Bwambale Rashid Ramadhan | Prognostics | Best Researcher Award

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Dr. Bwambale Rashid Ramadhan | Prognostics | Best Researcher Award

Lecturer at Islamic University in Uganda, Ugandađź“–

Dr. Bwambale Rashid Ramadhan is a computer scientist, researcher, and educator specializing in cybersecurity, artificial intelligence (AI), IoT, and embedded systems. He has extensive experience in machine learning, pattern recognition, network security, and software development, contributing to both academic research and industry applications. Currently serving as the Deputy Dean, Faculty of Science, at the Islamic University in Uganda (IUIU), he plays a vital role in academic leadership, research supervision, and curriculum development. His work focuses on AI-driven cybersecurity, IoT applications, and ICT solutions for digital transformation.

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Education Background🎓

  1. Ph.D. in Computer Engineering (Computer Science) – Eskisehir Technical University, Turkey
    • Focus: Pattern Recognition, Text Analytics, Advanced Algorithms, Information Security
  2. MSc in Computer Science – Universiti Teknologi Malaysia (CGPA: 4.0/4.0)
    • Focus: Machine Learning, Secure Software Development, AI in Decision Systems
  3. BSc in Computer Science – International University of Africa, Sudan (Second Upper Class)
    • Focus: Artificial Intelligence, Cryptography, Software Engineering, Computer Networks
  4. Postgraduate Diploma in Managing and Teaching at Higher Education (PGD-MATHE)
  5. CCNA Instructor Certification
  6. Certificates: Deep Neural Networks, Convolutional Neural Networks, AI Optimization

Professional Experience🌱

  1. Deputy Dean, Faculty of Science – Islamic University in Uganda (IUIU) (2021–Present)
    • Oversees academic programs, research projects, and faculty management
    • Lectures in Artificial Intelligence, Machine Learning, Neural Networks, IoT, Embedded Systems, and Cybersecurity
  2. Head of Computer Science Department – Islamic University in Uganda (2014–2017)
    • Led curriculum development, faculty coordination, research supervision, and departmental administration
  3. Graduate Assistant – Universiti Teknologi Malaysia (2011–2012)
    • Assisted in lecturing AI and cybersecurity courses and conducted research in information assurance and security
  4. Researcher – Intel Technologies Uganda Limited (2008–2009)
    • Developed enterprise software solutions, conducted system analysis, and implemented IT security protocols
  5. Software Developer – Garri Free Trade Zone, Sudan (2007–2008)
    • Worked on data management, software development, and system security solutions
Research Interests🔬

Research interests include:

  • Artificial Intelligence & Machine Learning – Applications in cybersecurity, pattern recognition, and decision systems
  • Cybersecurity & Information Assurance – Penetration testing, forensic analysis, vulnerability detection
  • IoT & Embedded Systems – Smart automation, security integration, and predictive maintenance
  • Software Engineering & Cloud Computing – Secure software design, AI-driven system development
  • Big Data & Data Science – Predictive analytics, AI-driven risk assessment, eLearning optimization

Author Metrics

  • Peer-Reviewed Research Papers published in Springer, IEEE, and high-impact journals
  • Published Works:
    • “A Deep Residual Sequential Autoencoder for Future State Estimation and Aiding Prognostics and Diagnostics in Machines” (Springer, Neural Computing & Applications, 2023)
    • “Review of Cloud Computing Framework for the Implementation of eLearning Systems” (NCHE, 2023)
    • “Verification of Quranic Verses in Audio Files using Speech Recognition Techniques” (Al-Madinah Conference, 2013)
    • Research on AI-driven defect detection, recommender systems, and cybersecurity enhancements
  • Supervised Master’s and Ph.D. students in AI, cybersecurity, and machine learning
Awards and Honors
  • Full Scholarship for Ph.D. Studies – Eskisehir Technical University, Turkey
  • Land Baden-WĂĽrttemberg Stipend – University of Hawaii (Master’s Research)
  • Fiat Panis Stipend – Senegalese Institute for Agricultural Research (Bachelor’s Research)
  • Best Research Paper Award – Universiti Teknologi Malaysia, AI & Cybersecurity Conference
  • Certified Ethical Hacker & Network Security Specialist
  • Recognized for Leadership – General Secretary, Uganda Students’ Union in Sudan
Publications Top Notes đź“„

