Khrystyna Lipianina-Honcharenko | Computer Science | Young Scientist Award

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Dr. Khrystyna Lipianina-Honcharenko | Computer Science | Young Scientist Award

Associate professor, Ph.D. in information technologies at West Ukrainian National University, Ukraine

Khrystyna Lipianina-Honcharenko is a promising candidate for the Young Scientist Award due to her strong academic background and substantial contributions to research in information technology, machine learning, and socio-economic modeling. Holding a PhD in Technical Sciences and serving as an Associate Professor at the West Ukrainian National University, she has extensive experience in both teaching and research. Khrystyna is involved in high-impact international projects, such as TruScanAI and Erasmus+ initiatives, demonstrating her leadership and collaboration in cutting-edge technological advancements. Her research on data analysis, simulation, and machine learning positions her at the forefront of modern scientific inquiry. While her proficiency in English and publication presence are notable, further enhancement of her language skills and expanding her network in global research circles could increase her influence. Overall, Khrystynaโ€™s innovative research and leadership make her a strong contender for the award, with significant potential for future contributions to the scientific community.

Professional Profileย 

Education๐ŸŽ“

Khrystyna Lipianina-Honcharenko has an extensive educational background, primarily from West Ukrainian National University, where she has completed multiple degrees. She holds a Bachelor’s degree in Economic Cybernetics (2007โ€“2011), followed by a Masterโ€™s in Information Technologies in Economics (2011โ€“2012). Khrystyna continued her academic journey as a postgraduate student at the Department of Economic Cybernetics and Informatics, earning a PhD in Technical Sciences in Information Technology (2019). Her academic pursuits are ongoing, as she is currently working towards her Doctor of Technical Sciences degree in the Department of Information Computer Systems and Control at the same university, which she is expected to complete in 2025. Her education reflects a strong foundation in both the technical and economic aspects of information systems, further enhanced by her focus on machine learning and data analysis. This solid academic background has significantly contributed to her research and teaching expertise.

Professional Experience๐Ÿ“

Khrystyna Lipianina-Honcharenko has a rich professional experience in academia, primarily at West Ukrainian National University (WNU). She began her career as a Laboratory Assistant in the Department of Economic Cybernetics and Informatics from 2012 to 2014, where she gained foundational experience in research and teaching. Khrystyna then advanced to the role of Lecturer in the same department from 2013 to 2020, and later became a Senior Lecturer in the Department of Information Computer Systems and Control from 2020 to 2021. Her expertise was further recognized when she was promoted to Associate Professor in 2021, a position she holds currently. Throughout her career, Khrystyna has not only contributed to teaching but has also been actively involved in research, particularly in areas such as machine learning, data analysis, and socio-economic modeling. Her experience spans both academic instruction and hands-on involvement in high-impact international research projects, highlighting her leadership and expertise.

Research Interest๐Ÿ”Ž

Khrystyna Lipianina-Honcharenkoโ€™s research interests lie at the intersection of information technology, machine learning, and socio-economic modeling. She is particularly focused on data analysis, simulation, and the application of artificial intelligence methods in cyber-physical systems. Her work explores the use of machine learning techniques to model and forecast socio-economic processes, aiming to improve decision-making in various fields, including economics and technology. Khrystyna has also contributed to innovative projects like TruScanAI, which uses AI to detect fake information, and Auralisation of Acoustic Heritage Sites, which combines augmented and virtual reality to preserve cultural heritage. Her research interests extend to structural and statistical identification of hierarchical objects, as well as the development of tools for analyzing complex systems. Through these endeavors, Khrystyna seeks to advance the integration of technology and data-driven methods in solving real-world challenges, particularly in the context of socio-economic systems and information technologies.

Award and Honor๐Ÿ†

Khrystyna Lipianina-Honcharenko has been recognized for her significant contributions to research and education, particularly in the fields of information technology and machine learning. While specific awards and honors are not detailed in the available information, her involvement in prestigious international projects such as Erasmus+ and her participation in high-impact research initiatives like TruScanAI and Auralisation of Acoustic Heritage Sites underscore her academic and professional recognition. These projects highlight her leadership and innovation, earning her respect within the academic community. Additionally, her active role in the Erasmus+ KA2 Work4CE program demonstrates her commitment to advancing higher education and interdisciplinary collaboration. Khrystynaโ€™s extensive publication record and contributions to scientific advancements further demonstrate her growing influence in her field. As she continues to contribute to international collaborations and projects, it is likely that her efforts will lead to more formal recognitions and awards, further solidifying her place as a leader in her research domain.

