Vaishnavee Rathod | Analysis | Best Researcher Award

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Ms. Vaishnavee Rathod | Analysis | Best Researcher Award

Research Scholar, Department of Computer Science Engineering SVNIT SURAT INDIA India

Vaishnavee Vijay Rathod is a Ph.D. candidate at SVNIT Surat, focusing on remote sensing and deep learning. She has earned her B.E., M.Tech., and is currently pursuing her Ph.D., with a specialized focus on satellite imaging, machine learning, and image processing. Vaishnavee has published multiple papers in renowned journals and conferences, contributing significantly to the fields of medical image analysis, remote sensing, and AI-based systems. She has also received recognition for her work, including the Best Paper Award at an international conference in 2020. Her innovative research on vehicle detection using computer vision and AI has gained attention in smart city development.

 

Publication Profile

Scopus Scholar

πŸŽ“ Education

Vaishnavee completed her B.E. in Electronics and Telecommunication in 2018 from Thakur College of Engineering and her M.Tech. in Electronics and Digital Image Processing from GHRIET Nagpur in 2020. She is currently pursuing a Ph.D. in Satellite Imaging and Deep Learning at SVNIT Surat, specializing in deep learning-driven satellite image classification. Throughout her academic journey, she has achieved distinction and published several research papers in prominent conferences and journals.

πŸ’Ό Experience

Vaishnavee has significant experience as a research scholar in deep learning and image processing. Her Ph.D. research includes the development of models for satellite image analysis and AI-based vehicle detection systems using UAV data. She has contributed to over 10 publications, including SCI-indexed journals and prestigious conferences. Additionally, Vaishnavee has been a recipient of several project funding awards, including SSIP 2.0 funding for her β€œRoadMitra” AI-based system project, showcasing her expertise in smart city technologies.

πŸ† Awards & Honors

Vaishnavee has been recognized for her research contributions, including the β€œBest Paper Award” at the International Conference on Engineering Systems Design and Optimization in 2020. She has received various honors, including funding under SSIP 2.0 for her smart city project β€œRoadMitra,” aimed at detecting road issues using UAVs. Additionally, she has won awards for her research excellence and has received numerous accolades for her participation in international conferences and workshops.

πŸ”¬ Research Focus

Vaishnavee’s research focuses on the intersection of satellite imaging, remote sensing, and deep learning. She works on developing deep learning models for efficient satellite image classification and vehicle detection systems using UAV technology. Her work also extends to image enhancement, biomedical image processing, and AI applications in smart city infrastructure. Vaishnavee’s research contributes to the development of innovative solutions for urban challenges, including road crack detection, traffic analysis, and the enhancement of healthcare technologies using AI.

Publication Top Notes

  • Title: Deep learning-driven UAV vision for automated road crack detection and classification
    • Authors: Rathod, V.V., Rana, D.P., Mehta, R.G.
    • Journal: Nondestructive Testing and Evaluation (2024)
    • Citations: 0
    • Status: Article in Press

 

  • Title: Road Crack Detection and Classification Using UAV and Deep Transfer Learning Optimization
    • Authors: Rathod, V., Rana, D., Mehta, R.
    • Journal: Journal of the Indian Society of Remote Sensing (2024)
    • Citations: 0
    • Status: Article in Press

 

  • Title: A computer vision approach to vehicle detection, classification, and tracking from UAV data for Indian traffic analysis
    • Authors: Rathod, V.V., Rana, D.P., Mehta, R.G., Nath, V.
    • Journal: IETE Journal of Research (2024)
    • Citations: 0
    • Status: Article in Press

 

  • Title: An Extensive Review of Deep Learning Driven Remote Sensing Image Classification Models
    • Authors: Rathod, V.V., Rana, D.P., Mehta, R.G.
    • Conference: Proceedings of the 2022 3rd International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT 2022)
    • Citations: 4

 

Jianping Zhang | Decision Sciences | Best Researcher Award

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Prof. Dr. Jianping Zhang | Decision Sciences | Best Researcher Award

Director of Uncrewed Aircraft Intelligent Traffic Technology Center at Second Research Institute of Civil Aviation Administration of China China

Zhang Jianping, born in 1976, is a renowned researcher and academic in the field of unmanned aircraft systems and air mobility. With a PhD in Engineering from Nanjing University of Aeronautics and Astronautics, he currently serves as a Researcher at the Second Research Institute of Civil Aviation Administration of China and as a Distinguished Professor at Southwest Jiaotong University. Zhang is the Director of the Civil Unmanned Aircraft Traffic Management Key Laboratory of Sichuan Province. He has made significant contributions to the development of unmanned aircraft traffic management (UTM) systems and international standards.

