Siliang Ma | Computer Science | Best Researcher Award

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Dr. Siliang Ma | Computer Science | Best Researcher Award

Senior Algorithm Engineer at School of Computer Science and Engineering, South China University of Technology, China

Dr. Siliang Ma, a Ph.D. candidate at South China University of Technology, is an accomplished researcher specializing in computer science with a focus on image processing and machine learning. With an excellent academic record, including a bachelor’s degree from South China Agricultural University (GPA: 3.99/5), Dr. Ma has made significant contributions to cutting-edge research. His works, published in esteemed journals such as Acta Automatica Sinica and Image and Vision Computing, address topics like calligraphy character recognition, multilingual scene text spotting, and efficient bounding box regression through novel loss functions like MPDIoU and FPDIoU. A skilled programmer proficient in Python, Java, and C#, he has developed robust image processing algorithms and software applications. Dr. Ma also contributes as a reviewer for leading conferences like ICRA and ICASSP, reflecting his commitment to advancing the research community. His innovative and impactful work positions him as a rising talent in computational science.

Professional Profile 

Education

Dr. Siliang Ma has a strong educational background in computer science and engineering. He is currently pursuing a Ph.D. at the South China University of Technology, where he has maintained an excellent GPA of 86.33/100. His doctoral research focuses on cutting-edge topics in image processing, machine learning, and computational algorithms, demonstrating both theoretical depth and practical relevance. Prior to this, Dr. Ma earned his bachelor’s degree from South China Agricultural University, graduating with a remarkable GPA of 3.99/5. His undergraduate studies in mathematics and informatics laid a solid foundation for his advanced research pursuits, equipping him with the analytical and technical skills essential for solving complex computational problems. Through rigorous academic training and dedication, Dr. Ma has excelled in his education, which is further reflected in his extensive publications in high-impact journals and his active engagement in academic conferences and peer reviews.

Professional Experience

Dr. Siliang Ma has gained valuable professional experience through diverse roles in research and industry, complementing his academic achievements. He interned as a Data Analyst at the China Construction Bank Guangdong Branch Technology Center, where he conducted financial data analysis using PostgreSQL, mastering database operations and complex linked table queries. As a Quality Engineer at the China Mobile Guangdong Branch Business Support Center, he developed a JavaWeb-based minimum feature set for user registration, login, and management, and implemented automated quality testing workflows using Jenkins. These roles allowed Dr. Ma to hone his skills in software development, data analysis, and quality assurance, showcasing his ability to translate theoretical knowledge into practical applications. Additionally, his expertise in programming and image processing has led to impactful contributions in academia, particularly in algorithm development. This blend of industrial and research experience positions Dr. Ma as a versatile professional in computer science and engineering.

Research Interest

Dr. Siliang Ma’s research interests lie at the intersection of computer vision, machine learning, and image processing. He is particularly focused on developing innovative algorithms and techniques for efficient and accurate object detection, scene text recognition, and character recognition. His work explores advanced loss functions, such as MPDIoU and FPDIoU, to optimize bounding box regression for both traditional and rotated object detection. Additionally, Dr. Ma has a keen interest in multilingual scene text spotting, where he leverages character-level features and benchmarks to improve the accuracy of text recognition across diverse languages. His research extends to robust graph learning and hypergraph-enhanced self-supervised models for social recommendation systems, showcasing his ability to address complex, real-world challenges. Through his work, Dr. Ma aims to bridge theoretical advancements with practical applications, contributing to the broader fields of artificial intelligence, data analysis, and computational optimization.

Award and Honor

Dr. Siliang Ma has been recognized for his academic and research excellence through various accolades and contributions. As a Ph.D. candidate at South China University of Technology, his consistent high performance, reflected in his impressive GPA, underscores his dedication to academic rigor. Although specific awards or honors are not explicitly listed in his profile, his role as a reviewer for prestigious conferences such as ICRA and ICASSP highlights his esteemed position within the research community. Dr. Ma’s impactful publications in top-tier journals and conferences, including Acta Automatica Sinica and Image and Vision Computing, further demonstrate the high regard in which his work is held. His innovative contributions to image processing and machine learning have earned him recognition as a rising talent in his field. These achievements reflect Dr. Ma’s commitment to advancing computational science and his growing influence in academic and professional circles.

