Marco Capogni | Data Science | Best Researcher Award

Published on by Computer Science Engineering

Prof. Dr. Marco Capogni | Data Science | Best Researcher Award

Researcher | ENEA – National Institute for Ionizing Radiation Metrology | Italy

Prof. Dr. Marco Capogni’s research focuses on the precise measurement and standardization of radionuclides, with a strong emphasis on ionizing radiation metrology and its applications in medicine, industry, and environmental monitoring. He has developed and maintained primary national standards for radioactivity, collaborating with international institutions such as the Bureau International des Poids et Mesures (BIPM) and contributing to interlaboratory comparisons to ensure global consistency in radionuclide measurements. His work includes the implementation of absolute measurement techniques and computational codes such as GEANT, MCNP, Penelope, and Fluka for both direct and indirect activity determination. Marco has been actively involved in projects producing medical radionuclides like Mo-99 and Cu-64, utilizing neutron activation and absolute or relative measurement systems, and has contributed to the development of innovative sources of fusion neutrons for radioisotope production under the Sorgentina-RF project. His expertise spans gamma spectrometry, liquid scintillation counting, and coincidence counting methods, and he has served as a member of international working groups including the International Committee for Radionuclide Metrology (ICRM) and the European Metrology Network for Radiation Protection (EURAMET). Marco has led and coordinated numerous European research projects funded by EMRP and EMPIR, focusing on robust production chains for medical radionuclides, radiological early warning networks, and metrology for decommissioning nuclear facilities. He has also contributed to the training of students at the master’s and doctoral levels in physics, engineering, and medical physics, supervising multiple theses on radionuclide metrology and measurement techniques. His work has resulted in significant publications, patents, and participation in international conferences, reflecting his leadership in metrological science and nuclear applications. Marco Capogni’s contributions demonstrate a blend of experimental expertise, computational proficiency, and collaborative engagement with international metrology and research networks, addressing challenges in nuclear measurement, radioprotection, and medical isotope production. He has achieved 1,882citations, authored 133 documents, and holds an h-index of 21.

Profiles: Scopus | ORCID
Featured Publication

Capogni, M., … (2024). Assessment of impurity production upon 14 MeV fusion neutron irradiation of both natural and isotopically enriched 100Mo samples. European Physical Journal Plus.
Citations: 1

Capogni, M., … (2024). Measurements of the absolute gamma-ray emission intensities from the decay of 166Ho. Applied Radiation and Isotopes.
Citations: 2

Capogni, M., … (2024). Future of 99Mo reactor-independent supply. Nature Reviews Physics.
Citations: 3

Capogni, M., … (2023). Analytical study of low energy proton interactions in the SORGENTINA’s fusion ion source-Part II: beam-gas: SORGENTINA ion beam interactions. European Physical Journal Plus.
Citations: 2

Capogni, M., … (2023). The international reference system for beta-particle emitting radionuclides: Validation through the pilot study CCRI(II)-P1.Co-60. Applied Radiation and Isotopes.
Citations: 5

Capogni, M., … (2023). The importance of uncertainty analysis and traceable measurements in routine quantitative 90Y-PET molecular radiotherapy: A multicenter experience. Pharmaceuticals.
Citations: 1

Capogni, M., … (2023). Experimental campaign on ordinary and baritic concrete samples for the SORGENTINA-RF plant: The SRF-bioshield tests. European Physical Journal Plus.
Citations: 3