Research Skill๐Ÿ”ฌ

Khrystyna Lipianina-Honcharenko possesses a diverse and robust set of research skills, particularly in the areas of data analysis, machine learning, and modeling of socio-economic processes. She is proficient in programming languages such as R and Python, which are essential for data processing, algorithm development, and machine learning applications. Her expertise extends to using various application packages like MS Excel, Mathcad, AnyLogic, and GeoDa, allowing her to model complex systems and analyze large datasets effectively. Khrystyna is well-versed in both qualitative and quantitative research methodologies, including structural and statistical identification of hierarchical objects, a skill she applied in projects related to cyber-physical systems. Her ability to combine technical knowledge with socio-economic modeling enables her to tackle interdisciplinary research challenges. Moreover, her involvement in international projects showcases her capacity for collaborative, cross-cultural research, further enhancing her adaptability and competence in applying advanced research techniques in diverse contexts.

Conclusion๐Ÿ’ก

Khrystyna Lipianina-Honcharenko is a strong candidate for the Young Scientist Award, thanks to her academic accomplishments, innovative research projects, and leadership in international collaborations. Her dedication to the field of information technology, machine learning, and socio-economic modeling positions her as an emerging scientist with significant potential for future contributions. With continued professional development in areas such as language proficiency and broader networking, Khrystyna could enhance her impact and further distinguish herself in her field.

Publications Top Notedโœ๏ธ

  • Title: Decision tree based targeting model of customer interaction with business page
    Authors: H Lipyanina, A Sachenko, T Lendyuk, S Nadvynychny, S Grodskyi
    Year: 2020
    Citations: 37

  • Title: Economic Crime Detection Using Support Vector Machine Classification
    Authors: A Krysovatyy, H Lipyanina-Goncharenko, S Sachenko, O Desyatnyuk
    Year: 2021
    Citations: 25

  • Title: Assessing the investment risk of virtual IT company based on machine learning
    Authors: H Lipyanina, V Maksymovych, A Sachenko, T Lendyuk, A Fomenko, I Kit
    Year: 2020
    Citations: 24

  • Title: Targeting Model of HEI Video Marketing based on Classification Tree
    Authors: H Lipyanina, S Sachenko, T Lendyuk, A Sachenko
    Year: 2020
    Citations: 22

  • Title: Concept of the intelligent guide with AR support
    Authors: K Lipianina-Honcharenko, R Savchyshyn, A Sachenko, A Chaban, I Kit
    Year: 2022
    Citations: 19

  • Title: Intelligent Method of a Competitive Product Choosing based on the Emotional Feedbacks Coloring
    Authors: R Gramyak, H Lipyanina-Goncharenko, A Sachenko, T Lendyuk
    Year: 2021
    Citations: 19

  • Title: Method of detecting a fictitious company on the machine learning base
    Authors: H Lipyanina, S Sachenko, T Lendyuk, V Brych, V Yatskiv, O Osolinskiy
    Year: 2021
    Citations: 17

  • Title: Multiple regression method for analyzing the tourist demand considering the influence factors
    Authors: V Krylov, A Sachenko, P Strubytskyi, D Lendiuk, H Lipyanina
    Year: 2019
    Citations: 13

  • Title: Recognizing the Fictitious Business Entity on Logistic Regression Base
    Authors: A Krysovatyy, K Lipianina-Honcharenko, S Sachenko, O Desyatnyuk
    Year: 2022
    Citations: 9

  • Title: ะกัƒั‡ะฐัะฝั– ั–ะฝั„ะพั€ะผะฐั†ั–ะนะฝั– ั‚ะตั…ะฝะพะปะพะณั–ั—
    Authors: ะžะ’ ะ’ะพะฒะบะพะดะฐะฒ, ะฅะ’ ะ›ั–ะฟโ€™ัะฝั–ะฝะฐ
    Year: 2017
    Citations: 9

