Alexander Minkin | Computer Science | Innovative Research Award

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Innovative Research Award

Alexander Minkin — Keldysh Institute of Applied Mathematics
Alexander Minkin
Affiliation Keldysh Institute of Applied Mathematics
Country Russia
Scopus ID 55828950500
Documents 19
Citations 153
h-index 5
Subject Area Computer Science
Event International Research Excellence Awards – Book of Award
ORCID 0000-0001-8789-0779

Alexander Minkin is a researcher affiliated with the Keldysh Institute of Applied Mathematics in Russia whose scholarly record is associated with computer science and computational research. The profile supplied for this recognition article records 19 documents, 153 citations, and an h-index of 5 in Scopus. [1] Publicly available researcher information also identifies Alexander S. Minkin with the Keldysh Institute of Applied Mathematics and describes research interests including machine learning, computer vision, decision trees, and feature selection. [2]

Abstract

This article presents a scholarly recognition profile of Alexander Minkin, a researcher associated with the Keldysh Institute of Applied Mathematics, Russia. According to the supplied Scopus profile information, his indexed research record comprises 19 documents, 153 citations, and an h-index of 5. [1] Public researcher information places his work within computer science and identifies areas including machine learning, computer vision, decision trees, and feature selection. [2]

Keywords

Alexander Minkin, Computer Science, Machine Learning, Computer Vision, Feature Selection, Decision Trees, Computational Research, Applied Mathematics, Keldysh Institute of Applied Mathematics, Research Impact, Scopus, Scholarly Communication.

Introduction

Research in computer science increasingly depends on methods capable of extracting meaningful information from large, complex, and heterogeneous datasets. Machine learning, computer vision, feature-selection techniques, and decision-tree methodologies are among the approaches used to develop computational models and support systematic decision-making. [2]

Research Profile

Alexander Minkin’s research profile is positioned at the intersection of computer science and computational methodology. Available researcher information identifies expertise in machine learning, computer vision, decision trees, and feature selection. [2]

Research Contributions

The available information supports several broad areas in which Minkin’s research profile can be considered. First, his association with machine learning reflects engagement with computational methods for learning patterns from data. Second, his identified expertise in feature selection relates to the methodological problem of determining which variables provide useful information for computational models. [2]

Publications

The supplied Scopus record reports 19 indexed documents associated with the researcher and 153 citations, with an h-index of 5. [1] These bibliometric indicators provide a quantitative description of the indexed scholarly record but should be interpreted in the context of disciplinary norms, publication chronology, collaboration patterns, and database coverage. [2]

Research Impact

The reported citation count of 153 and h-index of 5 indicate that the research record has received measurable scholarly attention within the indexed literature. Citation indicators are useful for describing research visibility, although they do not independently measure the quality, originality, societal value, or practical usefulness of individual contributions.[1]

Award Suitability

Based on the supplied bibliometric information and publicly identifiable research areas, Alexander Minkin’s profile is relevant to an award category emphasizing innovative research in computer science. The combination of documented scholarly output, citation activity, and an identified research focus on machine learning, computer vision, decision trees, and feature selection provides a reasonable academic basis for consideration. [1] [2]

Conclusion

Alexander Minkin’s research profile reflects a sustained engagement with computer science and computational methodologies, particularly machine learning, computer vision, decision trees, and feature selection. Publicly available information connects him with the Keldysh Institute of Applied Mathematics and identifies methodological work concerning the importance of highly correlated features. [2]

References

  1. Elsevier. (2026). Scopus author details: Alexander Minkin, Author ID 55828950500. Scopus.
    https://www.scopus.com/pages/authors/55828950500
  2. OpenReview. (2026). Alexander S. Minkin — Researcher, NLBDIS, Keldysh Institute of Applied Mathematics. Research profile and publicly listed research information.
    https://openreview.net/profile?id=~Alexander_S._Minkin1
  3. Restriction of macroscopic structural superlubricity due to structure relaxation by the example of twisted graphene bilayer
    https://doi.org/10.1103/physrevmaterials.9.024002
  4. ORCID. (2026). Alexander Minkin — ORCID record 0000-0001-8789-0779.
    https://orcid.org/0000-0001-8789-0779

