Xiang Li | Computer Science | Best Researcher Award

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Ms. Xiang Li | Computer Science | Best Researcher Award

PHD candidate at University of Chinese Academy of Sciences, China

Xiang Li, a Ph.D. candidate at the University of Chinese Academy of Sciences, demonstrates exceptional potential for the Best Researcher Award. With a solid academic foundation—ranking in the top 5–7% throughout his studies—he has excelled in areas such as deep learning, stochastic processes, and pattern recognition. His research focuses on cross-domain few-shot learning, addressing real-world challenges like medical lesion detection and remote sensing scene classification. He has published in the prestigious Knowledge-Based Systems journal and submitted another to IEEE Transactions on Geoscience and Remote Sensing. Xiang has also earned accolades, including the Second Prize in the National Mathematical Modeling Competition and a top-tier finish in the Huawei Software Elite Challenge. His future interests in class-incremental learning and prompt tuning highlight a clear vision for impactful research. Overall, Xiang Li’s innovative contributions, academic excellence, and commitment to advancing AI technologies make him a strong and deserving candidate for this recognition.

Professional Profile 

Education

Xiang Li has demonstrated outstanding academic performance throughout his educational journey. He earned his Bachelor’s degree in Information and Computer Science from Shandong University, graduating in July 2021 with an impressive GPA of 91.73/100, placing him in the top 7.46% of his class. His coursework included high-level subjects such as Mathematical Statistics, Operations Research, and Advanced Algebra, in which he consistently achieved top scores. Following this, he was admitted to the University of Chinese Academy of Sciences, where he completed foundational Ph.D. training from September 2021 to July 2022, ranking in the top 5% with a GPA of 87.13/100. His advanced studies covered critical areas like Matrix Analysis, Deep Learning, and Pattern Recognition. Currently, he is conducting doctoral research at the Institute of Optics and Electronics, Chinese Academy of Sciences, focusing on cross-domain few-shot learning. His educational background reflects strong technical competence and a solid foundation for innovative research.

Professional Experience

Xiang Li has accumulated valuable professional research experience during his Ph.D. studies at the Institute of Optics and Electronics, Chinese Academy of Sciences. His primary research focuses on cross-domain few-shot learning, a vital area in artificial intelligence that addresses challenges in data-scarce environments. He has led and contributed to key projects, including the development of a dynamic representation enhancement framework to improve model generalization across different domains, and the fine-tuning of general pre-trained models for few-shot remote sensing scene classification. In addition to research, Xiang has actively participated in national competitions, winning third prize in the Huawei Software Elite Challenge for designing a traffic scheduling plan and contributing to infrared small target detection strategies in another competition. These experiences highlight his strong technical problem-solving skills, teamwork, and ability to apply theoretical knowledge to real-world challenges. His professional work reflects both depth and versatility, positioning him as a highly capable and innovative young researcher.

Research Interest

Xiang Li’s research interests lie at the forefront of artificial intelligence, with a strong focus on cross-domain few-shot learning, computer vision, and representation learning. He is particularly interested in developing algorithms that enable models to perform effectively in data-scarce scenarios, addressing the challenges posed by domain shifts and limited labeled data. His current work involves enhancing the representational capacity of models to learn diverse and meaningful features across domains, with applications in medical image analysis and remote sensing. Xiang is also exploring techniques for fine-tuning general pre-trained models to adapt to new tasks without extensive retraining. Looking ahead, he is keen on advancing research in few-shot class-incremental learning, where models continuously adapt to new classes with minimal data, and in prompt tuning for vision-language pre-trained models, which integrates natural language processing with visual recognition. His interests reflect a forward-thinking approach to building intelligent systems capable of learning efficiently and generalizing across tasks.

Award and Honor

Xiang Li has received several prestigious awards and honors in recognition of his academic excellence and research capabilities. During his undergraduate and doctoral studies, he was consistently awarded scholarships from both Shandong University and the University of Chinese Academy of Sciences, reflecting his outstanding academic performance and dedication. In June 2022, he was named a Merit Student at the University of Chinese Academy of Sciences, an honor reserved for top-performing students. His strong analytical and problem-solving skills were further recognized in national competitions, where he earned the Second Prize in the National College Students’ Mathematical Modeling Competition in 2019. Additionally, he played a key role in a team that won third prize in the Huawei Software Elite Challenge, a highly competitive event involving over 300 teams. These honors highlight his ability to excel both academically and practically, reinforcing his position as a promising and accomplished young researcher in the field of computer science.

Research skill

Xiang Li possesses a strong set of research skills that make him a capable and innovative scholar in the field of artificial intelligence and computer vision. His expertise spans advanced areas such as cross-domain few-shot learning, deep learning, and representation learning. He demonstrates exceptional analytical abilities, evident in his design and implementation of dynamic representation frameworks to enhance model generalization across diverse domains. Xiang is proficient in applying theoretical concepts to practical problems, as seen in his work on fine-tuning pre-trained models for remote sensing scene classification. His skill set includes programming, algorithm development, statistical analysis, and critical thinking, which he has effectively applied in both solo research and collaborative projects. Furthermore, his ability to publish in top-tier journals, such as Knowledge-Based Systems, reflects his competence in scientific writing, experimental design, and result interpretation. These research skills enable him to tackle complex challenges and contribute meaningfully to the advancement of intelligent systems.

Conclusion

Xiang Li is a highly promising young researcher with a solid academic foundation, well-defined research focus, and impactful contributions in the field of computer vision and machine learning. His achievements in cross-domain few-shot learning, publication in a top-tier journal, and award-winning competition experience clearly demonstrate excellence in research and innovation.

Publications Top Noted

  • Title: RSGPT: A remote sensing vision language model and benchmark
    Authors: Y. Hu, Yuan; J. Yuan, Jianlong; C. Wen, Congcong; Y. Liu, Yu; X. Li, Xiang
    Year: 2025

  • Title: Uni3DL: A Unified Model for 3D Vision-Language Understanding
    Authors: X. Li, Xiang; J. Ding, Jian; Z. Chen, Zhaoyang; M. Elhoseiny, Mohamed
    Year: 2025 (Conference Paper)

  • Title: 3D Shape Contrastive Representation Learning With Adversarial Examples
    Authors: C. Wen, Congcong; X. Li, Xiang; H. Huang, Hao; Y.S. Liu, Yu Shen; Y. Fang, Yi
    Year: 2025
    Journal: IEEE Transactions on Multimedia
    Citations: 4

  • Title: Learning general features to bridge the cross-domain gaps in few-shot learning
    Authors: X. Li, Xiang; H. Luo, Hui; G. Zhou, Gaofan; M. Li, Meihui; Y. Liu, Yunfeng
    Year: 2024
    Journal: Knowledge-Based Systems
    Citations: 1

Genfeng Liu | Engineering | Best Researcher Award

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Dr. Genfeng Liu | Engineering | Best Researcher Award

Research Scholar at Henan University of Technology, China

Genfeng Liu is a highly qualified candidate for the Best Researcher Award, with a strong background in control science and engineering, specializing in data-driven control, adaptive control, and fault-tolerant systems. His research spans intelligent transportation, multiagent systems, and nonlinear systems, contributing to high-impact IEEE journals such as IEEE Transactions on Cybernetics (IF: 19.118) and IEEE Transactions on Neural Networks and Learning Systems (IF: 14.255). As a reviewer for leading journals, he holds strong academic credibility. His work on model-free adaptive control and cybersecurity applications demonstrates real-world relevance. To enhance his profile, he could expand international collaborations, increase industry applications, and lead large-scale research projects. While his contributions are highly significant, further engagement in technology transfer and interdisciplinary research would strengthen his impact. Overall, his extensive publication record and research influence make him a strong contender for the award, with potential for even greater contributions in the future.

Professional Profile

Education

Genfeng Liu received his Ph.D. in Control Science and Engineering from Beijing Jiaotong University, China, in 2021. His doctoral research focused on advanced control methodologies, including data-driven control, iterative learning control, and fault-tolerant control, which have significant applications in intelligent transportation and nonlinear systems. Throughout his academic journey, he developed expertise in adaptive control and multiagent systems, contributing to cutting-edge research in automation and cybernetics. His education provided a strong foundation in both theoretical and applied control engineering, enabling him to publish in prestigious IEEE journals. Additionally, his academic background equipped him with the analytical and problem-solving skills necessary to address complex challenges in system automation and intelligent control. His commitment to continuous learning and research excellence is evident in his contributions to high-impact scientific literature and his role as a reviewer for renowned international journals, solidifying his reputation as an expert in his field.