  • Title: Classification Method of Fictitious Enterprises Based on Gaussian Naive Bayes
    Authors: A Krysovatyy, H Lipyanina-Goncharenko, O Desyatnyuk, S Sachenko
    Year: 2021
    Citations: 8

  • Title: Intelligent information system for product promotion in internet market
    Authors: K Lipianina-Honcharenko, C Wolff, A Sachenko, O Desyatnyuk
    Year: 2023
    Citations: 7

  • Title: An intelligent method for forming the advertising content of higher education institutions based on semantic analysis
    Authors: K Lipianina-Honcharenko, T Lendiuk, A Sachenko, O Osolinskyi
    Year: 2021
    Citations: 7

  • Title: Intelligent waste-volume management method in the smart city concept
    Authors: K Lipianina-Honcharenko, M Komar, O Osolinskyi, V Shymanskyi
    Year: 2023
    Citations: 6

  • Title: Intelligent method for classifying the level of anthropogenic disasters
    Authors: K Lipianina-Honcharenko, C Wolff, A Sachenko, I Kit, D Zahorodnia
    Year: 2023
    Citations: 6

Fengyu Liu | Computer Science | Best Researcher Award

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Dr. Fengyu Liu | Computer Science | Best Researcher Award

PhD candidate at Southeast University, China

Fengyu Liu is a dedicated researcher specializing in deep learning, integrated navigation, intelligent unmanned systems, multi-sensor fusion, and SLAM (Simultaneous Localization and Mapping). He has authored 10 academic papers, including 5 SCI-indexed Q1 journal articles, and has contributed significantly to the fields of robotics and sensor technology. With 5 domestic invention patents and 1 PCT patent, his work demonstrates a strong focus on innovation. He has received numerous awards, including the National Scholarship and the Southeast University ‘Zhishan’ Scholarship, and has won four national and provincial-level first prizes in student competitions. He actively participates in academic conferences and serves as a reviewer for IEEE TIM, IEEE Sensor Journal, and MST journals. His research contributions and leadership in the academic community make him a promising figure in the field of intelligent navigation and robotics.

Professional Profile

Education

Fengyu Liu earned his B.S. degree in Electronic Science and Technology from the School of Instrument and Electronics, North University of China, in 2020. Currently, he is pursuing a Ph.D. in Instrument Science and Technology at the School of Instrument Science and Engineering, Southeast University, Nanjing, China. His doctoral research focuses on deep learning-driven navigation, SLAM, and multi-sensor fusion for intelligent unmanned systems. Throughout his academic journey, he has been recognized for his outstanding performance, receiving prestigious scholarships and awards for academic excellence and research contributions.

Professional Experience

During his undergraduate studies, Fengyu Liu served as the Chair of the Embedded Laboratory at the Innovation Elite Research Institute, where he led multiple student research projects. He has been actively involved in presenting at international conferences, including the 2023 International Conference on Robotics, Control, and Vision Engineering (Tokyo) and the China-Russia “Navigation and Motion Control” Youth Forum (2024, Nanjing). His research findings have been published in top-tier journals, and he has contributed as a reviewer for leading IEEE journals. His expertise in SLAM, sensor fusion, and AI-driven navigation technologies has led to patents and real-world applications, making him a key contributor to the advancement of autonomous systems and intelligent robotics.

Research Interests

Fengyu Liu’s research focuses on deep learning, integrated navigation, intelligent unmanned systems, multi-sensor fusion, and simultaneous localization and mapping (SLAM). His work explores advanced sensor fusion techniques, including the integration of LiDAR, cameras, inertial measurement units (IMUs), and deep learning models to enhance navigation accuracy and autonomy in complex environments. He is particularly interested in developing robust localization algorithms for dynamic and unstructured environments, with applications in robotics, autonomous vehicles, and aerospace navigation. His contributions to AI-driven SLAM and vision-based perception systems aim to improve real-time mapping, object recognition, and motion estimation for next-generation autonomous systems.

