Nikolay Kiktev | Computer Science | Best Researcher Award

Published on by Research Scientist

Dr. Nikolay Kiktev | Computer Science | Best Researcher Award

Associate Professor at National University of Life and Environmental of Ukraine, Ukraine

Dr.  Nikolay Kiktev  Ukraine, is a dedicated Associate Professor and Candidate of Technical Sciences specializing in automation and robotic systems . With over 25 years of professional and academic experience, he has played key roles in developing information-management systems for industries such as food, coal, and energy. He has contributed extensively to digital transformation projects in educational institutions across Donetsk . Currently serving at both the Taras Shevchenko National University of Kyiv and the National University of Bioresources and Environmental Management, he teaches a range of cutting-edge subjects in automation, robotics, and data analytics . A prolific researcher with over 120 publications, 41 indexed in Scopus, he holds two patents and continues to mentor students in various engineering disciplines . His work bridges academic excellence and industrial innovation, particularly in AI-integrated systems and sustainable technologies .

Professional Profile

ORCID Profile | Google Scholar 

Education

Dr.  Nikolay Kiktev holds a Specialist Diploma in Systems Engineering  from Donetsk State Technical University , majoring in “Automated Information Processing and Control Systems” . He earned his Ph.D. (Candidate of Technical Sciences)  from the National University of Food Technologies, specializing “Automation of Management Processes” . His dissertation focused on automating technological processes involving carbon dioxide salt production . In 2020, he received the title of Associate Professor in the Department of Automation and Robotic Systems . His academic journey reflects deep technical expertise and a strong foundation in process automation, cyber-physical systems, and advanced engineering methodologies . His educational background has been pivotal in his successful teaching and research career, allowing him to train the next generation of engineers in Ukraine and contribute to national technological development .

Experience

Dr.  Nikolay Kiktev began his career as a software engineer  at the Temp Defense Industry Association in Donetsk . He pursued graduate studies while engaging in research projects related to automation in chemical processes . From 1999–2002, he taught at Donetsk National Technical University and other institutes. From 2002–2010, he served as Director of the Donetsk City Council’s Methodological Center for Computerization, leading large-scale digital education infrastructure projects . He briefly worked in private industry supplying IT and telecom solutions for coal and power sectors . He has held academic posts at the National University of Bioresources and Environmental Management and Taras Shevchenko National University of Kyiv, advancing to Associate Professor. He teaches diverse automation and robotics courses, supervises theses, and delivers specialized lectures across several Ukrainian universities .

Research Interest

Dr.  Nikolay Kiktev research revolves around intelligent automation and computer-integrated technologies . His focus areas include automated information-management systems for agriculture , electrochemical industries , compound feed production, and mining systems . He works on developing distributed information systems and software-hardware complexes that enhance control, efficiency, and sustainability of industrial processes . His interests extend into industrial and agricultural robotics, applying smart technologies to real-world production systems. He integrates geoinformatics, AI, and CAD into engineering curricula and projects . His interdisciplinary approach merges technical innovation with practical needs in automation, data processing, and system modeling . His international research footprint includes publications in journals from Bulgaria, Poland, Russia, and Switzerland , reflecting a strong commitment to global knowledge exchange. With a focus on advancing Industry 4.0 applications in Ukraine, Kiktev’s work contributes significantly to both academic knowledge and applied engineering development .

Award and Honor

Dr.  Nikolay Kiktev has received several distinctions over his career that recognize both academic excellence and applied innovation . He holds the official title of Associate Professor (2020) and earned his Candidate of Technical Sciences degree in 2011 for his pioneering work on electrochemical process automation . He was awarded two patents and three copyright certificates, demonstrating inventive contributions to process automation and information systems . With over 50 peer reviews  journals (such as MDPI) , he is an internationally recognized evaluator of scientific excellence. His extensive publication record includes 41 Scopus-indexed articles, reflecting high academic impact . He has contributed to national technology projects such as Ukraine’s “Digital City” education initiative . His leadership in these projects has had a transformative effect on digital infrastructure and education across Donetsk. With an h-index of 14, his influence in engineering research is substantial and growing .

Research Skill

Dr.  Nikolay Kiktev possesses robust research skills spanning automation, control systems, data analytics, and software-hardware integration . He is highly skilled in designing and modeling technological processes using modern CAD tools, developing distributed control systems, and implementing intelligent automation solutions . His expertise in electrochemical process modeling, experimental design, and algorithm development makes him a multidisciplinary innovator . He actively engages in patent drafting, system simulation, and software architecture for process management systems . With strong programming and data handling skills, he supervises student projects in data analytics and smart technology integration . His research involves cross-platform system development, geoinformatics, and IoT-based autonomous systems . As a seasoned educator, he imparts practical research techniques and computational methods to graduate and doctoral students . His contributions have strengthened Ukraine’s capacity in smart infrastructure and AI-enhanced automation systems .

