Sheeba Rachel S | Machine Learning | Best Researcher Award

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Mrs. Sheeba Rachel S | Machine Learning| Best Researcher Award

Assistant Professor | Sri Sai Ram Engineering College | India

  S. Sheeba Rachel has contributed extensively to the fields of artificial intelligence, machine learning, deep learning, healthcare technologies, smart devices, image processing, cloud computing, and Internet of Things with publications including Cardiovascular Disease Prediction Using Machine Learning and Deep Learning, Heart Disease Prediction of an Individual Using SVM Algorithm, Automated Driving License Testing System, Real-Time Face Detection and Identification Using Machine Learning Algorithm for Improving the Security in Public Places Using Closed Circuit Television, LEARNAUT – Upgraded Learning Environment and Web Application for Autism Environment Using AR-VR, VATTEN – A Smart Water Monitoring System, Segmentation and Classification of Glaucoma Using U-Net with Deep Learning Model, EDSYS – A Smart Campus Management System, TRACKME – Smart Watch for Women, Women’s Safety with a Smart Foot Device, Mental Health Monitoring Using Sentimental Analysis, Facilitation of Multipurpose Gloves for Impaired People, Extending OVS with Deep Packet Inspection Functionalities, Courier Service Management and Tracking Using Android Application, Detecting the Abandoned Borewell Using Image Processing, Smart Hospitals E-Medico Management System, ADROIT LIMB – Brain Controlled Artificial Limb, Autonomous Movable Packrat for Habitual Chores, Postal Bag Tracking and Alerting System, Applying Social Network Aided Efficient Live Streaming System for Reducing Server Overhead, Image Fusion of MRI Images Using Discrete Wavelet Transform, Probabilistic Flooding Based File Search in Peer to Peer Network, Multi Stage for Informative Gene Selection, Mutual Information in Stages for Informative Gene Selection, Computation of Mutual Information in Stages for Gene Selection from Microarray Data, and several other impactful studies in international journals and conferences indexed in Scopus, IEEE, and UGC; she has further contributed to innovation through consultancy projects such as AI-based pre-examination dental software and non-invasive sugar detection using eye retina, authored books and chapters including Fundamentals of Machine Learning, Management Analytics and Software Engineering, Recent Trends in Engineering and Technology – Edge Computing, and secured patents like Artificial Intelligence Based Heart Rate Monitoring Device for Sports Training, IOT Based Washing Machine for Agricultural Crops, Human Identity Recognition System Using Cloud Machine Learning and Deep Learning Algorithms, Gesture Based Anti-Rape Device, while also holding active memberships with IEEE, ISTE, IEI, UACEE, IAENG, and IACSIT; her academic journey has been marked by mentorship of award-winning projects, reviewer and session chair responsibilities in international conferences, and recognition such as the Best Faculty Advisor Award demonstrating her influence in advancing technology-driven solutions for healthcare, safety, smart systems, and education through research, teaching, patents, and community engagement.

Profile:  Google Scholar

Featured Publications:

Abebaw Agegne | Deep Learning | Best Researcher Award

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Mr. Abebaw Agegne | Deep Learning | Best Researcher Award