Awards and Honors

Fengyu Liu has received multiple prestigious awards, including the National Scholarship and the Southeast University ‘Zhishan’ Scholarship, recognizing his academic excellence. He has won four first prizes at national and provincial-level university student competitions, demonstrating his problem-solving skills and technical expertise. His research has also been recognized at academic conferences, earning him the Outstanding Paper Award at the 2022 Science and Technology Workers Seminar of the Chinese Society of Inertial Technology. His participation in international research forums, such as the China-Russia “Navigation and Motion Control” Youth Forum (2024, Nanjing), further highlights his growing impact in the field.

Research Skills

Fengyu Liu possesses a diverse skill set in deep learning, computer vision, and multi-sensor data fusion, particularly for robotics and autonomous navigation. He is proficient in developing AI-based SLAM algorithms, sensor calibration techniques, and real-time embedded system implementations. His expertise extends to software tools and programming languages, including Python, MATLAB, C++, TensorFlow, and PyTorch, which he utilizes for machine learning and signal processing applications. He has hands-on experience with robotic perception systems, LiDAR-based mapping, and inertial navigation technologies, contributing to multiple high-impact research projects. Additionally, his role as a peer reviewer for IEEE TIM, IEEE Sensor Journal, and MST journals reflects his strong analytical and critical evaluation skills in cutting-edge research.

Conclusion

Fengyu Liu is a highly promising young researcher with strong academic contributions, patents, and international recognition. His research in SLAM, deep learning, and multi-sensor fusion aligns with cutting-edge advancements in robotics and AI. His leadership roles, awards, and editorial responsibilities further strengthen his profile.

For the Best Researcher Award, he is a strong candidate, but additional international collaborations, funded research projects, and industry partnerships could further enhance his competitiveness for top-tier global research awards.

Publications Top Noted

  • Confidence Factor Based Robust Localization Algorithm with Visual-Inertial-LiDAR Fusion in Underground Space

  • LiDAR-Aided Visual-Inertial Odometry Using Line and Plane Features for Ground Vehicles

    • Authors: Jianfeng Wu, Xianghong Cheng, Fengyu Liu, Xingbang Tang, Wengdong Gu
    • Year: 2025
    • DOI: 10.1109/TVT.2025.3527472

  • Spatial Feature Recognition and Layout Method Based on Improved CenterNet and LSTM Frameworks

  • Transformer-Based Local-to-Global LiDAR-Camera Targetless Calibration With Multiple Constraints

  • Spacecraft-DS: A Spacecraft Dataset for Key Components Detection and Segmentation via Hardware-in-the-Loop Capture

  • A Visual SLAM Method Assisted by IMU and Deep Learning in Indoor Dynamic Blurred Scenes

  • A Spatial Layout Method Based on Feature Encoding and GA-BiLSTM

  • Combination of Iterated Cubature Kalman Filter and Neural Networks for GPS/INS During GPS Outages

    • Authors: Fengyu Liu, Xiaohong Sun, Yufeng Xiong, Huang Haoqian, Xiao-ting Guo, Yu Zhang, Chong Shen
    • Year: 2019
    • DOI: 10.1063/1.5094559

Volodymyr Polishchuk | Computer Science | Best Researcher Award

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Prof. Volodymyr Polishchuk | Computer Science | Best Researcher Award

Uzhhorod National University, Ukraine

Volodymyr Polishchuk is a distinguished academic specializing in information technology, fuzzy systems, and decision-making models. Currently serving as a Professor at both Uzhhorod National University in Ukraine and the Technical University of Koลกice in Slovakia, he has made significant contributions to the fields of artificial intelligence, risk assessment, and sustainable tourism. With a career spanning over a decade, he has co-authored numerous publications, including journal articles and book chapters, focusing on the application of advanced decision models in various sectors. His research is internationally recognized, and he is an active member of several academic networks. He is known for his interdisciplinary approach, bridging information technology with real-world challenges such as healthcare, aviation education, and urban development.

