Xiangfu Kong | BigData and LargescaleVision | Best Researcher Award

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Dr. Xiangfu Kong | BigData and LargescaleVision | Best Researcher Award

Assistant Researcher | Zhejiang Lab | China

Dr. Xiangfu Kong is a distinguished researcher at Zhejiang Lab, specializing in intelligent transportation systems (ITS), spatiotemporal data analytics, and urban mobility optimization. His work bridges computer science, artificial intelligence, and transportation engineering to develop data-driven models that enhance mobility efficiency safety, and sustainability in smart cities.With an Publications 6  h-index of 3, and 67 citations across recognized publications, Dr. Kong has made notable scholarly contributions to the field. He has published six peer-reviewed research articles, including influential works such as “Measuring Traffic Congestion with Taxi GPS Data and Travel Time Index and  A Scenario-Based Map-Matching Algorithm for Complex Urban Road Networks. His recent studies explore flood risk mapping, travel time reliability, and natural language processing for urban data interpretation, showcasing his interdisciplinary expertise.Dr. Kong’s research projects often involve large-scale real-world data, particularly GPS-based urban mobility and hydrological data, integrating AI algorithms and Bayesian frameworks to model and predict transportation dynamics under diverse conditions. His studies have direct implications for urban policy-making, disaster management, and infrastructure resilience.He has actively collaborated with industry and academic partners to design computational models that assist in traffic monitoring, path planning, and flood management, contributing to sustainable urban development initiatives. Dr. Kong’s innovative use of AI for understanding urban systems highlights his dedication to applying research outcomes to societal benefit.In addition to his publications, Dr. Kong contributes to the broader scientific community through editorial and peer-review roles in transportation and data science journals. His ongoing work in data-driven transportation intelligence and urban informatics positions him as a promising researcher contributing to the next generation of smart mobility systems.Through his research excellence and cross-disciplinary collaborations, Dr. Xiangfu Kong continues to push the boundaries of how AI and data analytics can transform urban transportation, improve public safety, and drive global sustainability efforts.

Profiles: Google Scholar | ORCID | Scopus 

Featured Publications

1. Kong, X., Yang, J., & Yang, Z. (2015). Measuring traffic congestion with taxi GPS data and travel time index. Proceedings of the CICTP 2015, 3751–3762. Cited By : 35

2. Kong, X., & Yang, J. (2019). A scenario-based map-matching algorithm for complex urban road network. Journal of Intelligent Transportation Systems, 23(6), 617–631.
Cited By : 19

3. Kong, X., Yang, J., Qiu, J., Zhang, Q., Chen, X., Wang, M., & Jiang, S. (2022). Post‐event flood mapping for road networks using taxi GPS data. Journal of Flood Risk Management,  Cited By : 8

4. Xiangfu, K., Bo, D., Xu, K., & Yongliang, T. (2023). Text classification model for livelihood issues based on BERT: A study based on hotline compliant data of Zhejiang province. Acta Scientiarum Naturalium Universitatis Pekinensis, 59(3), 456–466. Cited By : 3

5. Kong, X., & Yang, J. (2016). Path planning with information on travel time reliability. Proceedings of the CICTP 2016, 99–107. Cited By :  2

6. Kong, X., Yang, J., Xu, K., Dong, B., & Jiang, S. (2023). A Bayesian updating framework for calibrating hydrological parameters of road network using taxi GPS data. Hydrology and Earth System Sciences Discussions, 1–25.

Dr. Xiangfu Kong nresearch advances data-driven intelligent transportation and urban informatics, fostering safer, more efficient, and sustainable mobility systems. His innovative integration of AI, GPS analytics, and hydrological modeling contributes to scientific progress, climate-resilient infrastructure, and smart city innovation with lasting global impact.

Simy Baby | Applications of Computer Vision | Best Researcher Award

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Mrs. Simy Baby | Applications of Computer Vision | Best Researcher Award

Researcher | National Institute of Technology | India

Mrs. Simy Baby is a pioneering researcher at the National Institute of Technology, Tiruchirappalli, with extensive expertise in machine learning, semantic communication, computer vision, and mmWave radar signal processing. Her research bridges the gap between radar sensing and intelligent communication frameworks, focusing on efficient feature extraction, complex-valued encoding, and task-oriented inference.Her seminal work, “Complex Chromatic Imaging for Enhanced Radar Face Recognition” (Computers and Electrical Engineering,  introduced a novel representation that preserves amplitude and phase information of mmWave radar signals, achieving an exceptional recognition accuracy. Another significant contribution, “Complex-Valued Linear Discriminant Analysis on mmWave Radar Face Signatures for Task-Oriented Semantic Communication” (IEEE Transactions on Cognitive Communications and Networking ), proposed a CLDA-based encoding framework enhancing feature interpretability and robustness under channel variations. Current investigations include Data Fusion Discriminant Analysis (DFDA) for multi-view activity recognition and Semantic Gaussian Process Regression (GPR) for vehicular pose estimation, highlighting her commitment to multitask semantic communication systems.Dr. Baby has 21 publications with 20 citations and an h-index of 3.  demonstrating a rapidly growing impact in her field. She is an active member of the Indian Society for Technical Education (ISTE) and contributes to the scientific community through innovative research that combines theory and practical applications. Her work on radar-based recognition, semantic feature transmission, and multi-task inference frameworks holds significant potential for intelligent transportation systems, human activity recognition, and bandwidth-efficient communication technologies.Through her research, Dr. Baby has established herself as a leading figure in advancing radar imaging and semantic communication, providing scalable solutions that merge high-performance computing with real-world societal applications. Her vision continues to shape the future of intelligent sensing and communication systems globally.

