Shakila Rahman | Deep Learning | Innovative Research Award

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Innovative Research Award

Shakila Rahman – American International University – Bangladesh

                  Shakila Rahman
Affiliation American International University – Bangladesh
Country Bangladesh
Scopus ID 57218573687
Documents 18
Citations 172
h-index 7
Subject Area Deep Learning
Event Global Tech Excellence Awards
ORCID 0000-0001-6375-4174

Shakila Rahman is affiliated with American International University – Bangladesh and has contributed to research in deep learning, intelligent systems, machine learning, and applied artificial intelligence. Her scholarly publications demonstrate interdisciplinary applications of advanced computational methods in engineering and healthcare while supporting practical industrial and environmental solutions.[1]

Abstract

This article presents an academic overview of Shakila Rahman’s research achievements supporting her recognition for the Innovative Research Award. Her scholarly work focuses on deep learning, federated learning, intelligent decision systems, environmental monitoring, and industrial automation. Through peer-reviewed publications, she has demonstrated practical applications of artificial intelligence for water quality assessment, privacy-preserving distributed learning, and automated defect detection. Her research combines computational innovation with real-world impact while contributing to scientific advancement, interdisciplinary collaboration, and technology-driven solutions across engineering and data science disciplines.[1]

Keywords

  • Deep Learning
  • Machine Learning
  • Federated Learning
  • Artificial Intelligence
  • Water Quality Prediction
  • Industrial Automation

Introduction

Shakila Rahman’s research emphasizes practical artificial intelligence solutions addressing engineering and environmental challenges through deep learning, intelligent analytics, and data-driven methodologies. Her work integrates computational efficiency with real-world implementation, supporting reliable decision-making, predictive modeling, and technological innovation while strengthening interdisciplinary collaboration across modern scientific and industrial research domains.[2]

Research Profile

Her scholarly profile demonstrates sustained contributions to deep learning, federated learning, computer vision, and intelligent engineering applications. With peer-reviewed publications indexed in recognized databases, measurable citation impact, and interdisciplinary collaborations, she continues advancing artificial intelligence research while supporting practical implementations across healthcare, manufacturing, and environmental monitoring systems.[1]

Research Contributions

Her research contributions include stacking ensemble learning for drinking water assessment, carbon-aware federated learning with privacy preservation, and deep learning models for automated printed circuit board inspection. These studies demonstrate methodological innovation while improving prediction accuracy, computational efficiency, security, and intelligent industrial quality assurance.[2][3]

Publications

Her publications highlight research spanning environmental analytics, federated artificial intelligence, computer vision, and industrial inspection. Published through internationally recognized venues, these studies demonstrate rigorous methodology, practical validation, and reproducible findings while contributing valuable knowledge to machine learning, engineering, and intelligent computational systems research.[2]

Research Impact

Her research has achieved measurable scholarly visibility through publications, citations, and interdisciplinary influence. The practical orientation of her studies supports environmental sustainability, privacy-aware distributed learning, and industrial automation, encouraging broader adoption of artificial intelligence techniques while inspiring continued innovation within academic and applied research communities.[1]

Award Suitability

Recognition through the Innovative Research Award appropriately reflects her documented academic productivity, interdisciplinary research excellence, and commitment to developing impactful artificial intelligence solutions. Her scholarly achievements demonstrate originality, practical significance, and sustained contributions that align with the objectives of the Global Tech Excellence Awards.[1]

Conclusion

Shakila Rahman’s academic record reflects continuous advancement in deep learning and intelligent computational research through impactful publications and measurable scholarly influence. Her contributions demonstrate scientific rigor, practical relevance, and interdisciplinary collaboration, supporting recognition as a deserving recipient of the Innovative Research Award for sustained excellence in research.[1]

References

  1. Elsevier. (n.d.). Scopus author details: Shakila Rahman, Author ID 57218573687. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57218573687
  2. Rahman, S., et al. (2025). Evaluating the Potability of Drinking Water Using Stacking Ensemble Machine Learning Technique.
    https://ieeexplore.ieee.org/document/11546276/
  3. Rahman, S., et al. (2025). FedEPL+: Carbon-Aware Client Selection With Valid Differential Privacy in Federated Learning.
    https://ieeexplore.ieee.org/document/11545760
  4. Rahman, S., et al. (2025). Real-Time Detection of Printed Circuit Board and Soldering Defects Using Deep Learning Techniques.
    https://ieeexplore.ieee.org/document/11545912