Saraswathy Shamini Gunasekaran | Computer Science | Research Excellence Award

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Assoc. Prof. Dr. Saraswathy Shamini Gunasekaran | Computer Science | Research Excellence Award

Taylor's University | Malaysia

Assoc. Prof. Dr. Saraswathy Shamini Gunasekaran is an accomplished researcher and academic specializing in Artificial Intelligence, with a strong focus on agent-based systems, intelligent autonomous systems, machine learning applications, smart energy systems, and climate change–related digital intelligence. Her scholarly impact is reflected in an h-index of 16, with 70 research documents generating 899 citations across international indexing platforms, demonstrating sustained influence in AI-driven and interdisciplinary research domains. Her work spans collective intelligence, knowledge transfer models, data mining, educational technologies, and intelligent digitalization, with publications appearing in IEEE conferences, international journals, and Springer book chapters. In addition to academic publishing, she has led significant intellectual property initiatives, including a granted patent on cooperative control systems for unmanned aerial platforms, utility innovations in autonomous multi-UAV task allocation, and copyrighted micro-credential programs. Her research excellence has been recognized through multiple prestigious awards, including international science communication accolades, industry research honors, and selection for global digital leadership programs. With over two decades of academic engagement and active research contributions, her profile reflects a strong integration of theoretical innovation, applied intelligence systems, and impactful scholarly dissemination across AI, energy, education, and digital transformation domains.

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View Scopus Profile

Featured Publications

Exploring the Roles of Agents and Multi-Agent in Improving Mobile Ad Hoc Networks
– International Symposium on Agents, Multi-Agent Systems and Robotics, ISAMSR, 2021

 

Bo Zhang | Computer Science | Research Excellence Award

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Assoc. Prof. Dr. Bo Zhang | Computer Science | Research Excellence Award

Northwest Polytechnic University | China

Assoc. Prof. Dr. Bo Zhang is an accomplished researcher whose work spans remote sensing, geospatial intelligence, environmental monitoring, and machine learning–driven Earth observation analytics. With 252 citations,  an h-index of 7, and 5, i10-index publications, his scholarly contributions demonstrate a growing and impactful presence in environmental data science. His research advances high-resolution satellite image processing, atmospheric pollutant estimation, digital elevation model reconstruction, and intelligent geospatial mapping. He has produced notable work on transfer learning–enhanced remote sensing, sparse-sample super-resolution mapping, neural-network–based PMx estimation, land surface temperature retrieval, and ozone concentration modeling. His publications in leading journals such as IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Science Bulletin, Remote Sensing, and Indoor and Built Environment highlight his expertise in integrating artificial intelligence with satellite observations to address environmental challenges. His research also contributes to epidemiological spatial analysis and geospatial data fusion, offering multidisciplinary value in Earth system science. Through continuous work on novel algorithms and high-fidelity environmental datasets, he has strengthened the scientific foundation for climate monitoring, pollution assessment, and large-scale geospatial modeling, positioning him as a significant contributor to advanced remote sensing and environmental informatics.

Profile : Scopus | Orcid | Google Scholar

Featured Publications

Yang, C., Zhang, B., Zhang, M., Wang, Q., & Zhu, P. (2025). Research on decision-making strategies for multi-agent UAVs in island missions based on Rainbow Fusion MADDPG algorithm. Drones, 9(10), 673.

Zhang, B., Shi, Z., Hong, D., Wang, Q., Yang, J., Ren, H., & Zhang, M. (2025). Super-resolution reconstruction of the 1 arc-second Australian coastal DEM dataset. Geo-Spatial Information Science, 1–21.


Zhang, B., Xiong, W., Ma, M., Wang, M., Wang, D., Huang, X., Yu, L., Zhang, Q., & others. (2022). Super-resolution reconstruction of a 3 arc-second global DEM dataset. Science Bulletin, 67(24), 2526–2530.