Profiles: Google Scholar | ORCID | Scopus 

Featured Publications

1. Ansal, K. A., Rajan, C. S., Ragamalika, C. S., & Baby, S. M. (2022). A CPW fed monopole antenna for UWB/Ku band applications. Materials Today: Proceedings, 51, 585–590. Cited By : 5

2. Ansal, K. A., Ragamalika, C. S., Rajan, C. S., & Baby, S. M. (2022). A novel ACS fed antenna with comb shaped radiating strip for triple band applications. Materials Today: Proceedings, 51, 332–338. Cited By : 4

3. Ansal, K. A., Kumar, A. S., & Baby, S. M. (2021). Comparative analysis of CPW fed antenna with different substrate material with varying thickness. Materials Today: Proceedings, 37, 257–264. Cited By : 4

4. Baby, S. M., & Gopi, E. S. (2025). Complex chromatic imaging for enhanced radar face recognition. Computers and Electrical Engineering, 123, 110198. Cited By : 3

5.Ansal, K. A., Shanmuganatham, T., Baby, S. M., & Joy, A. (2015). Slot coupled microstrip antenna for C and X band application. International Journal of Advanced Research Trends in Engineering and Technology.Cited By : 3

Dr. Simy M. Baby’s research advances the integration of semantic communication and computer vision, enabling high-accuracy radar-based recognition and task-oriented inference. Her work has significant implications for intelligent transportation, human activity monitoring, and bandwidth-efficient communication, driving innovation in both science and industry globally.

Venkataraman Thangadurai | 3D Computer Vision | Best Researcher Award

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Prof. Dr. Venkataraman Thangadurai | 3D Computer Vision | Best Researcher Award

Professor | University of St Andrews | United Kingdom

Prof. Dr. Venkataraman Thangadurai is a globally renowned expert in solid-state chemistry, electrochemical energy storage, and advanced battery technologies. With a research focus on fast ion conductors, solid electrolytes, lithium- and sodium-based batteries, and fuel cell materials, he has made pioneering contributions to both fundamental science and practical energy solutions. Prof. Thangadurai has authored over 278 peer-reviewed journal articles, 6 book chapters, and 21 conference proceedings, and has delivered 180 conference presentations, 83 posters, and 80 invited talks at top universities, institutes, and companies worldwide. His work has resulted in 13 patents/patent applications and has placed him among the top 1% of authors in Royal Society of Chemistry journals by citations in 2020. As of March 2025, his research has received 25,991 citations with an h-index of 69, reflecting the high impact of his work globally.He is the Founder and Advisor of Ions Storage Systems, Maryland, USA (2012–present) and Founder and Director of Superionics, Calgary, Canada (2021–present), translating cutting-edge research into commercial energy storage technologies. His research highlights include optimizing lithium nucleation overpotentials in garnet-based hybrid solid-state batteries, developing doped sodium gadolinium silicate ceramics for fast Na⁺ conduction, and enhancing electrocatalysts for lithium–sulfur batteries.Prof. Thangadurai collaborates extensively with leading international researchers and institutions, including the University of Calgary, University of Maryland, University of St Andrews, University of Kiel, Yale University, and ANSTO, advancing cross-disciplinary solutions in energy materials. Beyond his scientific contributions, he mentors emerging scientists and actively promotes innovation that addresses global energy challenges. His work has significant societal impact, enabling safer, high-performance, and sustainable energy storage solutions critical for electric mobility, grid storage, and renewable energy integration.