Jecha Jecha | Education and Outreach in Computer Vision | Young Scientist Award

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Young Scientist Award

Jecha Jecha
Affiliation Zanzibar University
Country Tanzania
Scopus ID 60416225500
Documents 2
Citations 1
h-index 1
Subject Area Education and Outreach in Computer Vision
Event Global Tech Excellence Awards

Jecha Jecha is affiliated with Zanzibar University in Tanzania and is associated with emerging academic activities in the interdisciplinary field of education and outreach in computer vision.[1] The researcher has been indexed within international bibliographic systems and demonstrates participation in scholarly dissemination connected to educational technology and computational learning methodologies.[2]

Abstract

This article provides a structured overview of the academic profile of Jecha Jecha, a researcher associated with Zanzibar University, Tanzania, whose scholarly interests are connected with educational applications of computer vision and outreach-oriented technological learning systems.[1] The profile highlights institutional affiliation, indexed publication activity, citation indicators, and the researcher’s relevance to contemporary academic recognition initiatives such as the Global Tech Excellence Awards.

Keywords

Computer Vision, Educational Technology, Academic Outreach, Emerging Research, Scholarly Communication, Digital Learning, Innovation Dissemination, Research Recognition, Technology Education, Global Tech Excellence Awards.

Introduction

Modern academic evaluation systems increasingly emphasize interdisciplinary innovation, digital knowledge dissemination, and socially impactful technological research. Researchers working within educational technology and computer vision outreach contribute to expanding computational literacy and supporting accessible learning ecosystems across global academic environments. Jecha Jecha’s scholarly profile reflects participation within this evolving academic landscape through indexed publication activity and institutional engagement in technology-oriented education initiatives.[1]

Research Profile

Jecha Jecha is affiliated with Zanzibar University and is indexed in the Scopus database under Author ID 60416225500.[1] Available bibliometric indicators identify two indexed documents, one citation, and an h-index value of 1, representing an emerging but formally recognized academic research profile.[1] The associated subject area includes Education and Outreach in Computer Vision, reflecting interdisciplinary engagement between computational technologies and educational communication systems.

Research Contributions

The research activities associated with Jecha Jecha are linked to broader discussions surrounding digital education, outreach methodologies, and computational learning frameworks. Educational applications of computer vision frequently contribute to technological accessibility, visual learning systems, and interactive knowledge dissemination mechanisms within academic institutions. Such interdisciplinary research areas are increasingly recognized for supporting innovation-driven educational development and inclusive technology awareness initiatives.

Publications

The Scopus-indexed profile associated with Jecha Jecha records two academic publications connected to educational and technology-oriented research themes.[1] Although the publication volume remains limited, indexed scholarly outputs indicate participation in peer-reviewed communication processes and international academic visibility systems.

  • Research publication concerning ergonomic mismatch between university student anthropometry and classroom furniture in Tanzania, contributing to educational environment assessment methodologies.
  • Research contribution related to ergonomic needs assessment and applied human factors methodologies in industrial and educational settings.

Research Impact

Research impact within emerging academic careers is commonly evaluated through publication indexing, citation development, institutional visibility, and thematic relevance. The inclusion of Jecha Jecha’s scholarly work within the Scopus database demonstrates participation in internationally recognized academic indexing systems.[1] Furthermore, thematic engagement with educational and technology-oriented research aligns with global priorities related to digital literacy, learning accessibility, and innovation-oriented knowledge dissemination.

Award Suitability

The Young Scientist Award category within the Global Tech Excellence Awards framework recognizes emerging researchers demonstrating academic promise, interdisciplinary engagement, and relevance to technological advancement initiatives.[3] Jecha Jecha’s profile aligns with several of these evaluation considerations through indexed scholarly participation, educational technology engagement, and interdisciplinary research visibility. The researcher’s affiliation with Zanzibar University additionally contributes to regional and international representation in technology-focused academic activities.[2]

Conclusion

Jecha Jecha represents an emerging researcher associated with interdisciplinary educational applications and technology-oriented outreach initiatives connected to computer vision and digital learning systems. Although the bibliometric indicators reflect an early-stage academic trajectory, the existence of indexed publications and participation in internationally visible scholarly databases demonstrate a foundation for future academic development.[1] The profile remains relevant to recognition programs emphasizing innovation, educational technology, and emerging scientific contribution within global research environments.[3]

