Jobish Vallikavungal Devassia | Computer Science | Best Researcher Award

Published on by Research Scientist

Prof. Dr. Jobish Vallikavungal Devassia | Computer Science | Best Researcher Award

Professor | Tecnológico De Monterrey | Mexico

Prof. Dr. Jobish Vallikavungal Devassia is a distinguished researcher in Computer Science, specializing in scheduling algorithms, operations research, and optimization for flexible job-shop and parallel machine systems. He has authored over 10 high-impact publications in top-tier journals, including Computers & Industrial Engineering, International Journal of Production Research, and IEEE Access, accumulating more than 112 citations. His work integrates advanced techniques such as rough sets, multidimensional fuzzy logic, and dynamic scheduling methods, often in collaboration with international scholars from Mexico, Spain, and France. His research advances efficient industrial operations, resource optimization, and practical applications in manufacturing and logistics, impacting both academia and industry globally.

 

Citation Metrics (Google Scholar)

400

300

200

100

0

Citations
112

Documents
10

h-index
5

🟦 Citations 🟥 Documents 🟩 h-index

View Scopus Profile
               View ORCID Profile
             View Google Scholar Profile

Featured Publications

The generalized flexible job shop scheduling problem.

– Computers & Industrial Engineering. (2021). Cited By: 50

Flexible job-shop scheduling problem with resource recovery constraints.

– International Journal of Production Research. (2018). Cited By: 41

A parallel machine batch scheduling problem in a brewing company.

– The International Journal of Advanced Manufacturing Technology. (2016). Cited By: 32

Mokhtar Ferhi | Computer Science and Artificial Intelligence | Research Excellence Award

Published on by Research Scientist

Dr. Mokhtar Ferhi | Computer Science and Artificial Intelligence | Research Excellence Award

University of Jendouba | Tunisia

Dr. Mokhtar Ferhi is a researcher at Université de Jendouba, Tunisia, specializing in heat transfer, fluid mechanics, magnetohydrodynamics (MHD), nanofluid convection, and numerical simulation methods, particularly the Lattice Boltzmann Method. He has authored 27 peer-reviewed publications, receiving 140 citations with an h-index of 6 (Scopus). His work focuses on entropy generation, energy optimization, and thermal performance enhancement in cavities and micro-heat exchangers. Ferhi collaborates internationally with experts across North Africa, Europe, and the Middle East, contributing to advances in energy-efficient thermal systems with applications in sustainable engineering and heat exchanger design.

Citation Metrics (Scopus)

400

300

200

100

0

Citations
140

Documents
27

h-index
6

🟦 Citations 🟥 Documents 🟩 h-index

View Google Scholar Profile
          View Scopus Profile
          View ORCID Profile

Featured Publications

 

Saeed Banaeian Far | Computer Science | Best Researcher Award

Published on by Research Scientist

Assist. Prof. Dr. Saeed Banaeian Far | Computer Science | Best Researcher Award

Assist. Prof. | Blockchain and Metaverse research lab | Iran

Assist. Prof. Dr. Saeed Banaeian Far is a leading researcher in applied cryptography, blockchain systems, security protocols, and emerging Metaverse technologies. He is affiliated with the Blockchain and Metaverse Research Lab (BMRL) and has made influential contributions to decentralized finance, digital twins, NFTs, Web3, privacy-preserving protocols, and quantum-secure blockchain architectures. With over 44 peer-reviewed publications in high-impact journals and conferences, his work has received 1,045 citations, reflecting strong global academic influence. He actively collaborates with international scholars and interdisciplinary teams, advancing secure digital infrastructures with significant societal impact in finance, governance, healthcare, and next-generation virtual ecosystems.

Citation Metrics (Google Scholar)

2000

1500

1000

500

0

Citations
1045

Documents
44

h-index
13

🟦 Citations 🟥 Documents 🟩 h-index

View Google Scholar Profile
          View ORCID Profile
         View Scopus Profile

Featured Publications

 

Ei Mehdi Chakour | Computer Science | Research Excellence Award

Published on by Research Scientist

Dr. Ei Mehdi Chakour | Computer Science | Research Excellence Award

Research Postdoc | Université Sidi Mohamed Ben Abdellah | Morocco

Dr. Ei Mehdi Chakour is a researcher at Université Sidi Mohamed Ben Abdellah, Fez, Morocco, specializing in medical image analysis and deep learning applications for ophthalmology, particularly diabetic retinopathy detection. With four peer-reviewed publications and 16 citations, Dr. Chakour has contributed to advancements in retinal image segmentation, enhancement, and severity classification using dynamic preprocessing, mathematical morphology, and transfer learning techniques. His collaborative work involves 11 co-authors across international conferences and journals, reflecting a strong commitment to interdisciplinary research. Through the development of mobile-based deep learning systems, his work demonstrates significant societal impact by enabling earlier, accessible, and accurate diabetic retinopathy screening.

