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

Published on by Computer Vision Awards

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/

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.

Barat Barati | Artificial Intelligence | Research Impact Award

Published on by Computer Vision Awards

Assist. Prof. Dr. Barat Barati | Artificial Intelligence | Research Impact Award

Medical Physics | Shoushtar Faculty of Medical Sciences | Iran

Assist. Prof. Dr. Barat Barati is a distinguished academician and researcher specializing in radiotherapy, artificial intelligence (AI), and computational simulation, with a career dedicated to advancing healthcare diagnostics and treatment through innovative research and teaching. Currently serving as a faculty member at Shoushtar Faculty of Medical Sciences, he integrates deep learning models with biomedical signal processing to address challenges in medical sciences, particularly brain tumor diagnosis and classification. He earned his doctoral degree with a specialization in artificial intelligence and simulation methods, where his PhD research introduced novel approaches by combining machine learning algorithms with Monte Carlo simulation tools such as MCNP, significantly advancing medical physics and diagnostic imaging. With a strong foundation in physics, mathematics, computer science, and biomedical technologies, Dr. Barati bridges engineering and medicine while enhancing his expertise through specialized training in programming, data analysis, and AI-driven healthcare applications. His research focuses on applying AI and computational simulations in radiotherapy and medical imaging, emphasizing brain tumor detection, classification, and radiation treatment modeling, while also extending to biomedical signal processing and machine learning applications for improved diagnostic accuracy and treatment planning. As a faculty member, he contributes to teaching, mentoring, research supervision, and interdisciplinary collaborations, publishing impactful work in Scopus-indexed journals. Recognized for his ability to mentor young researchers and his vision to advance precision medicine, Dr. Barati demonstrates leadership, innovation, and commitment to improving patient outcomes, making him a deserving candidate for the Best Researcher Award and an influential figure in the global scientific community.

Profile: Google Scholar | Scopus Profile | ORCID Profile

Featured Publications

  1. Noorimotlagh, Z., Mirzaee, S. A., Kalantar, M., Barati, B., Fard, M. E., & Fard, N. K. (2021). The SARS-CoV-2 (COVID-19) pandemic in hospital: An insight into environmental surfaces contamination, disinfectants’ efficiency, and estimation of plastic waste production. Environmental Research, 202, 111809.

  2. Mohseni, H., Amini, S., Abiri, B., Kalantar, M., Kaydani, M., Barati, B., Pirabbasi, E., & … (2021). Are history of dietary intake and food habits of patients with clinical symptoms of COVID-19 different from healthy controls? A case–control study. Clinical Nutrition ESPEN, 42, 280–285.

  3. Moghiseh, Z., Xiao, Y., Kalantar, M., Barati, B., & Ghahrchi, M. (2023). Role of bio-electrochemical technology for enzyme activity stimulation in high-consumption pharmaceuticals biodegradation. 3 Biotech, 13(5), 119.

  4. Barati, B., Erfaninejad, M., & Khanbabaei, H. (2025). Evaluation of effect of optimizers and loss functions on prediction accuracy of brain tumor type using a light neural network. Biomedical Signal Processing and Control, 103, 107409.

  5. Akbari, G., Mard, S. A., Savari, F., Barati, B., & Sameri, M. J. (2022). Characterization of diet based nonalcoholic fatty liver disease/nonalcoholic steatohepatitis in rodent models: Histological and biochemical outcomes. Universidad de Murcia, Departamento de Biología Celular e Histología.

Ms. Shan Jiang | AI Creativity | Best Researcher Award

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Ms. Shan Jiang | AI Creativity | Best Researcher Award

Shan Jiang at Shanghai Jiao Tong University, China

👨‍🎓 Profiles

Orcid

Publications

Another Kind of Authenticity: The Visual Simulacra of Artificial Intelligence

  • Authors: Shan Jiang, Kanghua Li
  • Journal: Digital Creativity
  • Year: 2025

The Generative Logic and Governance Strategies of New Cultural Productivity

  • Authors: Kanghua Li, Shan Jiang
  • Journal: FUJIAN TRIBUNE (The Humanities & Social Sciences Monthly)
  • Year: 2024

Revisit the myth of long-tail with agent-based models

  • Authors: Cheng Zeng, Shan Jiang
  • Journal: Dataset
  • Year: 2023

Turning Crisis into Opportunity– The New Driving Force of Shanghai’s Cultural Industry

  • Authors: Shan Jiang, Kanghua Li
  • Journal: Annual Report on Shanghai Cultural Industry Development
  • Year: 2021

Machine Learning and the Transformation of Cultural Production – A Perspective on AI Technology Development

  • Authors: Kanghua Li, Shan Jiang
  • Journal: Journal of Xiangtan University (Philosophy and Social Sciences)
  • Year: 2020