References

      1. Elsevier. (n.d.). Scopus author details: Jecha Jecha, Author ID 60416225500. Scopus.
        https://www.scopus.com/authid/detail.uri?authorId=60416225500
      2. Zanzibar University. (n.d.). Institutional academic and research information.
        https://zanvarsity.ac.tz/
      3. Global Tech Excellence Awards. (n.d.). Award categories and academic recognition framework.
        https://globaltechexcellence.com/

Assoc. Prof. Dr. Md. Jakir Hossen | Deep Learning for Computer Vision | Excellence in Research Award

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Kun Chen | Machine Learning for Computer Vision | Research Excellence Award

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Mr. Kun Chen | Machine Learning for Computer Vision | Research Excellence Award

East China Jiaotong University | China

Mr. Kun Chen is a postgraduate researcher at East China Jiaotong University, specializing in machine learning and data mining. His research focuses on clustering analysis and semi-supervised learning, contributing to advancing intelligent data-driven systems. He co-authored the article A Novel Semi-Supervised Clustering Algorithm Based on Ridge Regression with Optimal Scaling, published in Neurocomputing, demonstrating strong analytical and methodological innovation. Despite being in the early stage of his academic career, he shows promising potential through international collaboration and impactful research contributions aimed at improving data interpretation and decision-making across scientific and engineering domains.

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Sheilla Ann Pacheco | Deep Learning for Computer Vision | Top Researcher in Computer Vision Award

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Assist. Prof. Dr. Sheilla Ann Pacheco | Deep Learning for Computer Vision | Top Researcher in Computer Vision Award

North Eastern Mindanao State University | Philippines

Dr. Sheilla Ann Bangoy Pacheco is an Assistant Professor at North Eastern Mindanao State University, specializing in machine learning, image processing, and adversarial AI. Her research focuses on robust facial recognition, privacy-preserving federated learning, and healthcare analytics. Her work on SARGAN-based face recognition and adversarial defense contributes to the development of secure and resilient biometric systems. Actively collaborating with international researchers, she adopts interdisciplinary approaches to address real-world challenges, particularly in healthcare and data privacy. Her contributions reflect growing societal relevance and a commitment to advancing trustworthy and secure artificial intelligence systems.

Citation Metrics (Scopus)

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Featured Publications


Enhanced content-based image retrieval using multivisual features fusion.

– International Journal of Computers and Applications, 47(10), 835–856. (2025). Citefd By: 4

Hidden adversarial attack on facial biometrics: A comprehensive survey.

– Procedia Computer Science, 258, 1383–1390. (2025). Cited By: 2

A comprehensive survey on federated learning and its applications in health care.

– In 2024 6th IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) (pp. 407–412). IEEE. (2024).  Cited By: 1

Performance of students in computer programming: An analysis.

– International Journal of Engineering Research in Computer Science and Engineering (IJERCSE), 10(1). (2023).  

Jianjun Zhang | Deep Learning for Computer Vision | Best Innovation Award

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Prof. Jianjun Zhang | Deep Learning for Computer Vision | Best Innovation Award

Teacher | Guangzhou Huali College | China

Prof. Jianjun Zhang is an academic researcher specializing in business administration education, digital intelligence, and the integration of big data and AI in higher education. He has authored over 30 publications indexed in CNKI, including one SCI paper and two EI-indexed works, reflecting steady scholarly impact. Zhang has led seven national and provincial research projects and collaborates with multidisciplinary teams on digital transformation in education. His work emphasizes virtual simulation and intelligent learning systems to enhance teaching efficiency and practical skill development. Recognized as a “double-qualified” educator, he effectively bridges theory and practice, contributing to workforce readiness and innovation in modern business education.

 

Profiles : ORCID

 

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Naourez Benhadj | Deep Learning | Excellence in Research

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Prof. Naourez Benhadj | Deep Learning | Excellence in Research

Associate Professor | Ecole Nationale d’Ingénieurs de Sfax | Tunisian

Dr. Naourez Benhadj is a researcher at the Ecole Nationale d’Ingénieurs de Sfax (ENIS), Tunisia, specializing in electric machines, PMSM design, hybrid/electric vehicle energy management, and intelligent optimization techniques. With 32 scientific publications, 243 citations, and an h-index of 9, he has contributed significantly to fault detection, finite-element modeling, and advanced optimization algorithms, including recent work on transformer-based solar power prediction and PMSM design using chaotic PSO. Collaborating with over 30 international co-authors, his research supports sustainable mobility, smart energy systems, and high-efficiency electric transportation, fostering technological advancement and environmental impact on a global scale.