1. Hybrid Fuzzy Based Decision Model: A Case Study of Web Development Platforms Selection and Evaluation

  • Author: B.R. Ramadhan
  • Institution: Universiti Teknologi Malaysia
  • Year: 2013
  • Citations: 1
  • Summary: This study presents a hybrid fuzzy-based decision model for selecting and evaluating web development platforms. By integrating fuzzy logic and multi-criteria decision-making techniques, the research provides a structured approach for optimizing platform selection based on performance, scalability, and user requirements.

2. A Deep Residual Sequence Autoencoder for Future State Estimation and Aiding Prognostics and Diagnostics in Machines: A Case Study of Mechanical Rolling Elements

  • Authors: B.R. Ramadhan, P. Cahit
  • Journal: Neural Computing and Applications
  • Year: 2025
  • Pages: 1-20
  • Summary: This paper introduces an AI-driven deep residual sequence autoencoder for predicting future machine states and aiding in prognostics and diagnostics. The study applies deep learning techniques to mechanical rolling elements, enhancing fault detection, anomaly prediction, and system health monitoring for industrial machinery.

3. Review of Cloud Computing Framework for the Implementation of eLearning Systems

  • Authors: S.H. Asaba, A.A. Alli, S.A. Olawale, Y. Umar, A. Kasule, R.R. Bwambale
  • Journal: Uganda Higher Education Review Journal
  • Year: 2024
  • Summary: This comprehensive review evaluates cloud computing frameworks for eLearning system deployment. It discusses security, scalability, and cost-effectiveness, providing recommendations for institutions adopting cloud-based education technologies.

4. A Deep Learning Model for Prognostics and System Health Management (Prognostik ve sistem sağlığı yönetimi için derin bir öğrenme modeli)

  • Author: R.R. Bwambale
  • Institution: EskiĹźehir Technical University, Turkey
  • Year: 2022
  • Summary: This research proposes a deep learning-based prognostic model for system health management, leveraging AI algorithms to detect early-stage failures, predict system degradation, and optimize maintenance planning in industrial and cyber-physical systems.

Conclusion

Dr. Bwambale Rashid Ramadhan is a strong contender for the Best Researcher Award in Prognostics. His pioneering AI-driven research in predictive maintenance, cybersecurity, and IoT has made significant contributions to academia and industry. His leadership in research, impactful publications, and AI applications position him as a leading expert in AI-driven prognostics and system health management.

Konni Biegert | Apple Disorder Prediction | Best Researcher Award

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Dr. Konni Biegert | Apple Disorder Prediction | Best Researcher Award

Group Leader Plant Physiology at Kompetenzzetrum Obstbau Bodensee, Germanyđź“–

Dr. Konni Biegert is a Plant Physiology and Agricultural Technology expert with a rich academic background and extensive research experience in the field of horticulture and agriculture. Currently serving as the Working Group Leader of Plant Physiology and Technology at Kompetenzzentrum Obstbau Bodensee, Dr. Biegert has a proven track record of leading research projects and delivering innovative solutions for sustainable agriculture. His multidisciplinary expertise encompasses plant physiology, agricultural technology, and project management.