Pan, D., Zhang, M., & Zhang, B. (2021). A generic FCN-based approach for road-network extraction from VHR remote sensing images using OpenStreetMap as benchmarks. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.


Zhang, B., Zhang, M., Kang, J., Hong, D., Xu, J., & Zhu, X. (2019). Estimation of PMx concentrations from Landsat 8 OLI images based on a multilayer perceptron neural network. Remote Sensing, 11(6), 646.


Zhu, B., Liu, J., Fu, Y., Zhang, B., & Mao, Y. (2018). Spatio-temporal epidemiology of viral hepatitis in China (2003–2015): Implications for prevention and control policies. International Journal of Environmental Research and Public Health, 15(4), 661.

Chao Wang | Computer Science | Research Excellence Award

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Mr. Chao Wang | Computer Science | Research Excellence Award

North China University of Technology | China

Mr. Chao Wang is an accomplished researcher whose work spans vehicular networks, IoT security, blockchain mechanisms, and food engineering applications, reflecting a multidisciplinary impact. With 809 citations, an h-index of 12, and 16 i10-index publications, he has established a strong scholarly presence supported by numerous high-impact journal articles and competitive conference papers. His research contributions include advanced blockchain-based frameworks for secure communication, innovative privacy-preserving data-sharing models, anomaly detection algorithms for intelligent vehicles, and distributed system security. He has also co-authored influential studies on anti-glycation mechanisms, food bioactive compounds, and cellular protection. His publications from 2021 to 2025 demonstrate consistent output across IEEE Transactions, Future Generation Computer Systems, Food Biomacromolecules, and other reputable venues. His work on collaborative quality control, CAN bus anomaly detection, distributed GAN attack resistance, and multi-party payment channels represents notable advancements in secure systems. He has also contributed to reviews on AGEs inhibition, IoV security, NGS applicability, and blockchain-enabled vehicular applications. Beyond technical innovation, his research extends to biologically focused studies that explore glycation inhibition, fermentation mechanisms, and cellular oxidative protection. Across domains, his scholarly contributions continue to advance secure intelligent systems, data integrity solutions, and interdisciplinary applications, reinforcing his role as a productive and influential researcher.

Profiles : Orcid | Google Scholar

Featured Publications

Bao, C., Niu, Z., He, B., Li, Y., Han, S., Feng, N., Huang, H., Wang, C., Wang, J., & others. (2025). A novel high‐protein composite rice with anti‐glycation properties prepared with crushed rice flour, whey protein and lotus seed proanthocyanidins. Food Biomacromolecules, 2(1), 23–34.

He, Y., Zhou, Z., Wu, B., Xiao, K., Wang, C., & Cheng, X. (2024). Game-theoretic incentive mechanism for collaborative quality control in blockchain-enhanced carbon emissions verification. IEEE Transactions on Network Science and Engineering.

Li, Q., Xiao, K., Yi, C., Yu, F., Wang, W., Rao, J., Liu, M., Zhang, L., Mu, Y., Wang, C., & others. (2024). Inhibition and mechanism of protein nonenzymatic glycation by Lactobacillus fermentum. Foods, 13(8), 1183.

Wang, C., Xu, X., Xiao, K., He, Y., & Yang, G. (2024). Traffic anomaly detection algorithm for CAN bus using similarity analysis. High-Confidence Computing, 4(3), 100207.

Xiao, K., Li, J., He, Y., Wang, X., & Wang, C. (2024). A secure multi-party payment channel on-chain and off-chain supervisable scheme. Future Generation Computer Systems, 154, 330–343.

Feng, N., Feng, Y., Tan, J., Zhou, C., Xu, J., Chen, Y., Xiao, J., He, Y., Wang, C., & others. (2023). Inhibition of advance glycation end products formation, gastrointestinal digestion, absorption and toxicity: A comprehensive review. International Journal of Biological Macromolecules, 249, 125814.

Wu, Q., Kong, Y., Liang, Y., Niu, M., Feng, N., Zhang, C., Qi, Y., Guo, Z., Xiao, J., & others. (2023). Protective mechanism of fruit vinegar polyphenols against AGEs-induced Caco-2 cell damage. Food Chemistry: X, 19, 100736.