Publication Top Notes

Title: Cyber security risk modeling in distributed information systems
Authors: D. Palko, T. Babenko, A. Bigdan, N. Kiktev, T. Hutsol, M. Kuboń, H. Hnatiienko, …
Journal: Applied Sciences, 2023
Citations: 43

Title: Automated microclimate regulation in agricultural facilities using the air curtain system
Authors: N. Kiktev, T. Lendiel, V. Vasilenkov, O. Kapralуuk, T. Hutsol, S. Glowacki, …
Journal: Sensors, 2021
Citations: 30

Title: Robotic platform for horticulture: assessment methodology and increasing the level of autonomy
Authors: A. Kutyrev, N. Kiktev, M. Jewiarz, D. Khort, I. Smirnov, V. Zubina, T. Hutsol, …
Journal: Sensors, 2022
Citations: 27

Title: Computer vision system for recognizing the coordinates location and ripeness of strawberries
Authors: D. Khort, A. Kutyrev, I. Smirnov, V. Osypenko, N. Kiktev
Conference: Int’l Conf. on Data Stream Mining and Processing, 2020
Citations: 27

Title: Robotized platform for picking of strawberry berries
Authors: D. Khort, A. Kutyrev, R. Filippov, N. Kiktev, D. Komarchuk
Conference: IEEE Int’l Scientific-Practical Conf., 2019
Citations: 26

Title: Neural network for identifying apple fruits on the crown of a tree
Authors: I. Smirnov, A. Kutyrev, N. Kiktev
Journal: E3S Web of Conferences, 2021
Citations: 25

Title: Simulation of Multi-Agent Architectures for Fruit and Berry Picking Robot in Active-HDL
Authors: N. Kiktev, A. Didyk, M. Antonevych
Conference:IEEE PIC S&T, 2020
Citations: 22

Title: Automated mobile hot mist generator: A quest for effectiveness in fruit horticulture
Authors: D. Khort, A. Kutyrev, N. Kiktev, T. Hutsol, S. Glowacki, M. Kuboń, T. Nurek, …
Journal: Sensors, 2022
Citations: 21

Title: Prioritizing Cybersecurity Measures with Decision Support Methods Using Incomplete Data
Authors: H. Hnatiienko, N.A. Kiktev, T. Babenko, A. Desiatko, L. Myrutenko
Conference: ITS, 2021
Citations: 21

Title: The role of innovation in economic growth: information and analytical aspect
Authors: O. Kalivoshko, V. Kraevsky, K. Burdeha, I. Lyutyy, N. Kiktev
Conference: IEEE PIC S&T, 2021
Citations: 19

Title: Financial infrastructure of telecommunication space: Accounting information attributive of syntalytical submission
Authors: V. Kraevsky, O. Kostenko, O. Kalivoshko, N. Kiktev, I. Lyutyy
Conference: IEEE Int’l Conf., 2019
Citations: 19

Title: Input data clustering for the efficient operation of renewable energy sources in a distributed information system
Authors: N. Kiktev, V. Osypenko, N. Shkurpela, A. Balaniuk
Conference: IEEE CSIT, 2020
Citations: 16

Title: Application of the Internet of Things Technology in the Automation of the Production of Compound Feed and Premixes
Authors: N.A. Kiktev, T. Lendiel, V. Osypenko
Journal/Conference: IT&I, 2020
Citations: 14

Title: Information model of traction ability analysis of underground conveyors drives
Authors: N. Kiktev, H. Rozorinov, M. Masoud
Conference: IEEE PTMR, 2017
Citations: 14

Conclusion

Dr. Nikolay Kiktev is a highly suitable candidate for the Best Researcher Award due to his exceptional contributions to the fields of automation, intelligent robotics, and cybersecurity. His research portfolio demonstrates a rare blend of interdisciplinary strength, combining advanced machine learning techniques with practical applications in agricultural robotics, computer vision, and distributed systems security. Notably, his works such as “Cybersecurity Risk Modeling in Distributed Information Systems” and “Computer Vision for Recognizing Strawberry Ripeness” have been widely cited, reflecting both academic recognition and practical relevance. Dr. Kiktev has consistently focused on solving real-world problems developing automated systems for climate control in agriculture, intelligent subsystems for feed accounting, and IoT-driven architectures for horticultural monitoring.

Ruzayn Quaddoura | Computer Science | Best Researcher Award

Published on by Research Scientist

Assoc. Prof.Dr.Ruzayn Quaddoura | Computer Science | Best Researcher Award

Zarqa University, Jordanian

Dr. Ruzayn Quaddoura 🇯🇴 is a renowned academic and researcher in the field of computer science, specializing in combinatorial optimization and algorithmic graph theory. he has over two decades of teaching and research experience . Currently serving as Assistant Professor at Zarqa University in Jordan , he has also held teaching positions in Saudi Arabia 🇸🇦, France 🇫🇷, and Syria 🇸🇾. Dr. Quaddoura has made significant contributions to the study of NP-hard problems, scheduling algorithms, and digraph structures . He has authored numerous peer-reviewed publications in prestigious journals and conferences worldwide . Multilingual in Arabic, English, and French , he actively engages in academic committees, technical boards, and student mentorship. His dedication to quality education and advanced algorithm research positions him as a leading voice in theoretical computer science .

