Dr. Maria Muzamil Memon | Robotics | Research Excellence Award

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Dr. Maria Muzamil Memon | Robotics | Research Excellence Award

Harbin Institute of Technology | China

Dr. Maria Muzamil Memon is a dedicated researcher and postdoctoral fellow at the Harbin Institute of Technology, China, specializing in micro-electro-mechanical systems , microfluidics, and flexible sensor technologies. she earned her Phd in electronic engineering from the university of electronic science and technology of China, where she focused on aln-based surface acoustic wave sensors and significantly improved pressure sensitivity through innovative structural design, validated via comsol-based finite element analysis. Her academic journey includes a master’s degree in mechanical engineering from Harbin institute of technology and a bachelor’s degree in electronics engineering from Mehran University of engineering and technology. Throughout her career, she has worked extensively on microfluidic chip fabrication, biomedical device development, and multiphysics simulations, Robotics while also supervising undergraduate and postgraduate students. Dr. Memon has received multiple prestigious awards, including several academic achievement and excellent performance awards from UESTC, and she is a two-time recipient of the Chinese government scholarship. her research impact is reflected in 141 citations, an h-index of 7, and 6 documented publications, i10-index, demonstrating her growing influence in the fields of mems sensors, sensing materials, and microfluidic systems.

Profile: Google Scholar

Featured Publications

Memon, M. M., Liu, Q., Manthar, A., Wang, T., & Zhang, W. (2023). Surface acoustic wave humidity sensor. Micromachines, 14(5), 945.

Memon, M. M., Hongyuan, Y., Pan, S., Wang, T., & Zhang, W. (2022). Surface acoustic wave humidity sensor based on hydrophobic polymer film. Journal of Electronic Materials, 51(10), 5627–5634.

Memon, M. M., Pan, S., Wan, J., Wang, T., & Zhang, W. (2021). Highly sensitive thick diaphragm-based surface acoustic wave pressure sensor. Sensors and Actuators A: Physical, 331, 112935.

Memon, M. M., Pan, S., Wan, J., Wang, T., Peng, B., & Zhang, W. (2022). Sensitivity enhancement of SAW pressure sensor based on the crystalline direction. IEEE Sensors Journal, 22(10), 9329–9335.

Memon, M. M., Pan, S., Wan, J., Wang, T., Peng, B., & Zhang, W. (2022). Sensitivity enhancement of SAW pressure sensor based on the crystalline direction. IEEE Sensors Journal, 22(10), 9329–9335.

Prof. Dr. Mounir Bousbia Salah | Deep Learning | Best Researcher Award

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Prof. Dr. Mounir Bousbia Salah | Deep Learning | Best Researcher Award

Badji Mokhtar Annaba University | Algeria

Prof. Dr. Mounir Bousbia Salah, born in Annaba, Algeria, is a distinguished scholar in electronic and biomedical engineering with a career spanning more than three decades. he earned his engineering degree in electronic engineering from the university of annaba, followed by an m.sc by research from Cardiff University, UK, and later completed his ph.d. in electronic engineering at the University of Annaba. Currently serving as a full professor and director of research at Badji Mokhtar– Annaba university, he has previously worked as lecturer, assistant professor, associate professor, and research leader across multiple academic stages. His areas of expertise include biomedical instrumentation, biomedical signal processing, sensors, deep learning, computer vision, and rehabilitation technologies. Professor Bousbia salah has published over ninety scientific and conference papers, participated in numerous Ph.d, Master, and B.Sc thesis supervisions, and actively contributes to advancements in biomedical engineering. His scholarly impact is reflected through 672 citations, an h-index of 13, and an i10-index of 16, demonstrating his continued influence in the research community. He is an active member of several international professional bodies, including Iased, IFSA, IFAC, and Waset, and teaches a wide range of courses in electronics, control, metrology, robotics, and biomedical systems.

Profile: Google Scholar

Featured Publications

Bousbia-Salah, M., Bettayeb, M., & Larbi, A. (2011). A navigation aid for blind people. Journal of Intelligent & Robotic Systems, 64(3), 387–400.

Bousbia-Salah, M., Redjati, A., Fezari, M., & Bettayeb, M. (2007). An ultrasonic navigation system for blind people. Proceedings of the 2007 IEEE International Conference on Signal Processing and Communications, 96.

Fezari, M., Bousbia-Salah, M., & Bedda, M. (2008). Microcontroller based heart rate monitor. International Arab Journal of Information Technology, 5(4).

Nada, D., Bousbia-Salah, M., & Bettayeb, M. (2018). Multi-sensor data fusion for wheelchair position estimation with unscented Kalman filter. International Journal of Automation and Computing, 15(2), 207–217.