Shougui Zhang | High Performance Computing | Best Researcher Award

Published on by Computer Science Engineering

Prof. Dr. Shougui Zhang | High Performance Computing | Best Researcher Award

Teacher |Chongqing Normal University| China

Prof. Dr. Shougui Zhang is a distinguished scholar whose academic contributions focus primarily on computational mathematics, particularly in the field of numerical analysis and applied mathematics. His extensive research explores the numerical solution of partial differential equations (PDEs), an area that forms the foundation of many scientific and engineering applications. Zhang has made notable progress in the development and refinement of boundary element methods, which are efficient numerical techniques for solving boundary value problems that arise in physics and engineering disciplines such as fluid dynamics, elasticity, and electromagnetism. His work emphasizes mathematical rigor combined with computational efficiency, aiming to provide stable and accurate algorithms for complex real-world systems. A major aspect of his research involves variational inequalities, where he investigates computational methods for handling inequality constraints that frequently appear in optimization, contact mechanics, and obstacle problems. Zhang’s studies in this area contribute to bridging theoretical mathematical formulations with practical computational tools, enabling more precise simulations and analyses of nonlinear and constrained physical systems. His contributions extend beyond methodological innovation, influencing the design of advanced algorithms that improve the performance of numerical solvers and support the development of scientific computing frameworks. Over the years, he has published numerous research papers in recognized journals, reflecting a strong engagement with the global mathematical community. His interdisciplinary approach, combining mathematical theory, numerical techniques, and computational experimentation, enhances the understanding and application of PDE-based models across diverse domains. Zhang’s ongoing investigations into computational variational inequalities mark an important direction in applied mathematics, where numerical precision and computational feasibility must coexist. His research continues to play a key role in advancing the field of computational mathematics, fostering collaborations and innovative applications in scientific and engineering contexts. He has achieved 362 citations, authored 33 documents, and holds an h-index of 11.

Profiles: Scopus | ORCID
Featured Publication

Author(s). (2025). A self-adaptive alternating direction multiplier method for variational inequality in 2 domains. Applied Mathematics and Mechanics.

Author(s). (2025). Analysis of a Crank–Nicolson fast element-free Galerkin method for the nonlinear complex Ginzburg–Landau equation. Journal of Computational and Applied Mathematics. 7 Citations.

Author(s). (2024). Self-adaptive alternating direction method of multiplier for a fourth order variational inequality. Journal of Inequalities and Applications.

Miao Cui | Artificial Intelligence | Best Researcher Award

Published on by Computer Science Engineering

Prof. Miao Cui | Artificial Intelligence | Best Researcher Award

Professor |Dalian University of Technology| China

Professor Miao Cui focuses on the fields of digital transformation, innovation management, and data-driven business strategy, with extensive exploration in enterprise and community digitalization practices. Her research emphasizes how organizations orchestrate resources to adapt to digital economies, manage transformation, and foster innovation across various sectors, including state-owned enterprises, traditional manufacturing, high-tech firms, service industries, and non-profit community organizations. She has conducted in-depth case studies on more than 50 enterprises such as Haier, P&G, Inspur, and BBMW, as well as over 30 rural communities across China, providing valuable insights into digital capability development and data-oriented strategic renewal. Through her work, Miao Cui examines the interconnection between big data strategy and organizational growth, focusing on how data analysis informs decision-making, enhances resilience, and drives innovation in dynamic environments. Her studies extend to the role of information systems in enabling business transformation, ecosystem governance, and e-commerce-based social innovation, contributing significantly to both theory and practice in management sciences. Miao Cui’s research achievements include numerous high-impact publications in leading international journals such as the International Journal of Information Management, Information Systems Journal, and Journal of Strategic Information Systems, recognized as top-ranked in their field. Her scholarly contributions have been repeatedly highlighted through ESI highly cited and hot papers, reflecting the global relevance and influence of her work. Additionally, she has authored and edited multiple academic monographs, developed widely adopted management cases for Ivey Publishing, and received several awards for excellence in research and social science innovation. Her work has been cited extensively and applied in organizational and policy contexts, contributing to global discussions on digital transformation and innovation leadership. Miao Cui has 625 Citations, 26 Documents, and an h-index of 9. View h-index.