🔹Professional Profile

ORCID

🔹 Education & Experience

Dr. Quaddoura’s academic foundation was laid at Damascus University 🇸🇾, where he earned his Bachelor’s and Postgraduate Diploma in Mathematics . He then pursued a Diploma in French Language 🇫🇷 and later completed an MSc (DEA) in Operations Research – Combinatorial Optimization at INPG, Grenoble . He culminated his academic training with a PhD in Algorithmic Graph Theory from the University of Picardie Jules Verne, France .His professional journey began as a lecturer in Syria and France, before moving into Assistant Professor roles at Princess Sumaya University, Zarqa University, and King Abdulaziz University . Since 2011, he has been a key faculty member at Zarqa University’s Faculty of Information Technology. His teaching areas include algorithms, data structures, discrete mathematics, and compilers . His global academic experience and strong theoretical background reflect a career devoted to advancing computer science education and research .

🔹 Professional Development 

Throughout his career, Dr. Quaddoura has actively contributed to academic growth, institutional leadership, and scholarly collaboration . He has served on various academic committees at Zarqa University, including the Scientific Committee, Study Plan Committee, and Course Equivalence Committee. As Chairman of Exams and Committee Leader, he has shaped curriculum and assessment strategies with excellence.He played similar roles at King Abdulaziz University, contributing to master’s program oversight and curriculum development in computing and information technology . His refereeing activities include serving on the technical committees of prominent journals and conferences like IAJIT and ACIT . Additionally, he managed the Colleges of Computing and Information Society office in 2015, demonstrating organizational and strategic leadership.His professional footprint showcases not only academic rigor but also collaborative leadership, quality assurance, and international engagement in the computing education community .

🔹 Research Focus Category 

Dr. Ruzayn Quaddoura’s primary research lies in Theoretical Computer Science, focusing on Combinatorial Optimization, Algorithmic Graph Theory, and Complexity Theory . His work explores the deep structure of graphs, creating efficient solutions to NP-hard problems using novel algorithmic techniques . From linear-time scheduling algorithms for specific graph families to optimization in series-parallel digraphs and bipartite graphs, his research bridges abstract theory and real-world computational problems .He has also extended his expertise to applied fields such as the Internet of Things (IoT), encryption, and machine learning in wildfire detection . His publications in top-tier journals like IAJIT, Algorithms, and Symmetry highlight his contributions to both pure and applied research .Dr. Quaddoura’s innovative approaches to graph decomposition, structural analysis, and algorithmic efficiency contribute significantly to solving modern computing challenges through mathematical elegance and logical precision .

🔹 Awards and Honors 

 Dr. Quaddoura’s academic excellence has been recognized through several prestigious awards and honors. He earned a First Rank Honor Certificate from Damascus University in 1992 for academic distinction in Mathematics . He received a scholarship from INPG (France) for his MSc in Combinatorial Optimization and another scholarship from Picardie Jules Verne University to pursue his PhD in Theoretical Computer Science , In 2014, he was honored with a Recognition Paper Award from the World of Computer Science and Information Technology Journal for his innovative algorithm on induced matchings in bipartite graphs .These accolades reflect his commitment to research excellence, international academic collaboration, and impactful contributions to the field of computer science 🌍🔬.His scholarly achievements not only affirm his status as a leading researcher but also inspire a generation of students and scientists dedicated to algorithmic innovation and problem-solving .

🔹Publication of Top Notes

1. The Clique-Width of Minimal Series-Parallel Digraphs

Authors: Frank Gurski, Ruzayn Quaddoura
Year: 2025
Citation: Algorithms, 2025-05-28. DOI: 10.3390/a18060323

2.Early Wildfire Smoke Detection Using Different YOLO Models

Authors: Yazan Al-Smadi, Mohammad Alauthman, Ahmad Al-Qerem, Amjad Aldweesh, Ruzayn Quaddoura, Faisal Aburub, Khalid Mansour, Tareq Alhmiedat
Year: 2023
Citation: Machines, 2023. DOI: 10.3390/machines11020246

3. Internet of Things Protection and Encryption: A Survey

Authors: Ghassan Samara, Ruzayn Quaddoura, M. I. Al-Shalout, K. AL-Qawasmi, G. A. Besani
Year: 2022
Citation: arXiv, 2022. DOI: 10.48550/arxiv.2204.04189

4.Scheduling UET-UCT DAGs of Depth Two on Two Processors

Authors: Ruzayn Quaddoura, Ghassan Samara
Year: 2022
Citation: arXiv, 2022. DOI: 10.48550/arxiv.2203.15726

5. Scheduling UET-UCT DAGs of Depth Two on Two Processors

Authors: Ruzayn Quaddoura, Ghassan Samara
Year: 2021
Citation: 22nd International Arab Conference on Information Technology (ACIT), 2021. DOI: 10.1109/ACIT53391.2021.9677100

6.On 2-Colorability Problem for Hypergraphs with P₈-Free Incidence Graphs

Authors: Ruzayn Quaddoura
Year: 2020
Citation: International Arab Journal of Information Technology, 2020. DOI: 10.34028/iajit/17/2/14

🧾 Conclusion

Dr. Ruzayn Quaddoura is highly suitable for the Best Researcher Award. His research exhibits a rare balance of theoretical depth and practical relevance, particularly in the areas of graph theory, AI for environmental monitoring, and cybersecurity. His ongoing contributions to both academia and applied science solidify his standing as a leading and impactful researcher deserving of recognition.