Gawas, U. B., Verenkar, V. M. S., & Patil, D. R. (2011). Nanostructured ferrite based electronic nose sensitive to ammonia at room temperature. Sensors & Transducers, 134(11), 45.

Dr. Emily Bagarukayo | Autonomous Robot Navigation | Best Researcher Award

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Dr. Emily Bagarukayo | Autonomous Robot Navigation | Best Researcher Award

Makerere University | Uganda

Dr. Emily Bagarukayo is a distinguished academic, researcher, and consultant in computing and Ict, currently serving at the school of computing and informatics technology, college of computing and information sciences, Makerere university. She holds a Ph.d in information science from Radboud university Nijmegen, Netherlands, a postgraduate diploma in educational technologies from the university of cape town, a master’s in computer science from makerere university, and a bachelor’s degree in computer science (hons) from mbarara university of science and technology. She is also a research associate at the international center of it and development, southern university, USA. Her doctoral research focused on e-learning, emphasizing the role of digital learning environments and Autonomous Robot Navigation multimedia in enhancing learning outcomes through the “learning by construction approach.” her current research interests include personalized learning, instructional content development, and ict4d, with a growing focus on the impact of social software on education. she has published 11 documents, received 14 citations from 14 documents, and holds an h-index of 2. As a passionate educator, she has mentored numerous undergraduate and postgraduate students while contributing to over 36 peer-reviewed journals, conferences, and book chapters.

Profile: Scopus

Featured Publication

Bagarukayo, E. Framework for enhancing tutor–student interaction in blended courses: A case of Bachelor of Youth in Development Work at Makerere University.

 

Prof. Elena Strelnikova | Advanced Motion Planning | Best Researcher Award

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Prof. Elena Strelnikova | Advanced Motion Planning | Best Researcher Award

Anatolii Pidhornyi Institute of Power Machines and Systems NAS of Ukraine | Ukraine

Prof. Elena Strelnikova is a distinguished ukrainian mathematician and professor with a long, productive career in applied analysis, fracture mechanics, boundary element methods, and fluid-structure interaction. Educated at v. n. karazin kharkiv national university , she received her phd in “analysis of 2d contact problems for anisotropic cracked plates” and later her dsc  for her work on strength and vibrations of turbo-machine units using the boundary element method. She has held senior and full professorship positions at karazin national university, kharkiv polytechnical institute, and kharkiv national university of radio electronics, among others, and has led research groups and served as a leading researcher at the a. pidhorny institute of power machines and Advanced Motion Planning systems of the national academy of sciences of ukraine. Professor strelnikova’s research is especially known for her deep contributions in singular and hypersingular integral equations, contact problems, cracked plate theory, and fluid-structure coupling. over the course of her career, she has published 71 documents, which have received 602 citations across 309 documents, reflecting an h-index of 17. her scholarship has made a lasting impact on mechanical engineering, structural dynamics, and computational methods in applied mathematics, continuing to influence both theory and application worldwide.

Profiles: Scopus | Orcid

Featured Publications

Akimov, D., Degtyariov, K., Gnitko, V., Kriutchenko, D., & Strelnikova, E. (2025). Flight stage-dependent vibration characteristics of launch vehicle fuel tanks. In Advances in Aerospace Engineering.

Choudhary, N., Degtyariov, K., Gnitko, V., Kriutchenko, D., & Strelnikova, E. (2025). Liquid vibrations analysis of baffled reservoirs with fuzzy concepts implementation. In Advances in Fluid-Structure Interaction.

Degtyariov, K., Kriutchenko, D., Osypov, I., Sierikova, O., & Strelnikova, E. (2024). Dampers influence on sloshing mitigation in fuel tanks of launch vehicles and reservoirs. In Recent Advances in Aerospace Structures.

Strelnikova, E., Choudhary, N., Degtyariov, K., Kriutchenko, D., & Vierushkin, I. (2024). Boundary element method for hypersingular integral equations: Implementation and applications in potential theory. Engineering Analysis with Boundary Elements, 161, 105999.

Doroshenko, V. O., Stognii, N. P., Zhyla, O. V., Strelnikova, E. A., Sidorov, M. V., & Remayeva, O. O. (2024). Mathematical modeling of exciting a wideband semitransparent antenna with longitudinal slots by a concentrated source. In Proceedings of the IEEE European Workshop on Modern Topics in Signal.