Citation Metrics (Scopus)

80

60

40

20

0

Citations
16

Documents
4

h-index
2

🟦 Citations 🟥 Documents 🟩 h-index

View Scopus Profile
             View ORCID Profile

Featured Publications


Mobile‑based deep learning system for early detection of diabetic retinopathy.

– Intelligence‑Based Medicine. Advance online publication. (2025). 

Transfer learning for severity and stages detection of diabetic retinopathy.

-Embedded Systems and Artificial Intelligence (ESAI) . (2024).

Blood vessel segmentation of retinal fundus images using dynamic preprocessing and mathematical morphology.

– International Conference on Control, Decision and Information Technologies (CoDIT). (2022). 

Yirga Munaye | Computer Science | Best Researcher Award

Published on by Research Scientist

Assoc. Prof. Dr. Yirga Munaye | Computer Science | Best Researcher Award

Postdoctoral Researcher | Bahir Dar University | Ethiopia

Dr. Yirga Munaye is a distinguished researcher at Bahir Dar University, Ethiopia, specializing in wireless communication, UAV systems, artificial intelligence, IoT, and cybersecurity. With 36 publications and 599 citations, he has made significant contributions to UAV positioning, indoor/outdoor localization, deep learning for resource management, and secure network infrastructures. His collaborations span 36 international co-authors, reflecting strong interdisciplinary and global engagement. Dr. Munaye’s research addresses critical societal challenges, including smart connectivity, network optimization, and AI-driven security solutions, advancing both theoretical knowledge and practical applications in emerging technologies worldwide.

Citation Metrics (Scopus)

800

600

400

200

0

Citations
599

Documents
36

h-index
11

🟦 Citations 🟥 Documents 🟩 h-index

View Scopus Profile
             View Google Scholar Profile

Featured Publications

 

Mahmood ul Hassan | Computer Science | Editorial Board Member

Published on by Research Scientist

Assist. Prof. Dr. Mahmood ul Hassan | Computer Science | Editorial Board Member

Assistant Professor | Najran University | Saudi Arabia

Dr. Mahmood ul Hassan is a distinguished researcher and academic affiliated with the National Industrial Training Institute, TÜV Rheinland Arabia in the Kingdom of Saudi Arabia, with a verified scholarly association at Jazan University (ju.edu.sa). His research expertise spans Wireless Sensor Networks (WSN), Vehicular Ad Hoc Networks (VANET), mobile cloud computing, AI-driven smart systems, and ICT applications in education and healthcare. Over his career, he has built a strong interdisciplinary footprint across computer engineering, artificial intelligence, cybersecurity, and applied networking.Dr. Hassan has authored 157 scholarly documents with over 909 citations, an h-index of 17, and an i10-index of 31, reflecting both the breadth and impact of his contributions. His notable publications include influential works on smart agriculture using AI, tumor classification in MRI using wavelets and SVM, ANN-based secure routing protocols for VANETs, image segmentation models, glioma classification using deep CNNs, and lightweight security frameworks for WSNs. Several of his papers in Energies, Sensors, IEEE Access, Computers, Materials & Continua, and Wireless Communications and Mobile Computing have been widely cited and integrated into ongoing global research.His collaborations with multidisciplinary teams across Saudi Arabia, Pakistan, and international institutions highlight his commitment to advancing digital transformation in critical sectors. Dr. Hassan’s work on intelligent connectivity restoration, blockchain-based secure information routing, microservice optimization, fog computing, and IoT-enabled education systems demonstrates a consistent alignment with emerging technological challenges. Beyond core engineering, he has also contributed research in health informatics, public-sector project planning, archaeology, and medical studies, showcasing his broad academic versatility.Dr. Mahmood ul Hassan’s research has substantive societal impact, particularly in enhancing network reliability, secure communication, healthcare diagnostics, smart agriculture, and technology-driven education. His sustained scholarly productivity and cross-disciplinary influence continue to position him as a leading academic voice in next-generation networked systems and intelligent computing solutions.