Professional Profileย 

Education

Volodymyr Polishchuk holds a prestigious Doctor of Sciences (DrSc.) degree from Uzhhorod National University, where he also completed his undergraduate and graduate education. His academic journey in information technology, mathematics, and fuzzy systems laid a strong foundation for his future research and teaching. As a professor at the university, he has guided numerous students and collaborated on innovative projects. Additionally, his academic credentials are complemented by his position at the Technical University of Koลกice in Slovakia, where he continues to contribute to cutting-edge research in his fields of expertise. His educational background supports his broad interdisciplinary approach, allowing him to address complex problems in various domains such as tourism, healthcare, and risk management.

Professional Experience

Professor Polishchuk has been a dedicated faculty member at Uzhhorod National University since 2011, where he teaches and conducts research at the Faculty of Information Technology. Over the years, he has gained recognition for his expertise in decision-making models and fuzzy systems. In addition to his role at Uzhhorod, he has been a professor at the Technical University of Koลกice, Slovakia. His professional experience extends beyond teaching, as he has collaborated on numerous international research projects and published widely in top-tier journals. He has also worked on hybrid decision models for risk assessment in sectors such as sustainable tourism, healthcare, and aviation education. His leadership in academic research has earned him recognition through various academic platforms, and he continues to actively engage with the global research community.

Research Interests

Volodymyr Polishchukโ€™s research primarily focuses on information technology, fuzzy systems, and decision-making models, with a particular emphasis on their practical applications across various industries. He is deeply engaged in developing hybrid models for evaluating complex processes, such as tourism sustainability, risk assessment, and healthcare outcomes. His work also explores the integration of artificial intelligence in decision-making, specifically in aviation education and urban development. Additionally, he is interested in the application of multicriteria decision analysis (MCDA) in solving real-world challenges. Polishchuk’s interdisciplinary approach allows him to connect cutting-edge technology with pressing global issues, contributing valuable insights to sectors like smart cities, start-up financing, and pandemic management. His research has significant implications for optimizing resource allocation, improving system efficiency, and mitigating risks in both public and private sectors.

Awards and Honors

Throughout his academic career, Volodymyr Polishchuk has earned several prestigious honors and recognition for his contributions to research and education. His interdisciplinary approach to problem-solving has led to numerous successful collaborations with leading academic and industry experts across Europe. He has been acknowledged by his peers for his innovative contributions to the fields of fuzzy logic, decision support systems, and sustainability models. Polishchukโ€™s research papers are widely cited, indicating the significant impact his work has had on the academic community. His exceptional leadership in research has also helped foster international collaborations, particularly in the development of sustainable tourism models and risk assessment frameworks for emerging sectors. His continued excellence in academia and research is further demonstrated by his involvement in high-impact projects and his active participation in global conferences.

