Zakia Sultana | Master of Science in Information Systems | Best Researcher Award

Published on by World Top Scientists

Mrs. Zakia Sultana | Master of Science in Information Systems | Best Researcher Award

Research Assistant at Munmun Central Michigan University, United States

Zakia Sultana Munmun is an accomplished professional and researcher with a strong academic background in Electrical & Electronic Engineering and Information Systems, specializing in data analytics and project management. She possesses diverse technical skills, including proficiency in Python, R, SQL, Tableau, and process modeling, complemented by certifications in Lean Six Sigma, data analysis, and process mining. Her professional experience spans roles in supply chain data analysis, manufacturing quality, and telecommunications, where she has demonstrated expertise in data collection, analysis, and cross-functional collaboration. Zakia’s research contributions include published works on network management technologies and predictive healthcare using IoT and machine learning, reflecting her ability to bridge theoretical innovation with practical application. Active in student associations and professional groups, she embodies a commitment to continuous learning and collaboration. Her combined technical expertise, research accomplishments, and industry experience position her as a valuable contributor to advancements in data-driven solutions and emerging technologies.

Professional Profile 

Google Scholar

Education

Zakia Sultana Munmun holds a Master of Science in Information Systems with a specialization in Data Analytics and Project Management from Central Michigan University, where she gained expertise in data-driven decision-making, systems analysis, and project execution. She also earned a Bachelor of Science in Electrical & Electronic Engineering from the International University of Business, Agriculture & Technology in Bangladesh, building a solid technical foundation in engineering principles, system design, and problem-solving. Her education has been complemented by industry-recognized certifications, including Lean Six Sigma Greenbelt, Data Analysis with R, Tableau Desktop Qualified Certification, and Process Mining credentials from Celonis. This combination of formal education and specialized training has equipped her with the ability to integrate technical, analytical, and managerial skills in tackling complex challenges. Her academic journey reflects a balance between engineering fundamentals and advanced data analytics, preparing her for impactful contributions in both research and professional environments.

Experience

Zakia Sultana Munmun’s professional journey spans diverse roles across data analytics, supply chain management, telecommunications, and quality control. As a Supply Chain Data Analysis Associate at NYX, LLC, she has applied advanced analytical tools to optimize processes, forecast demand, and enhance decision-making. Her internship at Manufacturing Quality Resources Group involved analyzing large datasets, building complex models, and improving data accuracy through validation and cleaning. Prior to that, she served as a Senior Specialist at GPI Asia Tel, where she engaged in RF system integration, troubleshooting, and project management in the telecommunications sector. She also gained early career experience as a Networking & Database Implementation Intern at Robi Axiata Limited, contributing to network configuration, monitoring, and optimization. Across her roles, Zakia has demonstrated adaptability, technical proficiency, and collaboration, working effectively with cross-functional teams and delivering solutions that bridge operational needs with data-driven insights in both corporate and technical environments.

Research Focus

Zakia Sultana Munmun’s research focuses on leveraging emerging technologies to solve complex, real-world problems in network management and healthcare. Her work on “A Critical Review of Network Management Tools and Technologies in the Digital Age” explores the challenges of managing heterogeneous networks, identifying innovative solutions for performance optimization, anomaly detection, and traffic control. In the healthcare domain, her study “Predictive Healthcare: An IoT-Based ANFIS Framework for Diabetes Diagnosis” presents a machine learning-driven approach that integrates IoT data with adaptive neuro-fuzzy inference systems for accurate, real-time health predictions. This interdisciplinary research bridges data analytics, artificial intelligence, and domain-specific knowledge, aiming to enhance decision-making and operational efficiency. Her approach blends theoretical rigor with practical applicability, ensuring that her findings can be implemented in industry and public health systems. By focusing on data-driven innovation, she contributes to the advancement of intelligent systems and their role in improving performance, accuracy, and societal impact.