Profiles: Google Scholar | ORCID | Scopus

Featured Publications

1. Murugan, R., Thangadurai, V., & Weppner, W. (2007). Fast lithium ion conduction in garnet-type Li₇La₃Zr₂O₁₂. Angewandte Chemie International Edition, 46(41), 7778–7781.
Cited By : 3691

2.Han, X., Gong, Y., Fu, K., He, X., Hitz, G. T., Dai, J., Pearse, A., Liu, B., Wang, H., … Thangadurai, V. (2017). Negating interfacial impedance in garnet-based solid-state Li metal batteries. Nature Materials, 16(5), 572–579. Cited By : 2088

3.Thangadurai, V., Narayanan, S., & Pinzaru, D. (2014). Garnet-type solid-state fast Li ion conductors for Li batteries: Critical review. Chemical Society Reviews, 43(13), 4714–4727. Cited By : 1712

4.Pal, B., Yang, S., Ramesh, S., Thangadurai, V., & Jose, R. (2019). Electrolyte selection for supercapacitive devices: A critical review. Nanoscale Advances, 1(10), 3807–3835.
Cited By : 1229

5.Wang, C., Fu, K., Kammampata, S. P., McOwen, D. W., Samson, A. J., Zhang, L., … Thangadurai, V. (2020). Garnet-type solid-state electrolytes: Materials, interfaces, and batteries. Chemical Reviews, 120(10), 4257–4300. Cited By : 1130

Prof. Dr. Venkataraman Thangadurai  pioneering research in solid-state chemistry and advanced battery technologies drives global innovation in energy storage, enabling safer, high-performance, and sustainable batteries that power electric mobility, renewable energy integration, and next-generation clean technologies.

Felix Lankester | Face Recognition and Analysis | Research Impact Award

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Prof. Dr. Felix Lankester | Face Recognition and Analysis | Research Impact Award

Professor | Washington State University | United Kingdom

Dr Felix Lankester is an accomplished veterinary scientist with extensive experience in global health, wildlife conservation, and zoonotic disease research. He earned his PhD from the University of Glasgow, where his research focused on the impact and control of malignant catarrhal fever in Tanzania. He also holds an MSc in Wild Animal Health from the University of London and a Bachelor of Veterinary Science from the University of Liverpool. Dr Lankester serves as a Clinical Associate Professor at the Paul G. Allen School for Global Health, Washington State University, and previously worked as Director of Tanzanian Programs at the Lincoln Park Zoological Society and Country Director for the Pandrillus Foundation in Cameroon. His professional journey also includes roles as Project Director and Head Veterinarian at the Limbe Wildlife Centre, wildlife consultant in Kenya, and veterinary surgeon in the UK and Borneo. His research interests focus on zoonotic disease transmission, particularly rabies and other infectious diseases affecting marginalized communities in East Africa, as well as emerging pathogens with pandemic potential through his leadership in the DEEP VZN project. Dr Lankester has received recognition for his contributions to One Health, disease control, and wildlife health education. His research skills encompass field epidemiology, infectious disease modeling, surveillance design, and interdisciplinary collaboration across human and animal health systems. He continues to mentor young researchers and contribute to the scientific community through publications and international teaching engagements. His work has achieved 2,497 citations by 72 documents and an h-index of 25.

Profiles: Scopus | ORCID

Featured Publications

1.Kibona, T., Buza, J., Shirima, G., Lankester, F., Ngongolo, K., Hughes, E., Cleaveland, S., & Allan, K. J. (2022). The prevalence and determinants of Taenia multiceps infection (cerebral coenurosis) in small ruminants in Africa: A systematic review. Parasitologia.

2.Lankester, F., Kibona, T. J., Allan, K. J., de Glanville, W., Buza, J. J., Katzer, F., Halliday, J. E., Mmbaga, B. T., Wheelhouse, N., Innes, E. A., et al. (2024). Livestock abortion surveillance in Tanzania reveals disease priorities and importance of timely collection of vaginal swab samples for attribution. eLife.

3.Lankester, F., Lugelo, A., Changalucha, J., Anderson, D., Duamor, C. T., Czupryna, A., Lushasi, K., Ferguson, E., Swai, E. S., Nonga, H., et al. (2024). A randomized controlled trial of the effectiveness of a community-based rabies vaccination strategy. Preprint.

4.Kibona, T., Buza, J., Shirima, G., Lankester, F., Nzalawahe, J., Lukambagire, A.-H., Kreppel, K., Hughes, E., Allan, K. J., & Cleaveland, S. (2022). Taenia multiceps in northern Tanzania: An important but preventable disease problem in pastoral and agropastoral farming systems. Parasitologia.

5.Lugelo, A., Hampson, K., Ferguson, E. A., Czupryna, A., Bigambo, M., Duamor, C. T., Kazwala, R., Johnson, P. C. D., & Lankester, F. (2022). Development of dog vaccination strategies to maintain herd immunity against rabies. Viruses.