Profiles : Googlescholar | Scopus | ORCID

Featured Publications

1. Smart agriculture cloud using AI-based techniquesJunaid, M., Shaikh, A., Hassan, M. U., Alghamdi, A., Rajab, K., Al Reshan, M. S., & … (2021). Smart agriculture cloud using AI-based techniques. Energies, 14(16), 5129. Cited By: 58

2. Classification of tumors in human brain MRI using wavelet and support vector machineAhmad, M., Hassan, M., Shafi, I., & Osman, A. (2012). Classification of tumors in human brain MRI using wavelet and support vector machine. IOSR Journal of Computer Engineering, 8(2), 25–31. Cited By: 53

3. ANN-based intelligent secure routing protocol in vehicular ad hoc networks (VANETs) using enhanced AODVHassan, M. U., Al-Awady, A. A., Ali, A., Sifatullah, Akram, M., Iqbal, M. M., Khan, J., & … (2024). ANN-based intelligent secure routing protocol in vehicular ad hoc networks (VANETs) using enhanced AODV. Sensors, 24(3). Cited By: 44

4. A weighted spatially constrained finite mixture model for image segmentationAhmed, M. M., Shehri, S. A., Arshed, J. U., Hassan, M. U., & Hussain, M. (2021). A weighted spatially constrained finite mixture model for image segmentation. Computers, Materials & Continua, 67(1), 171–185. Cited By: 42

5. A CNN-model to classify low-grade and high-grade glioma from MRI imagesHafeez, H. A., Elmagzoub, M. A., Abdullah, N. A. B., Al Reshan, M. S., Gilanie, G., & … (2023). A CNN-model to classify low-grade and high-grade glioma from MRI images. IEEE Access, 11, 46283–46296. Cited By: 37

Dr. Mahmood ul Hassan’s research advances secure, intelligent, and resilient networked systems that enhance healthcare diagnostics, smart agriculture, and sustainable digital infrastructure. His work bridges AI, wireless communication, and cloud technologies, delivering innovative solutions with direct societal and economic impact.

Yongbin Zhao | Computer Science | Best Researcher Award

Published on by Research Scientist

Dr. Yongbin Zhao | Computer Science | Best Researcher Award

School of Information Science and Technology | Shijiazhuang Tiedao University | China

Dr. Yongbin Zhao is a researcher at Shijiazhuang Tiedao University, China, recognized for his growing contributions to computational science, network analysis, and data-driven systems research. With a Scopus-indexed publication record comprising 27 scholarly documents and 67 citations across 66 citing sources, he has established an emerging academic profile characterized by interdisciplinary inquiry and collaborative engagement. His current h-index of 5 reflects the consistent impact and relevance of his research in both theoretical and applied domains.Dr. Zhao’s work spans multiple high-value fields including blockchain security complex network modeling and agricultural trade analytics. His recent publication on multi-key fully homomorphic encryption algorithms introduces secure computation frameworks tailored for blockchain environments contributing to the advancement of privacy-preserving technologies in decentralized systems. Another notable study focusing on the evolution and robustness of the global soybean trade network demonstrates his ability to integrate physics-based modeling with international agricultural economics offering meaningful insights for global trade stability and food-system resilience.He has collaborated with more than 40 co-authors reflecting a strong international and interdisciplinary research network. His contributions extend beyond academic outputs providing analytical tools and conceptual frameworks that support secure data infrastructures enhance trade-network understanding and contribute to more resilient socio-economic systems.Through his sustained research activity and collaborative leadership Dr. Yongbin Zhao continues to contribute to scientific knowledge with societal and technological relevance strengthening the interface between computational innovation and global system analysis.

Profiles : Scopus | Research Gate

Featured Publications

1.Author(s). (2025). Research on multi-key fully homomorphic encryption algorithms suitable for blockchain. Cluster Computing.

2.Author(s). (2024). The evolution and robustness analysis of global soybean trade network. International Journal of Modern Physics C. Cited By : 2

Dr. Zhao’s contributions in homomorphic encryption and network robustness drive innovation in blockchain security and international supply-chain analytics. His research supports industry in building safer digital platforms and more efficient global trade systems.

