Zhang Zhenqian | Neuroscience | Best Researcher Award

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Mr. Zhang Zhenqian | Neuroscience | Best Researcher Award

University of Toyama | Japan

Mr. Zhang Zhenqian is a dedicated researcher whose work bridges artificial intelligence, machine learning, and meteorology, with an emphasis on developing advanced neural network models for predictive analytics. His recent publication, “RD2: Reconstructing the Residual Sequence via Under Decomposing and Dendritic Learning for Generalized Time Series Predictions,” featured in Neurocomputing (October 2025), showcases his innovative approach to enhancing time series forecasting accuracy through the integration of dendritic learning mechanisms and residual sequence reconstruction. Collaborating with Houtian He, Zhenyu Lei, Zihang Zhang, and Shangce Gao, Mr. Zhang contributes to advancing the computational intelligence field by addressing challenges in dynamic data modeling and predictive reliability. His research explores the intersection of data-driven modeling and environmental systems, offering valuable insights for improving real-world forecasting, particularly in meteorological and environmental applications. With a growing scholarly presence and contributions recognized through peer-reviewed international publications, Mr. Zhang exemplifies a new generation of researchers committed to interdisciplinary innovation. His work not only strengthens the theoretical foundations of artificial intelligence but also demonstrates its transformative potential in understanding and managing complex natural and engineered systems.

Profile : Orcid

Featured Publication

Zhang, Z., He, H., Lei, Z., Zhang, Z., & Gao, S. (2025). RD2: Reconstructing the residual sequence via under decomposing and dendritic learning for generalized time series predictions. Neurocomputing, 131867.

Simy Baby | Engineering | Best Researcher Award

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Mrs. Simy Baby | Engineering | Best Researcher Award

National Institute of Technology | India

Mrs. Simy Baby is an emerging researcher whose scholarly contributions center on semantic communications, machine learning, and computer vision, with a strong emphasis on communication-efficient feature extraction for edge inference tasks. She has authored 2 documents, received 2 citations, and holds an h-index of 1, reflecting the growing impact of her research in advanced communication technologies. Her publications in SCI-indexed journals, including Elsevier’s Computers & Electrical Engineering and IEEE Transactions on Cognitive Communications and Networking, demonstrate her commitment to innovation and excellence. Her study, “Complex Chromatic Imaging for Enhanced Radar Face Recognition”, introduced a novel complex-valued representation preserving amplitude and phase information of mmWave radar signals, achieving 99.7% recognition accuracy. Another major contribution, “Complex-Valued Linear Discriminant Analysis on mmWave Radar Face Signatures for Task-Oriented Semantic Communication”, proposed a CLDA-based encoding framework that improved feature interpretability and robustness under varying channel conditions. Her ongoing projects explore Data Fusion Discriminant Analysis (DFDA) for multi-view activity recognition and Semantic Gaussian Process Regression (GPR) for vehicular pose estimation, advancing the integration of semantic communication and computer vision. Mrs. Simy Baby’s research represents a vital step toward the development of intelligent, efficient, and adaptive communication systems for next-generation technologies.

Profiles : Scopus | Orcid | Google Scholar

Featured Publications

Baby, S. M., & Gopi, E. S. (2025). Complex valued linear discriminant analysis on mmWave radar face signatures for task-oriented semantic communication. IEEE Transactions on Cognitive Communications and Networking.

Baby, S. M., & Gopi, E. S. (2025, April). Complex chromatic imaging for enhanced radar face recognition. Computers and Electrical Engineering.