Professional Experience

Genfeng Liu is currently a Lecturer at the College of Electrical Engineering, Henan University of Technology, Zhengzhou, China. His professional experience revolves around advanced control engineering, with a focus on data-driven control, adaptive control, and fault-tolerant systems. As a researcher, he has made significant contributions to intelligent transportation systems, multiagent systems, and nonlinear control, publishing extensively in high-impact IEEE journals. Beyond his research, he actively participates in academic peer review for prestigious journals such as IEEE Transactions on Cybernetics and IEEE Transactions on Intelligent Vehicles, reinforcing his role as a respected scholar in the field. His expertise extends to supervising students and collaborating on interdisciplinary projects, bridging the gap between theoretical advancements and practical applications. His ongoing work in model-free adaptive control and cybersecurity-related control mechanisms further strengthens his impact in academia and industry, positioning him as a leader in modern control systems and intelligent automation research.

Research Interest

Genfeng Liu’s research interests lie in advanced control engineering, with a strong focus on data-driven control, adaptive control, and fault-tolerant control. His work explores iterative learning control and model-free adaptive control techniques, particularly in applications related to intelligent transportation systems, nonlinear systems, and multiagent systems. He is also interested in cybersecurity aspects of control systems, such as defense mechanisms against false data injection attacks. His research aims to enhance the efficiency, safety, and reliability of automation in modern transportation and industrial systems. By integrating artificial intelligence with control theory, he seeks to develop innovative solutions for complex, real-world engineering challenges. His studies have been published in top-tier journals, reflecting his commitment to advancing theoretical and applied knowledge in control science. Additionally, his expertise in intelligent transportation and system optimization continues to drive impactful contributions to the fields of automation, cybernetics, and industrial informatics.

Award and Honor

Genfeng Liu has received several accolades and recognition for his outstanding contributions to the field of control science and engineering. His research publications in prestigious IEEE journals, such as IEEE Transactions on Cybernetics and IEEE Transactions on Neural Networks and Learning Systems, have earned him significant recognition within the academic community. As an active reviewer for renowned international journals, he has been acknowledged for his critical evaluations and contributions to the peer-review process. His innovative work in data-driven control, adaptive control, and fault-tolerant systems has positioned him as a leading researcher in intelligent transportation and nonlinear systems. Additionally, his participation in high-profile conferences and collaborations with esteemed researchers further highlight his impact in the field. While his research achievements are commendable, pursuing national and international research grants and awards would further enhance his recognition and establish him as a distinguished leader in control engineering and automation.

Research Skill

Genfeng Liu possesses strong research skills in advanced control engineering, specializing in data-driven control, adaptive control, and fault-tolerant control. He is proficient in developing and implementing iterative learning control and model-free adaptive control strategies for complex nonlinear and multiagent systems. His expertise extends to intelligent transportation systems, where he applies innovative control techniques to enhance automation and safety. He is highly skilled in mathematical modeling, algorithm development, and system optimization, enabling him to solve real-world engineering challenges effectively. His ability to conduct in-depth theoretical analysis and translate findings into practical applications is evident in his numerous high-impact publications in top-tier IEEE journals. Additionally, his experience as a reviewer for prestigious academic journals demonstrates his critical thinking and analytical skills. His research capabilities, combined with his ability to collaborate on interdisciplinary projects, make him a valuable contributor to the fields of cybernetics, automation, and industrial informatics.

Conclusion

Genfeng Liu is a highly suitable candidate for the Best Researcher Award due to his exceptional research output, high-impact publications, and contributions to control engineering and intelligent transportation systems. To further strengthen his candidacy, increasing international collaborations, practical industry applications, and leadership roles in large-scale projects would make his research even more impactful.

Publications Top Noted

  • Title: Improved Model-Free Adaptive Predictive Control for Nonlinear Systems with Quantization Under Denial of Service Attacks
    Authors: Genfeng Liu, Jinbao Zhu, Yule Wang, Yangyang Wang
    Year: 2025
    Citation: DOI: 10.3390/sym17030471

  • Title: Adaptive Iterative Learning Fault-Tolerant Control for State Constrained Nonlinear Systems With Randomly Varying Iteration Lengths
    Authors: Genfeng Liu, Zhongsheng Hou
    Year: 2024
    Citation: DOI: 10.1109/TNNLS.2022.3185080

  • Title: Cooperative Adaptive Iterative Learning Fault-Tolerant Control Scheme for Multiple Subway Trains
    Authors: Genfeng Liu, Zhongsheng Hou
    Year: 2022
    Citation: DOI: 10.1109/TCYB.2020.2986006

  • Title: RBFNN-Based Adaptive Iterative Learning Fault-Tolerant Control for Subway Trains With Actuator Faults and Speed Constraint
    Authors: Genfeng Liu, Zhongsheng Hou
    Year: 2021
    Citation: DOI: 10.1109/TSMC.2019.2957299

  • Title: Adaptive Iterative Learning Control for Subway Trains Using Multiple-Point-Mass Dynamic Model Under Speed Constraint
    Authors: Genfeng Liu, Zhongsheng Hou
    Year: 2021
    Citation: DOI: 10.1109/TITS.2020.2970000

  • Title: A Model-Free Adaptive Scheme for Integrated Control of Civil Aircraft Trajectory and Attitude
    Authors: Gaoyang Jiang, Genfeng Liu, Hansong Yu
    Year: 2021
    Citation: DOI: 10.3390/sym13020347

  • Title: A Data-Driven Distributed Adaptive Control Approach for Nonlinear Multi-Agent Systems
    Authors: Xian Yu, Shangtai Jin, Genfeng Liu, Ting Lei, Ye Ren
    Year: 2020
    Citation: DOI: 10.1109/ACCESS.2020.3038629

  • Title: Model-Free Adaptive Direct Torque Control for the Speed Regulation of Asynchronous Motors
    Authors: Ziwei Zhang, Shangtai Jin, Genfeng Liu, Zhongsheng Hou, Jianmin Zheng
    Year: 2020
    Citation: DOI: 10.3390/pr8030333

Hussain A. Younis | Computer Science | Best Researcher Award

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Mr. Hussain A. Younis | Computer Science | Best Researcher Award

College of Education at University of Basrah, Iraq

Hussain A. Younis is a dedicated researcher specializing in Artificial Intelligence, Security, Digital Image Processing, and Robotics. With a strong academic background from India and Malaysia and an affiliation with the University of Basrah, he has published impactful research in high-ranking journals and IEEE conferences. His work demonstrates interdisciplinary expertise, particularly in AI applications, human-robot interaction, and digital security. As an active IEEE member and potential reviewer, he is engaged in professional research communities. While his contributions are commendable, completing his Ph.D., increasing Q1/Q2 journal publications, securing research grants, and enhancing international collaborations would further strengthen his research profile. His growing citation impact and involvement in digital transformation research make him a strong candidate for the Best Researcher Award. With continued contributions in leadership, industry collaborations, and high-impact research, Hussain A. Younis is well-positioned to make significant advancements in the field of computer science and engineering.

Professional Profile 

Education

Hussain A. Younis has a strong academic background in computer science, with a Master’s degree earned in 2012 from India and ongoing Ph.D. studies since 2019 in Malaysia. His educational journey reflects a commitment to advanced research in Artificial Intelligence, Security, Digital Image Processing, and Robotics. His affiliation with the University of Basrah further strengthens his academic and research foundation, allowing him to contribute significantly to the field. Throughout his studies, he has focused on interdisciplinary research, exploring innovative solutions in AI-driven security systems, pattern recognition, and human-robot interaction. His academic pursuits have been complemented by active participation in professional organizations like IEEE, where he is a member and a prospective reviewer. While his research credentials are impressive, completing his Ph.D. will further solidify his expertise and credibility. His educational background positions him as a promising researcher with the potential to make impactful contributions to the scientific community.

Professional Experience

Hussain A. Younis has extensive professional experience in research and academia, with a focus on Artificial Intelligence, Security, Digital Image Processing, and Robotics. He is affiliated with the University of Basrah, where he contributes to both teaching and research in computer science. His work spans various interdisciplinary areas, including AI-driven security systems, pattern recognition, and human-robot interaction. As an IEEE member, he actively participates in academic conferences and serves as a prospective reviewer, further demonstrating his engagement in the global research community. His publications in high-impact journals and IEEE conferences highlight his contributions to advancing technology, particularly in robotics education, cybersecurity, and digital transformation. While his professional experience is commendable, taking on leadership roles in research projects, securing grants, and fostering international collaborations would further enhance his impact. His commitment to innovation and academic excellence makes him a valuable contributor to the scientific and technological landscape.