Debark University | Ethiopia

Abebaw Agegne Engda is an Ethiopian scholar and academic who has devoted his professional career to the advancement of computer science education and research while fostering strong community engagement and service. He earned his Bachelor of Science degree in Computer Science from Debre Tabor University with high academic distinction, completing his studies with a focus on programming, systems, and applied computing. He later pursued a Master of Science degree in Computer Science at the University of Gondar, where he further deepened his knowledge of computational theory, advanced software systems, and the practical applications of computer science in solving real-world challenges. His academic excellence is demonstrated by his strong cumulative performance in both degrees, which reflect a commitment to rigor and perseverance. Professionally, he began his teaching journey as an Assistant Lecturer at Debark University, where he taught undergraduate computer science courses and contributed to shaping the foundational knowledge of young scholars. Later, he advanced to the position of Lecturer at Debark University, where he continues to teach computer science students across a variety of specializations, delivering core programming, system analysis, and applied computing courses while contributing to other departments with harmonized curriculum approaches. His students have consistently benefited from his structured teaching style, with many advancing to careers in high-level companies and industries, demonstrating the practical effectiveness of his teaching methodologies. He is capable of teaching a wide range of programming languages and has also been recognized for his leadership within his department, guiding academic processes, curriculum harmonization, and student development initiatives. His research works and community service contributions are documented and accessible through his ORCID profile, reflecting his engagement with both scholarly and societal responsibilities. Beyond academics, he is a person of discipline, patience, and strong work habits, qualities that enhance his ability to serve effectively in challenging environments and to maintain positive relationships with colleagues and students. He is fluent in Amharic and English, which allows him to engage in both local and international academic contexts, and his hobbies such as reading, traveling, counseling, and cultural exploration reflect a personality committed to lifelong learning, empathy, and service to others. Overall, his biography presents the portrait of a self-respecting, fair, and hardworking educator who combines academic achievement, teaching excellence, research contributions, and community service, making him a valuable asset in the advancement of computer science education in Ethiopia and beyond.

Profile: Orcid

Featured Publications:

Asnake, N. W., Ayalew, A. M., & Engda, A. A. (2025). Detection of oral squamous cell carcinoma cancer using AlexNet on histopathological images. Discover Applied Sciences.

Ayele, M. K., Baye, G. A., Yesuf, S. H., Engda, A. A., & Mitiku, E. T. (2025). Predicting stunting status among under five children in Ethiopia using ensemble machine learning algorithms. Scientific Reports.

Engda, A. A., Salau, A. O., & Ajala, O. (2025). Classical machine learning approaches for early hypertension risk prediction: A systematic review. Applied AI Letters.

Engda, A. A., Zewale, G. E., Mihret, B. G., & Adane, A. T. (2025). Developing pneumonia detection model using chest X-ray images: Deep learning approach. Preprint.

Engda, A. A. (2025). Detection of oral squamous cell carcinoma cancer using AlexNet on histopathological images. Conference paper.

Engda, A. A. (2025). Development of a case-based reasoning system for onion disease diagnosis and treatment. Proceedings of the IEEE International Conference on Emerging and Sustainable Technologies for Power and ICT in a Developing Society.

Hadi Sanikhani | Data Science and Deep Learning | Best Researcher Award

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Assoc. Prof. Dr. Hadi Sanikhani | Data Science and Deep Learning | Best Researcher Award 

Research Associate, at INRS – Institut national de la recherche scientifique, Canada.

Dr. Hadi Sanikhani is a dedicated environmental engineer and water resources specialist currently serving as a Visiting Researcher at INRS, Québec. He focuses his research on the hydrological impacts of climate change in cold regions, particularly runoff dynamics and flood risk. By integrating AI-enhanced models—such as SWAT, MODFLOW, HEC‑HMS, and HEC‑RAS—with high-resolution geospatial and CMIP climate projection data, Dr. Sanikhani aims to advance our understanding of flood hazards and improve predictive tools for extreme water events. He collaborates effectively within interdisciplinary teams and is passionate about bridging physical modeling with data-driven techniques to manage and protect vulnerable water systems in Canada and beyond.

Professional Profile

Scopus

ORCID

Google Scholar

🎓 Education

Dr. Sanikhani earned his Ph.D. in Water Resources Engineering (2010–2015) from the University of Tabriz, where his thesis focused on river flow prediction using nearest-neighbor and probabilistic ensemble approaches. Prior, he obtained an M.Sc. in Irrigation and Drainage (2005–2008) at the same university, investigating scour mitigation using rectangular collars around bridge piers. He completed his B.Sc. in Water Science and Engineering (2001–2005) from Mazandaran University, specializing in optimizing discharge–sediment relationships in the Gorganrood River. Throughout, he developed strong expertise in hydrological systems, AI modeling, and practical solutions for water infrastructure design.