Fahad Shahzad | Earth and Planetary Sciences | Best Researcher Award

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Dr. Fahad Shahzad | Earth and Planetary Sciences | Best Researcher Award

Beijing Forestry University | Pakistan

Dr. Fahad Shahzad is a leading researcher in Remote Sensing and Geospatial Analysis, specializing in environmental monitoring, vegetation dynamics, forest fire prediction, and sustainable forest management. With an h-index of 11, 19 published documents, and 353 total citations, his work demonstrates significant impact in applying advanced machine learning and geospatial techniques to ecological and environmental challenges. He has developed ensemble machine learning models for forest fire prediction in Pakistan and China, spatio-temporal analyses of vegetation stress under climatic variability, and biomass and carbon stock modeling in Northern China forests. His research also explores urban heat island effects and vegetation dynamics in major Pakistani cities, along with long-term land-use and forest fragmentation analyses in Portugal. Dr. Shahzad has contributed extensively to high-impact journals including Fire Ecology, Earth Science Informatics, Scientific Reports, and Ecological Informatics, and actively serves as a reviewer for leading international SCI journals. Collaborating with multidisciplinary teams across China, Pakistan, and Europe, he integrates tools such as R, Google Earth Engine, and GIS to generate data-driven insights for climate resilience and environmental management. His work bridges fundamental research and applied solutions, advancing predictive modeling and geospatial approaches for global sustainability, while mentoring early-career researchers and contributing to collaborative, cross-border scientific initiatives.

Profiles : Scopus | Orcid | Google Scholar

Featured Publications

Shahzad, F., Mehmood, K., Anees, S. A., Adnan, M., Muhammad, S., Haidar, I., Ali, J., Hussain, K., Feng, Z., & Khan, W. R. (2025). Advancing forest fire prediction: A multi-layer stacking ensemble model approach. Earth Science Informatics.

Hussain, K., Badshah, T., Mehmood, K., Rahman, A. U., Shahzad, F., Anees, S. A., Khan, W. R., & Yujun, S. (2025). Comparative analysis of sensors and classification algorithms for land cover classification in Islamabad, Pakistan. Earth Science Informatics.

Mehmood, K., Anees, S. A., Muhammad, S., Shahzad, F., Liu, Q., Khan, W. R., Shrahili, M., Ansari, M. J., & Dube, T. (2025). Machine learning and spatio temporal analysis for assessing ecological impacts of the Billion Tree Afforestation Project. Ecology and Evolution.

Ali, J., Haoran, W., Mehmood, K., Hussain, W., Iftikhar, F., Shahzad, F., Hussain, K., Qun, Y., & Zhongkui, J. (2025). Remote sensing and integration of machine learning algorithms for above-ground biomass estimation in Larix principis-rupprechtii Mayr plantations: A case study using Sentinel-2 and Landsat-9 data in northern China. Frontiers in Environmental Science.

Hussain, K., Mehmood, K., Anees, S. A., Ding, Z., Muhammad, S., Badshah, T., Shahzad, F., Haidar, I., Wahab, A., Ali, J., et al. (2025). Retraction notice to “Assessing forest fragmentation due to land use changes from 1992 to 2023: A spatio-temporal analysis using remote sensing data” [Heliyon 10 (2024) e34710]. Heliyon.

Anees, S. A., Mehmood, K., Raza, S. I. H., Pfautsch, S., Shah, M., Jamjareegulgarn, P., Shahzad, F., Alarfaj, A. A., Alharbi, S. A., Khan, W. R., et al. (2025). Spatiotemporal analysis of surface Urban Heat Island intensity and the role of vegetation in six major Pakistani cities. Ecological Informatics.

Anees, S. A., Mehmood, K., Khan, W. R., Shahzad, F., Zhran, M., Ayub, R., Alarfaj, A. A., Alharbi, S. A., & Liu, Q. (2025). Spatiotemporal dynamics of vegetation cover: Integrative machine learning analysis of multispectral imagery and environmental predictors. Earth Science Informatics.

Al-Tameemi, N., Zhang, X., Shahzad, F., Mehmood, K., Xiao, L., & Zhou, J. (2025). From trends to drivers: Vegetation degradation and land-use change in Babil and Al-Qadisiyah, Iraq (2000–2023). Remote Sensing.

Hussain, K., Mehmood, K., Yujun, S., Badshah, T., Anees, S. A., Shahzad, F., Nooruddin, Ali, J., & Bilal, M. (2024). Analysing LULC transformations using remote sensing data: Insights from a multilayer perceptron neural network approach. Annals of GIS.