Publications Top Noted

  1. Artificial Intelligence Technology for Assessing the Practical Knowledge of Air Traffic Controller Students Based on Their Responses in Multitasking Situations
    • Authors: Antoลกko, M., Polishchuk, V., Kelemen, M., Korniienko, A., Kelemen, M.
    • Year: 2025
    • Journal: Applied Sciences (Switzerland)
    • Volume: 15(1), 308
    • Citations: 0
  2. A large-scale decision-making model for the expediency of funding the development of tourism infrastructure in regions
    • Authors: Skare, M., Gavurova, B., Polishchuk, V.
    • Year: 2025
    • Journal: Expert Systems
    • Volume: 42(1), e13443
    • Citations: 1
  3. On Convergence of the Uniform Norm and Approximation for Stochastic Processes from the Space Fฯˆ(ฮฉ)
    • Authors: Rozora, I., Mlavets, Y., Vasylyk, O., Polishchuk, V.
    • Year: 2024
    • Journal: Journal of Theoretical Probability
    • Volume: 37(2), pp. 1627โ€“1653
    • Citations: 0
  4. THE IMPACT OF DIGITAL DISINFORMATION ON QUALITY OF LIFE: A FUZZY MODEL ASSESSMENT
    • Authors: Gavurova, B., Moravec, V., Hynek, N., Petruzelka, B., Stastna, L.
    • Year: 2024
    • Journal: Technological and Economic Development of Economy
    • Volume: 30(4), pp. 1120โ€“1145
    • Citations: 0
  5. An information-analytical system for assessing the level of automated news content according to the population structure โ€“ A platform for media literacy system development
    • Authors: Gavurova, B., Skare, M., Hynek, N., Moravec, V., Polishchuk, V.
    • Year: 2024
    • Journal: Technological Forecasting and Social Change
    • Volume: 200, 123161
    • Citations: 0
  6. Decision Support System Regarding the Possibility of Financing Cross-Border Cooperation Projects
    • Authors: Polishchuk, V., Kelemen, M., Polishchuk, I., Kelemen, M.
    • Year: 2024
    • Conference: CEUR Workshop Proceedings
    • Volume: 3702, pp. 58โ€“71
    • Citations: 0
  7. Hybrid Mathematical Model of Risk Assessment of UAV Flights Over Airports
    • Authors: Polishchuk, V., Kelemen, M., Kelemen, M., Scerba, M.
    • Year: 2024
    • Conference: New Trends in Civil Aviation
    • Citations: 0
  8. A Fuzzy Multicriteria Model of Sustainable Tourism: Examples From the V4 Countries
    • Authors: Skare, M., Gavurova, B., Polishchuk, V.
    • Year: 2024
    • Journal: IEEE Transactions on Engineering Management
    • Volume: 71, pp. 12182โ€“12193
    • Citations: 6
  9. Fuzzy multicriteria evaluation model of cross-border cooperation projects under resource curse conditions
    • Authors: Skare, M., Gavurova, B., Polishchuk, V.
    • Year: 2023
    • Journal: Resources Policy
    • Volume: 85, 103871
    • Citations: 3
  10. A fuzzy model for evaluating the level of satisfaction of tourists regarding accommodation establishments according to social class on the example of V4 countries
  • Authors: Skare, M., Gavurova, B., Polishchuk, V., Nawazish, M.
  • Year: 2023
  • Journal: Technological Forecasting and Social Change
  • Volume: 193, 122609
  • Citations: 7

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 Profileย 

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

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, Chinaย 

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 โ€œWorldโ€™s Top 2% Scientistโ€ 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 “Worldโ€™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 Zhuangโ€™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 noted๐Ÿ“œ
  • 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 Masterโ€™s degree in Computer Science from Hazara University, with a focus on wireless sensor networks. His thesis, titled โ€œAdaptive and Priority-Based Data Aggregation and Scheduling Model for Wireless Sensor Networks,โ€ 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 noted๐Ÿ“œ
  • 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), Chinaย 

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. ๐Ÿš€

Professional profile

Education๐Ÿ“š

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. ๐ŸŽ“๐Ÿ“š

Professional Experience๐Ÿ›๏ธ

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. ๐Ÿ› ๏ธ๐Ÿ“ˆ

Research Interest๐ŸŒ

Xuโ€™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. ๐Ÿง ๐Ÿ”

Awards and Honors๐ŸŽ“
  • Principal Investigator for multiple high-profile projects funded by the National Natural Science Foundation of China. ๐Ÿ†
  • Contributor to leading journals such as IEEE Transactions on Image Processing and IEEE/CVF International Conference on Computer Vision. ๐Ÿฅ‡๐Ÿ“œ
Achievements๐Ÿ…
  • Principal Investigator for multiple high-profile projects funded by the National Natural Science Foundation of China. ๐Ÿ†
  • Lead Author on influential papers in top journals such as IEEE Transactions on Image Processing and IEEE/CVF International Conference on Computer Vision. ๐Ÿ“œ
  • Innovator in Image Processing with a focus on deep learning, defocus deblurring, image inpainting, and texture representation. ๐Ÿง 
  • Received Grants from provincial and national science foundations for cutting-edge research projects. ๐Ÿ’ต
  • Contributed to High-Impact Publications with significant citations, reflecting the impact of research on the field. ๐Ÿ“š
  • Collaborated with Leading Researchers and institutions, enhancing the reach and application of his research findings. ๐Ÿค
Publications top noted๐Ÿ“œ
  • “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 ๐Ÿ“ˆ
  • “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 ๐Ÿ“ˆ
  • “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 ๐Ÿ“ˆ
  • “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 ๐Ÿ“ˆ
  • “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 ๐Ÿ“ˆ
  • “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 ๐Ÿ“ˆ
  • “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 ๐Ÿ“ˆ
  • “Deep scale-aware image smoothing”
    Authors: J Li, K Qin, R Xu, H Ji
    Conference: ICASSP 2022
    Year: 2022
    Citations: 7 ๐Ÿ“ˆ
  • “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 ๐Ÿ“ˆ
  • “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 ๐Ÿ“ˆ
  • “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 ๐Ÿ“ˆ
  • “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 ๐Ÿ“ˆ
  • “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 ๐Ÿ“ˆ

