Huiming Kang | Computer Science | Innovative Research Award

Published on by Young Scientists Awards
Huiming Kang
Affiliation Monash University
Country China
Scopus ID 60349792000
Documents 2
Citations 1
h-index 1
Subject Area Computer Science
Event International Young Scientists Award
ORCID 0009-0007-3008-6107

Innovative Research Award

Huiming Kang
Monash University

Huiming Kang, affiliated with Monash University, has established an emerging research profile within the field of Computer Science through peer-reviewed publications and internationally visible scholarly contributions. The available bibliometric indicators, including Scopus-indexed publications, citation performance, and ORCID researcher identification, provide an objective representation of academic productivity and research visibility. Such metrics are widely used for evaluating research excellence across universities and international scientific communities.[1]

Abstract

Huiming Kang is an emerging researcher whose academic activities focus on Computer Science. The available research indicators demonstrate participation in peer-reviewed scientific publishing with indexed contributions recognized by major scholarly databases. Research evaluation commonly incorporates publication output, citation performance, author identification systems, and international accessibility. These indicators collectively suggest continuous scholarly engagement while supporting transparency and research discoverability across the global academic community.[1]

Keywords

  • Computer Science
  • Scientific Research
  • Innovation
  • Research Impact
  • Scopus
  • ORCID
  • Academic Recognition

Introduction

Research excellence is evaluated through scholarly publications, citation analysis, collaboration, and the influence of scientific contributions. Digital researcher identifiers such as ORCID and Scopus Author ID have strengthened the transparency of academic evaluation by enabling accurate attribution of research outputs. Huiming Kang’s academic profile illustrates participation in this internationally recognized scholarly ecosystem through indexed publications and measurable citation performance. These indicators contribute to evidence-based assessment for research awards and professional recognition.[2]

Research Profile

Huiming Kang is affiliated with Monash University and has established an academic profile in Computer Science. The researcher maintains internationally recognized author identifiers including Scopus Author ID and ORCID, supporting visibility, publication tracking, and collaboration opportunities. According to the available bibliometric information, the researcher has published two indexed documents with citation activity and an h-index of one. Although still developing, these indicators represent meaningful participation within the international research community.[1]

Research Contributions

The research contributions associated with Huiming Kang emphasize scientific investigation, scholarly dissemination, and participation in peer-reviewed research. Academic publications contribute to the advancement of Computer Science by sharing methodologies, analytical findings, and technical knowledge with the wider research community. Continued publication and citation growth indicate the potential for expanding scientific influence and fostering future interdisciplinary collaborations.[3]

Publications

  • 2 Scopus-indexed research publications.
  • Internationally indexed scholarly outputs.
  • Research supported by persistent author identifiers.

Research Impact

Bibliometric indicators provide an objective measure of scientific communication and research influence. Citation counts, publication records, and author identification systems enable institutions and funding organizations to evaluate research visibility and scholarly engagement. Huiming Kang’s existing publication portfolio reflects an emerging research trajectory that may continue to develop through future investigations, collaborations, and peer-reviewed dissemination.[2]

Award Suitability

Based on publicly available academic indicators, Huiming Kang demonstrates characteristics that align with the objectives of the International Young Scientists Award. The combination of indexed publications, citation activity, recognized researcher identifiers, and active participation in Computer Science research provides measurable evidence supporting consideration for innovation-focused academic recognition. Award evaluation should additionally consider originality, scientific quality, ethical standards, and long-term research potential.[1]

Conclusion

Huiming Kang represents an emerging contributor to Computer Science research through peer-reviewed publications and internationally recognized scholarly identifiers. The available bibliometric evidence demonstrates active engagement in scientific publishing and supports objective assessment for academic recognition programs. Continued research productivity, collaboration, and publication are expected to strengthen future scientific impact while contributing to the advancement of knowledge within the discipline.[2]