Profile: Scopus 
Featured Publication

Author(s) unknown. (2025). Collaborative innovation network embeddedness and a firm’s technological impact: Does prior networking experience matter? Journal of Technology Transfer. Cited by 1

Author(s) unknown. (2025). An integrated approach to modeling the influence of critical factors in low-carbon technology adoption by chemical enterprises in China. Journal of Environmental Management. Cited by 2

Farrukh Dekhkonov | Computational Theory | Best Researcher Award

Published on by Computer Science Engineering

Assoc. Prof. Dr. Farrukh Dekhkonov | Computational Theory | Best Researcher Award

Associate Professor |Namangan State University | Uzbekistan

Associate Professor Farrukh Dekhkonov is a researcher in the field of mathematical physics, focusing on differential equations, partial differential equations, applied mathematics, integral equations, boundary value problems, control theory, and mathematical modeling. His work emphasizes the development and analysis of boundary and time-optimal control problems associated with parabolic and pseudo-parabolic equations, including multi-dimensional domains and systems with involution. By investigating the heating process of rods, plates, and other physical systems, Dekhkonov has contributed significantly to understanding the dynamics of thermal processes and optimal control strategies in various geometries and boundary conditions. His research integrates theoretical analysis with computational methods to design effective control protocols for complex systems, ensuring precise management of heat transfer and diffusion processes. Through rigorous mathematical modeling, he has addressed both classical and novel control challenges, offering solutions for one-dimensional, two-dimensional, and three-dimensional equations of parabolic and pseudo-parabolic types, including periodic and involution-influenced systems. Dekhkonov’s work encompasses time-optimal control, establishing frameworks that minimize the time required to achieve desired system states while adhering to boundary constraints, and extends to the analysis of higher-order parabolic equations. Collaborative efforts have expanded the scope of his research, applying advanced techniques to multi-dimensional and higher-order systems, ensuring the robustness of control strategies across diverse physical models. Contributions to journals such as Discrete and Continuous Dynamical Systems, Communications in Analysis and Mechanics, and Lobachevskii Journal of Mathematics highlight the combination of theoretical innovation and practical application in his research. His investigations provide a foundation for future studies in mathematical control theory, emphasizing efficiency, stability, and adaptability in complex dynamic systems. Farrukh Dekhkonov’s expertise bridges abstract mathematical concepts with applied problem-solving, facilitating advancements in the precise manipulation of thermal and diffusion processes. 126 Citations, 23 Documents, 9 h-index, View h-index

Profiles: Google Scholar | Scopus | ORCID
Featured Publication

Dekhkonov, F. N., & Kuchkorov, E. I. (2023). On the time-optimal control problem associated with the heating process of a thin rod. Lobachevskii Journal of Mathematics, 44(3), 1134–1144. Cited by 19

Dekhkonov, F. (2022). On a time-optimal control of thermal processes in a boundary value problem. Lobachevskii Journal of Mathematics, 43(1), 192–198. Cited by 17

Дехконов, Ф. Н. (2022). On the control problem associated with the heating process. Математические заметки СВФУ, 29(4), 62–71. Cited by 15

Dekhkonov, F. N. (2023). Boundary control problem for the heat transfer equation associated with heating process of a rod. Bulletin of the Karaganda University. Mathematics Series, 110(2), 63–71. Cited by 14

Dekhkonov, F. (2023). On a boundary control problem for a pseudo-parabolic equation. Communications in Analysis and Mechanics, 15(2), 289–299. Cited by14

Dekhkonov, F. N. (2024). On the control problem associated with a pseudo-parabolic type equation in an one-dimensional domain. International Journal of Applied Mathematics, 37(1), 109–118. Cited by 11