 

Citation Metrics (Scopus)

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h-index
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Featured Publications


Comparison of fuel consumption and emissions of two hybrid electric vehicle configurations.

-International Conference on Sciences and Techniques of Automatic Control and Computer Engineering. (2018) Cited By: 4

Design simulation and realization of solar battery charge controller using Arduino Uno..

-International Conference on Sciences and Techniques of Automatic Control and Computer Engineering . (2017) Cited By: 21

Torque ripple and harmonic density current study in induction motor: Two rotor slot shapes.

– International Review on Modelling and Simulations.(2007). Cited By: 5

Thermal modeling of permanent magnet motor with finite element method.

– International Conference on Sciences and Techniques of Automatic Control and Computer Engineering. (2014). Cited By: 5

Madhuri Rao | Machine Learning | Best Researcher Award

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Dr. Madhuri Rao | Machine Learning | Best Researcher Award

Senior Assistant Professor | MIT World Peace University | India

Dr. Madhuri Rao is a dedicated researcher and academic in computer science with expertise in wireless sensor networks, Internet of Things, artificial intelligence, blockchain, and cybersecurity, with her current work focusing on deep learning, cloud security, and healthcare applications. She earned her Ph.D. in Computer Science and Engineering from Biju Patnaik University of Technology, where her research emphasized energy-efficient object tracking in wireless sensor networks. Over her career, she has gained extensive professional experience as a faculty member, academic coordinator, research supervisor, and editorial board member, contributing significantly to both teaching and research. She has authored and co-authored numerous publications in reputed journals and conferences, including IEEE, Springer, Elsevier, and Scopus-indexed platforms, along with patents and book chapters that highlight her innovative approach. Her research interests span interdisciplinary applications of advanced technologies to address challenges in security, healthcare, and sustainability, with ongoing involvement in collaborative projects and international initiatives. She has received recognition through awards such as best paper honors and a best research scholar award, underscoring her contributions to the academic community. Her research skills include problem-solving, experimental design, data analysis, and guiding students at undergraduate, postgraduate, and doctoral levels, coupled with active roles as session chair, track chair, and guest lecturer in international conferences. She is also a life member of professional societies and holds certifications that strengthen her academic profile. Her impactful contributions are reflected in 116 citations and an h-index of 7.

Profile: Google Scholar | ORCID | ResearchGate | LinkedIn

Featured Publications

  1. Rao, M., & Kamila, N. K. (2021). Cat swarm optimization based autonomous recovery from network partitioning in heterogeneous underwater wireless sensor network. International Journal of System Assurance Engineering and Management, 1–15.

  2. Rao, M., Kamila, N. K., & Kumar, K. V. (2016). Underwater wireless sensor network for tracking ships approaching harbor. 2016 International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES), 1098–1102. IEEE.

  3. Rao, M., & Kamila, N. K. (2018). Spider monkey optimisation based energy efficient clustering in heterogeneous underwater wireless sensor networks. International Journal of Ad Hoc and Ubiquitous Computing, 29(1–2), 50–63.

  4. Chaudhury, P., Rao, M., & Kumar, K. V. (2009). Symbol based concatenation approach for text to speech system for Hindi using vowel classification technique. 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), 1393–1396. IEEE.

  5. Kumar, K. V., Kumari, P., Rao, M., & Mohapatra, D. P. (2022). Metaheuristic feature selection for software fault prediction. Journal of Information and Optimization Sciences, 43(5), 1013–1020.

Ahmet Kayabaşı| Artificial Intelligence | Best Researcher Award

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Prof. Dr. Ahmet Kayabaşı | Artificial Intelligence | Best Researcher Award

Professor | Karamanoglu Mehmetbey University | Turkey

Prof. Dr. Ahmet Kayabaşı is a distinguished academic in electrical-electronics engineering with expertise in artificial intelligence, antennas, biomedical signal processing, image processing, fuzzy logic, and power electronics. He earned his PhD in Electrical-Electronics Engineering from Selcuk University and has since built a strong academic career combining teaching, research, and leadership. His professional experience includes serving as Head of Department, Director of the Institute of Graduate Studies, and Senate Member, along with mentoring numerous MSc and PhD students. His research interests span interdisciplinary fields, applying advanced AI techniques in UAV swarm algorithms, smart agriculture, biomedical diagnostics, and energy-efficient power systems. He has been actively involved in TÜBİTAK and institutional projects, contributing to impactful solutions for both academia and industry. Recognized for his excellence, he has received awards such as Best Presenter Award at ICAT and has played vital roles in academic conferences and scientific communities. His research skills include developing intelligent systems, applying machine learning to engineering challenges, and designing novel antenna and biomedical applications. He has published widely in leading international journals indexed in IEEE, Scopus, and Web of Science, with notable contributions in Applied Thermal Engineering, Swarm and Evolutionary Computation, and Computers and Electronics in Agriculture. His academic excellence is reflected in 609 citations by 522 documents, 47 publications, and an h-index of 13.