Wang, C., Liu, X., He, Y., Xiao, K., & Li, W. (2023). Poisoning the competition: Fake gradient attacks on distributed generative adversarial networks. In Proceedings of the IEEE International Conference on Mobile Ad Hoc and Smart Systems.

Xu, X., Wang, L., Wang, C., Zhu, H., Zhao, L., Yang, S., & Xu, C. (2023). Intelligent connected vehicle security: Threats, attacks and defenses. Journal of Information Science & Engineering, 39(6).

Wang, C., Jiang, L., He, Y., Yang, G., & Xiao, K. (2023). Age of information-based channel scheduling policy in IoT networks under dynamic channel conditions. In China Conference on Wireless Sensor Networks (pp. 88–98).

Zhou, J., Wang, C., Luo, M., Liu, X., Xu, X., & Chen, S. (2023). Spatial-temporal based multi-head self-attention for in-vehicle network intrusion detection system. SSRN 4581213.

Wang, C., Wang, S., Cheng, X., He, Y., Xiao, K., & Fan, S. (2022). A privacy and efficiency-oriented data sharing mechanism for IoTs. IEEE Transactions on Big Data, 9(1), 174–185.

Li, Q., Li, L., Zhu, H., Yang, F., Xiao, K., Zhang, L., Zhang, M., Peng, Y., Wang, C., & others. (2022). Lactobacillus fermentum as a new inhibitor to control advanced glycation end-product formation during vinegar fermentation. Food Science and Human Wellness, 11(5), 1409–1418.

Wu, Q., Liang, Y., Kong, Y., Zhang, F., Feng, Y., Ouyang, Y., Wang, C., Guo, Z., & others. (2022). Role of glycated proteins in vivo: Enzymatic glycated proteins and non-enzymatic glycated proteins. Food Research International, 155, 111099.

Wang, C., Cheng, X., Li, J., He, Y., & Xiao, K. (2021). A survey: Applications of blockchain in the Internet of Vehicles. EURASIP Journal on Wireless Communications and Networking, 2021(1), 77.

Xu, S., Chen, X., Wang, C., He, Y., Xiao, K., & Cao, Y. (2021). A lattice-based ring signature scheme to secure automated valet parking. In Wireless Algorithms, Systems, and Applications.

Leema Nelson | Computer Science | Research Excellence Award

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Dr. Leema Nelson | Computer Science | Research Excellence Award

Chitkara University | India 

Dr. Leema Nelson is an accomplished researcher whose scholarly contributions span machine learning, clinical decision support systems, composite materials, signal processing, and intelligent diagnostic frameworks. With a total of 1206 citations, an h-index of 17, and 29 i10-index publications, her research demonstrates both depth and sustained impact across interdisciplinary domains. She has produced numerous high-quality peer-reviewed articles, many in leading Elsevier journals such as Applied Soft Computing and Materials & Design, focusing on neural network optimization, characterization of metal-matrix composites, wear modelling, and advanced computational methods. Her work in clinical data classification, including diabetes and PCOS diagnosis, highlights the integration of artificial intelligence into healthcare decision-making. In recent years, she expanded her research into video smoke detection, cyber-security–based email filtering, audio source separation, and welding parameter optimization using intelligent algorithms. Her studies in deepfake detection, text recognition, and clinical support systems reflect her continuing advancements in data-driven AI models. She has also contributed extensively to IEEE conferences, presenting innovations in masked face detection, ultrasound image analysis, mobile app frameworks, and disease prediction models. Overall, her scientific output reflects strong productivity, interdisciplinary expertise, and meaningful contributions to both computational intelligence and applied engineering research.

Profiles : Scopus | Orcid | Google Scholar

Featured Publications

Jibinsingh, B. R., & Nelson, L. (2025). FL-WOSP: Federated learning with Walrus Optimization for sepsis prediction using MIMIC-III physiological and clinical data. Pattern Recognition. Advance online publication.