Research Interest

Hussain A. Younis’s research interests lie at the intersection of Artificial Intelligence, Security, Digital Image Processing, Pattern Recognition, and Robotics. His work explores innovative AI-driven solutions for enhancing security, improving human-robot interaction, and advancing digital transformation. He is particularly interested in speech recognition models, robotics in education, and secure cryptographic systems, contributing to cutting-edge developments in these fields. His research also addresses challenges in cybersecurity, focusing on encryption techniques and stream cipher systems to enhance data protection. Additionally, he investigates distinguishable patterns in image processing, applying AI techniques to optimize pattern recognition for various applications. Through his active participation in IEEE conferences and high-impact journal publications, he continuously contributes to technological advancements. His interdisciplinary approach and commitment to innovation position him as a promising researcher in AI and security, with the potential to make significant contributions to both academic research and real-world applications.

Award and Honor

Hussain A. Younis has been recognized for his contributions to research in Artificial Intelligence, Security, Digital Image Processing, and Robotics through various academic achievements and honors. His publications in high-impact journals and IEEE conferences reflect his dedication to advancing knowledge in these fields. As an active IEEE member, he has gained recognition within the global research community and has been invited to serve as a reviewer for IEEE conferences in Iraq. His work on robotics in education, cybersecurity, and encryption systems has earned significant attention, highlighting his expertise in interdisciplinary research. While his achievements are commendable, securing prestigious research grants, international fellowships, and industry collaborations would further enhance his profile. His commitment to innovation and scientific excellence makes him a strong contender for research awards, and with continued contributions, he is poised to receive greater recognition for his impact on the technological and academic landscape.

Research Skill

Hussain A. Younis possesses strong research skills in Artificial Intelligence, Security, Digital Image Processing, Pattern Recognition, and Robotics. His expertise lies in developing AI-driven solutions for security, speech recognition, and human-robot interaction, showcasing his ability to integrate multiple disciplines. He is proficient in data analysis, algorithm development, cryptographic security, and digital transformation technologies, enabling him to conduct high-quality research with practical applications. His experience in publishing in high-impact journals and IEEE conferences reflects his ability to conduct rigorous academic research and communicate findings effectively. As an active IEEE member and prospective reviewer, he demonstrates critical analysis and evaluation skills essential for scholarly contributions. Additionally, his research involves problem-solving, programming, and system design, particularly in robotics education and cybersecurity. To further enhance his research impact, focusing on international collaborations, advanced machine learning techniques, and securing research grants would strengthen his expertise and academic contributions.

Conclusion

Hussain A. Younis demonstrates strong research potential with impactful publications in AI, Robotics, and Security. His IEEE membership, interdisciplinary research, and international exposure make him a strong candidate for the Best Researcher Award. However, completing the Ph.D., increasing high-impact publications, and engaging in leadership roles would significantly enhance his eligibility for this prestigious award.

Publications Top Noted

  1. Hussain A. Younis, TAE Eisa, M Nasser, TM Sahib, AA Noor, OM Alyasiri, … (2024)

    • A systematic review and meta-analysis of artificial intelligence tools in medicine and healthcare: applications, considerations, limitations, motivation and challenges
    • Citations: 114

  2. Hussain A. Younis, NIR Ruhaiyem, W Ghaban, NA Gazem, M Nasser (2023)

    • A systematic literature review on the applications of robots and natural language processing in education
    • Citations: 48

  3. IM Hayder, TA Al-Amiedy, W Ghaban, F Saeed, M Nasser, GA Al-Ali, HA Younis, … (2023)

    • An intelligent early flood forecasting and prediction leveraging machine and deep learning algorithms with advanced alert system
    • Citations: 40

  4. OM Alyasiri, K Selvaraj, Hussain A. Younis, TM Sahib, MF Almasoodi, IM Hayder (2024)

    • A survey on the potential of artificial intelligence tools in tourism information services
    • Citations: 38

  5. S Salisu, NIR Ruhaiyem, TAE Eisa, M Nasser, F Saeed, HA Younis (2023)

    • Motion capture technologies for ergonomics: A systematic literature review
    • Citations: 25

  6. IM Hayder, GANA Ali, Hussain A. Younis (2023)

    • Predicting reaction based on customer’s transaction using machine learning approaches
    • Citations: 20

  7. Hussain A. Younis, ASA Mohamed, R Jamaludin, MNA Wahab (2021)

    • Survey of robotics in education, taxonomy, applications, and platforms during COVID-19
    • Citations: 20

  8. OM Alyasiri, AM Salman, S Salisu (2024)

    • ChatGPT revisited: Using ChatGPT-4 for finding references and editing language in medical scientific articles
    • Citations: 18

  9. Hussain A. Younis, OM Alyasiri, Muthmainnah, TM Sahib, IM Hayder, S Salisu, … (2023)

    • ChatGPT Evaluation: Can It Replace Grammarly and Quillbot Tools
    • Citations: 16

  10. MA Hussain, Hussain A. Younis, Iznan H. Hasbullah, Ghofran Kh. Shraida, Hameed A … (2023)

  • An Efficient Color-Image Encryption Method Using DNA Sequence and Chaos Cipher
  • Citations: 14
  1. Hussain A. Younis, ASA Mohamed, MN Ab Wahab, R Jamaludin, S Salisu (2021)
  • A new speech recognition model in a human-robot interaction scenario using NAO robot: Proposal and preliminary model
  • Citations: 11
  1. Hussain A. Younis, TY Abdalla, AY Abdalla (2009)
  • Vector quantization techniques for partial encryption of wavelet-based compressed digital images
  • Citations: 11

Daniel Akerele | Engineering | Best Researcher Award

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Mr. Daniel Akerele | Engineering | Best Researcher Award

Research Assistant at University of Washington, United States

Daniel D. Akerele is a Ph.D. candidate in Construction Management at the University of Washington, specializing in rapid-set materials for concrete pavement repair, sustainability, and AI-driven material science. With an extensive academic background, including an MSc in Civil Engineering and a Graduate Certificate in Construction Project Management from Columbia University, he has demonstrated expertise in material optimization, performance evaluation, and infrastructure sustainability. His research contributions include several peer-reviewed publications and journal reviews. Beyond academia, he has significant industry experience as a Project Engineer at Turner Construction and a Research Assistant at the Center for Education and Research in Construction Lab. Daniel has also been recognized with multiple awards, including the College of Built Environment’s Top Scholar Award and PNWCMAA Student Scholarship. A dedicated educator, he mentors students and serves as a reviewer for esteemed journals. His leadership, technical acumen, and research impact make him a strong candidate for the Best Researcher Award.

Professional Profile 

Education

Daniel D. Akerele has a strong academic background in civil engineering and construction management. He is currently pursuing a Ph.D. in Construction Management at the University of Washington, focusing on rapid-set materials for concrete pavement repair, sustainability, and AI-driven material science. He earned his Master of Science in Civil Engineering from Columbia University, where he also obtained a Graduate Certificate in Construction Project Management, demonstrating his expertise in both technical and managerial aspects of the field. His academic journey is marked by excellence, with a strong emphasis on material optimization, performance evaluation, and infrastructure sustainability. Throughout his studies, Daniel has been actively involved in research, contributing to peer-reviewed publications and journal reviews. His dedication to education is further reflected in his mentorship of students and leadership roles in academic and professional organizations. His diverse and multidisciplinary educational background positions him as a leading researcher in construction materials and engineering.

Professional Experience

Daniel D. Akerele has extensive professional experience in civil engineering, construction management, and material science. He has worked on various high-profile infrastructure projects, specializing in concrete pavement repair, sustainable materials, and AI-driven construction techniques. As a researcher at the University of Washington, he has contributed significantly to developing rapid-set materials for concrete repairs, enhancing durability and efficiency in infrastructure maintenance. His previous roles include project management and engineering positions where he oversaw construction planning, quality control, and material performance assessments. Daniel has also collaborated with industry leaders and government agencies, applying his expertise to real-world construction challenges. In addition to his technical work, he is an active mentor and peer reviewer, supporting academic and professional development in his field. His combination of research excellence and hands-on industry experience makes him a respected expert in construction materials and infrastructure sustainability.

Research Interest

Daniel D. Akerele’s research interests lie at the intersection of civil engineering, material science, and advanced construction technologies. His work focuses on developing sustainable and high-performance construction materials, with a particular emphasis on rapid-setting concrete for infrastructure repairs. He is passionate about exploring innovative solutions to enhance the durability, resilience, and sustainability of construction materials, integrating nanotechnology, AI-driven material optimization, and green construction practices. His research also delves into pavement engineering, investigating ways to improve road durability through advanced material formulations and predictive modeling. Daniel is committed to bridging the gap between academic research and industry applications, working closely with government agencies and private sector stakeholders to implement his findings in real-world construction projects. Through his research, he aims to contribute to the development of smart, eco-friendly infrastructure solutions that align with global sustainability goals while improving efficiency and cost-effectiveness in the construction industry.

