deep learning, AI-assisted algorithms, and hybrid models, highlights his capacity for innovative problem-solving. Dr. Alharithi’s experience with diverse data types and applications, such as medical data categorization, oil spill detection, and IoT security, demonstrates his ability to tackle complex, interdisciplinary challenges. His strong analytical skills are evident from his impactful publications in high-impact journals like Sensors and Remote Sensing. Additionally, his adeptness in leveraging various computational techniques and his commitment to exploring novel solutions further underscore his research capabilities. Dr. Alharithi’s contributions reflect a deep understanding of both theoretical and practical aspects of his field, positioning him as a skilled researcher with a significant impact on advancing technology and knowledge.

Award and Recognition

Dr. Fahd Saad Alharithi’s research has garnered considerable recognition within the academic community. He has published extensively in high-impact journals, including Sensors, Remote Sensing, and Computers, Materials & Continua, showcasing his significant contributions to fields such as medical data categorization, oil spill detection, and AI-assisted algorithms. His innovative work, particularly in developing hybrid convolutional neural network models and intelligent systems for garbage detection, underscores his leadership in advancing technology. Although specific awards and formal recognitions are not detailed in his resume, Dr. Alharithi’s influential publications and his role in mentoring and educating future researchers highlight his exceptional impact in computer science. His involvement in volunteer activities and community service further demonstrates his commitment to fostering academic and professional excellence.

Conclusion

Dr. Taimoor Asim is a strong candidate for the Best Researcher Award due to his substantial contributions to Mechanical Engineering, particularly in fluid dynamics and renewable energy systems. His extensive research experience, leadership roles, and professional achievements make him a noteworthy contender. To strengthen his candidacy, he could focus on broadening his research impact, exploring diverse research areas, and enhancing community engagement related to his work. Overall, Dr. Asim’s profile reflects a high level of expertise and dedication, aligning well with the criteria for the Best Researcher Award.

Publications Top Notes

  1. Machine learning approaches for advanced detection of rare genetic disorders in whole-genome sequencing
    • Authors: Alzahrani, A.A., Alharithi, F.S.
    • Journal: Alexandria Engineering Journal
    • Year: 2024
    • Volume: 106, pp. 582–593
  2. IoT-enabled healthcare systems using blockchain-dependent adaptable services
    • Authors: Arul, R., Alroobaea, R., Tariq, U., Alharithi, F.S., Shoaib, U.
    • Journal: Personal and Ubiquitous Computing
    • Year: 2024
    • Volume: 28(1), pp. 43–57
    • Citations: 13
  3. A comprehensive cost performance analysis for a QoS-based scheme in network mobility (NEMO)
    • Authors: Hussein, L.F., Abass, I.A.M., Aissa, A.B., Alzahrani, A.A., Alharithi, F.S.
    • Journal: Alexandria Engineering Journal
    • Year: 2023
    • Volume: 76, pp. 349–360
    • Citations: 1
  4. Performance Analysis of Machine Learning Approaches in Automatic Classification of Arabic Language
    • Authors: Alharithi, F.S.
    • Journal: Information Sciences Letters
    • Year: 2023
    • Volume: 12(3), pp. 1563–1578
    • Citations: 1
  5. A blockchain-based hybrid platform for multimedia data processing in IoT-Healthcare
    • Authors: Taloba, A.I., Elhadad, A., Rayan, A., Alharithi, F.S., Park, C.
    • Journal: Alexandria Engineering Journal
    • Year: 2023
    • Volume: 65, pp. 263–274
    • Citations: 74
  6. Optimal Sizing and Deployment of Renewable Energy Generators in Practical Transmission Network Using Grid-Oriented Multiobjective Harmony Search Algorithm for Loss Reduction and Voltage Profile Improvements
    • Authors: Kumar, P., Swarnkar, N.K., Mahela, O.P., Mazon, J.L.V., Alharithi, F.S.
    • Journal: International Transactions on Electrical Energy Systems
    • Year: 2023
    • Volume: 2023, 6315918
    • Citations: 1
  7. Energy-Aware Live VM Migration Using Ballooning in Cloud Data Center
    • Authors: Gupta, N., Gupta, K., Qahtani, A.M., Singh, A., Goyal, N.
    • Journal: Electronics (Switzerland)
    • Year: 2022
    • Volume: 11(23), 3932
    • Citations: 4
  8. NLP-Based Application for Analyzing Private and Public Banks Stocks Reaction to News Events in the Indian Stock Exchange
    • Authors: Dogra, V., Alharithi, F.S., Álvarez, R.M., Singh, A., Qahtani, A.M.
    • Journal: Systems
    • Year: 2022
    • Volume: 10(6), 233
    • Citations: 7
  9. Deep learned BLSTM for online handwriting modeling simulating the Beta-Elliptic approach
    • Authors: Hamdi, Y., Boubaker, H., Rabhi, B., Dhahri, H., Alimi, A.M.
    • Journal: Engineering Science and Technology, an International Journal
    • Year: 2022
    • Volume: 35, 101215
    • Citations: 6
  10. A software for thorax images analysis based on deep learning
    • Authors: Almulihi, A.H., Alharithi, F.S., Mechti, S., Alroobaea, R., Rubaiee, S.
    • Book Chapter: Research Anthology on Improving Medical Imaging Techniques for Analysis and Intervention
    • Year: 2022
    • Pages: 1166–1178