Mehmood, K., Anees, S. A., Muhammad, S., Hussain, K., Shahzad, F., Liu, Q., Ansari, M. J., Alharbi, S. A., & Khan, W. R. (2024). Analyzing vegetation health dynamics across seasons and regions through NDVI and climatic variables. Scientific Reports.

Hussain, K., Mehmood, K., Anees, S. A., Ding, Z., Muhammad, S., Badshah, T., Shahzad, F., Haidar, I., Wahab, A., Ali, J., et al. (2024). Assessing forest fragmentation due to land use changes from 1992 to 2023: A spatio-temporal analysis using remote sensing data. Heliyon.

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.

Michele Buzzicotti | Physics and Astronomy | Best Paper Award

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Dr. Michele Buzzicotti | Physics and Astronomy | Best Paper Award

University of Rome Tor Vergata | Italy

Dr. Michele Buzzicotti is an accomplished physicist whose research bridges turbulence modeling, data-driven fluid dynamics, and machine learning applications in complex systems. He has authored 39 scientific documents, accumulating 703 citations across 513 records with an h-index of 17, reflecting his consistent scientific influence in computational physics and atmospheric modeling. Holding a Ph.D. in Physics from the University of Rome Tor Vergata (2017), his doctoral work focused on the effects of Fourier mode reduction in turbulence. Currently serving as a tenure-track faculty member at the Department of Physics, University of Rome Tor Vergata, Dr. Buzzicotti has held visiting research appointments at the Technical University of Eindhoven and the University of Rochester. His projects, such as the €1.3 million Italian Ministry of Research–funded “Data-driven and Equation-based Tools for Complex Turbulent Flows,” showcase his leadership in advancing AI-integrated turbulence studies. His publications in Nature Machine Intelligence, Physical Review Letters, and Europhysics Letters highlight pioneering contributions to stochastic modeling and generative AI for fluid dynamics. A reviewer for top-tier journals and a member of EUROMECH and APS, Dr. Buzzicotti continues to shape the future of theoretical and applied turbulence research through innovative interdisciplinary approaches.

Profiles : Scopus | Orcid | Google Scholar

Featured Publications

Li, T., Biferale, L., Bonaccorso, F., Buzzicotti, M., & Centurioni, L. (2025). Stochastic reconstruction of gappy Lagrangian turbulent signals by conditional diffusion models. Communications Physics.

Freitas, A., Um, K., Desbrun, M., Buzzicotti, M., & Biferale, L. (2025). Solver-in-the-loop approach to closure of shell models of turbulence. Physical Review Fluids.

Martin, J., Lübke, J., Li, T., Buzzicotti, M., Grauer, R., & Biferale, L. (2025). Generation of cosmic-ray trajectories by a diffusion model trained on test particles in 3D magnetohydrodynamic turbulence. The Astrophysical Journal Supplement Series.

Li, T., Tommasi, S., Buzzicotti, M., Bonaccorso, F., & Biferale, L. (2024). Generative diffusion models for synthetic trajectories of heavy and light particles in turbulence. International Journal of Multiphase Flow.

Khatri, H., Griffies, S. M., Storer, B. A., Buzzicotti, M., Aluie, H., Sonnewald, M., Dussin, R., & Shao, A. (2024). A scale‐dependent analysis of the barotropic vorticity budget in a global ocean simulation. Journal of Advances in Modeling Earth Systems.

Li, T., Biferale, L., Bonaccorso, F., Scarpolini, M. A., & Buzzicotti, M. (2024). Synthetic Lagrangian turbulence by generative diffusion models. Nature Machine Intelligence.

Li, T., Lanotte, A. S., Buzzicotti, M., Bonaccorso, F., & Biferale, L. (2023). Multi-scale reconstruction of turbulent rotating flows with generative diffusion models. Atmosphere, 15(1), 60.