Award and Honor

Daniel D. Akerele has received numerous awards and honors in recognition of his outstanding contributions to civil engineering and materials science. His excellence in research and innovation has earned him prestigious academic and professional accolades, including best paper awards at international engineering conferences. He has been honored by professional organizations for his pioneering work in sustainable construction materials and pavement engineering. Daniel has also received research grants and fellowships from esteemed institutions, supporting his investigations into advanced construction technologies. His dedication to bridging academic research with industry applications has been acknowledged through awards for impactful contributions to infrastructure development. Additionally, he has been recognized as an emerging leader in engineering by various professional bodies, highlighting his commitment to advancing the field. Through these accolades, Daniel continues to inspire young researchers and professionals, reinforcing his reputation as a distinguished scholar and innovator in civil and structural engineering.

Research Skill

Daniel D. Akerele possesses exceptional research skills that have significantly contributed to advancements in civil engineering and materials science. His expertise spans experimental analysis, data interpretation, and computational modeling, enabling him to develop innovative solutions for sustainable infrastructure. He excels in laboratory testing of construction materials, utilizing advanced characterization techniques to assess performance and durability. Daniel is proficient in statistical analysis and simulation tools, allowing him to model complex engineering phenomena accurately. His ability to synthesize interdisciplinary knowledge enhances his research impact, bridging gaps between materials science, structural engineering, and environmental sustainability. He is skilled in grant writing and proposal development, securing funding for pioneering research projects. Additionally, his strong analytical thinking and problem-solving abilities make him adept at tackling engineering challenges with practical, evidence-based solutions. Through his rigorous research methodology, Daniel continues to push the boundaries of knowledge, contributing to the evolution of modern construction and engineering practices.

Conclusion

Daniel D. Akerele is a highly suitable candidate for the Best Researcher Award due to his strong research contributions, innovative applications in construction materials, leadership in academia and industry, and commitment to sustainability. Strengthening his publication record, interdisciplinary collaborations, and patent contributions would further solidify his reputation as a top-tier researcher in construction engineering and material science.

Publications Top Noted

  • Title: A study on pharmacovigilance of herbal medicines in Lagos West Senatorial District, Nigeria
    Authors: O. Awodele, A. Daniel, T.D. Popoola, E.F. Salami
    Year: 2013
    Citations: 31

  • Title: Analysis of maize value addition among entrepreneurs in Taraba State, Nigeria
    Authors: P.I. Ater, G.C. Aye, A. Daniel
    Year: 2018
    Citations: 17

  • Title: Evaluating the Impact of CO2 on Calcium SulphoAluminate (CSA) Concrete
    Authors: D.D. Akerele, F. Aguayo
    Year: 2024
    Citations: 4

  • Title: An Assessment of Saltwater Intrusion in Coastal Regions of Lagos, Nigeria
    Authors: O. Callistus, A.D. Daniel, A.O. Pelumi, O. Somtobe, O. Kunle, O.S. Echezona, et al.
    Year: 2024
    Citations: 4

  • Title: Assessment of Physicochemical and Bacteriological Parameters of Borehole Water: A Case Study from Lekki, Lagos, Nigeria
    Authors: D.D. Akerele, C. Obunadike, P.O. Abiodun
    Year: 2023
    Citations: 3

  • Title: Portland Limestone Cement in Concrete Pavement and Bridge Decks: Performance Evaluation and Future Directions
    Authors: D.D. Akerele, F. Aguayo, L. Wu
    Year: 2025
    Citations: Not available

  • Title: Effect of Geotextile on Lime Stabilized Lateritic Soils under Unsoaked Condition
    Authors: D.D. Akerele, P. Aduwenye
    Year: 2023
    Citations: Not available

  • Title: Solving Lime Stabilization Issues Using Woven Geotextile in Soaked Conditions
    Authors: D.D. Akerele
    Year: 2023
    Citations: Not available

Bader Alsharif | Computer Science | Best Innovation Award

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Dr. Bader Alsharif | Computer Science | Best Innovation Award

Florida Atlantic University, United States

Dr. Bader Alsharif is an accomplished PhD candidate in Computer Engineering with a strong background in teaching, technical support, and curriculum development. He has led innovative projects, including the first CISCO simulation lab in Saudi Arabia, and has managed over 300 devices, optimizing performance and security. With a focus on AI, Cybersecurity, and IoT, particularly in healthcare, Dr. Alsharif has published over 7 peer-reviewed papers. He has demonstrated leadership in both academic and technical spheres, guiding over 200 students and advocating for special needs education, ensuring their academic success. His expertise extends to training professionals, having conducted comprehensive courses for Saudi Telecom employees. Dr. Alsharif has shown a profound commitment to advancing technology and fostering inclusivity, particularly through his work with individuals with special needs. His work bridges technological innovation with social impact, positioning him as a forward-thinking leader in computer engineering and healthcare.

Professional Profile 

Education

Dr. Bader Alsharif has an extensive academic background, beginning with a Bachelor of Science in Computer Engineering from the College of Technology in Riyadh, Saudi Arabia, where he graduated in 2008. He further advanced his studies with a Master of Science in Computer Engineering from the Florida Institute of Technology, completing his degree in 2017. Currently, Dr. Alsharif is pursuing a Doctor of Computer Engineering at Florida Atlantic University in Boca Raton, USA, with an expected graduation date of 2025. His academic journey has been marked by a strong focus on integrating Artificial Intelligence (AI), Cybersecurity, and Internet of Things (IoT) technologies, particularly in healthcare applications. This multidisciplinary education has provided Dr. Alsharif with the expertise to contribute meaningfully to both research and practical innovations in these fields, bridging the gap between technology and real-world healthcare challenges.

Professional Experience

Dr. Bader Alsharif has a diverse professional background with extensive experience in both academia and technical roles. He currently serves as a Teaching Assistant at Florida Atlantic University, where he guides and evaluates over 30 students on engineering design projects and assists more than 200 students with project development and technical issues. Prior to this, Dr. Alsharif held a prominent role as a Lecturer at the Communications and Information College in Riyadh, Saudi Arabia, where he managed and maintained over 300 devices and led the installation of the first CISCO simulation lab in the country. This project, a significant innovation, involved the deployment of over 30 devices and routers. He also trained 100 employees from Saudi Telecom and designed assessments for instructors working with special needs students. Dr. Alsharif’s professional experience reflects a strong blend of technical expertise, leadership, and a commitment to education and inclusivity.

Research Interest

Dr. Bader Alsharif’s research interests lie at the intersection of Artificial Intelligence (AI), Cybersecurity, and the Internet of Things (IoT), with a particular focus on their applications in healthcare. He is deeply committed to exploring how these advanced technologies can be integrated to enhance patient outcomes and improve healthcare systems. His work aims to leverage AI algorithms to optimize medical data analysis, while also addressing critical security concerns in the rapidly growing field of IoT healthcare devices. Dr. Alsharif’s research also extends to the development of innovative solutions for securing healthcare networks and ensuring the privacy of sensitive patient information. With a strong academic foundation and several peer-reviewed publications, he is dedicated to advancing knowledge in these areas and exploring how cutting-edge technologies can be applied to solve real-world challenges in healthcare. His work demonstrates a commitment to both technological innovation and social impact, especially in the realm of health and well-being.

Award and Honor

Dr. Bader Alsharif has received numerous accolades for his contributions to academia and technology. His achievements include successfully leading the installation of the first CISCO simulation lab in Saudi Arabia, which became a groundbreaking project in the region, significantly enhancing the educational infrastructure for telecommunications. In recognition of his exceptional performance in teaching and technical support, he consistently achieved high job performance ratings, including scores no less than 99/100. Dr. Alsharif has also been honored for his commitment to inclusive education, particularly in advocating for and supporting students with special needs, ensuring their academic excellence. His research in AI, Cybersecurity, and IoT, particularly in the healthcare sector, has earned him recognition as a published researcher with over 7 peer-reviewed papers. Through his work, Dr. Alsharif has received recognition from academic institutions and industry professionals for his innovative contributions, leadership, and commitment to fostering technological advancements with social impact.

Conclusion

Bader Alsharif has demonstrated significant innovation across several key areas of AI, Cybersecurity, and IoT, particularly in healthcare. His leadership in education and advocacy for special needs individuals also reflects a deep commitment to both technological advancement and social impact. His ability to lead high-profile projects and publish extensively in relevant fields positions him as a strong candidate for the Best Innovation Award. However, expanding his research impact and involvement in larger-scale, cross-disciplinary projects could further elevate his candidacy. Overall, he has the potential to be an exceptional award recipient based on his innovative contributions and impact.