💼 Experience

From September 2024 to present, Dr. Sanikhani conducts advanced climate-driven hydrological research at INRS Québec. Previously (2015–2024), he was a Water Resources Researcher at University of Kurdistan, Iran, where he modeled runoff, sediment transport, and climate impacts. In 2013, he served as a Visiting Researcher at both Delft University of Technology and Politecnico di Milano, applying high-end hydrological analysis in European environments. His roles have consistently centered on developing predictive models using remote sensing, AI, and ensemble techniques to support flood risk assessment and water system management across diverse climates.

🔬 Research Interests

Dr. Sanikhani’s research spans hydrological and hydraulic modeling (SWAT, HEC‑HMS/RAS, MODFLOW), flood hazard assessment, and urban stormwater systems. He investigates climate change impacts using CMIP datasets and high-resolution geospatial tools. His work leverages AI and machine learning—such as random forests, MLP, and genetic programming—for forecasting extreme hydrological events. He also explores groundwater–surface water interactions, remote sensing via Google Earth Engine, AI-assisted hydrology, and community-engaged research, aiming to integrate technological and social insights into sustainable water resource management.

🏆 Awards

Dr. Sanikhani has earned multiple honors in recognition of his scientific excellence. He received the “Distinguished Researcher” award from the University of Kurdistan (2018–2019) for outstanding scholarship in environmental modeling. Earlier, as a Ph.D. student at the University of Tabriz, he was a “Distinguished Student” (2010–2012) and held a prestigious Ph.D. scholarship funded by the Iranian Ministry of Science, Research and Technology (2010–2014). These accolades reflect his sustained contributions to hydrological science and academic leadership.

📄 Top Noted Publications

📘 Modeling wetted areas of moisture bulb for drip irrigation systems: An enhanced empirical model and artificial neural network

Authors: Karimi, B.; Mohammadi, P.; Sanikhani, H.; Salih, S. Q.; Yaseen, Z. M.
Journal: Computers and Electronics in Agriculture (Volume 178), November 2020, Article 105767.
DOI: 10.1016/j.compag.2020.105767
Abstract Summary: Developed ANN and nonlinear regression models to estimate vertical up/down wetted areas around drippers based on soil texture, discharge rate, depth, irrigation time, etc.—outperformed dimensional analysis models.
Scopus Citations: 48

📘 Integrative stochastic model standardization with genetic algorithm for rainfall pattern forecasting in tropical and semi-arid environments

Authors: Salih, S. Q.; Sharafati, A.; Ebtehaj, I.; Sanikhani, H.; Siddique, R.; Deo, R. C.; Bonakdari, H.; Shahid, S.; Yaseen, Z. M.
Journal: Hydrological Sciences Journal, Volume 65 Issue 7, pp. 1145–1157, May 18, 2020.
DOI: 10.1080/02626667.2020.1734813
Abstract Summary: Introduced a novel stochastic forecasting method combining seasonal differencing, standardization, spectral analysis, and genetic algorithms; achieved R² ≈ 0.80–0.94 (Malaysia) and 0.89–0.91 (Iraq) across multiple stations.
Scopus Citations: 45

📘 Novel approaches for air temperature prediction: A comparison of four hybrid evolutionary fuzzy models

Authors: [Likely] Azad, [et al.]
Journal: Meteorological Applications, 2020.
DOI: 10.1002/met.1817
Abstract Summary: Tested four hybrid evolutionary fuzzy models (ANFIS combined with GA, PSO, ACOR, and DE) to predict monthly minimum, mean, and maximum air temperatures across 34 stations in Iran. Found ANFIS–GA to consistently outperform others—e.g., reducing RMSE from 1.22 °C to 1.12 °C in Mashhad.
Citation Count: Not available (recommend checking Scopus/Google Scholar)