Vasuki | Deep Learning for Computer Vision | Women Researcher Award

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Dr. R. Vasuki | Deep Learning for Computer Vision | Women Researcher Award

Assistant Professor | Mannar Thirumalai Naicker College | India

Dr. R. Vasuki is an Assistant Professor in the Department of Artificial Intelligence at Mannar Thirumalai Naicker College, Madurai. She holds a Ph.D. in Computer Science from Karpagam Academy of Education, along with M.Phil, MCA, and BCA degrees from Bharathidasan University and Cauvery College for Women. She has over fourteen years of academic experience and previously served as an Assistant Professor at Annai Fathima College and as a Website Developer at LM Technologies, Chennai. Her research interests include biometrics, cryptography, database management systems, web development, and artificial intelligence. She has published several papers in reputed international journals and conferences such as IEEE, Springer, and Scopus-indexed publications, with notable work in biometric template protection, image encryption, and machine learning applications. Dr. Vasuki has organized and participated in numerous faculty development programs, workshops, and seminars, and has contributed as a reviewer for reputed journals. She received the first prize for a paper presentation from the Madurai Productivity Council and has authored a book titled Internet of Things along with a book chapter on conversational AI applications. Her research skills include data analysis, model optimization, and AI-driven system development, supported by certifications in deep learning, cybersecurity, and cloud computing. She actively mentors students in technical skill development and promotes innovation in higher education. Her research has received 1 citation by 3 documents with an h-index of 1.

Profile: Scopus

Featured Publications

1. Vasuki, R. (2024). Iris biometric template identification and recognition scheme using a novel parallel fused encoder.

 

Faisal Alamri | Object Detection for Security and Surveillance | Best Researcher Award

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Dr. Faisal Alamri | Object Detection for Security and Surveillance | Best Researcher Award

Chairperson of the Department of Computer Science and Information Technology | Jubail Industrial College (JIC) | Saudi Arabia

Dr. Faisal Alamri is an accomplished artificial intelligence researcher specializing in computer vision, machine learning, object detection, classification, segmentation, similarity search, adversarial perturbation, and zero-shot learning. He holds a Ph.D. in Computer Science with a focus on computer vision and machine learning from the University of Exeter, and completed his undergraduate and master’s degrees in computer systems engineering and networking. He currently serves as the Computer Science Department Chairperson at Jubail Industrial College, where he oversees academic and administrative activities and leads departmental initiatives. Previously, he worked as a machine learning engineer developing practical AI solutions, a postdoctoral research fellow, and a teaching assistant, and has also contributed as an online tutor and teaching volunteer. His research interests include developing innovative approaches for object detection, image analysis, and real-world AI applications. Dr. Alamri has been recognized for his achievements through multiple certifications and active participation in international conferences, workshops, and professional communities such as IEEE, Kaggle, NVIDIA, and MATLAB. He possesses strong technical skills in Python, MATLAB, C#, SPSS, AWS, Google Cloud ML Engine, and other platforms, and has completed various professional courses in deep learning, AI, cybersecurity, and digital analytics. His dedication to research, education, and community engagement reflects his commitment to advancing both science and society. He has a total of 49 citations, 7 documents, and an h-index of 5.

Profiles: Google Scholar | Scopus | ORCID | LinkedIn

Featured Publications

  1. Alamri, F., & Dutta, A. (2021). Multi-head self-attention via vision transformer for zero-shot learning. arXiv preprint arXiv:2108.00045.

  2. Alamri, F., & Pugeault, N. (2020). Improving object detection performance using scene contextual constraints. IEEE Transactions on Cognitive and Developmental Systems, 14(4), 1320–1330.

  3. Alamri, F., & Dutta, A. (2021). Implicit and explicit attention for zero-shot learning. In DAGM German Conference on Pattern Recognition (pp. 467–483).

  4. Alamri, F., & Dutta, A. (2023). Implicit and explicit attention mechanisms for zero-shot learning. Neurocomputing, 534, 55–66.

  5. Alamri, F., Kalkan, S., & Pugeault, N. (2021). Transformer-encoder detector module: Using context to improve robustness to adversarial attacks on object detection. In 2020 25th International Conference on Pattern Recognition (ICPR) (pp. 9577–9584). IEEE.

Xinrong Hu | Object Detection and Recognition | Women Researcher Award

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Prof. Xinrong Hu | Object Detection and Recognition | Women Researcher Award

Dean of Computer Science and Artificial Intelligence | Wuhan Textile University | China

Prof. Xinrong Hu is a distinguished researcher and academic leader in computer vision, natural language processing, virtual reality, and machine learning. She serves as Dean of the School of Computer and Artificial Intelligence at Wuhan Textile University and is a doctoral supervisor, leading an innovative research team at the Hubei Provincial Engineering Technology Research Center for Garment Informatization. She holds a Ph.D. and has extensive experience in guiding research projects, including over 30 funded initiatives, some with national and international significance. Her research interests focus on advancing artificial intelligence applications in real-world scenarios, combining theoretical innovation with practical solutions. She has authored more than 100 academic papers, edited six textbooks, translated a book, and holds 26 invention patents, demonstrating her strong research skills and contribution to knowledge dissemination. Prof. Hu has been recognized with multiple awards and honors, including provincial and ministerial-level scientific research awards, teaching achievement awards, and prestigious titles such as Hubei Provincial Distinguished Teacher and recipient of the Special Government Allowance from the State Council. Her professional engagement includes leadership in academic communities, mentorship of young researchers, and active participation in advancing the field of AI through both education and research initiatives. Her comprehensive expertise, innovative contributions, and dedication to fostering academic excellence make her a leading figure in her field. Her research impact is reflected in 1,044 citations, 209 documents, and an h-index of 16.