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. ๐ŸŒ๐Ÿ“š

Professional profile

Education๐Ÿ“š

Fouzia holds a Doctorate in Informatics from FSK UIT, Kรฉnitra, completed in May 2023. She also has a Masterโ€™s degree in Informatics, Graphics, and Imaging (M3I) from FSDM, Fes, obtained in July 2012, and a Bachelorโ€™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. ๐ŸŽ“๐Ÿ’ป

Professional Experience๐Ÿ›๏ธ

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 Supรฉrieure de Fรจs and the Ecole Nationale des Sciences Appliquรฉes de Fรจs. She also worked as a trainer at Atlas Engineering & Consulting Society, where she contributed her expertise in 2022-2023. ๐Ÿง‘โ€๐Ÿซ๐Ÿข

Research Interest๐ŸŒ

Fouziaโ€™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. ๐Ÿ”’๐Ÿ–ผ๏ธ

Achievements๐Ÿ…
  • ๐Ÿ… Doctorate in Informatics: Earned in May 2023 from FSK UIT, Kรฉnitra.
  • ๐Ÿ–ฅ๏ธ Masterโ€™s Degree in Informatics, Graphics, and Imaging (M3I): Completed in July 2012 from FSDM, Fes.
  • ๐Ÿ“œ Bachelorโ€™s Degree in Mathematics and Computer Science: Obtained in July 2008 from FSDM, Fes.
  • ๐Ÿง‘โ€๐Ÿซ Over a Decade of Teaching Experience: Teaching at the Office of Vocational Training and Employment Promotion since 2009.
  • ๐ŸŽ“ Visiting Professor: Held positions at Ecole Normale Supรฉrieure de Fรจs and Ecole Nationale des Sciences Appliquรฉes de Fรจs.
  • ๐Ÿ” Published Multiple Research Papers: Authored articles in reputable journals and conferences, focusing on advanced image encryption techniques.
  • ๐Ÿ“š Contributed to Book Chapters: Co-authored chapters on encryption algorithms in well-regarded publications.
  • ๐Ÿ—ฃ๏ธ Oral Presentations at International Conferences: Presented research findings at several prestigious conferences in Morocco and abroad.
  • ๐Ÿ’ผ Trainer at Atlas Engineering & Consulting Society: Provided professional training in 2022-2023.
  • ๐ŸŒ Multilingual: Fluent in Arabic, French, and English, enabling effective communication and collaboration across different regions.
Publications top noted๐Ÿ“œ
  • ๐Ÿ“ 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
  • ๐Ÿ“ 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
  • ๐Ÿ“ Color Image Encryption Using a Zigzag Transformation and Sineโ€“Cosine Maps
    • Authors: Elazzaby, F., Sabour, K.H., Elakkad, N., Torki, A., Rajkumar, S.R.
    • Year: 2023
    • Journal: Scientific African
    • Citations: 1
  • ๐Ÿ“ 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
  • ๐Ÿ“ 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
  • ๐Ÿ“ 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
  • ๐Ÿ“ 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. ๐ŸŒŸ

Professional profile

Education๐Ÿ“š

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 Experience๐Ÿ›๏ธ

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 Interest๐ŸŒ

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 Honors๐Ÿ†

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. ๐Ÿ† 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. ๐Ÿ“œ

Achievements๐Ÿ…

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 noted๐Ÿ“œ

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 noted๐Ÿ“œ
  • Article
    • Topic: A survey of large language models for cyber threat detection
    • Year: 2024
    • Journal: Computers and Security ๐Ÿ–ฅ๏ธ๐Ÿ”’
  • 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 ๐Ÿ”‹๐Ÿ“˜
  • 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 ๐Ÿ”‹๐Ÿ“š