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Education Background🎓

  1. Doctor of Agriculture (magna cum laude) – Hochschule Geisenheim University (Mar. 2023)
  2. Master of Science in Agriculture (very good) – University of Hohenheim, Stuttgart (Mar. 2016)
    • Master Thesis at the University of Hawaii, Manoa, USA (2014-2015), supported by the Land Baden-WĂĽrttemberg stipend
  3. Bachelor of Science in Agriculture (good) – University of Hohenheim, Stuttgart (Jan. 2013)
    • Bachelor Thesis at the Senegalese Institute for Agricultural Research, supported by the Fiat Panis stipend (2012)

Professional Experience🌱

  1. Kompetenzzentrum Obstbau Bodensee, Germany
    • Working Group Leader, Plant Physiology and Technology (Since Jan. 2021)
      • Leading a team focused on plant physiology, agricultural technology, and sustainability in fruit cultivation.
      • Overseeing research projects on plant development, maturation processes, and agricultural innovation.
  2. Kompetenzzentrum Obstbau Bodensee, Germany
    • Scientific Researcher & Project Manager (Dec. 2016 – Dec. 2021)
      • Managed several key projects, focusing on fruit cultivation technologies, plant health, and agricultural practices.
      • Developed strategies for sustainable agriculture and resource optimization in horticulture.
Research Interests🔬

Her research interests include:

  • Plant Physiology: Understanding plant growth, development, and responses to environmental stimuli.
  • Agricultural Technology: Implementation of cutting-edge technologies for sustainable farming, focusing on fruit crops and orchard management.
  • Horticulture and Crop Management: Enhancing productivity, resource management, and climate-resilient farming practices.
  • Plant Protection and Health Management: Exploring new methods for plant disease control and protection through biological and chemical treatments.

Author Metrics

  • Publications: Dr. Biegert has contributed to several research papers in plant physiology, agricultural technology, and horticulture, including high-impact journals and international conferences in the field.
  • Research Supervision: Has mentored graduate students and supervised research projects, fostering innovation and advancement in plant science and agriculture.
Awards and Honors
  1. Doctor of Agriculture (magna cum laude) – Awarded for academic excellence at Hochschule Geisenheim University.
  2. Land Baden-WĂĽrttemberg Stipend – For Master’s thesis research at the University of Hawaii.
  3. Fiat Panis Stipend – For Bachelor’s thesis research at the Senegalese Institute for Agricultural Research.
  4. Certificate of Plant Protection – Qualified to deploy and consult on plant protection methods in agricultural settings.
  5. CECRA Certificate – Recognized as a European Consultant in Rural Areas.
Publications Top Notes đź“„

1. Determining the Optical Properties of Apple Tissue and Their Dependence on Physiological and Morphological Characteristics During Fruit Maturation. Part 2: Mie’s Theory

  • Authors: Lohner, S.A., Biegert, K., Nothelfer, S., McCormick, R., Kienle, A.
  • Journal: Postharvest Biology and Technology
  • Volume: 181
  • Article: 111652
  • Year: 2021
  • Citations: 7
  • Summary: This study applies Mie’s theory to understand how the optical properties of apple tissue depend on various physiological and morphological characteristics during the maturation process. The research explores the relationship between the light scattering properties of apple tissue and its ripening stages.

2. Determining the Optical Properties of Apple Tissue and Their Dependence on Physiological and Morphological Characteristics During Maturation. Part 1: Spatial Frequency Domain Imaging

  • Authors: Lohner, S.A., Biegert, K., Nothelfer, S., McCormick, R., Kienle, A.
  • Journal: Postharvest Biology and Technology
  • Volume: 181
  • Article: 111647
  • Year: 2021
  • Citations: 23
  • Summary: This paper utilizes spatial frequency domain imaging to investigate the optical properties of apple tissue and their correlation with physiological and morphological characteristics during the maturation process. It highlights the use of advanced imaging to gain insights into fruit ripening.

3. Mulching as Alternative Orchard Floor Management in Apple Orchards Positively Affects Water Availability and Weed Control

  • Authors: Haug, A., Biegert, K., McCormick, R., Tagliavini, M., Keutgen, A.
  • Journal: Acta Horticulturae
  • Volume: 1373
  • Pages: 187–196
  • Year: 2023
  • Citations: 1
  • Summary: This research investigates how mulching as an alternative to traditional orchard floor management practices can improve water availability and weed control in apple orchards, contributing to more sustainable agricultural practices.