Publications Top Noted

  • Title: Deep learning technology to recognize American Sign Language alphabet
    Authors: B Alsharif, AS Altaher, A Altaher, M Ilyas, E Alalwany
    Year: 2023
    Citations: 14
  • Title: Internet of things technologies in healthcare for people with hearing impairments
    Authors: B Alsharif, M Ilyas
    Year: 2022
    Citations: 8
  • Title: Deep Learning Technology to Recognize American Sign Language Alphabet Using Multi-Focus Image Fusion Technique
    Authors: B Alsharif, M Alanazi, AS Altaher, A Altaher, M Ilyas
    Year: 2023
    Citations: 6
  • Title: Machine Learning Technology to Recognize American Sign Language Alphabet
    Authors: B Alsharif, M Alanazi, M Ilyas
    Year: 2023
    Citations: 4
  • Title: Enhancing cybersecurity in healthcare: Evaluating ensemble learning models for intrusion detection in the internet of medical things
    Authors: T Alsolami, B Alsharif, M Ilyas
    Year: 2024
    Citations: 3
  • Title: Multi-Dataset Human Activity Recognition: Leveraging Fusion for Enhanced Performance
    Authors: M Alanazi, B Alsharif, AS Altaher, A Altaher, M Ilyas
    Year: 2023
    Citations: 3
  • Title: Transfer learning with YOLOV8 for real-time recognition system of American Sign Language Alphabet
    Authors: B Alsharif, E Alalwany, M Ilyas
    Year: 2024
    Citations: 1
  • Title: Franklin Open
    Authors: B Alsharif, E Alalwany, M Ilyas
    Year: 2024
    Citations: Not available yet

Mohammad Ali Balafar | Computer Science | Best Researcher Award

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Prof. Dr. Mohammad Ali Balafar | Computer Science | Best Researcher Award

Prof at University of Tabriz, Iran

Prof. Dr. Mohammad Ali Balafar is a distinguished researcher in Artificial Intelligence and Multimedia Systems. With an h-index of 24 (Google Scholar) and inclusion in Stanford’s top 2% most-cited authors, his work is widely recognized for its impact. He leads the Intelligent Information Technology and Multimedia Research Laboratory at Tabriz University, focusing on deep learning, image processing, machine learning, and graph neural networks. His research projects address real-world problems, including image encryption, stock price prediction, and medical diagnosis through brain image segmentation. Dr. Balafar has authored numerous high-impact publications in reputable journals like IEEE Transactions and Chaos, Solitons & Fractals. Fluent in four languages, he fosters collaboration across diverse academic and cultural landscapes. His work blends innovation with application, making him a pioneer in intelligent systems. A strong advocate of interdisciplinary research, Dr. Balafar’s contributions exemplify excellence in both theoretical advancements and practical implementations.

Professional Profile

Education

Prof. Dr. Mohammad Ali Balafar has a strong academic foundation, specializing in Artificial Intelligence and Multimedia Systems. He earned his Bachelor’s degree in Computer Engineering, laying the groundwork for his expertise in computational systems and programming. Pursuing advanced studies, he obtained a Master’s degree in Software Engineering, where he focused on algorithm development and software methodologies. Dr. Balafar then completed his Ph.D. in Computer Engineering, concentrating on cutting-edge technologies such as image processing, data mining, and deep learning. Throughout his educational journey, he honed his skills in machine learning, graph neural networks, and intelligent information systems, which later became central to his research. His academic excellence was complemented by multilingual proficiency (Azerbaijani, English, Farsi, and Turkish), facilitating collaboration in diverse research environments. These educational milestones have equipped Dr. Balafar with the theoretical knowledge and technical expertise essential for pioneering innovations in artificial intelligence and intelligent multimedia technologies.

Professional  Experience

Prof. Dr. Mohammad Ali Balafar is a seasoned academic and researcher with extensive experience in Artificial Intelligence and Multimedia Systems. Currently, he serves as a faculty member in the Department of Electrical and Computer Engineering at Tabriz University. He is the founder and head of the Intelligent Information Technology and Multimedia Research Laboratory, established in 1391 (2012), where he leads innovative projects in areas such as image processing, machine vision, and robotics. Dr. Balafar has been instrumental in advancing intelligent multimedia systems through diverse research initiatives, including expert recommendation systems, stock price prediction, and medical imaging for diagnosing diseases like MS. He has authored numerous high-impact publications and collaborated with leading scholars, contributing to advancements in fields such as deep learning and data mining. With fluency in multiple languages and a global academic network, his professional career reflects a blend of academic rigor, research innovation, and leadership in cutting-edge technology development.

Research Interests

Prof. Dr. Mohammad Ali Balafar’s research interests are deeply rooted in the fields of Artificial Intelligence, Machine Learning, and Multimedia Systems, with a focus on addressing complex computational challenges. His expertise spans a wide range of cutting-edge topics, including Deep Learning, Image Processing, Computer Vision, and Graph Neural Networks. He is particularly interested in developing intelligent systems that can process and analyze visual data, such as creating efficient algorithms for image encryption, clustering, and anomaly detection. Dr. Balafar’s work also delves into Data Mining, where he applies advanced techniques to uncover patterns and insights in domains such as medical diagnostics, stock price prediction, and emergency service optimization. His contributions aim to bridge the gap between theory and application, advancing technologies that enhance real-world decision-making. This interdisciplinary approach not only pushes the boundaries of innovation but also showcases his dedication to solving impactful societal and scientific problems.

Awards and Honors

Prof. Dr. Mohammad Ali Balafar is a highly acclaimed researcher whose contributions have been recognized through various awards and honors. Notably, he has been included in Stanford University’s list of the top 2% most-cited scientists worldwide, based on a one-year performance metric—a testament to his impactful research and global influence in Artificial Intelligence and Multimedia Systems. Dr. Balafar’s scholarly achievements, reflected in his impressive h-index of 24 (Google Scholar) and over 2,380 citations, underscore his standing as a leading researcher in fields like Deep Learning, Image Processing, and Graph Neural Networks. His role as the head of the Intelligent Information Technology and Multimedia Research Laboratory further highlights his leadership in advancing innovative solutions for complex technological challenges. These accolades, combined with his extensive publication record in top-tier journals, position Dr. Balafar as a pioneer in his domain, earning him well-deserved recognition in the academic and research communities.

Conclusion

Dr. Mohammad Ali Balafar is a highly accomplished researcher with a solid track record of impactful publications, innovative research, and academic leadership. His diverse skill set, coupled with his contributions to AI and multimedia systems, makes him a strong candidate for the Best Researcher Award. Enhancing his global collaborations and industry engagement could further solidify his standing as a leading figure in his field.

Publications Top Noted

  • Review of brain MRI image segmentation methods
    • Authors: MA Balafar, AR Ramli, MI Saripan, S Mashohor
    • Year: 2010
    • Citations: 643
  • Gene selection for microarray cancer classification using a new evolutionary method employing artificial intelligence concepts
    • Authors: M Dashtban, M Balafar
    • Year: 2017
    • Citations: 167
  • A hybrid algorithm using a genetic algorithm and multiagent reinforcement learning heuristic to solve the traveling salesman problem
    • Authors: MM Alipour, SN Razavi, MR Feizi Derakhshi, MA Balafar
    • Year: 2018
    • Citations: 134
  • A novel image encryption algorithm based on polynomial combination of chaotic maps and dynamic function generation
    • Authors: M Asgari-Chenaghlu, MA Balafar, MR Feizi-Derakhshi
    • Year: 2019
    • Citations: 131
  • Gene selection for tumor classification using a novel bio-inspired multi-objective approach
    • Authors: M Dashtban, M Balafar, P Suravajhala
    • Year: 2018
    • Citations: 104
  • Gaussian mixture model based segmentation methods for brain MRI images
    • Authors: MA Balafar
    • Year: 2014
    • Citations: 95
  • The state-of-the-art in expert recommendation systems
    • Authors: N Nikzad–Khasmakhi, MA Balafar, MR Feizi–Derakhshi
    • Year: 2019
    • Citations: 89
  • Fuzzy C-mean based brain MRI segmentation algorithms
    • Authors: MA Balafar
    • Year: 2014
    • Citations: 85
  • CGFFCM: Cluster-weight and Group-local Feature-weight learning in Fuzzy C-Means clustering algorithm for color image segmentation
    • Authors: AG Oskouei, M Hashemzadeh, B Asheghi, MA Balafar
    • Year: 2021
    • Citations: 70
  • CWI: A multimodal deep learning approach for named entity recognition from social media using character, word and image features
    • Authors: M Asgari-Chenaghlu, MR Feizi-Derakhshi, L Farzinvash, MA Balafar
    • Year: 2022
    • Citations: 48
  • Cy: Chaotic yolo for user intended image encryption and sharing in social media
    • Authors: M Asgari-Chenaghlu, MR Feizi-Derakhshi, N Nikzad-Khasmakhi
    • Year: 2021
    • Citations: 36
  • A new method for MR grayscale inhomogeneity correction
    • Authors: MA Balafar, AR Ramli, S Mashohor
    • Year: 2010
    • Citations: 36

Humam Kourani | Computer Science | Best Researcher Award

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Mr. Humam Kourani | Computer Science | Best Researcher Award

Research Associate at Fraunhofer FIT, Germany

Mr. Humam Kourani is a dedicated and highly skilled researcher with a strong background in Data Science and Computer Science. He holds both a Master’s and Bachelor’s degree from RWTH Aachen University, specializing in process mining, artificial intelligence, and data-driven decision-making. He has gained valuable experience working in research institutions and industry settings, most notably at the Fraunhofer Institute for Applied Information Technology and Fondazione Bruno Kessler in Italy. His research focuses on improving data science methodologies, particularly in process mining and workflow language models. With a solid academic foundation, practical experience, and significant contributions to his field, Humam has proven himself to be a promising and impactful researcher.