Conclusion

Siliang Ma is a strong candidate for the Best Researcher Award due to his impressive academic record, significant publications, and technical expertise. His contributions to advanced image processing algorithms and innovative loss functions for object detection demonstrate technical ingenuity and research excellence. To further strengthen his profile, he could expand his research impact through interdisciplinary work, mentorship roles, and greater industry engagement.

Publications Top Noted

  • Title: FPDIoU Loss: A loss function for efficient bounding box regression of rotated object detection
    Authors: Siliang Ma, Yong Xu
    Year: 2024
    Citation: Ma, S., & Xu, Y. (2024). FPDIoU Loss: A loss function for efficient bounding box regression of rotated object detection. Image and Vision Computing. https://doi.org/10.1016/j.imavis.2024.105381
  • Title: Rethinking Multilingual Scene Text Spotting: A Novel Benchmark and a Character-Level Feature Based Approach
    Authors: Siliang Ma, Yong Xu
    Year: 2024
    Citation: Ma, S., & Xu, Y. (2024). Rethinking Multilingual Scene Text Spotting: A Novel Benchmark and a Character-Level Feature Based Approach. American Journal of Computer Science and Technology. https://doi.org/10.11648/j.ajcst.20240703.12

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
  • Title: Underwater image enhancement using a multiscale dense generative adversarial network
    Authors: Y Guo, H Li, P Zhuang
    Year: 2019
    Citations: 420
  • Title: Underwater image enhancement via minimal color loss and locally adaptive contrast enhancement
    Authors: W Zhang, P Zhuang, HH Sun, G Li, S Kwong, C Li
    Year: 2022
    Citations: 373
  • Title: Bayesian retinex underwater image enhancement
    Authors: P Zhuang, C Li, J Wu
    Year: 2021
    Citations: 255
  • Title: Underwater image enhancement with hyper-laplacian reflectance priors
    Authors: P Zhuang, J Wu, F Porikli, C Li
    Year: 2022
    Citations: 250
  • Title: Underwater image enhancement using an edge-preserving filtering retinex algorithm
    Authors: P Zhuang, X Ding
    Year: 2020
    Citations: 93
  • Title: Underwater image enhancement via weighted wavelet visual perception fusion
    Authors: W Zhang, L Zhou, P Zhuang, G Li, X Pan, W Zhao, C Li
    Year: 2023
    Citations: 86
  • Title: Removing stripe noise from infrared cloud images via deep convolutional networks
    Authors: P Xiao, Y Guo, P Zhuang
    Year: 2018
    Citations: 80
  • Title: Underwater image enhancement via piecewise color correction and dual prior optimized contrast enhancement
    Authors: W Zhang, S Jin, P Zhuang, Z Liang, C Li
    Year: 2023
    Citations: 77
  • Title: Non-uniform illumination underwater image restoration via illumination channel sparsity prior
    Authors: G Hou, N Li, P Zhuang, K Li, H Sun, C Li
    Year: 2023
    Citations: 54
  • Title: CVANet: Cascaded visual attention network for single image super-resolution
    Authors: W Zhang, W Zhao, J Li, P Zhuang, H Sun, Y Xu, C Li
    Year: 2024
    Citations: 49
  • Title: DewaterNet: A fusion adversarial real underwater image enhancement network
    Authors: H Li, P Zhuang
    Year: 2021
    Citations: 49
  • Title: SSTNet: Spatial, spectral, and texture aware attention network using hyperspectral image for corn variety identification
    Authors: W Zhang, Z Li, HH Sun, Q Zhang, P Zhuang, C Li
    Year: 2022
    Citations: 45
  • Title: Bayesian pan-sharpening with multiorder gradient-based deep network constraints
    Authors: P Guo, P Zhuang, Y Guo
    Year: 2020
    Citations: 41
  • Title: GIFM: An image restoration method with generalized image formation model for poor visible conditions
    Authors: Z Liang, W Zhang, R Ruan, P Zhuang, C Li
    Year: 2022
    Citations: 37