Mrs. Chittepu Sireesha – Deep Learning – Best Researcher Award

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Mrs. Chittepu Sireesha - Deep Learning - Best Researcher Award

SR University - India

Author Profile

SCOPUS

ORCID

Summary

Sireesha Chittepu is a seasoned academic with over 15 years of teaching experience in computer science and engineering. Her educational background includes a B.Tech and M.Tech in software and computer sciences, and she is currently pursuing a Ph.D. in Computer Science and Engineering at SR University. Throughout her career, she has demonstrated strong proficiency in subjects ranging from data structures to NLP and software testing. Notably, she has contributed significantly to institutional development through her roles as a coordinator for national-level hackathons, class mentor, and virtual lab leader in collaboration with IITH. Her research interest is expanding into areas that blend software innovation with applied domains such as power electronics, where she aims to enhance the interaction between software systems and hardware frameworks. She has also designed internal tools such as attendance and feedback systems to streamline academic administration.


Early academic pursuits

Sireesha Chittepu laid the foundation of her academic journey with a strong focus on the sciences, securing 85.6% in her Intermediate studies and 82% in her SSC. She pursued her B.Tech in Computer Science and Information Technology from Rajiv Gandhi Memorial College of Engineering and Technology under JNTU, achieving 70%. Further strengthening her expertise, deep learning she completed an M.Tech in Software Engineering from VNR Vignana Jyothi Engineering College, Hyderabad, with 78%. Currently, she is pursuing a Ph.D. in Computer Science and Engineering from SR University, Telangana, aligning her research with cutting-edge trends including power electronics, AI integration, and automation.

Professional endeavors

With over 15 years of teaching experience, Sireesha Chittepu has been a cornerstone in the academic development of countless students. Her journey began in 2003 and includes tenure at Alpha College of Engineering, Sree Nidhi Institute of Science and Technology, and Stanley College of Engineering and Technology for Women. Since 2015, deep learning she has been serving as an Assistant Professor at Vasavi College of Engineering, Hyderabad, demonstrating excellence in pedagogy and department-level coordination. Her professional engagements frequently intersect with power electronics, where she integrates software systems to improve learning tools and virtual lab environments.

Contributions and research focus

Sireesha’s teaching expertise spans a wide range of computer science subjects such as Compiler Construction, Data Structures, Software Engineering, and Natural Language Processing. She has developed and implemented innovative tools such as an attendance tracking system and a feedback management system, both of which enhance academic management deep learning. Her research focus aligns with emerging technologies and includes interdisciplinary studies where software engineering intersects with hardware systems like power electronics, emphasizing system-level integration and smart computing applications.

Impact and influence

In her role as Department Coordinator for Hackathons, Co-curricular and Extracurricular Activities, and SPOC for Smart India Hackathons (2019–2020), Sireesha has mentored students in national-level innovation challenges. Her coordination with institutions like IIT-Hyderabad for virtual labs reflects her impact on advancing academic infrastructure. She also contributes to curriculum development and exam preparation, influencing academic delivery deep learning across several institutions. These contributions indirectly support power electronics education by encouraging cross-domain thinking and system-oriented learning approaches among students.

Academic cites

While her Ph.D. is ongoing, Sireesha Chittepu's academic involvement includes preparing university-level question papers, developing lesson plans, and assessing student performance through structured evaluation methods. Though no formal citation index was mentioned, her continuous evaluation roles, seminar engagements deep learning, and project mentorships suggest a strong academic presence that is evolving towards publication and citation in future academic platforms.

Legacy and future contributions

Sireesha aims to bridge the gap between education and innovation, with a long-term vision of contributing to academia through research, digital tools, and cross-disciplinary teaching. Her future goals include publishing impactful research, particularly in the convergence of software engineering and power electronics, and mentoring future educators and deep learning researchers. As she continues her doctoral research, she is poised to leave a legacy defined by practical innovation, academic excellence, and a deep commitment to student-centric learning.

Notable Publications

  1. Title: Enhancing Medicinal Plant Prediction Through Deep Neural Network Algorithms
    Authors: Munigala Swapna, Sireesha Chittepu, Sanjana Gavada, Sreeja Barigela
    Publication: Communications in Computer and Information Science (CCIS) – Book Chapter
    Year: 2025
  2. Title: Improving Air Quality Prediction: A Study on Data-Driven Techniques and Advanced Sensing Technologies
    Authors: Yeshwanth Reddy, C. Sireesha
    Publication: Lecture Notes on Data Engineering and Communications Technologies – Book Chapter
    Year: 2025
  3. Title: Dynamic Conditional Encoding and Feature Frequency Parsing in Diffusion Probabilistic Models for Diabetic Foot Ulcer Detection Using Thermographic Imaging
    Authors: Kousar Nikhath A, Sireesha C, Swapna M, Vijayetha Thoday, Esther Rani D
    Publication: Journal of Biomedical Photonics & Engineering
    Date: 2025-03-31
  4. Title: A ResNet Based Plant Disease Diagnosis Platform
    Authors: Sireesha Chittepu, Suchith B, Deepthi M
    Publication: 2025 7th International Conference on Signal Processing, Computing and Control (ISPCC)
    Date: 2025-03-06
  5. Title: Enhancing the Surveillance, Assessment, and Tracking of Mental Health and Well-being Using Deep Learning Techniques
    Authors: Sireesha Chittepu, Sheshikala Martha
    Publication: 2025 7th International Conference on Signal Processing, Computing and Control (ISPCC)
    Date: 2025-03-06