Storer, B. A., Buzzicotti, M., Khatri, H., Griffies, S. M., & Aluie, H. (2023). Global cascade of kinetic energy in the ocean and the atmospheric imprint. Science Advances.

Li, T., Buzzicotti, M., Biferale, L., Bonaccorso, F., Chen, S., & Wan, M. (2023). Multi-scale reconstruction of turbulent rotating flows with proper orthogonal decomposition and generative adversarial networks. Journal of Fluid Mechanics.

Buzzicotti, M., Storer, B. A., Khatri, H., Griffies, S. M., & Aluie, H. (2023). Spatio‐temporal coarse‐graining decomposition of the global ocean geostrophic kinetic energy. Journal of Advances in Modeling Earth Systems.

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.*

 

Abhijeet Das | Environmental Science | Excellence in Environmental Sustainability Research

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Dr. Abhijeet Das | Environmental Science | Excellence in Environmental Sustainability Research

C.V. Raman Global University | India

Dr. Abhijeet Das is a distinguished researcher in Water Resource Engineering, with expertise spanning watershed hydrology, hydrological modeling, climate change impact assessment, water quality control, machine learning, and geoinformatics. He has authored 89 documents that have collectively garnered 205 citations across 103 documents, reflecting an h-index of 8, underscoring his growing academic influence. With over a decade of combined experience in teaching, consultancy, and research, he has actively contributed to international collaborative projects in Saudi Arabia, Tunisia, South Africa, the UK, USA, Oman, Syria, and Lebanon, focusing on sustainable water management, GIS applications, and machine learning-driven environmental assessments. His prolific research output is complemented by more than 30 patents filed/published in areas such as artificial intelligence, IoT-based water quality systems, waste management, and sustainable environmental technologies. In addition, Dr. Das has published several books covering civil engineering, wastewater management, artificial intelligence in education, research methodology, and cancer biology. His work has been recognized through multiple “Best Paper Awards,” international speaking invitations, and honors such as the “Inspiring Educator Award” and “Research Excellence Award.” Serving as a reviewer for leading international journals, he continues to advance interdisciplinary solutions addressing hydrological extremes, climate resilience, and sustainable resource management.

Profiles : Scopus | Orcid

Featured Publications

Das, A. (2025). An optimization based framework for water quality assessment and pollution source apportionment employing GIS and machine learning techniques for smart surface water governance. Discover Environment.

Das, A. (2025). Reimagining biofiltration for sustainable industrial wastewater treatment. [Journal name not specified].

Das, A. (2025). A data-driven approach utilizing machine learning (ML) and geographical information system (GIS)-based time series analysis with data augmentation for water quality assessment in Mahanadi River Basin, Odisha, India. Discover Sustainability.

Das, A. (2025). Evaluation and prediction of surface water quality status for drinking purposes using an integrated water quality indices, GIS approaches, and machine learning techniques. Desalination and Water Treatment.

Das, A. (2025). Bioplastics: A sustainable alternative or a hidden microplastic threat? Innovative Infrastructure Solutions.

Das, A. (2025). Surface water quality assessment for drinking and pollution source characterization: A water quality index, GIS approach, and performance evaluation utilizing machine learning analysis. Desalination and Water Treatment.

Das, A. (2025). Geographical Information System–driven intelligent surface water quality assessment for enhanced drinking and irrigation purposes in Brahmani River, Odisha (India). Environmental Monitoring and Assessment.

Das, A. (2025). Spatiotemporal evaluation and impact of superficial factors on surface water quality for drinking using innovative techniques in Mahanadi River Basin, Odisha, India. Journal of Hydrology Regional Studies.

Seyedeh Azadeh Fallah Mortezanejad | Mathematics | Best Researcher Award

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Dr. Seyedeh Azadeh Fallah Mortezanejad | Mathematics | Best Researcher Award

Jiangsu University | China

Author Profile

Scopus

Orcid

Google Scholar

Early Academic Pursuits

Dr. Seyedeh Azadeh Fallah Mortezanejad began her academic journey with a strong foundation in statistics, completing her undergraduate and postgraduate studies at Guilan University, Iran. Her master’s research on semi-parametric estimation of conditional copula reflected her interest in statistical theory and dependence structures. She advanced her academic training with doctoral research at Ferdowsi University of Mashhad, focusing on applications of entropy in statistical quality control, which laid the groundwork for her later interdisciplinary research.