Batra, H., & Nelson, L. (2024). ESD: E-mail spam detection using cybersecurity-driven header analysis and machine learning-based content analysis. International Journal of Performability Engineering, 20(4).

Nelson, L. (2024). Data-driven clinical decision support system using neural network topology optimization for PCOS diagnosis. Journal of Soft Computing and Data Mining.

Batra, H., & Nelson, L. (2024). A three-stage deepfake detection framework using deep learning models with multimedia data. International Journal of Intelligent Systems and Applications.

Shanmuga Priya, M., Pavithra, A., & Leema, N. (2024). Character/word modelling: A two-step framework for text recognition in natural scene images. Computer Science.

Batra, H., & Nelson, L. (2023). DCADS: Data-driven computer aided diagnostic system using machine learning techniques for polycystic ovary syndrome. International Journal of Performability Engineering, 19(3).

Kumar, V. A., Rao, C. V. R., & Leema, N. (2023). Audio source separation by estimating the mixing matrix in underdetermined condition using successive projection and volume minimization. International Journal of Information Technology, 15(4), 1831–1844.

Ramesh, A., Sivapragash, M., Ajith Kumar, K. K., & Leema, N. (2023). Investigating the quality of TIG-welded aluminium alloy 5086 using the online acoustic emission and optimization of welding parameters using global best-based modified artificial bee colony algorithm. Transactions of the Indian Institute of Metals, 1–14.

Pranshu Kumar Soni, & Leema, N. (2023). PCP: Profit-driven churn prediction using machine learning techniques in banking sector. International Journal of Performability Engineering, 19(5), 303–311.

Vettum Perumal, S., Suyamburajan, V., Chidambaranathan, V. S., & Nelson, L. (2023). Characterization of microstructure and mechanical behaviour in activated tungsten inert gas welded dissimilar AA joint of AA 5083 and AA 6061 alloys. Journal of the Institution of Engineers (India): Series D, 1–9.

Patruni Rajshekhar Rao | Computer Science | Best Researcher Award

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Mr. Patruni Rajshekhar Rao | Computer Science | Best Researcher Award

FTD Infocom Pvt Ltd | India

Mr. Patruni Rajshekhar Rao is an avionics research professional whose work integrates test and verification engineering, data analysis, and safety-critical system evaluation across aerospace platforms. His contributions span functional RTL verification, aerospace data analysis, and reliability assessment of embedded systems. His early work involved functional verification of ARINC818 protocol IP cores, where he designed assertion-based test benches using VHDL and file-driven debugging to enhance precision in timing-sensitive validation. He later expanded into flight data analysis for advanced aircraft systems such as the SARAS platform, performing hardware–software integration testing, developing low-level test cases, and analyzing stall-warning system performance. His research also includes pioneering efforts in software health management, where he explored self-healing software systems using AI-driven methods to automate fault detection and recovery in avionics architectures. He has contributed to safety-critical processes aligned with DO-178B and DO-254 standards, including MCDC-level testing for auto-generated code in A-FADEC systems and performing dynamic and static analysis to identify and mitigate software defects. Across conferences and journals, he has published studies on verification methodologies, safety criteria, IP-core validation procedures, and AI-based static analysis, reinforcing his role in advancing dependable avionics engineering.

Profile : Scopus

Featured Publications

Nanda, M., & Rao, P. R. (2018, May 17). Implementation and verification of an asynchronous FIFO under boundary conditions (Paper ID: NCESC18-181). National Conference on Electronics, Signals and Communication (NCESC-2018), GSSS Institute of Engineering & Technology for Women, Mysore.

Nanda, M., Jayanthi, J., & Rao, P. R. (2018, May 18–19). Aerospace compliant test bench to verify critical aerospace functionalities (Paper ID: CRP18-1007). 3rd International Conference on Recent Trends in Electronics, Information and Communication Technology (RTEICT-2018), Department of Electronics and Communication Engineering, SVCE, Bangalore.

Nanda, M., & Rao, P. R. (2018). An approach for generating self-checking test bench. International Journal for Research in Applied Science and Engineering Technology, 6(6). (Paper ID: IJRASET17914).