Profile: Google Scholar | Scopus | ORCID

Featured Publications

  1. Sabanci, K., Kayabasi, A., & Toktas, A. (2017). Computer vision‐based method for classification of wheat grains using artificial neural network. Journal of the Science of Food and Agriculture, 97(8), 2588–2593.

  2. Yigit, E., Sabanci, K., Toktas, A., & Kayabasi, A. (2019). A study on visual features of leaves in plant identification using artificial intelligence techniques. Computers and Electronics in Agriculture, 156, 369–377.

  3. Kayabasi, A., Toktas, A., Yigit, E., & Sabanci, K. (2018). Triangular quad-port multi-polarized UWB MIMO antenna with enhanced isolation using neutralization ring. AEU-International Journal of Electronics and Communications, 85, 47–53.

  4. Sabanci, K., Toktas, A., & Kayabasi, A. (2017). Grain classifier with computer vision using adaptive neuro‐fuzzy inference system. Journal of the Science of Food and Agriculture, 97(12), 3994–4000.

  5. Yildiz, B., Aslan, M. F., Durdu, A., & Kayabasi, A. (2024). Consensus-based virtual leader tracking swarm algorithm with GDRRT*-PSO for path-planning of multiple-UAVs. Swarm and Evolutionary Computation, 88, 101612.

Fatma Zahra Sayadi | Deep Learning | Best Innovation Award

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Prof. Fatma Zahra Sayadi | Deep Learning | Best Innovation Award

Associate Professor | University of Sousse | Tunisia

Fatma Elzahra Sayadi is a highly accomplished researcher and academic specializing in electronics and microelectronics, with current research focused on video surveillance systems, real-time processing, and signal compression. She earned her PhD in electronics for real-time systems from the University of Bretagne Sud in collaboration with the University of Monastir and has also completed her engineering and master’s studies in electrical and electronic systems. She has extensive professional experience as a maître de conférences and previously as a maître assistante and assistant technologist, teaching courses in microprocessors, multiprocessors, programming, circuit testing, and industrial electronics. Her research interests include signal processing, parallel architectures, microelectronics, real-time systems, and communication networks. She has actively participated in national and international research projects and collaborations with institutions in France, Italy, Germany, and Morocco. Her work has been published in over 37 journal articles, 40 conference papers, and six book chapters, and she has supervised several doctoral and master’s theses. She has been recognized with awards such as the first prize at the Women in Research Forum at the University of Sharjah and contributes to professional communities as a reviewer, evaluator, and organizer of academic events. She is skilled in research methodologies, signal and data analysis, electronic system design, and digital education innovation. Her academic contributions have been cited by 395 documents, with 69 documents contributing to her citations, and she has an h-index of 13.

Profile: Google Scholar | Scopus | ORCID 

Featured Publications

  1. Basly, H., Ouarda, W., Sayadi, F. E., Ouni, B., & Alimi, A. M. (2020). CNN-SVM learning approach based human activity recognition. In International Conference on Image and Signal Processing (pp. 271–281). 77 citations.

  2. Bouaafia, S., Khemiri, R., Sayadi, F. E., & Atri, M. (2020). Fast CU partition-based machine learning approach for reducing HEVC complexity. Journal of Real-Time Image Processing, 17(1), 185–196. 53 citations.

  3. Haggui, O., Tadonki, C., Lacassagne, L., Sayadi, F., & Ouni, B. (2018). Harris corner detection on a NUMA manycore. Future Generation Computer Systems, 88, 442–452. 48 citations.

  4. Basly, H., Ouarda, W., Sayadi, F. E., Ouni, B., & Alimi, A. M. (2022). DTR-HAR: Deep temporal residual representation for human activity recognition. The Visual Computer, 38(3), 993–1013. 40 citations.

  5. Bouaafia, S., Khemiri, R., Messaoud, S., Ben Ahmed, O., & Sayadi, F. E. (2022). Deep learning-based video quality enhancement for the new versatile video coding. Neural Computing and Applications, 34(17), 14135–14149. 35 citations.