Rana Ghazali | Data Science | Best Researcher Award

Published on by Computer Science Engineering

Dr. Rana Ghazali | Data Science | Best Researcher Award

Researcher |McMaster University | Iran

Dr. Rana Ghazali focuses on advancing intelligent computing systems through the integration of machine learning, reinforcement learning, and large language models to optimize big data and distributed computing environments. Her work bridges the domains of cloud computing, Hadoop-based systems, and intelligent caching to enhance computational performance and resource utilization in large-scale data frameworks. She has contributed to innovative algorithms such as CLQLMRS and H-SVM-LRU for improving cache locality and intelligent cache replacement in MapReduce job scheduling, combining machine learning with distributed system optimization. Rana’s research also extends to the design and analysis of routing protocols in mobile ad hoc networks, leveraging bio-inspired algorithms such as the Ant Colony Optimization method to improve network efficiency. Her current exploration includes the application of reinforcement learning in scheduling and performance enhancement for distributed computing platforms, with additional attention to emerging paradigms like edge, fog, and serverless computing. As a researcher affiliated with the Resource Allocation and Stochastic Systems Lab at McMaster University, she contributes to cutting-edge discussions on adaptive data management, cyber and network security, and intelligent system design. Rana’s expertise further encompasses data analytics, large language models, and the intersection of artificial intelligence with real-world computing challenges. She has served as a reviewer for leading international journals including Elsevier and Wiley publications and has participated in academic collaborations that explore deep learning and resource optimization in distributed architectures. Her research endeavors consistently emphasize scalable, secure, and intelligent computational systems that advance the performance of modern data-intensive applications. Rana Ghazali has 13 citations, 2 documents, and an h-index of 2.

Profiles: Google Scholar | Scopus | ORCID
Featured Publication

Ghazali, R., Down, D. G. (2025). Smart data prefetching using KNN to improve Hadoop performance. EAI Endorsed Transactions on Scalable Information Systems, 12(3). Cited by 1

Ghazali, R., Adabi, S., Rezaee, A., Down, D. G., & Movaghar, A. (2023). Hadoop-oriented SVM-LRU (H-SVM-LRU): An intelligent cache replacement algorithm to improve MapReduce performance. arXiv preprint arXiv:2309.16471. Cited by 2

Ghazali, R., Adabi, S., Rezaee, A., Down, D. G., & Movaghar, A. (2022). CLQLMRS: Improving cache locality in MapReduce job scheduler using Q-learning. Journal of Cloud Computing, 9. Cited by 9

Ghazali, R., Adabi, S., Down, D. G., & Movaghar, A. (2021). A classification of Hadoop job schedulers based on performance optimization approaches. Cluster Computing, 24(4), 3381–3403. Cited by 11

Ghazali, R., Down, D. G. (2025). A systematic overview of caching mechanisms to improve Hadoop performance. Concurrency and Computation: Practice and Experience, 37(25–26), e70337.

Samira Rahim | Computational Finance | Best Researcher Award

Published on by Computer Science Engineering

Assist. Prof. Dr. Samira Rahim | Computational Finance | Best Researcher Award

Assist. Prof. Dr | University of Sciences and Technology Houari Boumediene | Algeria

Samira Rahim specializes in the field of stochastic methods, statistical modeling, and time series analysis, with a strong focus on the development and application of advanced probabilistic techniques for real-world data interpretation. Her research explores complex stochastic models, particularly in financial and meteorological contexts, where uncertainty and periodicity play critical roles. Through her work on periodic GARCH and threshold GARCH models, Rahim has contributed to improving the understanding of volatility dynamics and the statistical behavior of financial series with cyclical characteristics. Her investigations emphasize the importance of model mixtures and the application of local asymptotic normality principles, offering deeper insights into how stochastic processes can better capture temporal dependencies in financial and environmental data. With a background in statistics and operational research, Rahim integrates both theoretical and applied perspectives to construct robust predictive and inferential frameworks. Her expertise extends to stochastic optimization and probabilistic analysis, contributing to the development of more adaptive forecasting tools for risk management and decision-making under uncertainty. Her academic contributions, including her publication in Communications in Statistics – Theory and Methods in 2025, demonstrate her commitment to advancing research in the mathematical and statistical sciences. Rahim’s long-term experience in academia and her engagement with research projects underscore her dedication to innovation in stochastic modeling and data-driven approaches. Her work aligns with contemporary challenges in data science, emphasizing the critical role of statistical methodologies in improving predictive accuracy and understanding complex systems governed by random processes. Samira Rahim’s research continues to strengthen the intersection of probability theory, operational research, and applied statistics, contributing significantly to both theoretical advancements and their practical implementations in diverse scientific domains.