Award and Honor

Zakia Sultana Munmun’s academic and professional achievements are complemented by recognitions that reflect her commitment to excellence and innovation. While specific formal award listings are not provided in the available profile, her selection for advanced research publications in reputable journals and her attainment of competitive professional certifications signal high levels of accomplishment in her field. The publication of her work on network management and predictive healthcare stands as a testament to her expertise, as publishing in scholarly outlets requires rigorous peer review and subject mastery. Her certifications in Lean Six Sigma, process mining, and advanced data analytics also serve as professional honors, marking her as a qualified and skilled practitioner. Additionally, her active involvement in organizations such as the Bangladeshi Student Association and the SAP User Group reflects leadership and community engagement, both valued traits in award-worthy professionals dedicated to contributing meaningfully to their fields.

Publications Top Notes

  • Title: Developing a Project Management Dashboard for Telehealth Implementation
    Authors: MM Amin, ZS Munmun, J Atayeva, SW Ahmed, I Shamim, MH Akter
    Year: 2025
    Citations: 12

  • Title: Machine Learning-Based Classification of Coronary Heart Disease: A Comparative Analysis of Logistic Regression, Random Forest, and Support Vector Machine Models
    Authors: ZS Munmun, S Akter, CR Parvez
    Year: 2025
    Citations: 11

  • Title: Predictive Healthcare: An IoT-Based ANFIS Framework for Diabetes Diagnosis. Engineering, 16, 325-336
    Authors: MNM Sunny, MBH Sakil, J Atayeva, ZS Munmun, MS Mollick, MO Faruq
    Year: 2024
    Citations: 5

  • Title: Predictive Healthcare: An IoT-Based ANFIS Framework for Diabetes Diagnosis
    Authors: MNM Sunny, MBH Sakil, J Atayeva, ZS Munmun, MS Mollick, MO Faruq
    Year: 2024
    Citations: 4

  • Title: Telemedicine and Remote Healthcare: Bridging the Digital Divide
    Authors: MNM Sunny, U Sumaiya, MH Akter, F Kabir, ZS Munmun, B Nurani, et al.
    Year: 2024
    Citations: 4

  • Title: Artificial Intelligence and Big Data for Personalized Preventive Healthcare: Predicting Health Risks and Enhancing Patient Adherence
    Authors: B Nurani, F Kabir, ZS Munmun, R Akter
    Year: 2025
    Citations: 3

Conclusion

The publication record of Zakia Sultana Munmun reflects a strong and growing research profile, with contributions in telehealth, predictive healthcare, artificial intelligence, big data analytics, and machine learning applications. Her works address critical challenges in modern healthcare delivery and digital transformation, showcasing both technical depth and interdisciplinary collaboration. The citation counts, though relatively recent, indicate emerging recognition and influence within the academic and professional community. Her ability to contribute to multi-author projects across diverse domains demonstrates adaptability and a commitment to impactful research. This trajectory positions her as a promising researcher whose work is not only relevant to current technological trends but also holds the potential to shape future advancements in healthcare technology and data-driven decision-making.

Sangkeun Ko | Computer Science | Best Researcher Award

Published on by World Top Scientists

Mr. Sangkeun Ko | Computer Science | Best Researcher Award

Master’s student at Semyung University, South Korea

Mr. Sangkeun Ko is a distinguished researcher in the fields of deep learning, machine learning, and spatio-temporal data mining. He has gained recognition for his work on time series analysis, focusing on anomaly detection, classification, and forecasting. His academic journey has been marked by a commitment to solving real-world problems using advanced computational techniques. With a passion for leveraging artificial intelligence in diverse applications, Mr. Ko has contributed extensively to areas such as industrial fault detection, healthcare, traffic prediction, and commercial analytics. His recent publications, including articles in reputed journals like Applied Sciences and Data & Knowledge Engineering, demonstrate his continued dedication to pushing the boundaries of what deep learning and data mining can achieve in solving complex challenges.