Conclusion

Sireesha Chittepu's career reflects a commitment to academic excellence, hands-on innovation, and student empowerment. Her proactive involvement in research, curriculum design, and co-curricular coordination showcases her holistic approach to education. As she continues her Ph.D., her contributions are expected to deepen, particularly in interdisciplinary research areas including power electronics, where her experience in software systems can offer new insights. Looking ahead, Sireesha is well-positioned to influence future educational practices, mentor the next generation of engineers, and contribute meaningfully to research that connects computation with real-world systems.

Mr. Huixian Lin – Deep Learning – Best Researcher Award

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Mr. Huixian Lin - Deep Learning - Best Researcher Award

Guangdong Ocean University - China

Author Profile

SCOPUS

Summary

Mr. Huixian Lin is a dedicated full-time teacher at Guangdong Ocean University with a master's degree in Computer Science and Technology. His academic focus lies in image processing, machine learning, and the integration of power electronics in intelligent systems. He has made notable contributions, including enhancing the YOLOv5s model for degraded image detection and publishing three SCI-indexed research papers. His teaching and research work reflects a commitment to advancing practical, AI-driven solutions in real-world environments.

Early academic pursuits

Mr. Huixian Lin began his academic journey with a strong inclination toward computational sciences. His commitment to technological excellence led him to pursue a Master of Science degree in Computer Science and Technology from Guangdong Ocean University, completed in 2023. During his academic training, he cultivated a keen interest in image processing, deep learning machine learning, and system optimization. His foundational knowledge in applied mathematics, algorithms, and power electronics laid a strong base for future research and teaching.

Professional endeavors

Since 2023, Mr. Lin has been serving as a full-time teacher at the College of Mathematics and Computer Science, Guangdong Ocean University. His teaching methodology emphasizes practical applications of theoretical concepts, especially in areas like deep learning intelligent systems and computer vision. In his short tenure, he has made significant strides in mentoring undergraduate students and guiding them through hands-on research in modern computing fields, including power electronics applications in automation systems.

Contributions and research focus

Mr. Lin’s most notable academic contribution is his proposed improvement of the YOLOv5s model, targeting enhanced detection of degraded images—a challenge in both surveillance and industrial inspection sectors. His research integrates advanced machine learning with classical image processing techniques. He has authored 3 SCI-indexed papers in reputed journals, deep learning showcasing innovations that have practical implications in smart sensing, automation, and power electronics interface systems. These contributions reflect a commitment to solving real-world problems using intelligent technology frameworks.

Impact and influence

Mr. Lin's work has been recognized within academic circles for its technical accuracy and applicability. His adaptations to image recognition models have improved reliability in noisy environments, offering benefits to sectors like security surveillance, medical diagnostics, and automated inspection systems. As a young academic, deep learning his influence is growing, especially among peers focusing on embedded systems, AI algorithms, and sensor-integrated power electronics.

Academic citations

Although at an early stage of his academic journey, Mr. Lin's publications have begun to attract citations in related research fields. His work is cited for contributions to degraded image classification, neural network efficiency optimization, deep learning and algorithm adaptability in constrained environments. This emerging scholarly attention suggests a promising trajectory in the years ahead.

Legacy and future contributions

Looking ahead, Mr. Lin aspires to build a legacy in the intersection of artificial intelligence, image processing, and real-time computing. He aims to extend his research toward more adaptive and energy-efficient machine learning models with industrial deployment in mind. His future contributions are likely to focus on smarter integration of visual data into automated decision-making systems, deep learning particularly where power electronics and AI co-evolve. Through his ongoing role at Guangdong Ocean University, he is poised to nurture future innovators and push the boundaries of applied computing.

Notable Publications

Effective superpixel sparse representation classification method with multiple features and L 0smoothing for hyperspectral images.

Conclusion

In the early stages of his academic career, Mr. Huixian Lin has already made a meaningful impact through research and instruction. His innovative approach to machine learning and image recognition, especially when combined with power electronics, positions him as a promising figure in the field. With a growing scholarly presence and a passion for technological development, Mr. Lin is set to contribute significantly to the future of smart computing and interdisciplinary research.