External Links

References

  1. Elsevier. (n.d.). Scopus author details: Huiming Kang, Author ID 60349792000. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=60349792000
  2. ORCID. (n.d.). Huiming Kang ORCID Record.
    https://orcid.org/0009-0007-3008-6107
  3. Digital Object Identifier Foundation. Example DOI Reference.
    https://doi.org/10.1109/5.771073

Divya Ramachandran | Computer Science | Young Researcher Award

Published on by Young Scientists Awards

Young Researcher Award

Divya Ramachandran
Researcher Divya Ramachandran
Affiliation PSNA College of Engineering and Technology
Country India
Scopus ID 57204412728
Documents 16
Citations 68
h-index 5
Subject Area Computer Science
Event International Young Scientists Award

Divya Ramachandran
PSNA College of Engineering and Technology

Divya Ramachandran is affiliated with PSNA College of Engineering and Technology, India, where her research activities contribute to the advancement of Computer Science through scholarly publications and collaborative academic initiatives. Her research profile reflects consistent participation in scientific communication, publication of peer-reviewed articles, and engagement with emerging technologies. Based on available scholarly metrics, her publication record includes sixteen indexed documents, sixty-eight citations, and an h-index of five, demonstrating measurable academic visibility within her research domain.[1]

Abstract

The Young Researcher Award recognizes emerging scholars whose research activities demonstrate originality, technical competence, and sustained academic development. Divya Ramachandran has established a growing publication profile in Computer Science through contributions to peer-reviewed journals and conference proceedings. Her research output reflects active participation in knowledge creation, interdisciplinary collaboration, and dissemination of scientific findings. Citation indicators further suggest that her published work has attracted scholarly attention within the research community, supporting continued academic growth and professional recognition.[1]

Keywords

Young Researcher Award, Computer Science, Academic Research, Scientific Publications, Scopus Author, Research Excellence, Innovation, Citation Analysis, Emerging Researcher, International Young Scientists Award.

Introduction

Academic research plays an important role in advancing scientific knowledge and technological innovation. Recognition programs such as the International Young Scientists Award encourage researchers to pursue high-quality investigations while promoting collaboration and knowledge exchange. Researchers at the early and middle stages of their careers contribute significantly to solving contemporary scientific and engineering challenges through evidence-based studies and publication of reproducible findings. Divya Ramachandran’s scholarly activities align with these objectives through continued engagement in Computer Science research and academic publishing.[2]

Research Profile

The available bibliographic indicators demonstrate a developing research portfolio consisting of sixteen indexed publications supported by sixty-eight scholarly citations and an h-index of five. These metrics indicate consistent research productivity and measurable influence within the Computer Science community. Affiliation with PSNA College of Engineering and Technology further reflects participation in institutional research, collaborative projects, and academic dissemination through recognized scholarly platforms.[1]

Research Contributions

  • Published peer-reviewed research in Computer Science.
  • Participated in academic conferences and scientific dissemination.
  • Supported interdisciplinary research and collaborative studies.
  • Contributed to ongoing technological and scientific advancement.

Publications

The research record includes journal articles and conference papers indexed within internationally recognized scholarly databases. These publications collectively contribute to the dissemination of research findings and provide a foundation for future investigations. The documented citation performance reflects continued academic engagement with published work.[3]

Research Impact

Research impact is commonly evaluated through publication quality, citation performance, collaboration, and contribution to scientific advancement. Divya Ramachandran’s citation profile indicates that her published work has been referenced by other researchers, demonstrating academic relevance and continued visibility. Such indicators contribute positively toward research recognition while encouraging future scholarly development.[1]

Award Suitability

Considering her documented scholarly publications, citation metrics, institutional affiliation, and continued research activity, Divya Ramachandran demonstrates characteristics commonly associated with candidates for the Young Researcher Award. Her developing research profile illustrates commitment to scientific inquiry, publication ethics, and academic collaboration. These factors align with the objectives of recognizing emerging researchers who contribute to the advancement of Computer Science through quality research and professional engagement.[4]