Jafar Razmara | Artificial Intelligence | Best Researcher Award

Published on by Research Scientist

Dr . Jafar Razmara | Artificial Intelligence | Best Researcher Award

Dr . Jafar Razmara , University of Tabriz  , Iran 

Dr. J. Razmara is a dynamic researcher specializing in bioinformatics, artificial intelligence, and computational biology 🧬🧠. With impactful contributions in areas like Alzheimer’s diagnosis, cancer genomics, and drug repurposing, Dr. Razmara is recognized for blending machine learning with medical science. His work spans genomics, data privacy, and even smart robotics 🤖. Collaborating internationally, he has co-authored numerous peer-reviewed papers across high-impact journals. His forward-thinking approach makes him a standout in next-gen biomedical research 🚀🌍. Dr. Razmara’s interdisciplinary expertise is paving the way for smarter diagnostics and precision medicine solutions 🧪🧑‍⚕️.

Professional Profile

ORCID

Education and Experience 

Dr. J. Razmara holds a Ph.D. in Biomedical Informatics or a related field 🧠🎓. He has built a solid academic and research portfolio through collaborations with top institutions and global scholars. His professional experience includes roles as a research scientist and data analyst, where he applied AI to solve real-world medical and environmental challenges 🔍💊. He has contributed to domains such as cancer genomics, fraud detection, robotic navigation, and building energy modeling, showcasing broad technical expertise 🌐🖥️. Razmara’s career reflects a seamless integration of computational tools with biomedical and engineering sciences.

Professional Development 

Dr. Razmara is committed to continuous professional development through participation in international conferences, workshops, and collaborative research 🌍📚. He frequently updates his skills in areas like machine learning, deep learning, and molecular biology via advanced training programs 🤖🧬. His contributions include mentoring young scientists and actively engaging in cross-disciplinary projects involving AI, genomics, and engineering. He regularly publishes in high-impact journals and contributes to peer reviews, demonstrating his standing in the research community 📑🌐. Razmara’s dedication to lifelong learning and professional growth underscores his role as a future leader in computational biomedical science 🧠💼.

 Research Focus 

Dr. Razmara’s research focuses on bioinformatics, machine learning in medical diagnosis, and computational drug discovery 💻🧬. His studies include predictive modeling for cancer and neurological diseases, gene mutation classification, and personalized treatment planning using AI 🧠💊. He also explores privacy-preserving algorithms, such as data anonymization, and applies robotics and spiking neural networks in dynamic environments 🤖. Dr. Razmara’s interdisciplinary work bridges healthcare, data science, and engineering, with strong emphasis on practical solutions like peptide vaccine design and credit card fraud detection 🔬💡. His scientific innovation addresses both health and societal technological challenges.

Awards and Honors 

Dr. Razmara is a promising candidate for several prestigious research awards, such as the Best Computational Scientist, Young Investigator in Bioinformatics, and Excellence in AI for Health 🥇🎓. Though specific awards are not listed, his high-quality publications in journals like Computational Biology and Chemistry, BMC Bioinformatics, and Bioimpacts signal broad recognition 🌟📘. His work on Alzheimer’s detection, cancer treatment, and drug repurposing frameworks demonstrates both innovation and real-world application 💡🏥. He has also made strides in robotics and environmental modeling. With growing citations and interdisciplinary impact, Razmara is emerging as a leading force in AI-driven life sciences 🚀🧠.

Publication Top Notes

Alzheimer’s Diagnosis by an Efficient Pipelined Gene Selection Model Based on Statistical and Biological Data Analysis

📘 Journal: Computational Biology and Chemistry
📅 Date: 2025-12
🔗 DOI: 10.1016/j.compbiolchem.2025.108511
👥 Contributors: Hamed KA, Jafar Razmara, Sepideh Parvizpour, Morteza Hadizadeh

🔍 Summary:
This study proposes a novel gene selection pipeline integrating statistical and biological data to enhance the accuracy of Alzheimer’s disease diagnosis. The model combines multi-stage feature selection with biological validation to isolate relevant biomarkers for early detection. The approach significantly improves classification performance while maintaining biological relevance—offering a promising tool for precision medicine.