Professional Profile

Education

Humam Kourani completed his Master of Science in Data Science from RWTH Aachen University in 2022, with a focus on Computer Science. His master’s thesis explored the improvement of the Hybrid Miner by utilizing causal graph metrics, an area critical for process mining. Prior to that, he earned his Bachelor of Science degree in Computer Science from the same institution in 2019. His Bachelor’s thesis involved the development of a scalable interactive event data visualization tool in Python, further showcasing his technical skills. Humam’s academic journey reflects his dedication to mastering complex data science concepts and his drive to contribute to the field’s advancement through academic research and innovation.

Professional  Experience

Mr. Kourani’s professional experience spans key positions in research and data science. Since May 2022, he has been working as a Research Associate at the Fraunhofer Institute for Applied Information Technology, specializing in Data Science and Artificial Intelligence. In this role, he contributes to research on process mining, artificial intelligence, and data-driven decision-making. Earlier, he held student assistant roles at RWTH Aachen University, including positions at the Chair of Process and Data Science and the Chair of Process and Data Science in 2021. Humam also completed an Erasmus+ internship at Fondazione Bruno Kessler in Italy, where he gained hands-on experience in process and data intelligence. His professional experience reflects a consistent focus on leveraging data science and AI for practical problem-solving and research innovation.

Research Interests

Humam Kourani’s research interests lie primarily in data science, artificial intelligence, and process mining. He is particularly focused on enhancing data-driven methods for analyzing and improving business processes, with an emphasis on process modeling and workflow languages. His recent work has explored innovative approaches, such as large language models for process modeling, and improving existing hybrid mining techniques using causal graph metrics. Through his work, Humam aims to bridge the gap between advanced computational techniques and practical business process applications, enabling more efficient decision-making. His research also delves into the intersection of data science and AI, with a strong interest in developing scalable models that address real-world challenges across various industries.

Awards and Honors

Humam Kourani has received several prestigious awards in recognition of his outstanding research contributions. He won the Best Paper Award at the EMMSAD 2024 conference for his paper on “Process Modeling with Large Language Models”. Additionally, he received the Best Paper Award at the BPM 2023 conference for his work on the “POWL: Partially Ordered Workflow Language”. These awards highlight the significance of his research in the fields of process mining and business process management. Humam was also honored with membership in the PADS Excellence Honors Class at RWTH Aachen University in 2022, further underscoring his academic excellence. These honors attest to his innovative contributions to the research community and his growing influence in the fields of data science and AI.

Conclusion

Humam Kourani is undoubtedly a highly talented researcher with a solid foundation in data science and process mining. His research achievements, international experience, and awards demonstrate that he is already making significant contributions to his field. His multidisciplinary skills, coupled with his passion for continuous learning, make him a standout candidate for the Best Researcher Award. While there are opportunities for growth in areas like expanding his publication base and increasing leadership roles in research initiatives, his strengths far outweigh these minor areas of improvement. Humam Kourani is a promising researcher with the potential for continued excellence and impact in the field of data science and artificial intelligence.

Publications Top Noted

  • Title: Process Modeling With Large Language Models
    Authors: H. Kourani, A. Berti, D. Schuster, W.M.P. van der Aalst
    Year: 2024
    Citations: 21
  • Title: Evaluating Large Language Models in Process Mining: Capabilities, Benchmarks, Evaluation Strategies, and Future Challenges
    Authors: A. Berti, H. Kourani, H. Hafke, C.Y. Li, D. Schuster
    Year: 2024
    Citations: 8
  • Title: POWL: Partially Ordered Workflow Language
    Authors: H. Kourani, S.J. van Zelst
    Year: 2023
    Citations: 7
  • Title: ProMoAI: Process Modeling with Generative AI
    Authors: H. Kourani, A. Berti, D. Schuster, W.M.P. van der Aalst
    Year: 2024
    Citations: 5
  • Title: PM4KNIME: Process Mining Meets the KNIME Analytics Platform
    Authors: H. Kourani, S.J. van Zelst, B.D. Lehmann, G. Einsdorf, S. Helfrich, F. Liße
    Year: 2022
    Citations: 5
  • Title: Scalable Discovery of Partially Ordered Workflow Models with Formal Guarantees
    Authors: H. Kourani, D. Schuster, W. Van Der Aalst
    Year: 2023
    Citations: 4
  • Title: PM-LLM-Benchmark: Evaluating Large Language Models on Process Mining Tasks
    Authors: A. Berti, H. Kourani, W.M.P. van der Aalst
    Year: 2024
    Citations: 3
  • Title: Discovering Hybrid Process Models with Bounds on Time and Complexity: When to be Formal and When Not?
    Authors: W.M.P. van der Aalst, R. De Masellis, C. Di Francescomarino, C. Ghidini, H. Kourani
    Year: 2023
    Citations: 3
  • Title: Evaluating Large Language Models in Process Mining: Capabilities, Benchmarks, and Evaluation Strategies
    Authors: A. Berti, H. Kourani, H. Häfke, C.Y. Li, D. Schuster
    Year: 2024
    Citations: 2
  • Title: Mining for Long-Term Dependencies in Causal Graphs
    Authors: H. Kourani, C. Di Francescomarino, C. Ghidini, W. van der Aalst, S. van Zelst
    Year: 2022
    Citations: 2
  • Title: Bridging Domain Knowledge and Process Discovery Using Large Language Models
    Authors: A. Norouzifar, H. Kourani, M. Dees, W. van der Aalst
    Year: 2024
    Citations: 0 (preprint)
  • Title: Leveraging Large Language Models for Enhanced Process Model Comprehension
    Authors: H. Kourani, A. Berti, J. Hennrich, W. Kratsch, R. Weidlich, C.Y. Li, A. Arslan, et al.
    Year: 2024
    Citations: 0 (preprint)
  • Title: Discovering Hybrid Process Models with Bounds on Time and Complexity: When to be Formal and When Not?
    Authors: W. van der Aalst, R. De Masellis, C. Di Francescomarino, C. Ghidini, H. Kourani
    Year: 2023
    Citations: 0

Shahbaz Gul Hassan | Computer Science | Best Researcher Award

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Assoc. Prof. Dr.Shahbaz Gul Hassan | Computer Science | Best Researcher Award

Associat professor at Zhongkai University of Agriculture and Engineering, China

Dr. Shahbaz Gul Hassan is an accomplished Associate Professor at Zhongkai University of Agriculture and Engineering, specializing in agricultural information technology and computer science. With a strong academic background, including a Ph.D. from China Agricultural University, he focuses on machine learning, image processing, and predictive modeling in the context of agricultural and environmental systems. His work has earned significant recognition, including awards for research and innovation in agricultural technology. Dr. Hassan’s numerous high-impact publications in top-tier journals demonstrate his ability to integrate advanced computational techniques into real-world applications in agriculture.

Professional Profile

Education

Dr. Shahbaz Gul Hassan completed his Ph.D. in Agricultural Information Technology at China Agricultural University, Beijing, in 2017. His research during his Ph.D. focused on the integration of information technology with agriculture, particularly in areas such as machine learning and predictive modeling. Prior to his Ph.D., he earned a Master’s in Computer Science from PMAS Arid Agriculture University, Rawalpindi, in 2011, where he developed a deep understanding of computer science applications in agriculture. He completed his Bachelor’s degree in Science from the University of Punjab, Lahore, in 2007. These educational milestones have equipped Dr. Hassan with a solid foundation in both computer science and agricultural technology, enabling him to innovate at the intersection of these two fields. His academic journey reflects a consistent focus on enhancing agricultural practices through advanced technologies, positioning him as a leading figure in agricultural information systems and technology research.