Conclusion

Divya Ramachandran’s scholarly profile represents a steadily developing academic career supported by peer-reviewed publications, measurable citation performance, and active institutional research participation. Recognition through the Young Researcher Award acknowledges ongoing contributions to Computer Science while encouraging continued excellence in research, innovation, and international academic collaboration.[4]

External Link

References

  1. Elsevier. (n.d.). Scopus author details: Divya Ramachandran, Author ID 57204412728. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57204412728
  2. Analyzing Urban Sprawl Through Aerial Views of Satellite Imagery using Geospatial Analysis with Generative AI.
    https://ieeexplore.ieee.org/document/11439040/
  3. Digital Object Identifier Foundation. DOI Handbook.
    https://doi.org/10.1109/5.771073
  4. Young Scientist Awards. International Young Scientists Award.
    https://youngscientistawards.com/

Laura Gatti | Engineering | Young Scientist Award

Published on by Young Scientists Awards

Young Scientist Award

Laura Gatti
Researcher Laura Gatti
Affiliation Politecnico di Torino
Country Italy
Scopus ID 60119120300
Documents 2
Citations 10
h-index 2
Subject Area Engineering
Event International Young Scientists Award
ORCID 0009-0004-4402-9910

Laura Gatti

Politecnico di Torino, Italy

Laura Gatti is an engineering researcher affiliated with Politecnico di Torino, Italy. Her scholarly work contributes to engineering research through peer-reviewed publications and measurable citation impact. According to publicly available academic indexing information, her research profile includes two indexed documents, ten citations, and an h-index of two, demonstrating the early development of a recognized scientific record.[1] Such indicators reflect continued engagement with scientific publication and participation in international research activities.[2]

Abstract

Laura Gatti’s academic profile represents an emerging contribution to engineering research. Her publications demonstrate participation in scholarly communication through peer-reviewed literature and indexed databases. Citation metrics indicate that her work has received attention within the research community, reflecting scientific relevance and the potential for future growth.[1]

Keywords

  • Engineering
  • Scientific Research
  • Innovation
  • Academic Publications
  • Young Scientist Award

Introduction

Early-career researchers play an important role in advancing engineering through innovative ideas and collaborative investigations. Laura Gatti’s scholarly profile illustrates continued participation in this process through scientific publications and research dissemination. Bibliometric indicators are commonly used to evaluate research visibility and complement qualitative assessment of academic achievements.[2]

Research Profile

The available Scopus profile records two indexed publications, ten citations, and an h-index of two. These metrics indicate an active research trajectory with measurable scholarly influence. Affiliation with Politecnico di Torino further reflects engagement within a respected academic environment dedicated to engineering education and research.[1]

Research Contributions

Laura Gatti has contributed to engineering scholarship through peer-reviewed publications that support scientific knowledge exchange. Her work demonstrates participation in collaborative research and the communication of technical findings to the wider academic community. Citation activity further indicates that these publications have been referenced by other researchers.[3]

Publications

The research record currently includes two Scopus-indexed documents. These publications contribute to the engineering literature and provide evidence of participation in scholarly publishing. Associated digital object identifiers (DOIs), where available, facilitate permanent identification and accessibility of published research.[3]

Research Impact

Research impact is assessed using both quantitative and qualitative indicators. Laura Gatti’s citation record and h-index demonstrate initial scholarly influence, while continued publication activity may further strengthen research visibility and interdisciplinary collaboration over time.[1]

Award Suitability

Based on publicly available academic information, Laura Gatti demonstrates characteristics commonly associated with emerging scientific researchers, including peer-reviewed publications, citation impact, institutional affiliation, and research engagement. Such achievements align with the objectives of recognition programs such as the International Young Scientists Award, which acknowledge promising contributions to scientific advancement while considering overall academic merit.[2]

Conclusion

Laura Gatti represents an emerging engineering researcher whose scholarly profile reflects active participation in scientific research and publication. Her measurable bibliometric indicators, combined with institutional affiliation and continued academic contributions, illustrate a developing research career with the potential for broader scientific impact. Continued publication and collaboration are expected to enhance future recognition within the engineering research community.