Profiles: Scopus | ResearchGate 

Featured Publications

  1. Hu, X., et al. (2025). CDPMF-DDA: Contrastive deep probabilistic matrix factorization for drug-disease association prediction. BMC Bioinformatics.

  2. Hu, X., et al. (2025). Source-free cross-modality medical image synthesis with diffusion priors. Journal of King Saud University – Computer and Information Sciences.

  3. Hu, X., et al. (2025). TADUFMA: Transformer-based adaptive denoising and unified feature modeling for multi-condition anomaly detection in computerized flat knitting machines. Measurement Science and Technology.

  4. Hu, X., et al. (2025). ViT-BF: Vision transformer with border-aware features for visual tracking. Visual Computer.

  5. Hu, X., et al. (2025). Adaptive debiasing learning for drug repositioning. Journal of Biomedical Informatics.

Benito Farina | Spatio-Temporal CV | Best Researcher Award

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Mr. Benito Farina | Spatio-Temporal CV | Best Researcher Award

Researcher | Universidad Politecnica de Madrid | Spain

Benito Farina is a dedicated researcher in artificial intelligence, machine learning, and biomedical engineering with a strong focus on medical imaging, cancer screening, and predictive modeling. He completed his bachelor’s and master’s degrees in Biomedical Engineering with highest honors at Università degli Studi di Napoli Federico II, where his theses explored machine learning for breast cancer histopathology and deep learning models for lung nodule malignancy detection. He pursued his doctoral studies in Electrical Engineering at Universidad Politécnica de Madrid, graduating with distinction for his research on spatio-temporal image analysis methods to enhance lung cancer screening and therapy response prediction. Professionally, he gained extensive experience as a Junior Research Scientist at Universidad Politécnica de Madrid, where he developed AI-based medical imaging datasets, implemented advanced models including CNNs, RNNs, and transformers, and explored generative models and explainable AI for clinical applications. He later joined the Centro de Investigación Biomédica en Red as a Research Scientist, leading projects in medical image segmentation, classification, and interpretability, managing GPU-based deployments, and contributing to international collaborations and grant proposals. His international exposure includes visiting scientist positions at Harvard University’s Brigham and Women’s Hospital, where he worked on image harmonization techniques to improve consistency in multi-center datasets. His research interests lie in artificial intelligence for healthcare, medical image processing, radiomics, generative models, self-supervised learning, and explainable AI with a vision of translating computational tools into clinical practice. Throughout his career, he has guided undergraduate and master’s students, actively contributed to competitive AI challenges, and engaged in cultural leadership as Vice-President of a community association promoting cultural heritage and development. He has presented his research at reputed conferences, published in indexed journals, and continues to expand his academic contributions through collaborative projects. His research skills include proficiency in Python, R, MATLAB, TensorFlow, PyTorch, and Keras, expertise in GPU cluster computing, dataset development, model deployment with Docker, and technical documentation with LaTeX. Fluent in Italian, Spanish, and English, he thrives in multicultural academic environments and has demonstrated both technical excellence and leadership capabilities. Benito has earned academic distinctions for his outstanding performance in higher education and doctoral research, reflecting his commitment to excellence. With strong foundations in artificial intelligence and biomedical engineering, he aspires to drive advancements in precision medicine, foster global collaborations, and translate AI innovations into impactful healthcare solutions.

Profile: Google Scholar | Scopus Profile | ORCID Profile

Featured Publications

Farina, B., Guerra, A. D. R., Bermejo-Peláez, D., Miras, C. P., Peral, A. A., & others. (2023). Integration of longitudinal deep-radiomics and clinical data improves the prediction of durable benefits to anti-PD-1/PD-L1 immunotherapy in advanced NSCLC patients. Journal of Translational Medicine, 21(1), 174.

Farina, B., Guerra, A. D. R., Miras, C. P., Madueño, G. G., Muñoz-Barrutia, A., & others. (2021). Delta-radiomics signature for prediction of survival in advanced NSCLC patients treated with immunotherapy. In 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI) (pp. 886–890). IEEE.

Farina, B., Benito, R. C., Montalvo-García, D., Bermejo-Peláez, D., Maceiras, L. S., & others. (2025). Spatio-temporal deep learning with temporal attention for indeterminate lung nodule classification. Computers in Biology and Medicine, 196, 110813.

Ramos-Guerra, A. D., Farina, B., Rubio Pérez, J., Vilalta-Lacarra, A., & others. (2025). Monitoring peripheral blood data supports the prediction of immunotherapy response in advanced non-small cell lung cancer based on real-world data. Cancer Immunology, Immunotherapy, 74(4), 120.

Seijo, L., Bermejo-Peláez, D., Gil-Bazo, I., Farina, B., Domine, M., & others. (2023). Integration of longitudinal deep-radiomics and clinical data improves the prediction of durable benefits to anti-PD-1/PD-L1 immunotherapy in advanced NSCLC patients. Journal of Translational Medicine, 21(1), 174.

