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).*

Su Cao| Big Data | Best Researcher Award

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Mr. Su Cao| Big Data | Best Researcher Award

China University of Mining and Technology |  China

Mr. Su Cao is a dedicated researcher in the field of Surveying and Mapping Science and Technology, currently pursuing his doctoral studies at the China University of Mining and Technology, Beijing. He earned his undergraduate degree in Surveying and Mapping Engineering from Jilin University and completed his master’s degree at Lanzhou Jiaotong University. With a strong academic foundation, his research focuses on multimodal data fusion, urban green space analysis, and sustainable urban planning. He has developed innovative methods for identifying and extracting the social functions of urban green spaces, constructing temporal change models with multi-level spatial gradients, and creating SDG-guided simulation approaches to predict future changes in green space distribution. His findings provide critical insights into Shanghai’s evolving green space patterns, highlighting the dominance of residential, commercial, and industrial green areas, while projecting long-term growth in conservation and community parks. Su Cao’s scholarly contributions include several high-quality publications as first author in leading journals such as Ecological Indicators, International Journal of Digital Earth, and ISPRS International Journal of Geo-Information. His research on the spatiotemporal evolution of social functions in multi-scale urban green spaces offers a valuable case study of Shanghai’s urban transformation. To date, his work has received 23 citations across 23 documents, reflecting strong academic recognition, and he has achieved an h-index of 2. At the age of 30, he demonstrates a combination of technical expertise, innovation, and future-oriented vision, contributing significantly to the advancement of geoinformatics, urban ecology, and sustainable city planning. With his growing achievements and impactful research, Su Cao is well-positioned to emerge as a leading scholar in his field, driving progress in the understanding and management of urban green infrastructure.

Featured Publications

Author(s). (2024). Multi-type and fine-grained urban green space function mapping based on BERT model and multi-source data fusion. International Journal of Digital Earth. Advance online publication.

Lakmini Prarthana Jayasinghe | Data Science | Best Researcher Award

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Dr Lakmini Prarthana Jayasinghe | Data Science | Best Researcher Award

Researcher, University of Southern Queensland, Australia 🌟

Lakmini Mudiyanselage is a dedicated researcher and academic with a passion for data science and artificial intelligence, specializing in hydrological forecasting and environmental applications. Based in Toowoomba, Queensland, she leverages her expertise to develop predictive models that address critical climate challenges in Australia, particularly in drought-prone regions. With a Ph.D. in Artificial Intelligence and a background in mathematics, Lakmini is committed to advancing scientific research through innovative data-driven methodologies and deep learning techniques.

Profile

Scopus

Education 🎓

Lakmini Mudiyanselage has a solid academic foundation marked by her advanced studies in artificial intelligence and mathematics. She earned her Doctor of Philosophy in Artificial Intelligence from the University of Southern Queensland in 2023, where she focused on cutting-edge AI applications to solve environmental challenges. Prior to this, she completed a Master of Philosophy in Mathematics in 2014 and a Bachelor of Science in Mathematics in 2008, both from the University of Kelaniya. This extensive background in mathematics and AI equips Lakmini with the analytical and computational skills needed to contribute significantly to data science and environmental studies, enabling her to develop sophisticated predictive models and insights that address critical climate issues.

Experience 🧑‍🏫

Researcher | UniSQ Advanced Data Analytic Lab (2020 – Present)
Lakmini leads efforts in predictive model development for hydrological parameters, working with extensive climate datasets to advance environmental forecasting. Her work has attracted significant funding and collaborative support, demonstrating her impact on hydrological research.

Senior Lecturer in Mathematical Sciences | Wayamba University of Sri Lanka (2010 – 2020)
In her decade-long academic career, Lakmini contributed to curriculum development and student mentorship. She also supervised student research and served as Acting Head of the Department, guiding students in mathematical modeling and AI-driven solutions.

Research Interest 🔍

Lakmini’s research focuses on leveraging artificial intelligence for environmental forecasting, with special attention to climate and hydrological data modeling. Her projects utilize hybrid machine learning models, such as Long Short-Term Memory networks, to enhance predictions related to evaporation, soil moisture, and evapotranspiration in regions affected by climate variability.

Awards 🏆

Lakmini Mudiyanselage has been honored with prestigious awards that reflect her academic excellence and research contributions. In 2023, she received the Award of Excellence in Doctoral Research from the University of Southern Queensland, recognizing her achievement of the highest possible result in her doctoral research examination. Earlier, in 2008, she was awarded the Physical Science Award in Mathematics by the Sri Lanka Association for the Advancement of Science for her groundbreaking work on confluent hypergeometric differential equations. These accolades underscore her dedication to advancing mathematical and AI-driven research, particularly in fields with impactful applications.

Publications 📚

“Development and Evaluation of Hybrid Deep Learning Long Short-Term Memory Network Model for Pan Evaporation Estimation” – Journal of Hydrology, 2022
Read here
Cited by multiple research articles examining predictive environmental modeling.

“Deep Multi-Stage Reference Evapotranspiration Forecasting Model: Multivariate Empirical Mode Decomposition Integrated with Boruta-Random Forest Algorithm” – IEEE Access, 2021
Read here
Referenced in studies focused on data-driven environmental predictions.

“Forecasting Multi-Step Soil Moisture with Three-Phase Hybrid Wavelet-Least Absolute Shrinkage Selection Operator-Long Short-Term Memory Network (moDWT-Lasso-LSTM) Model” – MDPI Water, 2023
Read here
Influential in soil moisture forecasting literature and widely cited in AI-based hydrological research.

Conclusion

Lakmini Mudiyanselage is an exceptional candidate for the Best Researcher Award. Her groundbreaking work in artificial intelligence and environmental data science addresses pressing global challenges, and her commitment to academic mentorship further underscores her dedication to scientific advancement and community service. Her accomplishments align well with the award’s objectives, making her highly deserving of this recognition.