References

  1. Elsevier. (n.d.). Scopus Author Details: Laura Gatti, Author ID 60119120300.
    https://www.scopus.com/authid/detail.uri?authorId=60119120300
  2. ORCID. (n.d.). Laura Gatti ORCID Record.
    https://orcid.org/0009-0004-4402-9910
  3. A deeper understanding of flooding dynamics in gas diffusion electrodes for CO2 electrolyzer: how interfacial pressure shapes gas–liquid stability.
    https://www.sciencedirect.com/science/article/pii/S1385894725122407

Tianshu Chen | Engineering | Best Researcher Award

Published on by Young Scientists Awards

Best Researcher Award

Tianshu Chen
Technische Universität Darmstadt, Germany

Tianshu Chen
Affiliation Technische Universität Darmstadt
Country Germany
Documents 10
Subject Area Engineering
Event International Young Scientists Award
ORCID 0009-0005-1933-7716

Tianshu Chen, affiliated with Technische Universität Darmstadt, Germany. The profile summarizes research activities, publication record, engineering expertise, and the relevance of these accomplishments to the International Young Scientists Award.[1]

Abstract

Tianshu Chen has contributed to engineering research through scholarly publications and academic collaboration. The available research output demonstrates participation in contemporary engineering topics while supporting the advancement of scientific knowledge. The academic record reflects an active research profile with ten published documents and continued involvement in higher education and research activities.[1]

Keywords

Engineering, Scientific Research, Academic Publications, Innovation, Higher Education, Research Excellence, International Collaboration, Technology.

Introduction

Engineering research contributes to technological advancement by developing practical solutions supported by scientific investigation. Researchers affiliated with internationally recognized universities play an important role in expanding knowledge through publications, collaborative projects, and innovation. Tianshu Chen’s academic activities demonstrate engagement with these objectives while contributing to ongoing engineering research.[2]

Research Profile

Affiliated with Technische Universität Darmstadt, Tianshu Chen has developed a research profile focused on engineering. The documented publication record reflects continued scholarly participation and dissemination of research findings through recognized academic channels. The profile illustrates dedication to scientific inquiry, technical development, and interdisciplinary collaboration within engineering.[1]

Research Contributions

  • Published engineering research in scholarly sources.
  • Supported academic collaboration and scientific communication.
  • Contributed to innovation through research-based methodologies.
  • Promoted knowledge dissemination within the engineering community.

Publications

The available academic record includes ten research documents covering engineering-related topics. These publications contribute to the exchange of scientific knowledge and provide evidence of continued scholarly engagement. Research publications remain an important indicator of academic productivity and professional development.[3]

Research Impact

The published work supports scientific discussion, encourages collaboration, and contributes to engineering research development. Through documented publications and institutional affiliation, the researcher demonstrates participation in the broader academic community and continued commitment to research excellence.[2]

Award Suitability

Based on the available academic profile, publication activity, and engineering research involvement, Tianshu Chen demonstrates characteristics commonly considered in academic recognition programs. Continued scholarly productivity, institutional affiliation, and contributions to engineering research support consideration for the International Young Scientists Award.[4]

Conclusion

Tianshu Chen’s academic profile reflects sustained participation in engineering research through publications and institutional engagement. The documented scholarly contributions align with the objectives of recognizing emerging researchers who support scientific advancement through research, innovation, and knowledge dissemination.[4]

External Link

References

  1. ORCID. (n.d.). Tianshu Chen – ORCID Profile.
    https://orcid.org/0009-0005-1933-7716
  2. Technische Universität Darmstadt. Research and Academic Information.
    https://www.tu-darmstadt.de/
  3. A Review of Stroboscopic and Phantom Array Effects in Light-Emitting Diode Lighting.
    https://www.mdpi.com/2076-3417/16/13/6357
  4. International Young Scientists Award. Award Information.
    https://youngscientistawards.com/

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.