Nanda, M., & Rao, P. R. (2018). Aerospace data bus safety criteria as per DO-254. International Journal of Research and Innovation in Applied Science, 3(6).

Nanda, M., & Rao, P. R. (2018). A procedure to verify and validate an FPGA level testing as per DO-254. International Journal of Research and Innovation in Applied Science, 3(6).

Nanda, M., & Rao, P. R. (2018). Verification cases and procedure for IP-core development. International Journal of Engineering Research and Advanced Technology. (ISSN 2454-6135).

Diogo Santiago | Computer Science | Best Researcher Award

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Mr. Diogo Santiago | Computer Science | Best Researcher Award

Oracle | Brazil

Mr. Diogo Santiago is a highly accomplished technology professional with extensive experience spanning software engineering, big data, and artificial intelligence. Beginning his career in 2009 as a software engineer developing major e-commerce platforms in Brazil, he transitioned into data engineering and science, mastering technologies like Hadoop, Spark, Hive, and Sqoop for large-scale data processing and migration. Since 2018, he has specialized in data science and AI, contributing to diverse projects in computer vision, anomaly detection, logistics optimization, and generative AI, including GAN and diffusion model applications for virtual try-on systems. As an AI Architect at Oracle for LATAM, he designs advanced AI architectures, supports clients with resource planning, and enhances model deployment efficiency through GPU optimization and large language model serving using vLLM and SGLang. His prior roles at Lambda3, Tivit, and Qintess involved developing ML models, data pipelines, and automation systems using cloud technologies such as GCP, AWS, and OCI. With multiple postgraduate qualifications in Big Data and Machine Learning for Finance, along with a Master’s in Medical Texture Imaging, he exemplifies innovation and leadership in merging AI research with scalable enterprise solutions.

Profile : Orcid

Featured Publication

Adorno, P. L. V., Jasenovski, I. M., Santiago, D. F. D. M., & Bergamasco, L. (2023, May 29). Automatic detection of people with reduced mobility using YOLOv5 and data reduction strategy. Conference paper.

 

Yaqin Wu | Computer Science | Excellence in Research Award

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Ms. Yaqin Wu | Computer Science | Excellence in Research Award

Shanxi Agricultural University | China

Ms. Yaqin Wu is an accomplished researcher and educator specializing in acoustic signal analysis, deep learning, and multimodal information fusion, with a research record reflecting 80 citations across 78 documents, 9 publications, and an h-index of 3. She holds a Master of Engineering in Electronic and Communication Engineering from Tianjin University and a Bachelor’s degree in Communication Engineering from Dalian Maritime University. Currently serving as a full-time faculty member at the School of Software, Shanxi Agricultural University, she teaches courses such as Speech Signal Processing, Natural Language Processing, and Human-Computer Interaction. Ms. Wu has led and contributed to several cutting-edge research projects, including pathological voice restoration, multimodal animal behavior monitoring, and AVS audio codec development. She has authored multiple SCI-indexed papers and holds several patents and software copyrights related to voice signal processing. Her technical proficiency spans Python, MATLAB, Linux systems, and MySQL databases. Notably, her master’s thesis earned the Outstanding Achievement Award of Engineering Master’s Practice from Tianjin University. Through her innovative contributions in signal processing and intelligent systems, Ms. Wu continues to advance the intersection of engineering and artificial intelligence research.

Profiles : Scopus | Orcid

Featured Publications

Zhang, J., Wu, Y., & Zhang, T. (2025). Fusing time-frequency heterogeneous features with cross-attention mechanism for pathological voice detection. Journal of Voice. Advance online publication.

Li, X., Wang, K., Chang, Y., Wu, Y., & Liu, J. (2025). Combining Kronecker-basis-representation tensor decomposition and total variational constraint for spectral computed tomography reconstruction. Photonics, 12(5), 492.