Profile

Scopus

πŸŽ“ Education 

Ph.D. in Engineering: Nanjing University of Aeronautics and Astronautics, specializing in air traffic management systems. Pioneered research in unmanned traffic management, urban air mobility, and advanced air mobility. Developed expertise in engineering solutions for intelligent air traffic systems during doctoral studies.

πŸ’Ό Experience

Researcher: Second Research Institute of Civil Aviation Administration of China; led national projects in UTM development. Distinguished Professor: Southwest Jiaotong University, mentoring PhD candidates in air mobility and traffic systems. Director: Civil Unmanned Aircraft Traffic Management Key Laboratory of Sichuan Province; guided innovations in air traffic systems.

πŸ† Awards & Honors

Special Award for Scientific and Technological Progress: China Communications and Transportation Association. First Prize: Civil Aviation Administration of China Science and Technology Award. Recognized for impactful contributions to UTM systems and intelligent air traffic management products.

πŸ”¬ Research Focus

Development of unmanned aircraft traffic management (UTM) systems. Urban air mobility (UAM) and advanced air mobility (AAM). International standards for unmanned aircraft systems (ISO 23629-9). Intelligent air traffic management systems for civil aviation applications.

Conclusion

Zhang Jianping’s exemplary achievements in unmanned aircraft traffic management and intelligent air traffic systems position him as a leading contender for the Best Researcher Award. His visionary leadership, significant contributions to both national and international projects, and ability to bridge the gap between research and real-world applications make him a deserving candidate. By expanding his global collaborations and mentoring future researchers, Zhang Jianping has the potential to continue making transformative advancements in the field of air mobility.

πŸ“šPublications 

  1. Title: SPE-SHAP: Self-paced ensemble with Shapley additive explanation for the analysis of aviation turbulence triggered by wind shear events
    • Year: 2024
    • Authors: Khattak, A.; Zhang, J.; Chan, P.-W.; Chen, F.

 

  1. Title: A New Frontier in Wind Shear Intensity Forecasting: Stacked Temporal Convolutional Networks and Tree-Based Models Framework
    • Year: 2024
    • Authors: Khattak, A.; Zhang, J.; Chan, P.-W.; Chen, F.; H. Almaliki, A.

 

  1. Title: Estimating Wind Shear Magnitude Near Runways at Hong Kong International Airport Using an Interpretable Local Cascade Ensemble Strategy
    • Year: 2024
    • Authors: Khattak, A.; Zhang, J.; Chan, P.-W.; Chen, F.; Almujibah, H.

 

  1. Title: Wind Shear and Aircraft Aborted Landings: A Deep Learning Perspective for Prediction and Analysis
    • Year: 2024
    • Authors: Khattak, A.; Zhang, J.; Chan, P.-W.; Hussain, A.; Almujibah, H.

 

  1. Title: AI-supported estimation of safety critical wind shear-induced aircraft go-around events utilizing pilot reports
    • Year: 2024
    • Authors: Khattak, A.; Zhang, J.; Chan, P.-W.; Chen, F.; Matara, C.M.

 

  1. Title: An optimisation model of hierarchical facility location problem for urban last-mile delivery with drones
    • Year: 2024
    • Authors: Zhang, G.; Zhang, J.; He, B.; Zhang, R.; Zou, X.

 

  1. Title: Estimating Turbulence Due to Low-Level Wind Shear in Airport Runway Zones Using TabNet-SHAP Framework
    • Year: 2024
    • Authors: Khattak, A.; Zhang, J.; Chan, P.-W.; Almaliki, A.H.; Mongina Matara, C.

 

  1. Title: Explainable Boosting Machine: A Contemporary Glass-Box Strategy for the Assessment of Wind Shear Severity in the Runway Vicinity Based on the Doppler Light Detection and Ranging Data
    • Year: 2024
    • Authors: Khattak, A.; Zhang, J.; Chan, P.-W.; Chen, F.; Almujibah, H.

 

  1. Title: Assessment of Wind Shear Severity in Airport Runway Vicinity using Interpretable TabNet approach and Doppler LiDAR Data
    • Year: 2024
    • Authors: Khattak, A.; Zhang, J.; Chan, P.-W.; Chen, F.

 

  1. Title: The Architecture of a Comprehensive System for Civil Unmanned Aerial Vehicle Traffic Management in Urban Low-Altitude Airspace
    • Year: 2023
    • Authors: Cao, L.; Xu, Q.; Chen, C.; Wu, Q.; Zhang, J.

 

Petros Barmpas | Statistical Analysis | Best Researcher Award

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Mr. Petros Barmpas | Statistical Analysis | Best Researcher Award 

Mr at University of The, Greece

Petros Barmpas is a dedicated researcher with a strong background in computer science and biomedical informatics. He has been advancing his academic journey at the University of Thessaly since 2020, following his graduation from the University of Patras with a degree in Computer Engineering and Informatics in 2019. His career is marked by an impressive series of publications and contributions to significant projects in the fields of machine learning, clustering methodologies, and the study of healthcare informatics. Petros is married and a proud father, balancing his professional and personal life with dedication and resilience. His contributions in AI and data analysis have cemented his reputation in both academic and practical applications of computational science.