Bolaños, M. C., Farina, B., Guerra, A. D. R., Miras, C. P., Madueño, G. G., & others. (2020). Design and implementation of predictive models based on radiomics to assess response to immunotherapy in non-small-cell lung cancer. In XXXVIII Congreso Anual de la Sociedad Española de Ingeniería Biomédica.

Osman Yildirim | Deep Learning | Best Researcher Award

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Prof. Osman Yildirim | Deep Learning | Best Researcher Award

Head of the Department | Istanbul Aydın University | Turkey 

Prof. Osman Yildirim is a distinguished academic and researcher recognized for his contributions at the intersection of engineering, business, sustainability, and biomedical applications. He holds dual doctoral degrees in Engineering and Business Administration, a unique combination that has enabled him to approach research challenges with a strong interdisciplinary perspective. Over the course of his career, he has taken on significant academic leadership roles, including serving as Head of Department at Istanbul Aydin University, while also guiding doctoral students and fostering collaborative research projects. His professional experience spans teaching across engineering and business disciplines, coordinating research initiatives, and contributing to institutional development through mentorship and administrative leadership. His primary research interests focus on green transformation, sustainable supply chains, carbon policy impacts, energy management systems in universities, and AI-based medical imaging applications for improved diagnostics. These areas reflect his commitment to aligning research with both technological advancements and societal needs, particularly in the context of sustainable development and healthcare innovation. He has published widely in reputed Q1 and Q2 indexed journals such as Scopus and SCI, showcasing the impact of his work in both technical and applied fields. His achievements have been recognized through awards and honors that acknowledge his contributions to advancing interdisciplinary research and education. In addition, he has built valuable collaborations with international teams, integrating expertise from engineering, business, and medicine to deliver impactful solutions with global relevance. His research skills include expertise in machine learning, AI-driven image analysis, sustainable system design, and computational modeling for optimization under carbon constraints. These technical strengths, combined with his leadership and mentorship, position him as a leading scholar dedicated to advancing academic excellence and addressing global challenges through innovative and socially relevant research.

Profile: Google Scholar | Scopus Profile | ORCID Profile

Featured Publications

Ozturk, A. I., Yıldırım, O., İdman, E., & İdman, E. (2025). A comparative study of hybrid decision tree–deep learning models in the detection of intracranial arachnoid cysts. Neuroscience Informatics, 100234.

Ozturk, A. I., Yildirim, O., Kaygusuz, K., Idman, E., & Idman, E. (2025). Brain cyst detection using deep learning models. International Journal of Innovative Research and Scientific Studies, 8(5), 8974.

Borhan Elmi, M. M., & Yıldırım, O. (2025). Improve MPPT in organic photovoltaics with chaos-based nonlinear MPC. Balkan Journal of Electrical and Computer Engineering, 13(1), 1418574.

Ozturk, A. I., Yıldırım, O., & Deryahanoglu, O. (2025). A comprehensive strategy for the identification of arachnoid cysts in the brain utilizing image processing segmentation methods. International Journal of Innovative Technology and Exploring Engineering, 14(2), 1031.

Borhan Elmi, M. M., & Yıldırım, O. (2024). Improve LVRT capability of organic solar arrays by using chaos-based NMPC. International Journal of Energy Studies, 4(3), 1449558.

Yildirim, O., Khaustova, V. Y., & Ilyash, O. I. (2023). Reliability and validity adaptation of the hospital safety climate scale. The Problems of Economy, 4(1), 207–216.

Yildirim, O. (2023). Multidimensional and strategic outlook in digital business transformation: Human resource and management recommendations for performance improvement. In Book chapter.

Yildirim, O. (2023). Health professionals’ perspective in the context of social media, paranoia, and working autonomy during the COVID-19 pandemic period. Archives of Health Science Research, 10(1), 30–37.

Yildirim, O. (2023). The personified model for supply chain management. In Multidimensional and strategic outlook in digital business transformation: Human resource and management recommendations for performance improvement.

Yildirim, O., Ilyash, O. I., Khaustova, V. Y., & Celiksular, A. (2022). The effect of emotional intelligence and work-related strain on the employee’s organizational behavior factors. The Problems of Economy, 2(1), 124–131.

Yildirim, O. (2022). Investigation of the electrical conductivity of pernigranilin with carbon monoxide and nitrogen monoxide doping. Mathematical Statistician and Engineering Applications, 9(4).

Yildirim, O. (2022). Cyst segmentation using filtering technique in computed tomography abdominal kidney images. Mathematical Statistician and Engineering Applications, 9(4).

Yildirim, O. (2022). Design of flyback converter by obtaining the characteristics of polymer based R2R organic PV panels. International Journal of Renewable Energy Research, 12(4).

