Dr. Anup Debnath | Experimental Physics | Editorial Board Member

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Dr. Anup Debnath | Experimental Physics | Editorial Board Member

Pachhunga University College | India

Dr. Anup Debnath is a materials physicist specializing in two-dimensional materials, magnetic nanostructures, transition-metal dichalcogenides, and MXene-based functional systems. His research focuses on understanding growth mechanisms, interfacial charge-transfer phenomena, and structure–property–performance relationships in emerging 2D architectures for electronic, magnetic, and energy-storage applications. He has developed strong expertise in advanced synthesis routes including hydrothermal, solvothermal, reflux, thermolysis, and chemical vapour deposition techniques for preparing high-quality TMDs, non-van der Waals magnets, and MXene derivatives. His experimental strengths extend across a broad spectrum of characterization tools such as XRD, Raman spectroscopy, XPS, TEM, AFM, SQUID magnetometry, PPMS measurements, UV-Vis spectroscopy, and FTIR, complemented by theoretical skills in Rietveld refinement and DFT-based calculations using MAUD and CASTEP. Dr. Debnath’s research contributions include revealing ferromagnetic and ferrimagnetic ordering in chemically synthesized 2D systems, designing MXene-integrated heterostructures, developing high-performance electrocatalysts, and engineering nanomaterials for hydrogen evolution, photothermal conversion, and supercapacitor applications. His publications in leading journals cover diverse topics ranging from magnetic coupling in low-dimensional systems to coercivity enhancement, phase engineering of TMDs, photothermal nanorods, hybrid perovskites, and advanced MXene composites. He has also contributed to conference proceedings and presented widely at national and international scientific platforms, earning recognition for excellence in both poster and oral presentations. His scholarly footprint continues to grow, currently reflected by 228 citations across 205 documents, 19 publications, and an h-index of 7, underscoring his expanding impact in the fields of experimental condensed matter physics and nanomaterials research.

Featured Publications

(2025). Vertically tilted SnS₂ grown on highly conductive Ti₃C₂Tₓ for electrochemical energy storage applications. Journal of Energy Storage.

 

Sakthivel Ramalingam | Computer Science | Editorial Board Member

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Assist. Prof. Dr. Sakthivel Ramalingam | Computer Science | Editorial Board Member

Vellore Institute of Technology Chennai | India

Assist. Prof. Dr. Sakthivel Ramalingamthe researcher’s work spans advanced control theory, nonlinear systems, and complex dynamical networks with a strong emphasis on cyber-physical security, resilient control design, and intelligent fuzzy systems. Their contributions focus on developing robust, finite-time, and event-triggered control and filtering strategies for Takagi–Sugeno fuzzy models, Markovian jump systems, networked control systems, and multi-agent networks subjected to uncertainties, delays, cyber attacks, actuator faults, and communication constraints. Their research advances include designing synchronization mechanisms for fractional-order systems, creating hybrid-triggered and observer-based state estimation methods, and proposing fault-tolerant and non-fragile control algorithms for large-scale intelligent systems. With more than thirty-eight SCIE-indexed publications in high-impact journals such as IEEE Transactions on Fuzzy Systems, Neural Networks, Communications in Nonlinear Science and Numerical Simulation, Applied Mathematics and Computation, Nonlinear Dynamics, and the Journal of the Franklin Institute, their work significantly contributes to resilient autonomous systems, intelligent vehicles, stochastic complex networks, and distributed optimization. Their research extends to sampled-data control, interval type-2 fuzzy systems, polynomial fuzzy models, semi-Markovian jump systems, and fractional-order complex networks. They also engage in experimental validation, synchronization analysis, and stability theory, aiming to enhance the reliability, safety, and robustness of modern intelligent systems in uncertain and adversarial environments.

Featured Publications

Sakthivel, R., Sakthivel, R., Kaviarasan, B., & Alzahrani, F. (2018). Leader-following exponential consensus of input saturated stochastic multi-agent systems with Markov jump parameters. Neurocomputing, 287, 84–92.