Profiles: Google Scholar
Featured Publication

Rahim, S., & Guerbyenne, H. (2025). Local asymptotic normality in periodic threshold GARCH and periodic GARCH models. Communications in Statistics – Theory and Methods, 1–20.

Ayman Al Hayek | Education Technology | Best Researcher Award

Published on by Computer Science Engineering

Dr. Ayman Al Hayek | Education Technology | Best Researcher Award

Diabetes Educator| Prince Sultan Military Medical City | Saudi Arabia

Dr. Ibrahim A. Al-Ghofaili is a distinguished medical professional specializing in family medicine, diabetes, and obesity management, with extensive clinical, research, and leadership experience across Saudi Arabia’s premier medical institutions. His professional journey includes key roles at Prince Sultan Military Medical City and the Ministry of Defense, progressing from family medicine registrar to consultant and head of the Diabetic Center. His research interests bridge preventive, metabolic, and endocrine health, emphasizing diabetes management, obesity medicine, and public health awareness. Al-Ghofaili’s scholarly work includes studies on the use of alternative medicine by cancer patients, prevalence of osteoporosis risk factors among young Saudi women, incidence and risk factors of hypoglycemia in chronic kidney disease, and factors influencing diabetic foot self-care efficacy. His active participation in international conferences such as the ADA Symposium, European Association for the Study of Diabetes (EASD), and Obesity Week highlights his dedication to advancing clinical knowledge and integrating global best practices into Saudi healthcare. Through various leadership and educational initiatives, he has contributed significantly to developing training frameworks and health service standards within the Saudi Commission for Health Specialties. His engagement in medical education, executive healthcare leadership programs, and obesity-focused fellowships underscores his multidisciplinary expertise in both patient-centered care and healthcare management. Ibrahim A. Al-Ghofaili’s research aims to enhance chronic disease prevention and management, aligning with national and international goals for improved metabolic health outcomes and lifestyle interventions. His continued academic involvement and clinical leadership position him as an influential figure in advancing family and lifestyle medicine, diabetes care, and obesity research in the Gulf region. Ibrahim A. Al-Ghofaili has achieved 1,193 citations, 48 documents, and an h-index of 18 according to the latest research metrics.

Profiles: Scopus | ORCID
Featured Publication

Al-Ghofaili, I. A., et al. (2025). Impact of transitioning from conventional blood glucose monitoring to continuous glucose monitoring on glycemic control and self-management in adults with type 2 diabetes on oral glucose-lowering medications. Diabetes Research and Clinical Practice.

Al-Ghofaili, I. A., et al. (2025). Evaluating the effect of Semaglutide as add-on therapy on glycemic control and continuous glucose monitoring outcomes in adults with type 1 diabetes: A two-year real-world data study. Journal of Diabetes and Its Complications.

Al-Ghofaili, I. A., et al. (2025). Evaluation of glycemia risk index and continuous glucose monitoring-derived metrics in type 1 diabetes: A real-world observational study. Journal of Diabetes and Metabolic Disorders.