Imtiaz Ahmad | Computer Science | Best Researcher Award

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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

Ruotao Xu | Computer Science | Best Researcher Award

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Dr. Ruotao Xu | Computer Science | Best Researcher Award

Associate Researcher at Institute of Super Robotics(Huangpu), China 

Ruotao Xu is a dedicated researcher specializing in robotics, computer vision, and image processing. As a Postdoctoral Researcher and Associate Researcher, he is at the forefront of exploring advanced techniques in deep learning and image analysis. 🚀

Professional profile

Education📚

Ruotao Xu earned his Ph.D. in Electrical Engineering, where he focused on image processing and robotics. His educational background provides a solid foundation for his research endeavors and contributions to the field. 🎓📚

Professional Experience🏛️

Currently serving as a Postdoctoral Researcher at the Institute of Robotics and Automatic Information Systems, Xu has led several significant research projects. His role includes managing projects funded by national and provincial science foundations and contributing to various high-impact publications. 🛠️📈

Research Interest🌐

Xu’s research interests lie in deep learning, image processing, defocus deblurring, image inpainting, and texture representation. He is particularly focused on developing innovative solutions and technologies in these areas to advance the field of computer vision. 🧠🔍

Awards and Honors🎓
  • Principal Investigator for multiple high-profile projects funded by the National Natural Science Foundation of China. 🏆
  • Contributor to leading journals such as IEEE Transactions on Image Processing and IEEE/CVF International Conference on Computer Vision. 🥇📜
Achievements🏅
  • Principal Investigator for multiple high-profile projects funded by the National Natural Science Foundation of China. 🏆
  • Lead Author on influential papers in top journals such as IEEE Transactions on Image Processing and IEEE/CVF International Conference on Computer Vision. 📜
  • Innovator in Image Processing with a focus on deep learning, defocus deblurring, image inpainting, and texture representation. 🧠
  • Received Grants from provincial and national science foundations for cutting-edge research projects. 💵
  • Contributed to High-Impact Publications with significant citations, reflecting the impact of research on the field. 📚
  • Collaborated with Leading Researchers and institutions, enhancing the reach and application of his research findings. 🤝
Publications top noted📜
  • “Multi-view 3D shape recognition via correspondence-aware deep learning”
    Authors: Y Xu, C Zheng, R Xu, Y Quan, H Ling
    Journal: IEEE Transactions on Image Processing
    Year: 2021
    Citations: 40 📈
  • “Structure-texture image decomposition using discriminative patch recurrence”
    Authors: R Xu, Y Xu, Y Quan
    Journal: IEEE Transactions on Image Processing
    Year: 2020
    Citations: 20 📈
  • “Attention with structure regularization for action recognition”
    Authors: Y Quan, Y Chen, R Xu, H Ji
    Journal: Computer Vision and Image Understanding
    Year: 2019
    Citations: 19 📈
  • “Removing reflection from a single image with ghosting effect”
    Authors: Y Huang, Y Quan, Y Xu, R Xu, H Ji
    Journal: IEEE Transactions on Computational Imaging
    Year: 2019
    Citations: 19 📈
  • “Factorized tensor dictionary learning for visual tensor data completion”
    Authors: R Xu, Y Xu, Y Quan
    Journal: IEEE Transactions on Multimedia
    Year: 2021
    Citations: 17 📈
  • “Image quality assessment using kernel sparse coding”
    Authors: Z Zhou, J Li, Y Quan, R Xu
    Journal: IEEE Transactions on Multimedia
    Year: 2020
    Citations: 13 📈
  • “Cartoon-texture image decomposition using orientation characteristics in patch recurrence”
    Authors: R Xu, Y Xu, Y Quan, H Ji
    Journal: SIAM Journal on Imaging Sciences
    Year: 2020
    Citations: 10 📈
  • “Deep scale-aware image smoothing”
    Authors: J Li, K Qin, R Xu, H Ji
    Conference: ICASSP 2022
    Year: 2022
    Citations: 7 📈
  • “Enhancing texture representation with deep tracing pattern encoding”
    Authors: Z Chen, Y Quan, R Xu, L Jin, Y Xu
    Journal: Pattern Recognition
    Year: 2024
    Citations: 6 📈
  • “No-reference image quality assessment using dynamic complex-valued neural model”
    Authors: Z Zhou, Y Xu, R Xu, Y Quan
    Conference: 30th ACM International Conference on Multimedia
    Year: 2022
    Citations: 4 📈
  • “Deeply exploiting long-term view dependency for 3D shape recognition”
    Authors: Y Xu, C Zheng, R Xu, Y Quan
    Journal: IEEE Access
    Year: 2019
    Citations: 4 📈
  • “Deep blind image quality assessment using dual-order statistics”
    Authors: Z Zhou, Y Xu, Y Quan, R Xu
    Conference: IEEE International Conference on Multimedia and Expo (ICME)
    Year: 2022
    Citations: 3 📈
  • “Wavelet analysis model inspired convolutional neural networks for image denoising”
    Authors: R Xu, Y Xu, X Yang, H Huang, Z Lei, Y Quan
    Journal: Applied Mathematical Modelling
    Year: 2024
    Citations: 2 📈