Uwayesu Happy Edwards | Engineering | Excellence in Research Award

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Mr. Uwayesu Happy Edwards | Engineering | Excellence in Research Award

Suzhou university of science and technology | China

Mr. Uwayesu Happy Edwards the research focuses on environmental engineering, natural resource assessment, wastewater treatment modeling, hydropower system analysis, and climate-related environmental degradation across East and Central Africa. Recent work investigates the factors driving water quality changes in Lake Bunyonyi, integrating ecological metrics with habitat-impact assessments. Studies on wastewater treatment processes include large-scale evaluation of ASM1 parameters under subtropical climatic conditions, using long-term WWTP monitoring data to improve predictive reliability and optimize treatment efficiency. Broader environmental impact assessments examine risk patterns in natural resource zones across Southern Nigeria, Ibo regions, and Uganda’s Kitezi landfill, applying quantitative environmental models to evaluate pollution, habitat stress, and human–ecosystem interaction. Additional research explores deforestation-driven climate change in Morogoro, Tanzania, emphasizing the environmental implications for EPA-related conservation missions. Work on hydropower comparability analyzes the performance, sustainability, and environmental footprints of hydropower relative to fossil fuels and other energy systems in developing countries, contributing to renewable-energy assessment frameworks. Complementary studies investigate biomass arrangement effects on aquatic ecosystems, using vibrational analysis to evaluate impacts on fish habitats in Lake Victoria. Across these projects, the research integrates environmental modeling, climate assessment, water-resource analytics, and sustainable energy evaluation to support data-informed environmental management and policy development.

Featured Publications

Uwayesu, H. E., & Mulangila, J. (2025). Factor contributing to change of water in Lake Bunyonyi [Dataset]. Figshare. https://doi.org/10.6084/m9.figshare.30041587

Uwayesu, H. E. (2025). Address of Edwards line of emissions in reducing/positive impact to climate [Dataset]. OSF. https://doi.org/10.17605/osf.io/csz8x

 Uwayesu, H. E. (2025). Environmental impact and risk assessment of natural resource areas around Southern Nigeria, particularly Ibo and Uganda in the Kitezi landfill [Dataset]. Harvard Dataverse. https://doi.org/10.7910/DVN/EJ4Z7E

 Uwayesu, H. E. (2025). Evaluation of ASM1 parameters using large-scale WWTP monitoring data from a subtropical climate in Entebbe [Dataset]. Harvard Dataverse. https://doi.org/10.7910/DVN/BG5VJB

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

Mujeeb Abiola Abdulrazaq | engineering | Young Scientist Award

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Mr. Mujeeb Abiola Abdulrazaq l engineering
| Young Scientist Award

University of North Carolina at Charlotte | United States

Mr. Mujeeb Abiola’s research focuses on advancing transportation safety and efficiency through data-driven methodologies and emerging technologies. His work extensively employs large-scale traffic and crash data, including millions of federal highway administration records, to investigate the spatiotemporal dynamics of pedestrian crashes and the evolution of crash hotspots. Utilizing advanced statistical and machine learning models, he has developed predictive frameworks that outperform traditional Highway Safety Manual standards, providing robust insights into risk factors and injury severity in both human-driven and autonomous vehicle contexts. His research on connected and autonomous vehicles (CAVs) has led to the development of traffic control algorithms that significantly enhance safety, operational efficiency, and environmental sustainability in freeway work zones. Furthermore, his studies integrate GPU-accelerated data processing, simulation-based optimization, and multi-level heterogeneity modeling to evaluate vulnerable road user behavior and assess dynamic collision risks. Through simulation platforms such as VISSIM and SUMO, combined with Python-based data analysis and GIS applications, his work systematically addresses complex traffic scenarios, including merging, diverging, and weaving segments, while also accounting for seasonal variations and temporal constraints in crash determinants. His contributions include empirical analyses of autonomous vehicle incidents, methodological advancements in microsimulation accuracy, and development of actionable strategies for real-world traffic management, ultimately aiming to improve roadway safety, inform policy, and guide evidence-based planning in modern transportation systems.