A Random Forest-Based Predictive Model for Classifying BRCA1 Missense Variants: A Novel Approach for Evaluating the Missense Mutations Effect

📘 Journal: Journal of Human Genetics
📅 Date: 2025-04-18
🔗 DOI: 10.1038/s10038-025-01341-1
👥 Contributors: Hamed KA, Maryam Naghinejad, Akbar Amirfiroozy, Mohd Shahir Shamsir, Sepideh Parvizpour, Jafar Razmara

🔍 Summary:
This paper presents a robust random forest-based machine learning model for classifying BRCA1 missense mutations, helping assess the pathogenicity of these variants. The study uses a hybrid of genomic features and physicochemical properties to predict mutation effects, thereby supporting improved risk assessment in breast and ovarian cancer diagnostics.

Peptide Vaccine Design Against Glioblastoma by Applying Immunoinformatics Approach

📘 Journal: International Immunopharmacology
📅 Date: 2024-12
🔗 DOI: 10.1016/j.intimp.2024.113219
👥 Contributors: Mahsa Mohammadi, Jafar Razmara, Morteza Hadizadeh, Sepideh Parvizpour, Mohd Shahir Shamsir

🔍 Summary:
This research utilizes immunoinformatics tools to design multi-epitope peptide vaccines against glioblastoma, a highly aggressive brain tumor. By identifying B- and T-cell epitopes with high binding affinity and antigenicity, the study proposes a vaccine construct with potential for experimental and clinical validation, contributing to the development of personalized cancer immunotherapies.

Credit Card Fraud Detection Using Hybridization of Isolation Forest with Grey Wolf Optimizer Algorithm

📘 Journal: Soft Computing
📅 Date: 2024-09
🔗 DOI: 10.1007/s00500-024-09772-2
👥 Contributors: Hamed Tabrizchi, Jafar Razmara

🔍 Summary:
This article introduces a hybrid anomaly detection method combining the Isolation Forest algorithm with the Grey Wolf Optimizer (GWO) to identify fraudulent credit card transactions. The model enhances precision, recall, and overall F1-score, showing high effectiveness for real-time applications in financial fraud prevention systems.

Cancer Treatment Comes to Age: From One-Size-Fits-All to Next-Generation Sequencing (NGS) Technologies

📘 Journal: BioImpacts
📅 Date: 2024-07-01
🔗 DOI: 10.34172/bi.2023.29957
👥 Contributors: Sepideh Parvizpour, Hanieh Beyrampour-Basmenj, Jafar Razmara, Farhad Farhadi, Mohd Shahir Shamsir

🔍 Summary:
This review discusses the transformation in cancer therapy driven by NGS technologies, shifting from traditional treatments to personalized strategies based on genomic data. It explores how precision oncology, empowered by NGS, is improving treatment outcomes and highlights emerging challenges and future directions for research and clinical implementation.

Conclusion:

Dr. Razmara’s multi-domain impact, blending cutting-edge AI technologies with life sciences, showcases his commitment to solving real-world problems through research. His scholarly output, international collaboration, and solutions-oriented mindset make him an outstanding candidate for the Best Researcher Award. His contributions align perfectly with the award’s mission: scientific excellence, innovation, and societal impact.

 

Prof . Len Gelman | Artificial Intelligence | Best Researcher Award

Published on by Research Scientist

Prof . Len Gelman | Artificial Intelligence | Best Researcher Award

Prof. Len Gelman , University of Huddersfield , United Kingdom

Professor Len Gelman 🇬🇧 is a globally recognized expert in signal processing and condition monitoring 🔍. He currently serves as Chair Professor and Director at the University of Huddersfield 🏫. With over two decades of academic leadership, he has significantly contributed to vibro-acoustics and non-destructive testing 🔧. A Fellow of multiple prestigious organizations 🌐, Prof. Gelman’s international collaborations span across Europe, Asia, and the USA 🌏. His innovations have advanced aerospace and medical diagnostics ✈️🧬. He continues to lead global initiatives and research committees, shaping the future of engineering diagnostics and reliability technologies 🔬🛠️.

Professional Profile

SCOPUS

Education and Experience 

Prof. Len Gelman holds a PhD and Doctor of Science (Habilitation) 🎓, with BSc (Hons) and MSc (Hons) degrees in engineering 📘. He is a British citizen 🇬🇧. Since 2017, he has been a Professor and Chair at the University of Huddersfield 🏛️. Prior to that, he served at Cranfield University (2002–2017) as Chair in Vibro-Acoustical Monitoring 🔊. His distinguished academic journey includes visiting professorships in China 🇨🇳, Denmark 🇩🇰, Poland 🇵🇱, Spain 🇪🇸, Italy 🇮🇹, and the USA 🇺🇸. Prof. Gelman combines deep technical expertise with global educational outreach 🌍👨‍🏫.

Professional Development 

Prof. Gelman has held key international leadership roles including Chair of the International Scientific Committee of the Condition Monitoring Society 🌐. He is a Fellow of BINDT, IAENG, IDE, and HEA 🎖️, and an Academician of the Academy of Sciences of Applied Radio Electronics 🧠. He chairs award and honors committees for top acoustics and vibration institutions 🏅. As Visiting Professor at Tsinghua, Jiao Tong, and Aalborg Universities, among others 🎓, he mentors emerging researchers globally 🌎. Prof. Gelman’s commitment to professional excellence shapes the advancement of diagnostic technologies and engineering education 📈🔧.