Fouzia Elazzaby | Computer Science | Best Researcher Award

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Ms. Fouzia Elazzaby | Computer Science | Best Researcher Award

Docteur at Universite ibn tofail, Morocco

Fouzia Elazzaby is a dedicated and accomplished academic with a passion for computer science and its applications. Based in Fes, Morocco, she has made significant strides in her field through teaching, research, and practical projects. Known for her strong organizational skills, teamwork, and commitment to continuous learning, she is a valuable contributor to both the academic and professional communities. 🌍📚

Professional profile

Education📚

Fouzia holds a Doctorate in Informatics from FSK UIT, Kénitra, completed in May 2023. She also has a Master’s degree in Informatics, Graphics, and Imaging (M3I) from FSDM, Fes, obtained in July 2012, and a Bachelor’s degree in Mathematics and Computer Science from the same institution, earned in July 2008. Her academic journey started with a Baccalaureate in Experimental Sciences in 2004. 🎓💻

Professional Experience🏛️

With over a decade of teaching experience, Fouzia has been an educator at the Office of Vocational Training and Employment Promotion since 2009. Additionally, she has served as a visiting professor at the Ecole Normale Supérieure de Fès and the Ecole Nationale des Sciences Appliquées de Fès. She also worked as a trainer at Atlas Engineering & Consulting Society, where she contributed her expertise in 2022-2023. 🧑‍🏫🏢

Research Interest🌐

Fouzia’s research interests lie primarily in the field of image encryption and the application of chaotic systems in computer science. She has published several papers on innovative encryption schemes, using complex mathematical theories like the Heisenberg group and Zigzag transformations. Her work contributes to advancing security in digital imaging, making her a key player in her area of expertise. 🔒🖼️