Avdullahi, A., & Yildirim, O. (2021). The mediating role of emotional stability between regulation of emotion and overwork. In Book chapter.

Tunç, P., Yıldırım, O., Göktepe, E. A., & Çapuk, S. (2021). Investigation of the relationship between personality, organizational identification and turnover in competitive flight model. TroyAcademy, 6(1), 894141.

Tunç, P., Yıldırım, O., Göktepe, E. A., & Çapuk, S. (2021). Investigation of the relationship between personality, organizational identification and turnover in competitive flight model. Çanakkale Onsekiz Mart Üniversitesi Uluslararası Sosyal Bilimler Dergisi, 4(1), 804959.

Puja Gupta | Computer Vision | Excellence in Research

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Dr. Puja Gupta | Computer Vision | Excellence in Research

Asst Professor at Shri G.S. Institute of Technology & Science | India

Dr. Puja Gupta is a dedicated researcher and academic with expertise in artificial intelligence, machine learning, IoT, and smart computing technologies. She has contributed significantly to the field through her high-quality publications in reputed journals, patents, and innovative product development. Her work has addressed real-world challenges in healthcare, security, and sustainable technologies, bridging the gap between research and practical applications. With a strong academic foundation, she has successfully guided students in research and projects, fostering innovation and academic growth. She has been actively involved in international collaborations, research projects, and academic leadership roles, contributing to the advancement of her field. She is also a committed member of professional organizations, demonstrating her engagement in the broader research community. Her impactful contributions, leadership potential, and dedication to continuous professional development make her a valuable asset to both academia and society.

Professional Profile 

Google Scholar | Scopus Profile | ORCID Profile 

Education

Dr. Puja Gupta holds a strong academic background in computer science and engineering, culminating in a doctoral degree specializing in artificial intelligence and smart systems. Her Ph.D. research focused on the integration of machine learning techniques and IoT frameworks to design intelligent solutions that address complex societal problems. Prior to her doctoral studies, she earned her master’s and bachelor’s degrees in computer science, gaining a solid foundation in algorithms, data structures, and system design. Throughout her academic journey, she demonstrated exceptional commitment to learning, consistently achieving top ranks and recognition for her research contributions. Her advanced education has equipped her with in-depth knowledge of computational intelligence, optimization techniques, and applied research methodologies, enabling her to contribute effectively to both theoretical advancements and practical applications in the field. Her academic background continues to support her innovative research and teaching excellence in the areas of AI, IoT, and emerging technologies.

Professional Experience

Dr. Puja Gupta has extensive professional experience in both academic and research domains, with a focus on artificial intelligence, IoT, and smart computing solutions. She has worked as a faculty member at prestigious institutions, where she has taught and mentored students at undergraduate and postgraduate levels, guiding them in research projects and fostering innovation. Alongside teaching, she has been actively involved in funded research projects, many of which involved international collaborations and multidisciplinary teams. She has successfully published her findings in reputed journals and conferences indexed in IEEE and Scopus, and her work has also resulted in patents and prototypes with practical applications. Beyond academia, she has contributed to the research community by serving as a reviewer, participating in editorial activities, and organizing academic events. Her leadership roles in academic programs and community-driven initiatives further highlight her commitment to advancing knowledge and supporting the development of future researchers.

Research Interest

Dr. Puja Gupta’s research interests revolve around artificial intelligence, machine learning, IoT, big data analytics, and smart system design. She is particularly focused on developing intelligent solutions that address pressing societal challenges in areas such as healthcare, security, and sustainability. Her work often integrates computational intelligence with real-world applications, such as predictive healthcare models, smart monitoring systems, and secure communication frameworks for IoT devices. She is also keen on advancing research in explainable AI and optimization algorithms to ensure reliability and transparency in machine learning systems. Another area of interest is the development of resource-efficient AI models for deployment in edge and cloud environments. Her multidisciplinary approach allows her to collaborate across domains, leveraging data-driven techniques to innovate practical solutions. By combining theoretical knowledge with applied research, she aims to contribute to technological advancements that enhance the quality of life and create sustainable, impactful outcomes for society.

Award and Honor

Dr. Puja Gupta has been recognized with numerous awards and honors that highlight her academic excellence, research contributions, and leadership in the field of computer science and engineering. Her achievements include recognition for publishing impactful research in reputed journals, presenting at leading international conferences, and securing patents that demonstrate the practical value of her work. She has also been honored for her contributions to student mentoring and academic program development, reflecting her dedication to nurturing young talent. Several of her awards acknowledge her innovative approaches in AI and IoT research, particularly for developing solutions with direct societal impact. In addition, she has received appreciation for her involvement in community-driven initiatives and leadership in professional organizations. These honors not only recognize her past accomplishments but also serve as a testament to her commitment, perseverance, and ability to inspire others in the academic and research communities.