4. Chlorophyll- and Anthocyanin-rich Cell Organelles Affect Light Scattering in Apple Skin

  • Authors: Lohner, S.A., Biegert, K., Hohmann, A., McCormick, R., Kienle, A.
  • Journal: Photochemical and Photobiological Sciences
  • Volume: 21, Issue 2
  • Pages: 261–273
  • Year: 2022
  • Citations: 7
  • Summary: This study explores how chlorophyll- and anthocyanin-rich organelles in apple skin contribute to light scattering, affecting the optical properties of apple fruits and influencing their appearance and ripening.

5. Effects of Laser Scanner Quality and Tractor Speed to Characterise Apple Tree Canopies

  • Authors: Siefen, N., McCormick, R.J., Vogel, A.M., Biegert, K.
  • Journal: Smart Agricultural Technology
  • Volume: 4
  • Article: 100173
  • Year: 2023
  • Citations: 5
  • Summary: This research examines the effects of laser scanner quality and tractor speed on the accuracy of apple tree canopy characterization. The findings are significant for improving precision agriculture techniques used in apple orchards.

Conclusion

Dr. Konni Biegert is a highly deserving candidate for the Best Researcher Award. His outstanding research in plant physiology, horticultural technology, and agricultural sustainability has greatly contributed to innovations in orchard management and fruit cultivation. His impactful publications, research leadership, and interdisciplinary approach to solving agricultural challenges underscore his role as a pioneer in advancing sustainable farming practices.

Expanding his industry collaborations, international presence, and cross-disciplinary engagement will only further enhance his ability to transform agriculture and plant sciences for the betterment of global food security and sustainability.

Touraj BaniRostam | Big Data | Best Researcher Award

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Assist. Prof. Dr. Touraj BaniRostam | Big Data | Best Researcher Award

Assistant Professor at University of Niagara Falls Canada, Canadađź“–

Dr. Touraj BaniRostam is a seasoned Computer Science and Artificial Intelligence (AI) expert with extensive academic and industry experience. Holding a Ph.D. in Computer Science, he is currently a Full-Time Faculty Member and Assistant Professor at the University of Niagara Falls, Canada. With a strong focus on AI, machine learning (ML), data analytics, and intelligent autonomous agents, Dr. BaniRostam is committed to advancing the fields of AI, cognitive science, and philosophy of AI. He has significantly contributed to the academic community, having supervised over 85 master’s and 5 Ph.D. students and published various impactful research works in AI, machine learning, cognitive science, and multi-agent systems.

Profile

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Education Background🎓

  • Ph.D. in Computer Science – Science and Research Branch, Islamic Azad University, Tehran, Iran (Sep. 2006 – Sep. 2011)
  • M.Sc. in Philosophy of Science – Science and Research Branch, Islamic Azad University, Tehran, Iran (Sep. 2016 – Sep. 2019)
  • M.A. in Psychology – Islamic Azad University, Tehran, Iran (Feb. 2014 – Sep. 2016)
  • M.Sc. in Artificial Intelligence – Science and Research Branch, Islamic Azad University, Tehran, Iran (Sep. 2001 – Sep. 2004)
  • B.Sc. in Computer Hardware – Islamic Azad University, Central Tehran Branch, Tehran, Iran (Sep. 1996 – Jul. 2000)

Professional Experience🌱

1. University of Niagara Falls, Canada

  • Full-Time Faculty Member & Assistant Professor (May 2024 – Present)
    • Data Analytics, Medical Computing, and Data Visualization
    • Teaching various courses related to data analytics, business intelligence, and medical & scientific computing.

2. International Business University (IBU), Toronto, Canada

  • Lecturer (Adjunct Professor) (Jan 2024 – Present)
    • Courses: Business Analytics, Technology Literacy, and Digital Transformation, including cloud computing, AI & machine learning, and cybersecurity compliance.