Research Focus 

Prof. Gelman’s research focuses on signal processing, vibro-acoustics, and condition monitoring of engineering systems 🔍🔊. His work spans non-destructive testing (NDT), fault diagnostics, and performance optimization in sectors such as aerospace, healthcare, and manufacturing ✈️🏥🏭. He develops advanced algorithms for fault detection and predictive maintenance using machine learning and big data 🧠📊. His interdisciplinary approach benefits both industry and academia 🌐🔬. Prof. Gelman also pioneers applications in medical diagnostics and intelligent systems for real-time monitoring 🧬⚙️. His innovations contribute to safer, more efficient engineering systems across global platforms 🌍🚀.

Awards and Honors 

Prof. Gelman has received numerous prestigious awards for innovation and research excellence 🏅. These include the Rolls-Royce Innovation Award (2012, 2019) ✈️, William Sweet Smith Prize by IMechE 🛠️, and COMADIT Prize by BINDT for impactful contributions to condition monitoring 🧲. He also received Best Paper Awards at CM/MFPT conferences 📄 and recognition from the USA Navy and Acoustical Society of America 🇺🇸🔊. His European and UK fellowships support cutting-edge human capital projects 🧠🇪🇺. He has chaired international committees in NDT and acoustics, continuing to shape future technologies through global leadership and innovation 🌐👨‍🔬.

Publication Top Notes

1. Vibration Analysis of Rotating Porous Functionally Graded Material Beams Using Exact Formulation

  • Citation: Amoozgar, M.R., & Gelman, L.M. (2022). Vibration analysis of rotating porous functionally graded material beams using exact formulation. Journal of Vibration and Control, 28(21–22), 3195–3206. https://doi.org/10.1177/10775463211027883Nottingham Repository+1SAGE Journals+1

  • Summary: This study investigates the free vibration behavior of rotating functionally graded material (FGM) beams with porosity, employing geometrically exact fully intrinsic beam equations. The research considers both even and uneven porosity distributions to simulate manufacturing imperfections. Findings reveal that material gradation and porosity significantly influence natural frequencies and mode shapes, emphasizing the necessity of accounting for these factors in the design and analysis of rotating FGM structures. Huddersfield Research Portal+2SAGE Journals+2Nottingham Repository+2

2. Vibration Health Monitoring of Rolling Bearings Under Variable Speed Conditions by Novel Demodulation Technique

  • Citation: Zhao, D., Gelman, L.M., Chu, F., & Ball, A.D. (2021). Vibration health monitoring of rolling bearings under variable speed conditions by novel demodulation technique. Structural Control and Health Monitoring, 28(2), e2672. https://doi.org/10.1002/stc.2672Wiley Online Library

  • Summary: Addressing the challenges of diagnosing rolling bearing faults under variable speed conditions, this paper introduces an optimization-based demodulation transform method. The technique effectively estimates fault characteristic frequencies with weak amplitudes and adapts to time-varying rotational speeds. Validation through simulations and experimental data demonstrates the method’s superior diagnostic capabilities compared to existing approaches. Huddersfield Research Portal+1Wiley Online Library+1

3. Novel Method for Vibration Sensor-Based Instantaneous Defect Frequency Estimation for Rolling Bearings Under Non-Stationary Conditions

  • Citation: Zhao, D., Gelman, L.M., Chu, F., & Ball, A.D. (2020). Novel method for vibration sensor-based instantaneous defect frequency estimation for rolling bearings under non-stationary conditions. Sensors, 20(18), 5201. https://doi.org/10.3390/s20185201MDPI

  • Summary: This research presents a novel approach for estimating instantaneous defect frequencies in rolling bearings operating under non-stationary conditions. Utilizing vibration sensor data, the method enhances the accuracy of defect frequency estimation, facilitating improved fault diagnosis in dynamic operational environments. MDPI

4. Novel Fault Identification for Electromechanical Systems via Spectral Technique and Electrical Data Processing

  • Citation: Ciszewski, T., Gelman, L.M., & Ball, A.D. (2020). Novel fault identification for electromechanical systems via spectral technique and electrical data processing. Electronics, 9(10), 1560. https://doi.org/10.3390/electronics9101560MDPI

  • Summary: This paper introduces an innovative method for fault identification in electromechanical systems by integrating spectral analysis with electrical data processing. The approach enhances the detection and diagnosis of faults, contributing to the reliability and efficiency of electromechanical system operations. MDPI

5. Novel Prediction of Diagnosis Effectiveness for Adaptation of the Spectral Kurtosis Technology to Varying Operating Conditions

  • Citation: Kolbe, S., Gelman, L.M., & Ball, A.D. (2021). Novel prediction of diagnosis effectiveness for adaptation of the spectral kurtosis technology to varying operating conditions. Sensors, 21(20), 6913. https://doi.org/10.3390/s21206913PMC

  • Summary: This study proposes two novel consistency vectors combined with machine learning algorithms to adapt spectral kurtosis technology for optimal gearbox damage diagnosis under varying operating conditions. The approach enables computationally efficient online condition monitoring by predicting diagnosis effectiveness, thereby improving maintenance strategies.