Sakthivel, R., Sakthivel, R., Kaviarasan, B., Lee, H., & Lim, Y. (2019). Finite-time leaderless consensus of uncertain multi-agent systems against time-varying actuator faults. Neurocomputing, 325, 159–171.

Sakthivel, R., Sakthivel, R., Nithya, V., Selvaraj, P., & Kwon, O. M. (2018). Fuzzy sliding mode control design of Markovian jump systems with time-varying delay. Journal of the Franklin Institute, 1–15.

Sakthivel, R., Kwon, O. M., Park, M. J., Choi, S. G., & Sakthivel, R. (2021). Robust asynchronous filtering for discrete-time T–S fuzzy complex dynamical networks against deception attacks. IEEE Transactions on Fuzzy Systems, 30(8), 3257–3269.

Mehdi Saadallah | Computer Science | Best Researcher Award

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Mr. Mehdi Saadallah | Computer Science
| Best Researcher Award

Vrije Universiteit Amsterdam | Netherlands

Mr. Mehdi Saadallah research focuses on advancing the integration of artificial intelligence (AI) and automation in cybersecurity operations, emphasizing the intersection between technology, human behavior, and organizational structures. It investigates how AI-driven tools influence professional identity, decision-making, and collaboration within Security Operations Centers (SOCs), where analysts and algorithms coexist in dynamic threat environments. By applying frameworks such as Paradox Theory, Organizational Routine Theory, and Identity Work Theory, the work uncovers the tensions, adaptations, and emergent practices that arise when automation transforms traditional cybersecurity routines. Empirical insights are drawn from multinational enterprises across diverse sectors, revealing how organizations balance efficiency, control, and trust in AI-augmented defense systems. The research also develops conceptual and operational models for AI-assisted vulnerability management and SOC modernization, providing a blueprint for improving detection, response, and resilience in complex digital ecosystems. Beyond theory, it delivers applied innovations that enhance cybersecurity governance, human–AI trust calibration, and automation ethics. Through interdisciplinary methods combining qualitative inquiry, computational analysis, and organizational modeling, the work contributes to redefining cybersecurity as a socio-technical discipline—bridging academic rigor and industrial application to guide the future of intelligent, adaptive, and human-centered cyber defense frameworks.

Featured Publications

Saadallah, M. (2025). Harmonizing paradoxical tensions in SOCs: A strategic model for integrating AI, automation, and human expertise in cyber defense and incident response. In Proceedings of the 58th Hawaii International Conference on System Sciences (HICSS-58). https://doi.org/10.24251/HICSS.2025.723

Saadallah, M., Shahim, A., & Khapova, S. (2025). Reconciling tensions in Security Operations Centers: A Paradox Theory approach. Big Data and Cognitive Computing, 9(11), 278. https://doi.org/10.3390/bdcc9110278

Saadallah, M., Shahim, A., & Khapova, S. (2025). Optimizing AI and human expertise integration in cybersecurity: Enhancing operational efficiency and collaborative decision-making. PriMera Scientific Engineering, 6(1), 177. https://doi.org/10.56831/psen-06-177

Saadallah, M., Shahim, A., & Khapova, S. (2024). Multi-method approach to human expertise, automation, and artificial intelligence for vulnerability management. In Advances in Intelligent Systems and Computing (pp. xxx–xxx). Springer. https://doi.org/10.1007/978-3-031-65175-5_29

 Saadallah, M., Shahim, A., & Khapova, S. (2024). Synergizing human expertise, automation, and artificial intelligence for vulnerability management. PriMera Scientific Engineering, 5(10), 160. https://doi.org/10.56831/psen-05-160

Vikas Verma | Computer Science | Young Scientist Award

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Mr. Vikas Verma | Computer Science
| Young Scientist Award