Achievements🏅
  • 🏅 Doctorate in Informatics: Earned in May 2023 from FSK UIT, Kénitra.
  • 🖥️ Master’s Degree in Informatics, Graphics, and Imaging (M3I): Completed in July 2012 from FSDM, Fes.
  • 📜 Bachelor’s Degree in Mathematics and Computer Science: Obtained in July 2008 from FSDM, Fes.
  • 🧑‍🏫 Over a Decade of Teaching Experience: Teaching at the Office of Vocational Training and Employment Promotion since 2009.
  • 🎓 Visiting Professor: Held positions at Ecole Normale Supérieure de Fès and Ecole Nationale des Sciences Appliquées de Fès.
  • 🔐 Published Multiple Research Papers: Authored articles in reputable journals and conferences, focusing on advanced image encryption techniques.
  • 📚 Contributed to Book Chapters: Co-authored chapters on encryption algorithms in well-regarded publications.
  • 🗣️ Oral Presentations at International Conferences: Presented research findings at several prestigious conferences in Morocco and abroad.
  • 💼 Trainer at Atlas Engineering & Consulting Society: Provided professional training in 2022-2023.
  • 🌍 Multilingual: Fluent in Arabic, French, and English, enabling effective communication and collaboration across different regions.
Publications top noted📜
  • 📝 A New Encryption Scheme for RGB Color Images by Coupling 4D Chaotic Laser Systems and the Heisenberg Group
    • Authors: Elazzaby, F., Akkad, N.E., Sabour, K., Kabbaj, S.
    • Year: 2024
    • Journal: Multimedia Tools and Applications
    • Citations: 4
  • 📝 The Coupling of a Multiplicative Group and the Theory of Chaos in the Encryptions of Images
    • Authors: Elazzaby, F., Elakkad, N., Sabour, K.
    • Year: 2024
    • Journal: International Arab Journal of Information Technology
    • Citations: 1
  • 📝 Color Image Encryption Using a Zigzag Transformation and Sine–Cosine Maps
    • Authors: Elazzaby, F., Sabour, K.H., Elakkad, N., Torki, A., Rajkumar, S.R.
    • Year: 2023
    • Journal: Scientific African
    • Citations: 1
  • 📝 A New Contribution of Image Encryption Based on Chaotic Maps and the Z/nZ Group
    • Authors: Elazzaby, F., Akkad, N.E., Sabour, K., Kabbaj, S.
    • Year: 2023
    • Journal: Journal of Theoretical and Applied Information Technology
    • Citations: 2
  • 📝 An RGB Image Encryption Algorithm Based on Clifford Attractors with a Bilinear Transformation
    • Authors: Elazzaby, F., Akkad, N.E., Sabour, K., Kabbaj, S.
    • Year: 2022
    • Conference: Lecture Notes in Networks and Systems
    • Citations: 4
  • 📝 Advanced Encryption of Image Based on S-Box and Chaos 2D (LSMCL)
    • Authors: Elazzaby, F., Akkad, N.E., Kabbaj, S.
    • Year: 2020
    • Conference: 1st International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET)
    • Citations: 9
  • 📝 A New Encryption Approach Based on Four-Square and Zigzag Encryption (C4CZ)
    • Authors: Elazzaby, F., El Akkad, N., Kabbaj, S.
    • Year: 2020
    • Conference: Advances in Intelligent Systems and Computing
    • Citations: 13

Sachin Kumar Verma | Computer Science | Best Researcher Award

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Mr. Sachin Kumar Verma | Computer Science | Best Researcher Award

Senior Executive and Researcher at Samsung SDS, India

Sachin Kumar Verma is a highly skilled researcher and developer with a robust educational background in Computer Science and Engineering. Holding an M.Tech from IIITDM Jabalpur and a B.Tech from NIET Gr. Noida, Verma has demonstrated a strong grasp of advanced topics such as Machine Learning and IoT. His technical proficiency spans programming languages, hardware integration, and dynamic problem-solving. 🌟

Professional profile

Education📚

Sachin Kumar Verma has a solid educational foundation in Computer Science and Engineering. He completed his M.Tech from IIITDM Jabalpur with a CGPA of 7.7 and his B.Tech from NIET Gr. Noida with a CGPA of 7.14. This educational background provides him with a strong theoretical and practical understanding of the field, which is crucial for research excellence.

Professional Experience🏛️

Sachin’s experience at Samsung SDS as a Senior Executive and Software Developer Intern showcases his practical expertise in software development, specifically in ABAP, SAP S/4 HANA, and SAP Hybris Marketing. His role involved advanced programming and project work, which enhances his research and problem-solving skills. Additionally, his position as a Teaching Assistant at IIITDM Jabalpur allowed him to impart knowledge on data structures and algorithms, further enriching his research skills through teaching.

Research Interest🌐

During his tenure at IIITDM Jabalpur, Verma worked on a significant research project related to mitigating DAO Insider Attacks in RPL-based IoT Networks. This work, published in the IEEE Region 10 Symposium, reflects his ability to address complex problems in IoT networks. This research demonstrates his capability to contribute to the field of computer science and engineering through practical and theoretical advancements.