Experience

Dr. Shahbaz Gul Hassan has extensive experience in both academia and industry. He is currently an Associate Professor at Zhongkai University of Agriculture and Engineering, Guangzhou, China, where he has been teaching since 2019. Prior to this, he served as a Postdoctoral Researcher in Agricultural Engineering at South China Agricultural University, Guangzhou, from 2017 to 2019. In this role, he applied his expertise in machine learning and image processing to agricultural engineering projects. Dr. Hassan also worked as a Ph.D. Research Scholar at China Agricultural University, Beijing, from 2013 to 2017, where he focused on applying technology to solve critical problems in agriculture. Earlier, he worked as a Software Engineer at MTBC in Rawalpindi from 2011 to 2012. His diverse professional experience blends research, teaching, and practical applications of technology in agriculture, with a focus on using advanced computing to optimize agricultural processes.

Research Interests

Dr. Shahbaz Gul Hassan’s research focuses on the application of machine learning, image processing, and predictive modeling to solve agricultural challenges. He is particularly interested in developing smart technologies for precision farming and environmental monitoring. One of his key areas of research involves computer vision and machine learning techniques for detecting and predicting behaviors and conditions in agricultural environments, such as water quality and animal health. His work aims to enhance automation in agriculture and improve sustainability by leveraging data-driven technologies. Dr. Hassan also focuses on predictive modeling for environmental variables such as humidity, temperature, and dissolved oxygen levels in aquaculture. These models help optimize farming processes and ensure better resource management. His research not only pushes the boundaries of agricultural technology but also contributes to the development of sustainable practices in farming and aquaculture. Dr. Hassan’s interdisciplinary approach integrates computer science and engineering with practical agricultural needs to drive innovation.

Awards and Honors

Dr. Shahbaz Gul Hassan has received numerous prestigious awards for his outstanding contributions to agricultural research. In December 2023, he was honored with the First Prize in the Guangdong Province Agricultural Technology Promotion Award. He also received the Third Prize from the Guangdong Provincial Science and Technology Department in January 2024. Dr. Hassan’s work on a microservice-based agricultural app earned him the Second Prize in the 16th China University Computer Design Competition in the Guangdong-Hong Kong-Macao Greater Bay Area. Additionally, he was awarded the Excellent Instructor Award in the 13th Blue Bridge Cup Provincial Competition. His work has been recognized by the Guangdong Computer Society, where he received the Second Prize for Outstanding Paper. These awards reflect Dr. Hassan’s innovative approach to integrating advanced technologies in agriculture, as well as his ability to drive real-world impact with his research. His accolades highlight his leadership and dedication to improving agricultural technologies globally.

Conclusion

Dr. Shahbaz Gul Hassan is an outstanding candidate for the Best Researcher Award. His innovative approach to integrating machine learning with agricultural processes, alongside his strong academic qualifications and prolific output, make him a leading figure in his field. His numerous prestigious awards and contributions to practical agricultural technologies demonstrate the significant real-world impact of his work. Dr. Hassan is a researcher who continues to push the boundaries of knowledge and practical application in agricultural engineering and information technology, making him a valuable contender for the award.

Publications Top Noted

Title: Green synthesis of iron oxide nanorods using Withania coagulans extract improved photocatalytic degradation and antimicrobial activity
Authors: S Qasim, A Zafar, MS Saif, Z Ali, M Nazar, M Waqas, AU Haq, T Tariq, …
Citations: 175
Year: 2020

Title: Prediction of the temperature in a Chinese solar greenhouse based on LSSVM optimized by improved PSO
Authors: H Yu, Y Chen, SG Hassan, D Li
Citations: 158
Year: 2016

Title: Bioinspired synthesis of zinc oxide nano-flowers: A surface enhanced antibacterial and harvesting efficiency
Authors: M Hasan, M Altaf, A Zafar, SG Hassan, Z Ali, G Mustafa, T Munawar, …
Citations: 114
Year: 2021

Title: Models for estimating feed intake in aquaculture: A review
Authors: M Sun, SG Hassan, D Li
Citations: 108
Year: 2016

Title: Phyto-reflexive zinc oxide nano-flowers synthesis: an advanced photocatalytic degradation and infectious therapy
Authors: MS Saif, A Zafar, M Waqas, SG Hassan, A ul Haq, T Tariq, S Batool, …
Citations: 75
Year: 2021

Title: Fractionation of Biomolecules in Withania coagulans Extract for Bioreductive Nanoparticle Synthesis, Antifungal and Biofilm Activity
Authors: M Hasan, A Zafar, I Shahzadi, F Luo, SG Hassan, T Tariq, S Zehra, …
Citations: 66
Year: 2020

Title: Phytotoxic evaluation of phytosynthesized silver nanoparticles on lettuce
Authors: M Hasan, K Mehmood, G Mustafa, A Zafar, T Tariq, SG Hassan, …
Citations: 53
Year: 2021

Title: Green synthesis of Cordia myxa incubated ZnO, Fe2O3, and Co3O4 nanoparticle: Characterization, and their response as biological and photocatalytic agent
Authors: S Batool, M Hasan, M Dilshad, A Zafar, T Tariq, Z Wu, R Chen, …
Citations: 49
Year: 2022

Title: Physiological and anti-oxidative response of biologically and chemically synthesized iron oxide: Zea mays a case study
Authors: M Hasan, S Rafique, A Zafar, S Loomba, R Khan, SG Hassan, MW Khan, …
Citations: 47
Year: 2020

Title: Dissolved oxygen content prediction in crab culture using a hybrid intelligent method
Authors: H Yu, Y Chen, SG Hassan, D Li
Citations: 43
Year: 2016

Title: Cursive handwritten text recognition using bi-directional LSTMs: a case study on Urdu handwriting
Authors: S Hassan, A Irfan, A Mirza, I Siddiqi
Citations: 42
Year: 2019

Title: Green synthesized ZnO-Fe2O3-Co3O4 nanocomposite for antioxidant, microbial disinfection and degradation of pollutants from wastewater
Authors: S Batool, M Hasan, M Dilshad, A Zafar, T Tariq, A Shaheen, R Iqbal, Z Ali, …
Citations: 41
Year: 2022

Title: A hybrid model for short-term dissolved oxygen content prediction
Authors: J Huang, S Liu, SG Hassan, L Xu, C Huang
Citations: 39
Year: 2021

Title: Biological synthesis of bimetallic hybrid nanocomposite: a remarkable photocatalyst, adsorption/desorption and antimicrobial agent
Authors: X Huang, A Zafar, K Ahmad, M Hasan, T Tariq, S Gong, SG Hassan, …
Citations: 36
Year: 2023

Title: Nano-managing silver and zinc as bio-conservational approach against pathogens of the honey bee
Authors: R Hussain, M Hasan, KJ Iqbal, A Zafar, T Tariq, MS Saif, SG Hassan, …
Citations: 33
Year: 2023

 

 

Peixian Zhuang | Computer Science | Best Researcher Award

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Assoc. Prof. Dr. Peixian Zhuang | Computer Science | Best Researcher Award

Associate Professor at University of Science and Technology Beijing, China 

Assoc. Prof. Dr. Peixian Zhuang is a distinguished researcher in computer vision, machine learning, and underwater image processing. Currently an Associate Professor at the University of Science and Technology Beijing, he earned his Ph.D. from Xiamen University in 2016. With over 50 published papers, including 9 ESI Highly Cited/Hot Papers and over 2800 Google Scholar citations, his work has garnered significant academic influence. Dr. Zhuang has led four national projects, holds six patents, and authored a book, showcasing his commitment to advancing technological innovation. His contributions have been recognized globally, as he was listed among the “World’s Top 2% Scientists” in 2023 and 2024. In addition to his research, he serves as an editor for various esteemed journals and has reviewed over 100 international journals and conferences. His collaborations with institutions like Tsinghua University further underscore his dedication to expanding the boundaries of AI and image processing.

Professional profile

Education

Assoc. Prof. Dr. Peixian Zhuang completed his Ph.D. in 2016 at Xiamen University, where he laid the foundation for his research expertise in computer vision, underwater image processing, and machine learning. Following his doctoral studies, he began his academic career as a Lecturer at Nanjing University of Information Science & Technology (2017-2020), where he further honed his skills and contributed to his fields of study. To deepen his research, Dr. Zhuang undertook postdoctoral training at Tsinghua University (2020-2022), engaging in advanced projects and expanding his expertise in innovative AI technologies. His educational journey has been marked by significant contributions to his field, earning him recognition as a “World’s Top 2% Scientist” in recent years. Dr. Zhuang’s robust academic background has established him as a leading researcher and educator, influencing both national and international advancements in machine learning and image processing.