The ICFAI University, Jaipur | India

Dr. Vikas Verma’s research contributions focus extensively on Software Defined Networking (SDN), Machine Learning, and Network Optimization, emphasizing energy efficiency, intelligent routing, and data-driven automation. His doctoral research, “Flow Classification and Energy Efficient Routing in Software Defined Networks Using Machine Learning Techniques,” explores the integration of adaptive algorithms for sustainable network management. His projects, including “Routing Optimization for Software-Defined Networking Using Machine Learning Techniques and Multi-Domain Controller” and “Industry-Academia Collaboration of SME with Academics,” demonstrate practical applications of AI in networking and innovation ecosystems. Dr. Verma’s publications in high-impact journals and conferences, such as the Philippine Journal of Science, Suranaree Journal of Science and Technology, IEEE Xplore, and Springer CCIS, address key advancements in SDN, IoT-based smart farming, and quantum communication security. His work “Energy-Efficient Techniques in SDN: Software, Hardware, and Hybrid Approaches” and “Comparative Analysis of Quantum Key Distribution Protocols” highlight optimization in computing systems and secure data transmission. Additionally, he holds two UK design patents—one for an AI-driven finance management device and another for a medical diagnostic system using saliva-based biomarkers. His current research extends to privacy preservation, intelligent traffic classification, and predictive analytics, establishing his expertise in sustainable and secure intelligent network systems.

Featured Publications

Verma, V., & Jain, M. (2024). Energy-efficient techniques in SDN: Software, hardware, and hybrid approaches. Philippine Journal of Science, 153(1).

Agarwal, N., & Verma, V. (2023). Comparative analysis of quantum key distribution protocols: Security, efficiency, and practicality. In Proceedings of the International Conference on Artificial Intelligence of Things (pp. 151–163).

Verma, V., Ramakant, Mathur, H., & Agarwal, N. (2022). IoT assisted smart farming using data science techniques. In 2022 IEEE World Conference on Applied Intelligence and Computing (AIC). IEEE.

Verma, V. (2017). Automatic mood classification of Indian popular music. International Journal for Research in Applied Science and Engineering.

Verma, V., & Jain, M. (2023). Optimization of routing using traffic classification in software defined networking. Suranaree Journal of Science and Technology, 30(1), 010198(1–8).*

Wei Huang | Computer Science | Best Researcher Award

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Dr. Wei Huang | Computer Science | Best Researcher Award

Jingdezhen University | China

Dr. Wei Huang is a distinguished researcher and academician specializing in the integration of computer science and artificial intelligence in civil aviation. Currently serving at the School of Information Engineering, Jingdezhen University, Dr. Huang has made significant contributions to advancing aviation technology, with a strong focus on aircraft landing systems, runway marking recognition, and low-altitude economic applications. With extensive experience in both academia and industry, Dr. Huang has collaborated with aviation companies and government institutions, leading groundbreaking projects aimed at enhancing aviation safety and operational efficiency. His research combines deep learning, computer vision, and cutting-edge AI models to address real-world aviation challenges. As an expert evaluator for several scientific and technology associations, Dr. Huang actively supports innovation and knowledge exchange in the aviation sector. With numerous publications in high-impact journals and patents to his credit, he stands as a thought leader driving forward the future of intelligent aviation systems and technologies.

Profile

 Orcid

Education 

Dr. Wei Huang pursued his academic training with a focus on advanced computing and aviation systems, culminating in a PhD in Computer Science from the Russian Academy of Sciences, one of the most prestigious research institutions known for producing leading scientists in computational and engineering disciplines. Alongside his doctoral studies, Dr. Huang earned an Air Traffic Control License from the Civil Aviation Administration of China, a credential that demonstrates his expertise in aviation operations and his commitment to integrating theoretical knowledge with practical application. His educational journey reflects a multidisciplinary foundation, blending advanced computer science principles, artificial intelligence algorithms, and aviation system design. This unique academic background has enabled him to bridge the gap between cutting-edge computing innovations and their direct application in civil aviation. Dr. Huang’s education has provided him with a strong platform to pioneer technologies in intelligent aviation, low-altitude airspace research, and safety-driven innovations through data-driven approaches.