3. Georgian College, Barrie Campus, Canada

  • Lecturer (Part-Time) (May 2023 – Present)
    • Courses: Reinforcement Learning, System Vision, and Conversational AI.

4. Humber College, Toronto, Canada

  • Lecturer (Part-Time) (Jan 2024 – Aug 2024)
    • Emerging Technologies in AI, Generative AI, and Quantum Computing.

5. Durham College, Oshawa, Canada

  • Lecturer (Part-Time) (Jan 2024 – May 2024)
    • AI Algorithms and teaching various machine learning techniques such as supervised, unsupervised learning, and ensemble methods.

6. DAPCCO, Toronto, Canada

  • Research Manager (Part-Time) (Apr 2023 – Apr 2024)
    • Research on AI for intelligent waterproofing estimation, including machine learning, data mining, and deep neural networks.

7. Islamic Azad University, Tehran, Iran

  • Faculty Member & Assistant Professor (Feb 2007 – Feb 2023)
    • Taught courses on machine learning, business intelligence, intelligent decision support systems, and supervised numerous student theses in AI, data mining, and big data.

8. Islamic Azad University, Central Organization, Tehran, Iran

  • Vice Chancellor of Science and Engineering (Jun 2022 – Feb 2023)
    • Led the development of AI curricula and served as a policy advisor for AI development across the university system.
Research Interests🔬

Her research interests include:

  • Artificial Intelligence & Machine Learning: Development of intelligent systems, deep learning, and autonomous agents.
  • Data Science & Analytics: Applications of data mining, predictive modeling, and business intelligence.
  • Cognitive Science & Philosophy of AI: Exploring human cognition and decision-making through AI and cognitive models.
  • Multi-Agent Systems: Designing and analyzing autonomous agents in distributed systems.
  • Medical AI: Applications of AI in healthcare, including disease prediction and diagnostics.

Author Metrics

  1. Publications:
    • Published in journals such as PLOS One, SN Computer Science, BMC Bioinformatics, and presented at IEEE conferences such as ICASSP and CONECCT.
    • Notable papers on AI for medical diagnostics and autonomous vehicles.
  2. Supervision:
    • Successfully supervised 85 master’s and 5 Ph.D. students, with a focus on AI and machine learning in diverse applications.
Awards and Honors
  1. Full Scholarship for Ph.D. – Awarded based on academic excellence and research contributions.
  2. Rank 1 in Visvesvaraya PhD Fellowship Entrance Test (2024) – Kalinga Institute of Industrial Technology, MeitY (Govt. of India).
  3. Qualified GATE (2022) – Computer Science & Information Technology.
  4. Qualified JEE (2018) – Secured admission in IIIT Gwalior.
Publications Top Notes đź“„

1. Classification of Pima Indian Diabetes Dataset using Ensemble of Decision Tree, Logistic Regression, and Neural Network

  • Authors: M. Abedini, A. Bijari, T. Banirostam
  • Journal: International Journal of Advanced Research in Computer and Communication Engineering
  • Year: 2020
  • Citations: 37
  • Summary: This paper presents an ensemble approach combining decision tree, logistic regression, and neural network for classifying the Pima Indian Diabetes Dataset, improving prediction accuracy and robustness in medical data analysis.

2. Resolving Cold Start and Sparse Data Challenge in Recommender Systems using Multi-Level Singular Value Decomposition

  • Authors: K.V. Rodpysh, S.J. Mirabedini, T. Banirostam
  • Journal: Computers & Electrical Engineering
  • Volume: 94
  • Article: 107361
  • Year: 2021
  • Citations: 23
  • Summary: This research addresses the cold start and sparse data challenges in recommender systems, utilizing multi-level singular value decomposition to enhance system performance and recommendation accuracy in real-time applications.