📘 Monthly long-term rainfall estimation in Central India using M5Tree, MARS, LSSVR, ANN and GEP models

Authors: Mirabbasi, R.; Kisi, O.; Sanikhani, H.; Gajbhiye Meshram, S.
Journal: Neural Computing and Applications, 2019; exact volume/issue TBD.
Abstract Summary: Compared five different data-driven models (M5Tree, MARS, LSSVR, ANN, GEP) for monthly rainfall estimation based on data from 61 stations in Madhya Pradesh and Chhattisgarh. LSSVR achieved the highest accuracy (RMSE ≈ 13.93 mm, MAE ≈ 9.52 mm, R² ≈ 0.995), whereas GEP performed worst (RMSE ≈ 36.74 mm)
Citation Count: Not listed (suggest using Google Scholar/Scopus)

Conclusion

Dr. Hadi Sanikhani is a strong and suitable candidate for the Best Researcher Award, especially in the domain of water resources, flood modeling, and climate change resilience. His interdisciplinary, AI-enhanced approach to environmental modeling and international research collaborations distinguish him as an impactful and forward-thinking researcher.

Jihong Wang | Data Science and Deep Learning | Best Academic Researcher Award

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Ms. Jihong Wang | Data Science and Deep Learning | Best Academic Researcher Award 

Ms. Jihong Wang, at The University of Hong Kong, China.

Jihong Wang is a robotics and autonomous systems engineer pursuing an MSE in Innovative Design and Technology at The University of Hong Kong (expected July 2025). With a robust foundation from a B. Eng in Robot Engineering at Beijing University of Technology (2020–2024; CGPA 3.49/4.0), Jihong combines theoretical excellence with real-world innovation. Their passion lies in intelligent transportation, UAV/robotic control systems, and federated learning. Through multiple competitive academic projects—ranging from autonomous intersection navigation to solar-tracking innovations—they demonstrate skill in MATLAB, STM32, and AI algorithms. Recipient of Huawei Future Star Scholarship (2023), national contest wins, and multiple patents, Jihong brings creativity, technical depth, and academic rigor. Their goal: to develop cutting-edge, robust control strategies that improve safety and efficiency in next-gen autonomous systems.

Professional Profile

Google Scholar

🎓 Education

Jihong’s academic journey began at Beijing University of Technology (Sep 2020–Jul 2024), where they earned a B. Eng in Robot Engineering with a CGPA of 3.49/4.0; a stellar junior-year CGPA of 3.85/4.0 reflected exceptional performance across modules. Key coursework included Data Structures & Algorithms (95), Modern Control Theory (89), Machine Vision (89), Multi‑Robot Modeling (96), Electric Machines & Motion Control (93), and High‑Level Programming (92), laying a strong theoretical and applied foundation. Building on this, Jihong began MSE studies in Innovative Design & Technology at The University of Hong Kong in September 2024, with expected graduation in July 2025. Here, advanced design methodologies, emerging technology applications, and multidisciplinary collaboration foster deeper expertise in autonomous system design and research innovation.

💼 Experience

Jihong’s practical experience encompasses academic, research, and professional roles. In academia, they’ve led projects such as autonomous intersection control, solar‑tracking STM32 systems, and robot‑car Bluetooth control, applying embedded systems and AI. Their professional engagements include roles at China Aerospace Standardization Institute (intern, Jun–Jul 2023), where they earned high marks (94/100) in standards integration and technical documentation; Bamba Technology Co. (editorial intern, Jul–Sep 2022), overseeing content revision and meeting summaries; and Orang International Translation Center (translation assistant, Sep–Oct 2020), converting multimedia content into accurate manuscripts. Each role showcases attention to technical detail, communication, and cross-functional teamwork. In graduate research ongoing since mid‑2024, Jihong is designing fault‑tolerant control systems for tiltrotor UAVs and federated‑learning algorithms. Their combined work experience supports their ambition to merge robotics, machine learning, and control theory into real‑world systems.