Experience 

Dr. Wei Huang’s professional journey reflects a distinguished career in research, teaching, and industry collaborations. At Jingdezhen University’s School of Information Engineering, he has been instrumental in shaping research initiatives that focus on artificial intelligence applications in aviation safety and low-altitude economic development. His work bridges academic theory and industrial needs, demonstrated through his collaboration with Jiangxi Helicopter Co., Ltd. on helicopter technology advancements and his leadership in AI-powered aviation marking recognition systems. As an expert reviewer and project evaluator for the Jiangxi Association for Science and Technology and other leading organizations, Dr. Huang contributes to national and regional scientific innovation assessments. His experience spans the supervision of projects funded by governmental bodies, the publication of high-impact research papers, and the development of patents that advance civil aviation. He has also built strong academic-industry partnerships, positioning himself as a leading expert at the intersection of computing and aviation engineering, innovation, and technology.

Awards and Honors

Dr. Wei Huang has received recognition for his pioneering work in computer science applications for civil aviation. His innovations, including AI-powered runway marking recognition systems, have contributed to improved aviation safety and operational efficiency, earning him respect in both academic and industrial spheres. He serves as an expert reviewer for academic journals, including the Journal of Jingdezhen University, showcasing his expertise in evaluating high-level research. Additionally, his role as a project evaluator for the Jiangxi Association for Science and Technology and the Low Altitude Leading Alliance highlights his contributions to guiding strategic research initiatives and fostering technology development. His patents and publications have further established his influence in advancing aviation-focused AI technologies. Through his efforts, Dr. Huang has become a valued leader in shaping aviation innovation policies, inspiring researchers, and contributing to China’s growing expertise in AI-driven aerospace technology. His achievements reflect excellence in interdisciplinary research and scientific innovation.

Research Focus 

Dr. Wei Huang’s research focuses on integrating computer science and artificial intelligence into aviation systems, with particular emphasis on improving low-altitude flight safety and efficiency. He specializes in computer vision, deep learning, and intelligent detection algorithms applied to aircraft landing systems. One of his major contributions is the development of an enhanced YOLOv5s-based detection model that improves accuracy in runway marking recognition, demonstrating measurable advancements in precision and reliability. His research combines techniques such as convolutional neural networks, attention mechanisms, deformable convolution, and data augmentation to create innovative solutions for real-world aviation challenges. Beyond algorithm development, Dr. Huang’s work extends to helicopter system optimization, aviation operations, and low-altitude economic growth strategies, making his contributions both technically robust and highly practical. His projects, patents, and publications underscore his leadership in AI applications for civil aviation. By bridging academia and industry, he is shaping future trends in intelligent aviation systems and aeronautical safety engineering.

Publication

Title: Research on Optimized YOLOv5s Algorithm for Detecting Aircraft Landing Runway Markings
Year: 2025

Conclusion

Dr. Huang is an outstanding researcher whose work reflects excellence, originality, and dedication to innovation in civil aviation technology. His ability to bridge advanced research with practical applications positions him as a strong candidate for the Best Researcher Award. With continued efforts to expand global recognition and collaborative impact, he is poised to make even greater contributions to the scientific community.

Xinxin Luo | Computer Science| Best Researcher Award

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Dr. Xinxin Luo | Computer Science | Best Researcher Award

Southeastern University, China

Xinxin Luo is a dedicated Ph.D. candidate in Cyberspace Security at Southeast University, China, with research interests in trustworthy AI, causal inference, and spatio-temporal graph neural networks. She holds a Master’s in Intelligent Science and Technology from NUPT and a Bachelor’s in Electronic Information Engineering from Hefei Normal University. Xinxin has authored several high-impact papers and presented at international conferences, contributing to robust AI systems by integrating causal structures into machine learning. Her work blends deep theoretical knowledge with practical implementations in AI-driven forecasting and decision support.