3. Design, Modeling and Experimental Analysis of Wheeled Mobile Robot

  • Authors: M.H. Korayem, T. Banirostam
  • Conference: 3rd IFAC Symposium on Mechatronic Systems
  • Pages: 629-634
  • Year: 2004
  • Citations: 23
  • Summary: The paper presents the design, modeling, and experimental analysis of a wheeled mobile robot, with a focus on the integration of mechatronic systems for autonomous robotic applications.

4. Employing Singular Value Decomposition and Similarity Criteria for Alleviating Cold Start and Sparse Data in Context-Aware Recommender Systems

  • Authors: K.V. Rodpysh, S.J. Mirabedini, T. Banirostam
  • Journal: Electronic Commerce Research
  • Volume: 23, Issue 2
  • Pages: 681-707
  • Year: 2023
  • Citations: 19
  • Summary: This paper further builds upon the cold start issue in context-aware recommender systems, applying singular value decomposition and similarity criteria to address data sparsity and improve recommendation accuracy.

5. Functional Control of Users by Biometric Behavior Features in Cloud Computing

  • Authors: H. Banirostam, E. Shamsinezhad, T. Banirostam
  • Conference: Intelligent Systems Modeling & Simulation (ISMS-IEEE)
  • Year: 2013
  • Citations: 19
  • Summary: This study explores the use of biometric behavior features to provide functional control of users in cloud computing environments, enhancing security and user authentication in distributed systems.

Conclusion

Dr. Touraj BaniRostam is a highly deserving candidate for the Best Researcher Award. His exceptional academic track record, contributions to AI and machine learning, leadership in educational curricula development, and impactful research in fields such as healthcare AI, autonomous systems, and data science make him a leading figure in the field. Expanding his efforts to industry collaborations, increasing his participation in global conferences, and focusing on scalability and commercialization will further solidify his impact on the global research and technology landscape.

Pritam Chakraborty | Image Processing | Best Researcher Award

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Mr. Pritam Chakraborty | Image Processing | Best Researcher Award

Research Scholar at Kalinga Institute of Industrial Technology, Indiađź“–

Dr. Pritam Chakraborty is a dedicated researcher in computer vision, image segmentation, and autonomous vehicle technology, specializing in deep learning and machine learning applications. Currently pursuing his Ph.D. under the Visvesvaraya PhD Scheme (MeitY, Govt. of India) at Kalinga Institute of Industrial Technology, his work focuses on real-time image segmentation for autonomous vehicles in unstructured environments. His research contributions extend to medical imaging, game theory, and AI-driven healthcare predictions.

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Education Background🎓

  1. Ph.D. in Information Technology (Ongoing) – Kalinga Institute of Industrial Technology (2023 – Present)
    • Topic: Image segmentation for autonomous vehicles in unstructured environments
  2. Integrated Postgraduate (B.Tech + M.Tech) in Information Technology – Indian Institute of Information Technology, Gwalior (2018 – 2023)
    • Thesis: Semantic Segmentation using Modified Deeplab V3 Plus for Autonomous Vehicles
  3. Higher Secondary (Science) – Bidhan Chandra Institution (2016 – 2018)

Professional Experience🌱

Dr. Chakraborty has been actively involved in academic research, data-driven AI applications, and deep learning innovations. His expertise spans machine learning, neural networks, and game theory-based AI modeling. He has contributed to multiple high-impact journal publications and IEEE conference proceedings, presenting novel AI frameworks for real-time segmentation, medical diagnostics, and autonomous driving technologies. His work integrates AI-driven decision-making models, stroke prediction, and computer vision advancements for real-world applications.