Profile:  Google Scholar 

Featured Publications

  • Abdulrazaq, M. A., & Fan, W. D. (2024). Temporal dynamics of pedestrian injury severity: A seasonally constrained random parameters approach. International Journal of Transportation Science and Technology, 9.

  • Abdulrazaq, M. A., & Fan, W. (2025). A priority based multi-level heterogeneity modelling framework for vulnerable road users. Transportmetrica A: Transport Science, 1–34. https://doi.org/10.1080/23249935.2025.2516817

  • Abdulrazaq, M. A., & Fan, W. (2025). Seasonal instability in crash determinants: A partially temporally constrained modeling analysis. SSRN 5341417. https://doi.org/10.2139/ssrn.5341417

Sheharyar Khan | Engineering | Young Scientist Award

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Dr. Sheharyar Khan l Engineering
| Young Scientist Award

Shandong University | Pakistan

Dr. Sheharyar Khan is a distinguished computer scientist and software engineer with extensive expertise in software engineering, artificial intelligence, and cybersecurity, specializing in IoMT edge-cloud frameworks and network intrusion detection systems. Currently a Postdoctoral Research Fellow at Shandong University, he leads independent and collaborative research initiatives, designing experiments, analyzing data, and publishing findings in high-impact journals. His doctoral research at Northwestern Polytechnical University focused on optimization-based hybrid offloading frameworks for IoMT in edge-cloud healthcare systems, demonstrating the integration of advanced computing techniques with practical healthcare applications. Dr. Khan has made significant contributions to explainable AI and hybrid ensemble machine learning, as seen in publications such as “HCIVAD: Explainable hybrid voting classifier for network intrusion detection systems” and “Consensus hybrid ensemble machine learning for intrusion detection with explainable AI”. With prior experience as a lecturer and IT specialist, he combines academic rigor with practical software development expertise. Dr. Khan has 104 citations across 10 documents, an h-index of 6, an i10-index of 5, is indexed under Scopus Author ID 57221647889, and holds ORCID 0000-0002-0089-0168, reflecting his impact on the field. Recognized for his analytical skills, innovation, and interdisciplinary research, he continues to advance secure, intelligent, and explainable computing systems for both academic and real-world applications.

Profile: Scopus | Google Scholar | Orcid | Researchgate 

Featured Publications

Khan, S., Liu, S., Pan, L., & Mei, G. (2025). Optimization-based hybrid offloading framework for IoMT in edge-cloud healthcare systems. Future Generation Computer Systems, 108163. https://doi.org/

Ahmed, S. K. M. T. S., Jiangbin, Z., & Khan, S. (2025). HCIVAD: Explainable hybrid voting classifier for network intrusion detection systems. Cluster Computing, 28(343). https://doi.org/

Ahmed, M. T. S., Jiangbin, Z., & Khan, S. (2024). Consensus hybrid ensemble machine learning for intrusion detection with explainable AI. Journal of Network and Computer Applications, 5*. https://doi.org/

Khan, S., Jiangbin, Z., & Ali, H. (2024). Soft computing approaches for dynamic multi-objective evaluation of computational offloading: A literature review. Cluster Computing, 27(9), 12459–12481. https://doi.org/

Yogesh Thakare | Engineering | Best Researcher Award

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Yogesh Thakare | Engineering | Best Researcher Award

Dr Yogesh Thakare, Ramdeobaba University, Nagpur, India

Dr. Yogesh Thakare 🎓 is an accomplished researcher and educator in Electronics and Communication Engineering. He earned his Ph.D. (2020) from SGB Amravati University, specializing in DRAM design using submicron technology 💾. Currently an Assistant Professor at Shri Ramdeobaba College of Engineering & Management, Nagpur 👨‍🏫, he has published in SCIE and Scopus-indexed journals 📑. His research spans FPGA architectures, AI, IoT, and biomedical systems 🤖. A GATE qualifier (94.92%), he has led government-funded projects 💰 and organized AI & IoT workshops 🏗️. Passionate about innovation, he contributes to cutting-edge electronics and computing technologies ⚡.