Mitsuru Endo | Computational Theory | Best Researcher Award

Published on by Computer Science Engineering

Prof. Dr. Mitsuru Endo | Computational Theory | Best Researcher Award

Professor Emeritus| Tokyo Institute of Technology | Japan

Mitsuru Endo has made distinguished contributions to applied mechanics and vibration engineering, focusing on the dynamic behavior of continua and structures and the development of advanced noise and vibration control systems. His work bridges theoretical mechanics and practical applications in acoustic control, offering innovative solutions for vibration reduction in engineering systems. Endo has pioneered the extension of Southwell-Dunkerley methods for synthesizing frequencies, contributing to a deeper understanding of vibrational modes in complex systems. His research on flexural vibrations of rotating rings and deformation theories for beams, plates, and cylindrical shells has advanced modeling precision in mechanical structures. By introducing alternative formulations for Timoshenko beam and Mindlin plate models, Endo improved computational accuracy in vibration analysis. His innovative “one-half order shear deformation theory” redefined how transverse shear deformation is represented in structural mechanics, influencing global research on elasticity and composite structures. Endo’s extensive publications in leading journals such as the Journal of Sound and Vibration and the International Journal of Mechanical Sciences have established a strong foundation for future explorations in vibration modeling, acoustic optimization, and structural mechanics. His studies integrate both analytical and experimental perspectives, driving advancements in passive and active noise control technologies essential to aerospace, automotive, and civil engineering applications. The recognition of his work through multiple prestigious awards underscores his impact in mechanical sciences and engineering research, with 440 citations, 64 documents, and an h-index of 8.

Profiles: Scopus | ORCID
Featured Publication

Endo, M. (2013). Study on direct sound reduction structure for reducing noise generated by vibrating solids. Journal of Sound and Vibration, 332, 2643–2658. 5 citations

Endo, M. (2015). Study on an alternative deformation concept for the Timoshenko beam and Mindlin plate models. International Journal of Engineering Science, 87, 32–56. 34 citations

Endo, M. (2016). An alternative first-order shear deformation concept and its application to beam, plate and cylindrical shell models. Composite Structures, 146, 50–61. 17 citations

Endo, M. (n.d.). Study on the characteristics of noise reduction effects of a sound reduction structure. Conference Paper. 1 citation

Fu Chaotian | Software Engineering | Best Researcher Award

Published on by Computer Science Engineering

Dr. Fu Chaotian | Software Engineering | Best Researcher Award

Researcher | China Coal Technology and Engineering Group Shenyang Research Institute Co., Ltd. | China

Tian Fuchao, a distinguished researcher and chief scientist at the China Coal Technology and Engineering Group Shenyang Research Institute Co., Ltd., has established a remarkable career focusing on mine fire and gas disaster prevention under coupled conditions, industrial environmental gas spectroscopy, and advanced instrument development. His extensive research has led to the successful completion of 34 major research projects, the publication of 87 peer-reviewed journal papers, 5 books, and 69 patents, showcasing a strong integration of theory and industrial application. With an SCI h-index of 16, his work demonstrates both depth and impact within the field of coal mine safety and gas control technologies. Tian Fuchao has played a central role in developing state-of-the-art scientific instruments such as the TZX-1000 atmospheric environment monitoring system, TZX-3000 intelligent gas adsorption-desorption platform, and TZX-7000 laser-based multi-parameter detection system, which have been widely adopted across China. His research collaborations with the National Natural Science Foundation of China have focused on the mechanisms of gas desorption and spontaneous combustion in coal under multi-field coupling, contributing significantly to understanding disaster mechanisms in deep mining environments. Additionally, his editorial roles in Journal of China Coal Society and China Safety Science Journal reflect his leadership in advancing scientific communication. As a professional member of the China Occupational Safety and Health Association’s Youth Committee, Tian Fuchao continues to promote innovation and safety culture in industrial research. His ongoing efforts toward coupling analysis, predictive modeling, and intelligent monitoring technologies mark him as a leading figure in coal mine safety and environmental engineering. Tian Fuchao’s pioneering research continues to advance global understanding of energy safety and sustainable industrial operations, with 1,140 citations from 1,000 documents, 98 publications, and an h-index of 19.

Profiles:  Scopus | ORCID
Featured Publication

Thermal stability of acidified coal under coupled pressure and temperature effects. Thermal Science and Engineering Progress.

Explosion behavior of premixed LPG-hydrogen/air mixtures in a square duct. Physics of Fluids.