Research Interests🔬

Her research interests include:

  • Autonomous Vehicles & Image Segmentation (Deep Learning for Real-time Road Analysis)
  • Medical AI & Predictive Analytics (Stroke Prediction & Hemorrhage Detection)
  • Machine Learning & Game Theory in Healthcare
  • Convolutional Neural Networks (CNNs) & Pyramid Networks for Image Processing

Author Metrics

  • Journal Articles: Published in SN Computer Science, BMC Bioinformatics, and IEEE Transactions on Intelligent Transportation Systems (communicated)
  • Conference Papers: Presented at IEEE ICASSP, IEEE CONECCT (IISc Bangalore), IEEE AITU Digital Generation
  • H-Index & Citations: Growing impact in AI-driven image segmentation and medical diagnostics
Awards and Honors
  • Rank 1 in Visvesvaraya PhD Fellowship Entrance Test (2024) – KIIT, MeitY (Govt. of India)
  • GATE Qualified (2022) – Computer Science & Information Technology
  • JEE Qualified (2018) – Secured admission in IIIT Gwalior
Publications Top Notes đź“„

1. OptiSelect and EnShap: Integrating Machine Learning and Game Theory for Ischemic Stroke Prediction

  • Authors: P. Chakraborty, A. Bandyopadhyay, S. Parui, S. Swain, P.S. Banerjee, T. Si, …
  • Journal: PLOS One
  • Status: Communicated
  • DOI: 10.21203/rs.3.rs-3841050/v1
  • Year: 2024
  • Summary: This paper presents the integration of machine learning and game theory for predicting ischemic stroke, exploring how these techniques can enhance diagnostic accuracy in medical predictions.

2. IndiRTS: Real-Time Segmentation for Autonomous Vehicles for Indian Conditions

  • Authors: P. Chakraborty, A. Bandyopadhyay, R. Ghosh, R. Sarkar
  • Journal: SN Computer Science
  • Volume: 6, Issue 2
  • Pages: 1-13
  • Year: 2025
  • DOI: 10.1007/s42979-025-00788-z
  • Summary: This research proposes IndiRTS, a real-time image segmentation model for autonomous vehicles tailored for Indian driving conditions, focusing on improving the safety and efficiency of self-driving cars in challenging environments.

3. Predicting Stroke Occurrences: A Stacked Machine Learning Approach with Feature Selection and Data Preprocessing

  • Authors: P. Chakraborty, A. Bandyopadhyay, P.P. Sahu, A. Burman, S. Mallik, …
  • Journal: BMC Bioinformatics
  • Volume: 25, Issue 1
  • Article: 329
  • Year: 2024
  • Summary: This paper introduces a stacked machine learning model for stroke occurrence prediction, incorporating feature selection and data preprocessing to enhance the model’s diagnostic reliability.

4. PyramidNet: Image Segmentation Model for Autonomous Vehicles for Indian Conditions

  • Authors: P. Chakraborty, A. Bandyopadhyay
  • Conference: 10th IEEE International Conference on Electronics, Computing, and Communication Technologies (CONECCT)
  • Location: IISc Bangalore
  • Year: 2024
  • Summary: The paper discusses the development of PyramidNet, an image segmentation model specifically designed for autonomous vehicles operating under Indian environmental conditions, improving vehicle navigation and road safety.

5. Automated Detection of Intracranial Hemorrhage using Convolutional Neural Networks

  • Authors: P. Chakraborty, A. Bandyopadhyay, M. Misra, P. Gupta, T.H. Sardar, …
  • Conference: 2024 IEEE AITU: Digital Generation
  • Pages: 20-26
  • Year: 2024
  • DOI: 10.1109/IEEECONF61558.2024.10585483
  • Summary: This work explores the use of convolutional neural networks (CNNs) for the automated detection of intracranial hemorrhage, showcasing the application of deep learning techniques in medical diagnostics.

Conclusion

Dr. Pritam Chakraborty is a highly deserving candidate for the Best Researcher Award, thanks to his innovative research, strong academic record, and interdisciplinary expertise. His work has the potential to transform the fields of autonomous driving and medical AI, and with some additional focus on scaling and global visibility, he will undoubtedly continue to make game-changing contributions.