Awards and Honors🏆

Sachin Kumar Verma has been recognized for his contributions to the field through notable awards and achievements. He secured 1st prize at a Hackathon held at Sharda University in 2019 and played a key role in the Smart India Hackathon (SIH) 2019 as a volunteer. 🏆 His research work, particularly on mitigating DAO Insider Attacks in IoT networks, has been published in prestigious IEEE conferences, showcasing his dedication to addressing complex technological challenges. 📜

Achievements🏅

His project on S-MAV (Smart Vehicle) utilized a range of technologies and tools, demonstrating his ability to apply theoretical knowledge to real-world problems. Winning 1st prize in a Hackathon and volunteering for the Smart India Hackathon further illustrate his commitment to innovation and problem-solving in technology.

Publications top noted📜

Yiren Chen | Computer Science | Best Researcher Award

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Dr Yiren Chen | Computer Science | Best Researcher Award

research associate at Institute of Information Engineering, Chinese Academy of Sciences, China

Dr. Yiren Chen, a PhD student at the Institute of Information Engineering, Chinese Academy of Sciences, specializes in cyberspace security. He has contributed to various projects, including Internet of Vehicles security management (2022), robot simulation control (2021), and the development of a white flow filtering system (2021). Currently, he is focusing on the application of large language models in cyberspace security (2023-present). Dr. Chen has published two SCI-indexed papers, one EI-indexed conference paper, and a book. His notable work includes the paper “A Survey of Large Language Models for Cyber Threat Detection,” published in the journal Computers & Security in 2024. This paper highlights the significant advancements and key issues in network threat detection using large language models and proposes future research directions. Dr. Chen’s ongoing research and practical contributions to cybersecurity have been instrumental in developing products applied in national security projects.

Professional profile

Education and Qualifications

Dr. Yiren Chen is a PhD student at the Institute of Information Engineering, Chinese Academy of Sciences. His specialization is in Cyberspace Security, particularly in Cyberspace Security Situation Awareness. His educational background and current academic pursuits demonstrate a strong foundation in a highly relevant and specialized area.

Research Experience and Projects

Dr. Chen has participated in various significant research projects, including:

  • Internet of Vehicles security management (2022)
  • Robot simulation control and white flow filtering system development (2021)
  • Application research of large language models on cyberspace security (2023-present)

His involvement in these projects highlights his practical and theoretical contributions to the field of cybersecurity.

Publications and Academic Achievements

Dr. Chen has published two SCI-indexed papers, one EI-indexed conference paper, and authored a book. This publication record is impressive for a PhD student and indicates active and successful engagement in research. Notably, his paper titled “A Survey of Large Language Models for Cyber Threat Detection” has been published in a recognized journal, reflecting the relevance and impact of his work.

Contributions to Research and Development

Dr. Chen’s research focuses on applying large language models like GPT and BERT to cybersecurity challenges. His contributions include developing practical products for national security projects and publishing research papers that explore the application and potential of these models in cyber threat detection. His work is forward-looking and addresses key issues in the field, making significant strides in both theoretical and practical aspects of cybersecurity.

Conclusion

Considering Dr. Chen’s strong educational background, active research involvement, notable publications, and contributions to cybersecurity, he appears to be a highly deserving candidate for the “Best Researcher Award.” His work not only advances the academic field but also has practical implications for national security, highlighting his comprehensive impact on the discipline.

Publications top noted📜
  • Article
    • Topic: A survey of large language models for cyber threat detection
    • Year: 2024
    • Journal: Computers and Security 🖥️🔒
  • Conference Paper
    • Topic: Towards the Digital Twin Model of Li-Ion Batteries: State-of-Health (SoH) Prediction
    • Year: 2023
    • Journal: Lecture Notes in Electrical Engineering 🔋📘
  • Conference Paper (Open Access)
    • Topic: The Scheme for SOC Estimation of Lithium-ion Batteries based on EQ-OCV-Ah-EKF
    • Year: 2023
    • Journal: Journal of Physics: Conference Series 🔋📚