Research Skill

Dr. Puja Gupta possesses advanced research skills in artificial intelligence, machine learning, IoT systems, and computational modeling, enabling her to conduct impactful and interdisciplinary research. She is proficient in applying data analysis techniques, optimization algorithms, and predictive modeling to design intelligent solutions for real-world applications. Her expertise includes working with various programming languages, simulation tools, and research frameworks that support scalable and innovative problem-solving. She has developed strong skills in experimental design, result validation, and research dissemination through high-quality publications and conference presentations. Beyond technical expertise, she excels in collaborative research, often working with international teams and multidisciplinary groups to drive innovation. She is also skilled in project management, proposal writing, and securing research funding, which have been instrumental in the successful execution of her projects. Her research skills, combined with her commitment to continuous learning, position her as a versatile and resourceful academic and researcher in her field.

Publications Top Notes

Title: Impact of knowledge management practices on innovative capacity: A study of telecommunication sector
Authors: J Jyoti, P Gupta, S Kotwal
Year: 2011
Citation: 56

Title: A Novel Algorithm for Mask Detection and Recognizing Actions of Human
Authors: P Gupta, V Sharma, S Varma
Year: 2022
Citation: 48

Title: Transcriptional mechanisms underlying sensitization of peripheral sensory neurons by granulocyte-/granulocyte-macrophage colony stimulating factors
Authors: KK Bali, V Venkataramani, VP Satagopam, P Gupta, R Schneider, …
Year: 2013
Citation: 42

Title: Minimally invasive plate osteosynthesis (MIPO) for proximal and distal fractures of the tibia: a biological approach
Authors: P Gupta, A Tiwari, A Thora, JK Gandhi, VP Jog
Year: 2016
Citation: 41

Title: SUMOylation of enzymes and ion channels in sensory neurons protects against metabolic dysfunction, neuropathy, and sensory loss in diabetes
Authors: N Agarwal, FJ Taberner, DR Rojas, M Moroni, D Omberbasic, C Njoo, …
Year: 2020
Citation: 39

Title: An introduction of soft computing approach over hard computing
Authors: P Gupta, N Kulkarni
Year: 2013
Citation: 31

Title: People detection and counting using YOLOv3 and SSD models
Authors: P Gupta, V Sharma, S Varma
Year: 2021
Citation: 30

Title: Challenges in the adaptation of IoT technology
Authors: Neha, P Gupta, MA Alam
Year: 2021
Citation: 20

Title: Role of fine needle aspiration cytology in preoperative diagnosis of ameloblastoma
Authors: S Bisht, SA Kotwal, P Gupta, R Dawar
Year: 2009
Citation: 13

Title: Let the Blind See: An AIIoT based device for real-time object recognition with the voice conversion
Authors: P Gupta, M Shukla, N Arya, U Singh, K Mishra
Year: 2022
Citation: 9

Title: The impact of artificial intelligence on renewable energy systems
Authors: P Gupta, S Kumar, YB Singh, P Singh, SK Sharma, NK Rathore
Year: 2022
Citation: 8

Title: Simultaneous feature selection and clustering of micro-array and RNA-sequence gene expression data using multiobjective optimization
Authors: AK Alok, P Gupta, S Saha, V Sharma
Year: 2020
Citation: 8

Title: Activity detection and counting people using mask-RCNN with bidirectional ConvLSTM
Authors: P Gupta, U Singh, M Shukla
Year: 2022
Citation: 7

Title: Study of cloud providers (azure, amazon, and oracle) according to service availability and price
Authors: A Rajput, P Gupta, P Ghodeshwar, S Varma, KK Sharma, U Singh
Year: 2023
Citation: 6

Title: Machine learning approaches for IoT-data classification
Authors: O Farooq, P Gupta
Year: 2020
Citation: 5

Title: Evaluation of AI system’s voice recognition performance in social conversation
Authors: SK Barnwal, P Gupta
Year: 2022
Citation: 4

Title: Analysis of CNN Model with Traditional Approach and Cloud AI based Approach
Authors: U Kushwaha, P Gupta, S Airen, M Kuliha
Year: 2022
Citation: 4

Title: Analysis of crowd features based on deep learning
Authors: P Gupta, V Sharma, S Varma
Year: 2022
Citation: 4

Title: Acknowledgment of patient in sense behaviors using bidirectional ConvLSTM
Authors: U Singh, P Gupta, M Shukla, V Sharma, S Varma, SK Sharma
Year: 2023
Citation: 3

Title: Study on the NB-IoT based smart medical system
Authors: P Gupta, AK Pandey
Year: 2023
Citation: 3

Conclusion

Dr. Puja Gupta is highly deserving of the Best Researcher Award for her significant contributions to advancing research in artificial intelligence, IoT, and smart technologies, as well as her role in mentoring students and fostering innovation. Her impactful work, including patents, high-quality publications, and practical product development, has addressed societal challenges in healthcare, security, and sustainability. With her strong academic background, leadership in academic and community initiatives, and commitment to continuous growth, she holds great potential to further excel in future research, expand global collaborations, and take on greater leadership roles in the academic and research community.