 

Mijanur Rahaman | Machin Learning | Excellence in Research

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Mr. Mijanur Rahaman | Machin Learning | Excellence in Research

Assistant Professor at Bangladesh University of Business and Technology (BUBT), Bangladesh.

Mr. Mijanur Rahaman is an accomplished professional with a rich background in academia, research, and teaching. With extensive experience in computer science and engineering, he has excelled in various roles, including Assistant Professor and Lecturer at Bangladesh University of Business & Technology (BUBT). His research interests span diverse areas such as peer-to-peer payment systems, job skill requirements during the COVID-19 pandemic, and web programming curriculum reform. Mr. Rahaman’s contributions to scholarly literature include several published articles in prestigious journals and conferences. Beyond academia, he has actively engaged in software development, database administration, and website design, demonstrating his versatility and technical prowess. His commitment to education, coupled with strong research and technical skills, positions him as a valuable asset in the field of computer science.

Professional Profiles:

Education

Mr. Mijanur Rahaman holds a Bachelor of Science in Engineering (B.Sc. Engg.) in Computer Science & Engineering (CSE) from Bangladesh University of Business & Technology (BUBT). He completed his higher secondary education (Higher Secondary School Certificate – HSC) in Science from Lakshmipur Govt. College and his secondary education (Secondary School Certificate – SSC) in Science from Rakhalia High School, both in Bangladesh. Additionally, he is currently pursuing a Master of Science (MSc) in Computer Science (MScCS) from the American International University-Bangladesh (AIUB).

Professional Experience

Mr. Mijanur Rahaman has extensive professional experience in the field of education and technology. He has served as an Assistant Professor of Computer Science and Engineering (CSE) at Bangladesh University of Business & Technology (BUBT) since February 2016. Prior to this, he worked as a Lecturer in CSE at BUBT from October 2011 to January 2015, and as a Teaching Assistant (TA) in CSE from March 2011 to September 2011. Additionally, he worked as a Part-time Lecturer at Dhaka Edinburgh International College from August 2010 to October 2010. In these roles, he has been actively involved in teaching, curriculum development, and student mentorship, demonstrating his commitment to academic excellence and professional development.

Research Interest

Mr. Mijanur Rahaman’s research interests primarily revolve around areas such as applied research methodology, digitalization, software development, job skill analysis, quantum computing, cloud computing, and web programming. He has contributed to various publications covering topics such as near field peer-to-peer payment systems, IT-software job skill requirements during the COVID-19 pandemic, optimal job allocation algorithms, state-of-the-art reformation of web programming course curriculums, and quantum computing as a service (Qcaas). His diverse research interests reflect his dedication to exploring cutting-edge technologies and their applications in solving real-world problems.