Profile

Scopus

Education πŸŽ“

Petros Barmpas embarked on his academic career by completing high school in Kozani from 2010 to 2013. He then pursued his Bachelor’s degree in Computer Engineering and Informatics at the University of Patras, graduating in 2019. Currently, Petros is enrolled at the University of Thessaly, where he has been deepening his expertise in Computer Science and Biomedical Informatics since 2020. His educational path has equipped him with extensive knowledge and skills in computational algorithms, data analysis, and advanced informatics, supporting his impactful research and publications.

Experience πŸ§‘β€πŸ«

Since the onset of his academic pursuits, Petros Barmpas has engaged in various research initiatives, focusing on the practical application of AI and machine learning. His participation in the ATHLOS project exemplifies his commitment to large-scale data analysis and unsupervised learning. Throughout his time at the University of Thessaly and during collaborations with peers, he has demonstrated exceptional expertise in hierarchical clustering, ensemble regressors, and hyperdimensional computing approaches. Petros’ professional journey showcases a consistent dedication to solving complex computational problems and advancing methodologies in biomedical informatics.

Awards and Honors πŸ†

Petros Barmpas has earned recognition through numerous high-impact publications in prestigious conferences and journals. His co-authored works in IEEE Congresses and journals like the Springer Journal Health Information Science and Systems underscore his influential presence in the research community. The breadth of his collaborative work reflects the trust and respect he commands among fellow researchers, showcasing his commitment to advancing scientific understanding in AI applications and public health data analysis.

Research Focus πŸ”

Petros Barmpas’ research primarily explores innovative approaches to machine learning and data clustering. His interests lie in hyperdimensional computing, hierarchical clustering ensemble methods, and the development of predictive models for healthcare studies. He has applied these techniques to diverse areas, including opioid management during the pandemic and socio-demographic analysis in large-scale studies. Through collaborations and independent work, Petros has contributed to refining algorithms that enhance prediction power and adaptability, positioning his work at the intersection of AI and public health informatics.

ConclusionπŸ“

Petros Barmpas is a distinguished researcher with significant accomplishments in computer science and biomedical informatics. His extensive publication record, innovative contributions to data clustering and health informatics, and strong collaborative efforts underscore his suitability for the Best Researcher Award. Addressing areas such as publishing in higher-impact journals and increasing international engagement could further augment his candidacy. Overall, his academic achievements, commitment to interdisciplinary research, and consistent output make him a deserving nominee for recognition.

Publications πŸ“š

  1. Conference Paper: Hyperdimensional Computing Approaches in Single Cell RNA Sequencing Classification
    Year: 2024
    Authors: Barmpas, P., Tasoulis, S.K., Georgakopoulos, S.V., Plagianakos, V.P.
    Source: 2024 IEEE Congress on Evolutionary Computation, CEC 2024 – Proceedings

 

  1. Conference Paper: HCER: Hierarchical Clustering-Ensemble Regressor
    Year: 2024
    Authors: Barmpas, P., Anagnostou, P., Tasoulis, S.K., Georgakopoulos, S.V., Plagianakos, V.P.
    Source: Communications in Computer and Information Science, 2141 CCIS, pp. 369–378

 

  1. Article (Open access): The Development and Validation of the Pandemic Medication-Assisted Treatment Questionnaire for the Assessment of Pandemic Crises Impact on Medication Management and Administration for Patients with Opioid Use Disorders
    Year: 2023
    Authors: Leventelis, C., Katsouli, A., Stavropoulos, V., Veskoukis, A.S., Tsironi, M.
    Source: NAD Nordic Studies on Alcohol and Drugs, 40(1), pp. 76–94

 

  1. Conference Paper: Neural Networks Voting for Projection Based Ensemble Classifiers
    Year: 2023
    Authors: Anagnostou, P., Barmpas, P., Tasoulis, S.K., Georgakopoulos, S.V., Plagianakos, V.P.
    Source: Proceedings of the 2023 IEEE International Conference on Big Data, BigData 2023, pp. 4567–4574

 

  1. Article (Open access): A Divisive Hierarchical Clustering Methodology for Enhancing the Ensemble Prediction Power in Large Scale Population Studies: The ATHLOS Project
    Year: 2022
    Authors: Barmpas, P., Tasoulis, S., Vrahatis, A.G., Plagianakos, V.P., Panagiotakos, D.
    Source: Health Information Science and Systems, 10(1), 6