Victor R.L. Shen | Computer Science | Best Researcher Award

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Prof. Dr. Victor R.L. Shen | Computer Science | Best Researcher Award

National Taipei University | Taiwan

Prof. Dr. Victor R. L. Shen is a highly accomplished scholar and Professor Emeritus in the Department of Computer Science and Information Engineering at National Taipei University, Taiwan. With an extensive academic background, including a Ph.D. in Computer Science from National Taiwan University, he has dedicated decades to advancing research and education in artificial intelligence, Petri net theory, fuzzy logic, cryptography, e-learning systems, IoT, and intelligent computing. Over his distinguished career, he has published 78 documents that collectively received 840 citations across 696 sources, earning him an h-index of 15, reflecting both the depth and impact of his contributions. Beyond his prolific research, Prof. Shen has held prominent academic leadership positions, including Dean, Chairman, and CEO roles at National Taipei University and Ming Chi University of Technology, shaping academic programs and fostering innovation. His global recognition includes visiting professorships, membership in leading professional organizations such as IEEE, ACM, and IET, and numerous prestigious awards for teaching, research, and innovation. With sustained contributions in smart systems, advanced computing, and AI-driven education, Prof. Shen continues to influence the global academic community, leaving a legacy of excellence in both research and pedagogy.

Profiles : Scopus | Orcid

Featured Publications

Yang, C.-Y., Lin, Y.-N., Shen, V. R. L., Shen, F. H. C., & Lin, Y.-C. (2025). Petri net modeling and analysis of an IoT-enabled system for real-time monitoring of eggplants. Systems Engineering.

Yang, C.-Y., Lin, Y.-N., Shen, V. R. L., Shen, F. H. C., & Jheng, W.-S. (2025). A novel IoT-enabled system for real-time monitoring home appliances using Petri nets. IEEE Canadian Journal of Electrical and Computer Engineering.

Chang, J.-C., Chen, S.-A., & Shen, V. R. L. (2024). Smart bird identification system based on a hybrid approach: Petri nets, convolutional neural and deep residual networks. Multimedia Tools and Applications, 83(12), 34795–34823.

Yang, C.-Y., Lin, Y.-N., Shen, V. R. L., Tung, Y.-C., & Lin, J.-F. (2024). A novel IoT-enabled system for real-time face mask recognition based on Petri nets. IEEE Internet of Things Journal, 11(4), 6992–7001.

Yang, C.-Y., Lin, Y.-N., Wang, S.-K., Shen, V. R. L., & Lin, Y.-C. (2024). An edge computing system for fast image recognition based on convolutional neural network and Petri net model. Multimedia Tools and Applications, 83(5), 12849–12873.

Yang, C.-Y., Hwang, M.-S., Tseng, Y.-W., Yang, C.-C., & Shen, V. R. L. (2024). Advancing financial forecasts: Stock price prediction based on time series and machine learning techniques. Applied Artificial Intelligence, 38(1), 1–24.

Lin, Y.-N., Wang, S.-K., Chiou, G.-J., Yang, C.-Y., Shen, V. R. L., & Su, Z. Y. (2023). Development and verification of an IoT-enabled air quality monitoring system based on Petri nets. Wireless Personal Communications, 131(1), 63–87.*

 

Swathi Priyadarshini Tigulla | Computer Science | Best Researcher Award

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Dr. Swathi Priyadarshini Tigulla | Computer Science | Best Researcher Award

Osmania University | India

Author Profile

Scopus

Early Academic Pursuits

Dr. Swathi Priyadarshini Tigulla laid the foundation of her academic journey with a degree in Information Technology, followed by a master’s program in Information Technology with a specialization in network security. Her pursuit of advanced knowledge culminated in a doctoral degree in Computer Science and Engineering from Osmania University. From the beginning, she demonstrated a strong inclination toward solving computational problems and a keen interest in the emerging domains of artificial intelligence, machine learning, and network security.

Professional Endeavors

Her professional career reflects an extensive teaching and mentoring journey across reputed institutions. She began her career as an Assistant Professor in engineering colleges where she taught computer science, network security, and software engineering, and guided student projects. Over the years, she progressed to significant academic roles, including serving as Head of the Department, coordinating extracurricular activities, and contributing to student training and placement. Presently, she continues her academic engagement as an Assistant Professor specializing in artificial intelligence and machine learning, while also actively mentoring projects and participating in innovative academic initiatives such as GEN-AI teams and project schools.