🔬 Research Interest

Jihong’s research focuses on advanced control, robotics, and distributed AI systems. Key interests include:

  • Model Predictive Control (MPC): Designing algorithms for UAVs and autonomous vehicles that account for disturbances and system uncertainties.

  • Fault‑tolerant control: Developing robust frameworks for tiltrotor UAVs experiencing partial power loss or mechanical failures.

  • Federated learning & fuzzy clustering: Creating privacy‑aware, distributed unsupervised learning models (e.g., ECM algorithm) for decentralized sensor networks.

  • Collaborative autonomy: Integrating real‑time traffic signal data with autonomous vehicle control to optimize safety and efficiency at intersections.

  • Embedded and aerial robotics: Deploying STM32‑based systems for solar tracking and robot arms and exploring innovations in aerial‑target detection and SLAM in dynamic environments.

Jihong combines control theory, machine vision, federated AI, and embedded systems to push the boundaries of intelligent, resilient, and cooperative robotic systems.

🏅 Awards

Jihong’s achievements include:

  • Winner, National Academic English Vocabulary Contest for College Students (2023)

  • Huawei Future Star Scholarship (2023)

  • Four utility‑model patents & two software copyrights (2022–2023)

  • School‑level Innovation & Entrepreneurship Awards (2022, 2023)

  • First Prize, School‑level Writing Contest Preliminaries (2022)

  • “S Award,” American University Mathematical Modeling Competition (2021)

  • Third Prize, School‑level Poetry Conference (2021)

  • Third Prize, University‑level Knowledge Contest (2020)

These honors reflect Jihong’s academic strength, innovativeness, and interdisciplinary excellence in technical writing, modeling, and creativity.

📄Top Noted Publications

Here are Jihong’s key publications (each listed with hyperlink, year, journal, and one-line citation count if available):

1. “Research on Autonomous Vehicle Control based on Model Predictive Control Algorithm”

  • Conference: IEEE ICDSCA 2024

  • Publisher: IEEE

  • Citations: 5

2. Feng et al., “Research on Move‑to‑Escape Enhanced Dung Beetle Optimization and Its Applications”

  • Journal: Biomimetics, 2024

  • Citations: 8

3. Wei et al., “AFO‑SLAM: an improved visual SLAM in dynamic scenes…”

  • Journal: Measurement Science and Technology, 2024

  • Citations: 6

4. Jia & Wang, “A Control Strategy and Simulation for Precision Control of Robot Arms”

  • Conference: ICIR 2024

  • Publisher: ACM

  • Citations: 3

5. Wang & Jia, “Research on UAV Trajectory Tracking Control Based on Model Predictive Control”

  • Conference: IEEE ICETCI 2024

  • Publisher: IEEE

  • Citations: 4

6. Xiong et al., “A Sinh Cosh Enhanced DBO Algorithm Applied to Global Optimization Problems”

  • Journal: Biomimetics, 2024

  • Citations: 7

7. Wang et al., “Research on the External Structure and Control System Design of Biomimetic Robots”

  • Conference: ICISCAE 2023

  • Publisher: IEEE

  • Citations: 2

📝 Under Review

8. “FAS‑YOLO: Enhanced Aerial Target Detection…”

  • Journal: Remote Sensing

  • Status: Under Review

9. Xu et al., “MASNet: Mixed Artificial Sample Network for Pointer Instrument Detection”

  • Journal: IEEE Transactions on Instrumentation and Measurement

  • Status: Under Review

Conclusion

Jihong Wang is a highly promising candidate for the Best Academic Researcher Award, especially in the student or early-career researcher category. The profile reflects a mature understanding of advanced robotics, intelligent systems, and real-world engineering problems, backed by publications, practical projects, and international experiences.