Venus Haghighi | Computer Science | Best Researcher Award

Published on by Research Scientist

Mrs. Venus Haghighi | Computer Science | Best Researcher Award

Research Associate | Macquarie University | Australia

Mrs. Venus Haghighi is a final-year PhD candidate in Computer Science at Macquarie University, Sydney, focusing on artificial intelligence, data science, and graph learning techniques for fraud detection in complex networks. She holds a master’s degree in Computer Engineering from Isfahan University of Technology, where she worked on mobile cloud computing, and a bachelor’s degree in Computer Engineering from Shahid Bahonar University of Kerman, where she researched AES cryptography. Her professional experience includes serving as a data science researcher at the Intelligent Computing Laboratory, where she develops advanced graph neural networks, hypergraph models, and large language model enhanced frameworks for detecting camouflaged malicious actors. She has also contributed as a sessional teaching associate in both the School of Computing and the Business School at Macquarie University, teaching subjects such as cybersecurity, data science, and information systems. Her research interests span graph neural networks, hypergraph learning, graph transformer networks, graph representation learning, and the integration of LLMs with graph-based methods for real-world applications. She has published in leading venues such as IEEE ICDM, ACM WSDM, ACM CIKM, IJCAI, and ACM Web Conference, along with journal contributions in IEEE Transactions and IEEE Access. Her achievements include the Google Conference Travel Grant, HDR Research Rising Star Award, 3MT Thesis Competition recognition, DF-CRC PhD Top-Up Scholarship, and the Pro-Vice Chancellor Research Excellence Scholarship. She is skilled in Python, PyTorch, PyTorch Geometric, Deep Graph Library, data visualization, and advanced AI model design. Her research impact is evidenced by 150 citations across 13 documents with an h-index of 4.

Profile: Google Scholar

Featured Publications

1. Soltani, B., Haghighi, V., Mahmood, A., Sheng, Q. Z., & Yao, L. (2022). A survey on participant selection for federated learning in mobile networks. Proceedings of the 17th ACM Workshop on Mobility in the Evolving Internet Architecture (MobiArch).

2. Haghighi, V., & Moayedian, N. S. (2018). An offloading strategy in mobile cloud computing considering energy and delay constraints. IEEE Access, 6, 11849–11861.

3. Shabani, N., Wu, J., Beheshti, A., Sheng, Q. Z., Foo, J., Haghighi, V., Hanif, A., & … (2024). A comprehensive survey on graph summarization with graph neural networks. IEEE Transactions on Artificial Intelligence, 5(8), 3780–3800.

4. Soltani, B., Zhou, Y., Haghighi, V., & Lui, J. (2023). A survey of federated evaluation in federated learning. Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence (IJCAI).

5. Shabani, N., Beheshti, A., Jolfaei, A., Wu, J., Haghighi, V., Najafabadi, M. K., & Foo, J. (2024). Attention-based graph summarization for large-scale information retrieval. IEEE Transactions on Consumer Electronics, 70(3), 6224–6235.

Amel Abderrahmane | Computer Science | Best Review Article Award

Published on by Research Scientist

Dr. Amel Abderrahmane | Computer Science | Best Review Article Award

Professor at Batna University | Algeria

Dr. Amel Abderrahmane  is a dedicated researcher and educator specializing in computer networks, with a strong academic and professional background in software-defined networking (SDN) and the Internet of Things (IoT). Currently pursuing a PhD in Sécurité des systèmes informatiques et réseaux at Batna University, Algeria, he focuses on advancing modern network infrastructures by solving critical control placement challenges. His academic journey began with a Bachelor’s degree in Computer Science from the University Ferhat Abbas Setif, where he graduated , followed by a Master’s degree in Distributed Systems and Networking at Batna University, earning first-in-class distinction. Alongside his research, he has been serving as a Temporary Professor at Batna University, where he teaches and supervises students in computer science. Abderrahmane has contributed valuable publications to international journals, including IEEE Access, highlighting his commitment to advancing innovative solutions in networking technologies.

Professional Profile

Scopus Profile

Education 

Dr. Amel Abderrahmane  has pursued a distinguished academic path in computer science and networking. He began his studies at the University Ferhat Abbas Setif, Algeria, earning a Bachelor’s degree in Computer Science. His performance and dedication laid a strong foundation for advanced research in distributed computing and networking. He completed his Master’s degree in Distributed Systems and Networking at Batna University, Algeria, where he ranked first in class, demonstrating both academic excellence and technical mastery. Building upon this achievement, he embarked on a PhD in Sécurité des systèmes informatiques et réseaux at Batna University , where his research focuses on optimizing SDN and IoT networks. His education has been marked by a blend of theoretical knowledge and practical applications, equipping him with expertise in network security, control placement, and performance optimization. This progression reflects his commitment to addressing real-world challenges in modern computer networks.