Professional Endeavors

Following her doctoral studies, Dr. Mortezanejad pursued postdoctoral research at Jiangsu University in China, under the guidance of Professor Ruochen Wang. Supported by the National Natural Science Foundation of China, her work explored advanced applications of statistical inference in engineering systems. Her professional engagements span teaching, collaborative research, and presenting at international conferences, reflecting her role as both a researcher and an academic contributor.

Contributions and Research Focus

Her research lies at the intersection of statistics, data science, and engineering. She has significantly contributed to areas such as time series analysis, dependence data, deep learning, and statistical quality control. Her expertise in copula functions and entropy has enabled novel methods for addressing challenges in multivariate data analysis and control charts. More recently, her work integrates machine learning and physics-informed neural networks for solving complex problems in multivariate time series and image processing.

Accolades and Recognition

Dr. Mortezanejad’s scholarly contributions have been recognized through numerous publications in leading journals, including Entropy, Sankhya B, and Physica A. She has been invited to present her findings at international workshops in Germany, France, Vietnam, and Spain, underscoring her recognition in global research communities. Her role as a reviewer for reputed journals and conferences further reflects her professional standing in the field.

Impact and Influence

Through her interdisciplinary research, Dr. Mortezanejad has bridged the gap between theoretical statistics and practical applications in fields such as healthcare, engineering, and financial modeling. Her contributions to statistical quality control, machine learning applications, and Bayesian inference have influenced both academic discourse and applied research, making her work relevant across diverse scientific domains.

Legacy and Future Contributions

With her strong background in both theoretical and applied statistics, Dr. Mortezanejad is poised to continue advancing research in modern statistical methods, particularly in integrating entropy-based approaches with machine learning. Her future work is expected to focus on enhancing predictive analytics, developing robust statistical tools for big data, and contributing to sustainable innovations in engineering and healthcare.

Publications

Article: Physics-Informed Neural Networks with Unknown Partial Differential Equations: An Application in Multivariate Time Series
Authors: Seyedeh Azadeh Fallah Mortezanejad, Ruochen Wang, Ali Mohammad-Djafari
Journal: Entropy
Year: 2025

Article: Variational Bayesian Approximation (VBA): Implementation and Comparison of Different Optimization Algorithms
Authors: Seyedeh Azadeh Fallah Mortezanejad, Ali Mohammad-Djafari
Journal: Entropy
Year: 2024

Article: Variational Bayesian Approximation (VBA) with Exponential Families and Covariance Estimation
Authors: Seyedeh Azadeh Fallah Mortezanejad, Ali Mohammad-Djafari
Journal: Physical Sciences Forum
Year: 2023

Article: Variational Bayesian Approximation (VBA): A Comparison between Three Optimization Algorithms
Authors: Seyedeh Azadeh Fallah Mortezanejad, Ali Mohammad-Djafari
Journal/Conference: MaxEnt 2022 (Conference Proceedings)
Year: 2023

Article: Evaluation of Anti-lice Topical Lotion of Ozonated Olive Oil and Comparison of its Effect with Permethrin Shampoo
Authors: Omid Rajabi, Atoosa Haghighizadeh, Seyedeh Azadeh Fallah Mortezanejad, Saba Dadpour
Journal: Reviews on Recent Clinical Trials
Year: 2022

Conclusion

Dr. Seyedeh Azadeh Fallah Mortezanejad’s career reflects a rare blend of statistical rigor, innovative application, and international recognition. Her early commitment to statistical theory, coupled with her interdisciplinary contributions, has positioned her as a rising figure in applied statistics and data science. With her expanding research footprint, she is set to leave a lasting impact on statistical research and its applications in science, technology, and industry.

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.