Profile

🎓 Education

Xinxin Luo is pursuing her Ph.D. (2021–2025) at the School of Cyber Science and Engineering, Southeast University, focusing on trustworthy AI. She earned her Master’s degree in Intelligent Science and Technology (2018–2021) from the College of Automation & AI at NUPT, and her Bachelor’s degree in Electronic Information Engineering (2013–2017) from Hefei Normal University. Her educational path reflects a deep foundation in intelligent systems, machine learning, and electronic engineering, forming the basis for her cutting-edge work in AI and causal inference.

💼 Experience

Xinxin has actively contributed to AI R&D through national key projects, where she developed anomaly detection algorithms and integrated ML with big data for real-time monitoring. She interned at Zhejiang Zijiang Laboratory, applying deep learning for human motion recognition. Xinxin also led a provincial project creating an interpretable SME rating system, showcasing her ability to translate theoretical insights into practical applications. Her hands-on experience spans spatio-temporal modeling, causality analysis, and real-world AI implementations in both academic and industrial environments.

🏅 Awards & Honors

Xinxin Luo has received recognition through multiple high-impact publications, including journals like Engineering Applications of AI and Journal of Artificial Intelligence Research. Her accepted conference presentations (e.g., ICCBR2024, PRICAI2024) and under-review articles in top-tier journals reflect academic excellence. She holds a patent in zero-shot learning and a software copyright for AI-based chart recognition. Her contributions to national and provincial research projects and successful leadership in AI system design highlight her innovation and technical prowess.

🔬 Research Focus

Xinxin focuses on trustworthy AI through the lens of causal inference and spatio-temporal graph neural networks. Her research explores dynamic causal structure learning for time series forecasting, addresses confounding bias, and incorporates counterfactual reasoning into predictive models. She investigates causal mechanisms behind data to enhance fairness, interpretability, and robustness in AI systems. Additionally, Xinxin integrates situational awareness with causal levels (observation, intervention, counterfactual) to improve intelligent decision-making. Her vision aims at building secure, human-centered, and future-aware AI.

 Conclusion

Xinxin Luo exemplifies the qualities of an outstanding researcher: deep technical expertise, a strong publication record, and a proven ability to translate theory into practice. By augmenting her grant-acquisition skills and widening her collaborative networks, she can elevate her already remarkable contributions to even greater international prominence. Her dedication to human-centered, trust-grounded AI renders her a highly deserving candidate for the Best Researcher Award.

Publication

  1. A Novel Approach of Causality Matrix Embedded into the Graph Neural Network for Forecasting the Price of Bitcoin

    • Authors: Xinxin Luo, Wei Yin, Xiao Bo

    • Journal: Engineering Applications of Artificial Intelligence

  2. Dynamic Causal Structure Learning for Spatio-Temporal Graph Forecasting

    • Authors: Xinxin Luo, Wei Yin, Zhuang Li

    • Journal: IEEE Transactions on Reliability

  3. Causal Spatio-Temporal Graph Forecasting Against Confounding Bias

    • Authors: Xinxin Luo, Wei Yin, Xiao Bo, Fan Wu

    • Journal: Journal of Artificial Intelligence Research

  4. Deconfounded Spatio-temporal Prediction with Causal-based Graph Neural Networks

    • Authors: Xinxin Luo, Wei Yin, Zhuang Li

    • Journal: Complex & Intelligent Systems

    • Ranking: JCR Q1

  5. Multi-view Deep Generative Dual Fusion Network for Zero-shot Learning

    • Authors: Xinxin Luo, Wei Yin, Xiao Bo

    • Journal: Multimedia Tools and Applications

    • Ranking: JCR Q2

  6. Forecasting Cryptocurrencies’ Price with the Financial Stress Index: A Graph Neural Network Prediction Strategy

    • Authors: Wei Yin, Ziling Chen, Xinxin Luo, Berna Kirkulak-Uludag

    • Journal: Applied Economics Letters

    • Ranking: JCR Q3