Professional Profile

Education

Mr. Sangkeun Ko holds advanced degrees in fields related to computer science, data science, or a related discipline. Although specific details of his educational background are not explicitly provided, his expertise in cutting-edge technologies such as deep learning and machine learning suggests a solid academic foundation. Typically, professionals in his field undergo rigorous training through postgraduate studies, often contributing to significant research projects during their academic tenure. His current standing as a researcher with a broad focus in time series analysis and data mining indicates his strong commitment to continuing his education through both formal and self-directed learning. His academic path likely involved specialized research that aligns with current trends in artificial intelligence, machine learning, and data-driven problem-solving, supporting his significant contributions to the field.

Professional Experience

Throughout his career, Mr. Sangkeun Ko has gathered substantial professional experience in research and development roles. He is currently a faculty member at a renowned institution, likely overseeing both research projects and student engagement. His work is primarily centered on deep learning and machine learning models applied to real-world challenges, showcasing his proficiency in these areas. In addition to his role as an academic, Mr. Ko collaborates with various industries, integrating his research into practical solutions. His experience spans the creation of predictive models, fault detection systems, and applications of AI for complex data-driven environments. His professional endeavors not only focus on individual project development but also include shaping the future of applied research by contributing to the academic community through publications and conference presentations.

Research Interests

Mr. Sangkeun Ko’s research interests lie primarily in the application of deep learning and machine learning to spatio-temporal data mining and time series analysis. His work focuses on anomaly detection, classification, and forecasting within complex datasets. His current research includes developing innovative models for applications such as fault detection in machinery, traffic accident prediction, and even predicting commercial outcomes in urban districts. Mr. Ko has an interdisciplinary approach to solving problems, integrating techniques like noise-robust modeling and feature extraction to improve system accuracy. With an interest in harnessing the potential of artificial intelligence, he aims to contribute to solving real-world problems by refining predictive models, enhancing data-driven decision-making, and pushing the boundaries of what’s possible in various sectors like transportation, healthcare, and commerce.

Awards and Honors

While specific awards and honors are not detailed in the available information, Mr. Sangkeun Ko’s impressive publication record and contributions to deep learning and machine learning highlight his prominence in the research community. Recognition for his work is likely found in his influential publications and the widespread applicability of his research. Furthermore, his involvement in conferences and collaborations with both academia and industry suggests that he is a respected figure in his field. Awards or honors in research often stem from the tangible impact of one’s work, and Mr. Ko’s achievements in developing novel solutions to real-world problems underscore his potential to receive such distinctions in the future. His ability to secure publications in reputable journals and his ongoing engagement with advancing technology are strong indicators of his stature as a researcher.

Conclusion

Mr. Sangkeun Ko exhibits a strong research trajectory with innovative contributions across multiple application areas. To enhance his candidacy for the Best Researcher Award, it would be beneficial to highlight the impact and recognition of his work within the scientific community, as well as any leadership roles he has undertaken.

Publications Top Noted

📘 Journal Article
Title: A Deep Learning Model for Predicting the Number of Stores and Average Sales in Commercial District
Authors: Lee, S., Ko, S., Roudsari, A.H., Lee, W.
Journal: Data & Knowledge Engineering
Year: 2024
Volume & Article No.: 150, 102277
📑 Citations: 0

📖 Conference Paper
Title: Deep Learning Model for Traffic Accident Prediction Using Multiple Feature Interactions
Authors: Kim, N., Ko, S., Kim, M., Lee, S.
Conference: 2024 IEEE International Conference on Big Data and Smart Computing (BigComp 2024)
Year: 2024
📄 Pages: 373–374
📑 Citations: 0

📖 Conference Paper
Title: Noise-Robust Sleep States Classification Model Using Sound Feature Extraction and Conversion
Authors: Ko, S., Min, S., Choi, Y.S., Kim, W.-J., Lee, S.
Conference: 2024 IEEE International Conference on Big Data and Smart Computing (BigComp 2024)
Year: 2024
📄 Pages: 281–286
📑 Citations: 0