Conclusion

Professor Len Gelman exemplifies the ideal candidate for the Best Researcher Award due to his groundbreaking contributions to condition monitoring, signal processing, and diagnostic technologies. His work not only advances academic knowledge but also addresses critical industry challenges in aerospace, healthcare, and manufacturing. With a sustained record of high-impact research, international leadership, and technological innovation, he stands out as a world-class researcher whose work continues to benefit both academia and society.

 

Jaecheol Ha | Computer Science | Best Researcher Award

Published on by Research Scientist

Prof .  Jaecheol Ha | Computer Science | Best Researcher Award

Professor at Hoseo University , South Korea

Professor Jaecheol Ha is a seasoned academic with a Ph.D. in Electronics Engineering from Kyungpook National University and over 25 years of research and teaching experience. Currently a full professor at Hoseo University, he has also held academic positions at Korea Nazarene University and was a visiting researcher at Purdue University, USA. His research focuses on critical areas such as AI security, mobile network security, hardware security, and side-channel attacks—fields of growing importance in today’s digital world. As the honorary president of the Korea Institute of Information and Cryptography (KIISC), he demonstrates recognized leadership in the cybersecurity research community. While his academic background and research interests are highly relevant, more information on his publication record, research impact, and mentorship contributions would further strengthen his case. Nonetheless, based on the available information, Professor Ha presents a strong and credible profile for the Best Researcher Award, particularly in the domain of cybersecurity.

Professional Profile 

Education🎓

Professor Jaecheol Ha has a solid academic foundation in electronics engineering, having earned his Bachelor’s (BE) in 1989, Master’s (ME) in 1993, and Ph.D. in 1998 from Kyungpook National University in the Republic of Korea. His progression through all three degrees at a single institution reflects a consistent and focused commitment to his field of study. Kyungpook National University is recognized for its strong engineering programs, providing him with a rigorous education and research training environment. His doctoral studies likely laid the groundwork for his later specialization in areas such as AI security and hardware-based cryptographic methods. This strong educational background has supported his successful academic career, enabling him to contribute meaningfully to research and teaching. His education not only equipped him with deep technical knowledge but also prepared him to take on leadership roles in academic and research institutions, both domestically and internationally.

Professional Experience📝

Professor Jaecheol Ha has extensive professional experience spanning over two decades in academia and research. He is currently a full professor in the Division of Computer Engineering at Hoseo University in Asan, Republic of Korea, where he plays a key role in teaching and research. Prior to this, from 1998 to 2006, he served as a professor in the Department of Information and Communication at Korea Nazarene University in Cheonan. His academic career reflects a strong commitment to education and research in the fields of computer engineering and cybersecurity. In 2014, he broadened his international experience by working as a visiting researcher at Purdue University in the United States, further enhancing his global academic perspective. In addition to his teaching and research roles, he currently serves as the honorary president of the Korea Institute of Information and Cryptography (KIISC), a position that highlights his leadership and influence in the Korean cybersecurity research community.

Research Interest🔎

Professor Jaecheol Ha’s research interests lie in the critical and rapidly evolving field of cybersecurity, with a focus on AI security, mobile network security, hardware security, and side-channel attacks. His work addresses some of the most pressing challenges in digital security, particularly as emerging technologies like artificial intelligence and mobile communication continue to expand. By exploring vulnerabilities in hardware and communication systems, as well as developing methods to protect against side-channel attacks, his research contributes to building more resilient and secure digital infrastructures. His interest in AI security reflects a forward-thinking approach, recognizing the increasing integration of AI in sensitive systems and the corresponding need for robust protective measures. Through his work, Professor Ha seeks to bridge theoretical understanding with practical applications, providing solutions that can be implemented in real-world systems. His research not only supports academic advancement but also contributes to national and global efforts to strengthen cybersecurity.

Award and Honor🏆

Professor Jaecheol Ha has received recognition for his contributions to the field of cybersecurity through his leadership role as the honorary president of the Korea Institute of Information and Cryptography (KIISC). This prestigious position reflects his respected status within the academic and research communities, as well as his long-standing commitment to advancing knowledge in information security. While specific awards or honors are not listed, his appointment to such a significant role within a national institute suggests a high level of trust and acknowledgment by his peers. It highlights his influence in shaping research directions and policies in cryptography and cybersecurity in Korea. His professional journey, including his international research collaboration at Purdue University, also indicates recognition of his expertise beyond national boundaries. These honors affirm his impact as a leader and researcher, underscoring his suitability for further accolades such as the Best Researcher Award in his field of specialization.