Contributions and Research Focus

Dr. Tigulla’s research is strongly anchored in artificial intelligence, machine learning, and soft computing, with a particular focus on healthcare applications such as heart stroke prediction models. Her publications have proposed innovative approaches that integrate clustering, classification, and deep learning techniques to enhance medical predictions, combining accuracy with practical applicability. Beyond healthcare, her work also explores security strategies in cloud computing and data-driven approaches to protect systems from vulnerabilities. This blend of healthcare informatics and cyber security positions her research at the intersection of technology and community impact.

Accolades and Recognition

Her expertise has been recognized through publications in reputed international journals such as Measurement: Sensors and Journal of Positive School Psychology, along with contributions to international conferences under IEEE. She has served as a reviewer for scholarly journals and academic book chapters, demonstrating her standing as a trusted evaluator in her field. Her involvement as an organizer of technical workshops, hackathons, and project expos reflects her commitment to academic innovation and student skill development, further reinforcing her recognition as a versatile academic leader.

Impact and Influence

The impact of Dr. Tigulla’s work is evident in both her research outcomes and her teaching contributions. Her models for heart stroke prediction contribute significantly to community health by combining artificial intelligence with real-world medical applications. As an educator, she has influenced generations of students by equipping them with knowledge in machine learning, artificial intelligence, and advanced computational concepts. Her leadership in academic events has fostered a culture of innovation, creativity, and hands-on learning among students, thereby extending her influence beyond traditional teaching.

Legacy and Future Contributions

Dr. Tigulla’s legacy is one of blending research excellence with community benefit. By focusing on both healthcare prediction models and system security, she has addressed two domains of immense social importance—public health and digital trust. Looking forward, her future contributions are expected to further deepen the integration of artificial intelligence into real-world applications, enhance her role as a reviewer and academic guide, and continue her efforts to shape students into innovative researchers and industry-ready professionals.

Publications

Article: Developing Heart Stroke Prediction Model using Deep Learning with Combination of Fixed Row Initial Centroid Method with Naïve Bayes, Decision Tree, and Artificial Neural Network
Authors: T. Swathi Priyadarshini, Vuppala Sukanya, Mohd Abdul Hameed
Journal: Measurement: Sensors
Year: 2024

Article: Collaboration of Clustering and Classification Techniques for Better Prediction of Severity of Heart Stroke using Deep Learning
Authors: T. Swathi Priyadarshini, Vuppala Sukanya, Mohd Abdul Hameed
Journal: Measurement: Sensors
Year: 2025

Article: Deep Learning Prediction Model for Predicting Heart Stroke using the Combination Sequential Row Method Integrated with Artificial Neural Network
Authors: T. Swathi Priyadarshini, Mohd Abdul Hameed, Balagadde Ssali Robert
Journal: Journal of Positive School Psychology
Year: 2022

Article: Methods of Hidden Pattern Usage in Cloud Computing Security Strategies with K-means Clustering
Authors: T. Swathi Priyadarshini, Dr. S. Ramachandram
Journal: AIJREAS
Year: 2021

Article: A Review on Security Issue Solving Methods in Public and Private Cloud Computing
Authors: T. Swathi Priyadarshini, S. Ramachandram
Journal: IJMTST
Year: 2020

Conclusion

Dr. Swathi Priyadarshini Tigulla embodies the qualities of an academician and researcher who successfully bridges the gap between theoretical advancements and community impact. Her journey, marked by academic rigor, extensive teaching experience, and impactful research, showcases her dedication to advancing artificial intelligence and machine learning for practical applications. Recognized as both a researcher and a mentor, she continues to inspire through her contributions in education, healthcare, and cyber security. In conclusion, her career highlights a sustained commitment to knowledge, innovation, and community-oriented research, establishing her as a distinguished academic voice in the field of computer science and engineering.