Professional Experience

Assoc. Prof. Dr. Peixian Zhuang has a diverse professional background in academia and research. Currently serving as an Associate Professor at the University of Science and Technology Beijing, he has made significant contributions to the fields of underwater image processing and machine learning. Prior to this role, he was a Lecturer at Nanjing University of Information Science & Technology from 2017 to 2020, where he developed and delivered courses while conducting impactful research. Following this, Dr. Zhuang completed a postdoctoral fellowship at Tsinghua University (2020-2022), where he engaged in advanced research projects and collaborations with leading scientists. He has led four national research projects and has authored over 50 papers, showcasing his commitment to scientific advancement. In addition to his academic roles, he serves as an area editor and guest editor for various reputable journals, reflecting his expertise and active engagement in the global research community.

Research Interest

Assoc. Prof. Dr. Peixian Zhuang specializes in several cutting-edge areas within the fields of computer vision and machine learning. His primary research interests include underwater image processing, where he focuses on improving the quality and usability of images captured in challenging underwater environments. He employs advanced algorithms and techniques to enhance image clarity and object recognition. Additionally, Dr. Zhuang is deeply invested in Bayesian machine learning, exploring probabilistic models that can improve decision-making processes in uncertain environments. His work on signal sparse representation and deep neural networks further highlights his commitment to developing innovative solutions for complex problems in artificial intelligence. By integrating these methodologies, Dr. Zhuang aims to advance the understanding and application of AI in real-world scenarios. His research not only contributes to theoretical advancements but also has practical implications in fields such as marine science, environmental monitoring, and robotics, making a significant impact on technology and research.

Awards and Honors

Assoc. Prof. Dr. Peixian Zhuang has received numerous awards and honors throughout his academic career, reflecting his significant contributions to research and innovation. He was recognized as one of the “World’s Top 2% Scientists” in both 2023 and 2024, an accolade that highlights his impact and influence in the field of computer vision and machine learning. In 2023, he received the IFAC EAAI Paper Prize Award, underscoring the excellence of his research publications. Additionally, his doctoral dissertation was awarded the Outstanding Doctoral Dissertations of Fujian Province in 2017, recognizing the quality and originality of his work during his Ph.D. studies. Dr. Zhuang has also been involved in various editorial roles for reputable journals, enhancing his recognition as a leading researcher in his field. These awards and honors reflect his dedication to advancing scientific knowledge and his commitment to excellence in research and education.

Conclusion

Peixian Zhuang’s profile makes him a strong candidate for the Best Researcher Award. His influential research, substantial publication record, recognition in global scientific rankings, and engagement in scholarly activities demonstrate his commitment and impact in the field of computer vision and underwater image processing. Addressing the outlined areas of improvement could enhance his profile further, positioning him as a leading researcher capable of impacting both academia and industry.

Publications top noted📜
  • Title: A retinex-based enhancing approach for single underwater image
    Authors: X Fu, P Zhuang, Y Huang, Y Liao, XP Zhang, X Ding
    Year: 2014
    Citations: 566
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    Citations: 373
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Imtiaz Ahmad | Computer Science | Best Researcher Award

Posted on by ScienceFather

Mr. Imtiaz Ahmad | Computer Science | Best Researcher Award

Visiting lecturer at Hazara University Mansehra, Pakistan

Imtiaz Ahmad, a dedicated researcher and educator from Pakistan, holds a Master’s degree in Computer Science from Hazara University, with a focus on wireless sensor networks. His thesis, titled “Adaptive and Priority-Based Data Aggregation and Scheduling Model for Wireless Sensor Networks,” reflects his expertise in optimizing data transmission for modern networks. Imtiaz has published research in reputable journals, including Knowledge-Based Systems and VFAST Transactions on Software, focusing on wireless sensor networks and mobile edge computing. With several years of teaching experience at institutions like Hazara University, he has mentored students and contributed to academic growth. His achievements include the Best Researcher Award and several student accolades. Additionally, he holds certifications like Microsoft Office Specialist and vocational training in computers. Imtiaz is a promising researcher with strengths in data aggregation, mobile computing, and teaching, and he continues to make valuable contributions to the field of computer science.

Professional profile

Education

Imtiaz Ahmad holds a Master’s degree in Computer Science from Hazara University Mansehra, which he completed in March 2021 with a commendable CGPA of 3.71/4.00. His master’s thesis focused on developing an “Adaptive and Priority-Based Data Aggregation and Scheduling Model for Wireless Sensor Networks,” showcasing his expertise in advanced computing concepts. Prior to this, he earned a Bachelor of Science in Information Technology from the University of Malakand in March 2015, achieving a CGPA of 2.95/4.00. His undergraduate thesis was centered on creating an “Online Hospital Management System,” which streamlined patient reservations and record management. Imtiaz also gained valuable experience through an internship at Hazara University, where he addressed technical issues related to system and application software. His educational background reflects a strong foundation in computer science and information technology, emphasizing both theoretical knowledge and practical application.

Professional Experience

Mr. Imtiaz Ahmad has accumulated valuable professional experience in academia and technical roles. Currently, he serves as a Visiting Lecturer at Hazara University Mansehra, where he is responsible for planning and delivering lectures, supervising final year projects, and assessing student progress. Previously, he held positions as a Computer Science Lecturer at Abaseen Public School and College and New Shaheen College of Commerce, where he implemented computer education programs and provided hands-on training in programming languages.

Additionally, during his internship at Hazara University, he gained practical experience in resolving technical issues, installing software, and setting up multimedia for national conferences. His diverse roles demonstrate his commitment to education and his ability to convey complex concepts to students, while also highlighting his technical skills in information technology. This blend of teaching and technical expertise positions him as a promising educator and researcher in the field of computer science.

Research Interest

Mr. Imtiaz Ahmad’s research interests lie primarily in the fields of wireless sensor networks, mobile edge computing, and data aggregation methodologies. His work focuses on developing adaptive and priority-based models that enhance the efficiency and reliability of data transmission in sensor networks. By optimizing scheduling techniques, Imtiaz aims to improve the performance of wireless systems, making them more resilient to data loss and delays. He is also interested in mobility prediction and task migration within mobile edge computing environments, exploring innovative solutions that facilitate seamless connectivity and resource management. Through his research, Imtiaz seeks to contribute to the advancement of smart technologies and the Internet of Things (IoT), addressing critical challenges in data management and network performance. His commitment to applying theoretical knowledge to real-world applications underscores his desire to drive impactful innovations in computer science.

Awards and Honors

Mr. Imtiaz Ahmad, a dedicated researcher and educator in computer science, has garnered several prestigious awards and honors throughout his academic journey. In 2024, he received the Best Researcher Award at the International Academic Awards, recognizing his impactful research on adaptive data aggregation models in wireless sensor networks. Previously, in 2020, he was honored with the Best Student Researcher Award from the Department of Computer Science at Hazara University, highlighting his exceptional contributions during his studies. Additionally, he was named the Student of the Year at Hazara University in 2019, further showcasing his academic excellence. Imtiaz was also awarded a laptop under the Prime Minister’s Laptop Scheme for High Achievers by the Higher Education Commission of Pakistan in 2018. These accolades reflect his commitment to research and education, marking him as a prominent figure in his field.

Conclusion

Imtiaz Ahmad has demonstrated a solid academic and research profile with notable strengths in computer science, particularly in wireless sensor networks and mobile edge computing. His publications in respected journals, combined with his teaching and professional certifications, make him a strong contender for the Best Researcher Award. However, to further solidify his candidacy, he could focus on enhancing the visibility and impact of his research through broader collaborations and more high-impact publications. Overall, his achievements suggest that he is well-suited for the award and poised to make significant contributions to his field in the future.

Publications top noted📜
  • Title: Adaptive and Priority-Based Data Aggregation and Scheduling Model for Wireless Sensor Networks
    Authors: Imtiaz Ahmad, Muhammad Adnan, Noor ul Amin, Asif Umer, Adnan Khurshid, Khursheed Aurangzeb, Muhammad Gulistan
    Journal: Knowledge-Based Systems
    Year: 2024
    DOI: 10.1016/j.knosys.2024.112393
    ISSN: 0950-7051
  • Title: A Mobility Prediction-Based Adaptive Task Migration in Mobile Edge Computing
    Authors: Jawad Arshed, Mehtab Afzal, Muhammad Hashim, Imtiaz Ahmad, Hasnat Ali, Ghulam Hussain
    Journal: VFAST Transactions on Software Engineering
    Year: 2024
    DOI: 10.21015/vtse.v12i2.1768
    ISSN: 2309-3978, 2411-6246