Teaching Experience

Mr. Mijanur Rahaman has extensive teaching experience spanning over a decade. His teaching journey started as a Teaching Assistant (TA) in the Department of Computer Science and Engineering (CSE) at Bangladesh University of Business & Technology (BUBT) in March 2011. From there, he progressed to the role of Lecturer in CSE, where he continued to impart knowledge and motivate students until January 2015. Subsequently, he transitioned to the position of Assistant Professor of CSE at BUBT, where he has been serving since February 2016. Throughout his tenure, Mr. Rahaman has demonstrated a commitment to challenging and inspiring students through in-depth lectures, discussions, and hands-on learning experiences. Additionally, he has actively contributed to curriculum development and research activities, enriching the academic environment of his institution.

Award and Honors

Mr. Mijanur Rahaman has received numerous awards and honors throughout his career, recognizing his significant contributions to academia and programming contests. Notably, he excelled as a contestant in prestigious competitions like the ACM-ICPC Asia Regional Dhaka Site in 2010, where his team achieved the 13th place, and the 2008 Asia Dhaka Contest. His achievements also include receiving an Honorable Mention at the AB Bank-IUT 2nd National ICT Fest 2009. Beyond his individual accomplishments, Mr. Rahaman has demonstrated leadership as a coach for programming contest teams and as Chief Judge at the BUBT Intra-university Programming Contest 2015. Moreover, he has been invited to high-profile events such as the ICPC World Final 2021 and the ACM-ICPC Asia Dhaka Regional Contest, where he served in key roles. His active involvement in academic and extracurricular activities further underscores his commitment to fostering learning and innovation within the community.

Research Skills

Mr. Mijanur Rahaman possesses advanced research skills honed through years of academic and professional experience. His expertise encompasses various methodologies, including quantitative and qualitative research methods, literature reviews, experimental design, and data analysis techniques. He is proficient in utilizing statistical software such as SPSS, R, and Minitab for data analysis and interpretation. Moreover, Mr. Rahaman demonstrates strong critical thinking abilities, enabling him to formulate research questions, develop hypotheses, and design rigorous research studies. His keen attention to detail ensures the accuracy and reliability of research findings, while his effective communication skills enable him to disseminate research results through scholarly publications and presentations. Additionally, he stays abreast of emerging trends and best practices in his field, continually refining his research skills to contribute meaningfully to the advancement of knowledge in computer science and related disciplines.

Publications

  1. Utilizing EfficientNet for sheep breed identification in low-resolution images
    • Authors: Himel, G.M.S.; Islam, M.M.; Rahaman, M.
    • Year: 2024
    • Citations: 0
    • Type: Article
  2. Vision Intelligence for Smart Sheep Farming: Applying Ensemble Learning to Detect Sheep Breeds
    • Authors: Himel, G.M.S.; Islam, M.M.; Rahaman, M.
    • Year: 2024
    • Citations: 1
    • Type: Article
  3. An Empirical Analysis of IT-Software Job Skill Requirements During COVID-19 Pandemic Period in Bangladesh
    • Authors: Rahaman, M.; Islam, M.M.; Rahman, M.S.
    • Year: 2023
    • Citations: 0
    • Type: Conference Paper
  4. Knowledge, attitude, and practice of a local community towards the prevention and control of rabies in Gaibandha, Bangladesh
    • Authors: Rahaman, M.M.; Siddiqi, U.R.; Sabuj, A.A.M.; Ghosh, S.; Uddin, N.
    • Year: 2020
    • Citations: 7
    • Type: Article
  5. State-of-the-art reformation of web programming course curriculum in digital Bangladesh
    • Authors: Kar, S.; Islam, M.M.; Rahaman, M.
    • Year: 2020
    • Citations: 3
    • Type: Article