Dynamic viscosity evolution characteristics of ScCO₂ and its effects on carbon geological sequestration efficiency. Energy.

Precise gas control technology for fault fracture zones in deep roadways. Physics of Fluids.

Roof fracture dynamics and synergistic control strategies for mitigating coal mine gas disasters in goaf regions. Mining, Metallurgy and Exploration.

Arif Basgumus | Mobile Computing | Best Researcher Award

Published on by Computer Science Engineering

Dr. Arif Basgumus | Mobile Computing | Best Researcher Award

Associate Professor | Bursa Uludag University | Turkey

Dr. Arif Basgumus is a distinguished Associate Professor at Bursa Uludag University, whose research profoundly advances wireless communication, signal processing, and next-generation network systems. His extensive contributions encompass cognitive radio networks, non-orthogonal multiple access (NOMA), reconfigurable intelligent surfaces (RIS), cooperative communications, integrated sensing and communication (ISAC), and physical layer security. Dr. Arif Basgumus has developed robust models for interference alignment, hybrid RF/VLC systems, and UAV-assisted network architectures, contributing significantly to 5G and 6G technology evolution. His studies integrate theoretical modeling with artificial intelligence applications, enhancing the efficiency and reliability of communication frameworks. Actively collaborating with industrial partners such as ASELSAN, HAVELSAN, and TUSAŞ, he bridges academic innovation with practical defense and aerospace applications. His authorship spans influential journals including IEEE Access, IET Communications, and Digital Signal Processing, reflecting a consistent research impact in signal optimization, deep learning-aided communications, and security enhancement in RIS-assisted systems. He has guided numerous graduate theses, emphasizing interdisciplinary approaches across electrical, electronics, and computer engineering. His projects funded by TUBITAK and other research councils explore UAV communication, smart vehicle systems, and optical sensor networks, fostering sustainable and intelligent connectivity. Dr. Arif Basgumus has also co-authored several books and chapters on communication systems, cognitive networks, and artificial intelligence in engineering. His long-standing involvement in international collaborations and IEEE activities highlights a leadership role in shaping the technological foundations of future communication infrastructures, with 256 citations, 48 documents, and an h-index of 10 (View h-index).

Profiles:  Google Scholar | Scopus | ORCID
Featured Publication

Alakoca, H., Namdar, M., Aldirmaz-Colak, S., Basaran, M., & Basgumus, A. (2022). Metasurface manipulation attacks: Potential security threats of RIS-aided 6G communications. IEEE Communications Magazine, 61(1), 24–30. Citations: 43

Bayhan, E., Ozkan, Z., Namdar, M., & Basgumus, A. (2021). Deep learning-based object detection and recognition of unmanned aerial vehicles. In Proceedings of the 3rd International Congress on Human-Computer Interaction, Optimization and Robotic Applications. Citations: 41

Ozkan, Z., Bayhan, E., Namdar, M., & Basgumus, A. (2021). Object detection and recognition of unmanned aerial vehicles using Raspberry Pi platform. In Proceedings of the 5th International Symposium on Multidisciplinary Studies and Innovative Technologies. Citations: 34

Altuncu, A., & Basgumus, A. (2005). Gain enhancement in L-band loop EDFA through C-band signal injection. IEEE Photonics Technology Letters, 17(7), 1402–1404. Citations: 27

Basgumus, A., Durak, F. E., Altuncu, A., & Yilmaz, G. (2015). A universal and stable all-fiber refractive index sensor system. IEEE Photonics Technology Letters, 28(2), 171–174. Citations: 26

Umakoglu, I., Namdar, M., Basgumus, A., Kara, F., Kaya, H., & Yanikomeroglu, H. (2021). BER performance comparison of AF and DF assisted relay selection schemes in cooperative NOMA systems. In Proceedings of the 2021 IEEE International Black Sea Conference on Communications and Networking. Citations: 22