Publication Profile

Google Scholar

Academic Excellence

Dr. Yogesh Thakare earned his Ph.D. in Electronics Engineering from SGB Amravati University in 2020, focusing on Dynamic Random Access Memory (DRAM) design using submicron technology ⚡🔬. His academic journey reflects excellence, having completed his M.Tech with Distinction (85.10%) 🎓🏆 and his B.E. with First-Class (72.62%) 📚✨. With a strong foundation in electronics and a passion for advanced semiconductor technologies, Dr. Thakare has made significant contributions to memory design and innovation. His expertise in microelectronics and circuit design continues to drive advancements in the field, shaping the future of high-performance computing and digital storage solutions 💡🔍.

Funded Research & Grants

Dr. Yogesh Thakare has demonstrated exceptional research leadership by securing ₹24.6 Lakhs from CSIR for developing an automated water distribution system 💧🔬. His innovative approach aims to enhance water management efficiency through automation, contributing to sustainable resource utilization 🌱💡. This significant funding underscores his expertise in engineering solutions that address real-world challenges 🏗️⚙️. With a strong commitment to technological advancement, Dr. Thakare continues to drive impactful research that promotes water conservation and smart distribution systems 🌍📊. His work not only fosters scientific progress but also supports community welfare by ensuring efficient and equitable water access 🚰✅.

Experience

Dr. Yogesh Thakare is an experienced educator with over 14 years of teaching in top engineering institutes 🏫, including Shri Ramdeobaba College of Engineering and Management, Nagpur. As an Assistant Professor, he has played a key role in shaping technical education 📚. His passion for emerging technologies has led him to organize numerous workshops on Artificial Intelligence 🤖, the Internet of Things 🌐, and Machine Learning 📊, empowering students with cutting-edge knowledge. Through his dedication to academic excellence and innovation, Dr. Thakare continues to inspire the next generation of engineers and researchers 🚀.

Research Focus

Dr. Yogesh Thakare’s research spans electronics, artificial intelligence, IoT, and machine learning 🤖📡. His work includes DRAM memory design 🏗️💾, FPGA-based cryptography 🔐, and deepfake detection using neural networks 🕵️‍♂️🎭. He has contributed to environmental intelligence systems 🌱📊, weather prediction for agriculture 🌦️🚜, and smart monitoring technologies 📡🏠. Additionally, he has explored cortisol detection for stress monitoring 🧪⚕️ and crime reporting frameworks 🚔📜. His interdisciplinary research integrates hardware and AI-driven solutions, making impactful advancements in computing, security, and human well-being 🔬💡. His innovative approach bridges technology and real-world applications, enhancing automation, safety, and intelligence. 🚀

Publication Top Notes

Intelligent Life Saver System for People Living in Earthquake Zone.

An Effect of Process Variation on 3T-1D DRAM

Analysis of power dissipation in design of capacitorless embedded DRAM

IoT-Enabled Environmental Intelligence: A Smart Monitoring System

Detection of Deepfake Video Using Residual Neural Network and Long Short-Term Memory.

A Read-out Scheme of 1T-1D DRAM Design with Transistor Assisted Decoupled Sensing Amplifier in 7 nm Technology

Enhancing weather prediction and forecasting for agricultural applications using machine learning

FPGA Implementation of Compact Architecture for Lightweight Hash Algorithm for Resource Constrained Devices

Crafting visual art from text: A generative approach

Cortisol Detection Methods for Stress Monitoring: Current Insight and Future Prospect: A Review

An Ensemble Learning with Deep Feature Extraction Approach for Recognition of Traffic Signs in Advanced Driving Assistance Systems

Development and design approach of an sEMG-based Eye movement control system for paralyzed individuals