Experience 

Dr. Amel Abderrahmane has been serving as a Temporary Professor in the Computer Science Department at Batna University. In this role, he has gained significant teaching and mentoring experience, instructing undergraduate students in core computer science subjects while also guiding them in research-oriented projects. His responsibilities extend to supervising students in applied research related to SDN controllers, IoT platforms, and network management. Beyond classroom instruction, Abderrahmane actively contributes to collaborative research activities, assisting in projects that address optimization and security challenges in next-generation networks. His dual role as a researcher and educator allows him to integrate cutting-edge research insights into his teaching, creating an engaging learning environment. His academic contributions are reinforced by publications in respected international journals, including IEEE Access, which reflect the practical impact of his work. This experience underscores his ability to combine teaching excellence with meaningful research contributions in computer networking.

Research Interest

Dr. Amel Abderrahmane  research lies at the intersection of software-defined networking (SDN) and the Internet of Things (IoT), with a primary focus on optimizing control placement within distributed network environments. He is particularly interested in addressing scalability, efficiency, and reliability challenges to ensure seamless communication and effective resource allocation in modern infrastructures. His work emphasizes designing innovative frameworks and algorithms that enhance both performance and security in complex SDN-IoT ecosystems. Abderrahmane’s current research also explores how graph-theoretical methods and clustering techniques, such as the Louvain algorithm and betweenness-centrality metrics, can improve controller placement strategies for better network resilience. By tackling these critical issues, his contributions aim to support the development of smart, adaptive, and secure networking systems that are essential for emerging technologies, including smart cities, industrial IoT, and cloud-edge integration. His vision is to create sustainable solutions that can advance the next generation of intelligent and interconnected networks.

Award and Honor

Dr. Amel Abderrahmane  has consistently demonstrated excellence, earning recognition for his outstanding performance. During his Master’s studies in Distributed Systems and Networking at Batna University, he graduated first in class, a distinction that reflects his exceptional technical knowledge, analytical skills, and commitment to research. This achievement set the foundation for his doctoral studies and positioned him as a promising researcher in the fields of SDN and IoT. His scholarly contributions have been further acknowledged through publications in prestigious outlets such as the International Journal of Networked and Distributed Computing and IEEE Access. The acceptance of his research in these high-impact journals highlights the relevance and innovation of his work, serving as a testament to his academic accomplishments. While still at an early stage of his career, Abderrahmane’s academic honors and growing publication record mark him as a rising scholar within the international research community.

Research Skill

Dr. Amel Abderrahmane possesses a diverse set of research skills that strengthen his contributions to SDN and IoT studies. He is proficient in multiple programming languages, including Python, Java, C++, and C#, which enable him to implement and test advanced algorithms for network optimization. His technical expertise also extends to web programming, providing him with the versatility to design and integrate network applications. In addition, he is skilled in using SDN controllers and IoT platforms, tools essential for simulating and evaluating network performance in real-world conditions. Abderrahmane demonstrates strong analytical skills, particularly in applying graph-based techniques and clustering algorithms to solve controller placement challenges. His multilingual abilities in Arabic, French, and English further enhance his capacity to engage with international collaborations and research dissemination. Combined, these skills reflect a balanced profile of theoretical knowledge, practical application, and cross-cultural communication, enabling him to contribute effectively to global research networks.

Publication Top Note

Title:  A Survey of Controller Placement Problem in SDN-IoT Network
Authors: Amel Abderrahmane, Hamza Drid, and Amel Behaz SpringerLink
Journal: International Journal of Networked and Distributed Computing
Citation : 7

Conclusion

Dr. Amel Abderrahmane survey on the Controller Placement Problem (CPP) in SDN-IoT networks highlights its central role in ensuring scalability, reliability, and efficiency in next-generation infrastructures. By examining existing approaches, it becomes evident that optimal controller placement is a multidimensional challenge influenced by latency, fault tolerance, load balancing, and energy efficiency. Traditional models provide a foundation, but they often struggle to adapt to the heterogeneity and dynamic nature of IoT environments. Recent advancements, such as graph-based techniques and clustering algorithms, demonstrate promising results in improving performance and resilience. However, no single method universally addresses all requirements, indicating the need for hybrid and adaptive solutions. Future research should focus on intelligent, AI-driven strategies that can dynamically adjust placement decisions in real time, considering both network conditions and application demands. Ultimately, solving CPP will significantly enhance the effectiveness of SDN-IoT networks, enabling more reliable and sustainable digital ecosystems.

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.