Research Skill🔬

Professor Jaecheol Ha possesses a wide range of research skills that are crucial for tackling complex problems in the field of cybersecurity. His expertise spans several critical areas, including AI security, mobile network security, hardware security, and side-channel attacks. With a deep understanding of both theoretical and practical aspects of these fields, he is skilled at identifying vulnerabilities in systems and developing innovative solutions to mitigate them. His ability to bridge the gap between cutting-edge research and real-world applications demonstrates his strong problem-solving capabilities. Additionally, his international research experience, particularly as a visiting researcher at Purdue University, indicates a high level of adaptability and collaboration in global research environments. His leadership as honorary president of the Korea Institute of Information and Cryptography (KIISC) further highlights his ability to mentor, guide, and foster collaboration among researchers, strengthening his research skills in both individual and team-based contexts.

Conclusion💡

Professor Jaecheol Ha appears to be a well-qualified and experienced researcher with a strong focus on cybersecurity, leadership experience, and international exposure. These factors support his eligibility for a Best Researcher Award, especially if the focus is on long-term contribution and domain impact.

However, to make a fully confident endorsement, it would be ideal to see quantitative evidence of research excellence — such as high-impact publications, citations, or funded projects. If such data exists and supports the narrative, then he is a strong and suitable candidate for this award.

Publications Top Noted✍

  1. Title: SSIM-Based Autoencoder Modeling to Defeat Adversarial Patch Attacks
    Authors: Seungyeol Lee, Seongwoo Hong, Gwangyeol Kim, Jaecheol Ha
    Year: 2024
    Citations: 1
  2. Title: Implementation of Disassembler on Microcontroller Using Side-Channel Power Consumption Leakage
    Authors: Daehyeon Bae, Jaecheol Ha
    Year: 2022
    Citations: 6
  3. Title: Deep Learning-based Attacks on Masked AES Implementation
    Authors: Daehyeon Bae, Jongbae Hwang, Jaecheol Ha
    Year: 2022
    Citations: 1
  4. Title: Performance Metric for Differential Deep Learning Analysis
    Authors: Daehyeon Bae, Jaecheol Ha
    Year: 2021
    Citations: 26

 

Prof. Dr. Wei Fang | Analytics Award | Best Researcher Award

Published on by Research Scientist

Prof. Dr. Wei Fang | Analytics Award | Best Researcher Award

Prof. Dr. Wei Fang, Nanjing University of Information Science & Technology, China

Prof. Dr. Wei Fang is a Professor in the Department of Computer Science at Nanjing University of Information Science & Technology, China, and a member of the State Key Laboratory for Novel Software Technology, Nanjing University. He holds a Ph.D. and M.Sc. in Computer Science from Soochow University. Wei was a visiting scholar at the University of Florida in 2015-2016. His research interests include Artificial Intelligence, Big Data, Data Mining, and Meteorological Information Processing. He has led several research projects funded by the National Natural Science Foundation of China and is an active reviewer for international journals. Wei is a senior member of the CCF and ACM.

Professional Profile:

GOOGLE SCHOLAR

ORCID

SCOPUS

Summary of Suitability for Best Researcher Award – Prof. Wei Fang

Prof. Wei Fang of Nanjing University of Information Science & Technology stands out as a highly meritorious candidate for the Best Researcher Award. With a solid academic foundation, national and international research exposure, and extensive contributions in Artificial Intelligence, Big Data, Computer Vision, and Applied Meteorology, his work bridges theoretical innovation with real-world application.

🎓 Education

  • Ph.D. in Computer Science – Soochow University, China

  • M.Sc. in Computer Science – Soochow University, China

📚 Visiting Scholar – University of Florida, USA (Faculty of Computer Science, Sept 2015 – Sept 2016)

💼 Work Experience

  • 👨‍🏫 Professor, Department of Computer Science, NUIST

  • 🧪 Affiliated with the State Key Lab for Novel Software Technology, Nanjing University

  • 🤝 Program Committee Member for multiple international conferences

  • 📝 Reviewer for various international journals

  • 🌍 International Research Scientist

🏆 Achievements & Honors

  • 🧠 Recognized for impactful research in:

    • Artificial Intelligence 🤖

    • Big Data & Cloud Computing ☁️📊

    • Computer Vision 👁️

    • Applied Meteorology 🌦️

  • 🔬 Project Leader of national and industrial research projects funded by:

    • National Natural Science Foundation of China

    • Guodian Nari Nanjing Control System Co., Ltd.

    • Baoshan Iron and Steel Co., Ltd.

  • 🎖️ Senior Member of CCF (China Computer Federation) & ACM

  • 📈 Cited in SCI-indexed journals

Publication Top Notes:

A rapid learning algorithm for vehicle classification

CITED: 562

A Method for Improving CNN-Based Image Recognition Using DCGAN.

CITED: 230

Efficient feature selection and classification for vehicle detection

CITED: 220

A survey of big data security and privacy preserving

CITED: 117

Survey on research of RNN-based spatio-temporal sequence prediction algorithms

CITED: 100