Richard Usang | Environmental Monitoring | Best Researcher Award

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Dr. Richard Usang | Environmental Monitoring | Best Researcher Award

Senior Data Scientist at Heineken Uk, United Kingdomđź“–

Dr. Richard Usang is a distinguished Chemistry expert and Data Scientist with extensive experience in industrial and environmental chemistry, AI-driven analytics, and machine learning applications. His expertise spans advanced chemical research, data-driven decision-making, and AI model evaluation. With a Ph.D. in Industrial & Environmental Chemistry and an MSc in Data Science, he bridges the gap between scientific research and AI innovations. Dr. Usang has led impactful projects in predictive modeling, process optimization, and sustainability-driven solutions across various industries.

Profile

Orcid Profile

Education Background🎓

  • Ph.D. Industrial & Environmental Chemistry – University of Ibadan, Nigeria (2021)
  • MSc Data Science – University of Sussex, UK (2023)
  • MSc Industrial Chemistry (Top 1%) – University of Ibadan, Nigeria (2014)
  • BSc Chemistry (Ranked 1st) – Benue State University, Nigeria (2011)

Professional Experience🌱

Dr. Usang is currently a Senior Data Scientist at Heineken UK, where he applies AI and machine learning to optimize production processes and marketing strategies. He previously served as a Lead Data Analyst at EMCOR UK, significantly improving data processing efficiency. His earlier roles at The Heineken Company included Data Scientist and Brewing Process Specialist, where he spearheaded predictive modeling projects, optimized brewing parameters, and contributed to sustainability initiatives. He also held an academic position as a Research Assistant at the University of Ibadan, conducting groundbreaking research on chemical processes and environmental impact assessments.

Research Interests🔬

Her research interests include:

  1. AI-driven chemical data analysis and predictive modeling
  2. Sustainable industrial processes and environmental chemistry
  3. Machine learning applications in materials science
  4. AI content evaluation and Natural Language Processing in Chemistry

Author Metrics

Dr. Usang has authored several peer-reviewed publications on environmental chemistry, AI-driven water quality assessments, and sustainable waste management. Notable works include:

  1. “Integrating Principal Component Analysis, Fuzzy Inference Systems, and Advanced Neural Networks for Enhanced Estuarine Water Quality Assessment.”
  2. “Synthesis, Aqueous Solubility Studies, and Antifungal Activity Test of Some Tributyltin(IV) Carboxylates.”
  3. Conference presentations at TU Braunschweig, Germany, and the University of Ibadan, Nigeria.
Awards and Honors
  • Recognized among the Top 1% in MSc Industrial Chemistry
  • Best Graduate (1st Rank) in BSc Chemistry at Benue State University
  • Contributor to multiple industry-driven AI and sustainability initiatives
Publications Top Notes đź“„
1.  Integrating Principal Component Analysis, Fuzzy Inference Systems, and Advanced Neural Networks for Enhanced Estuarine Water Quality Assessment
  • Authors: Richard O. Usang, Bamidele I. Olu-Owolabi, Kayode O. Adebowale
  • Journal: Journal of Hydrology: Regional Studies
  • Publication Date: January 2025
  • DOI: 10.1016/j.ejrh.2025.102182
  • ISSN: 2214-5818
Abstract:

This research integrates Principal Component Analysis (PCA), Fuzzy Inference Systems (FIS), and Advanced Neural Networks to develop a more robust and precise estuarine water quality assessment model. The study applies machine learning and statistical techniques to improve water quality monitoring, pollution prediction, and ecological sustainability. By leveraging fuzzy logic and artificial intelligence, the proposed framework enhances the decision-making process for environmental management.

Key Highlights:
  • PCA for Data Reduction: Identifies key water quality parameters affecting estuarine ecosystems.
  • Fuzzy Inference Systems: Enhances interpretability and decision-making in water quality assessment.
  • Advanced Neural Networks: Improves prediction accuracy for water pollution trends and environmental impact analysis.
  • Application in Environmental Sustainability: Provides insights into climate change effects on estuarine water systems.

Conclusion

Dr. Richard Usang is an outstanding researcher whose interdisciplinary expertise, AI-driven environmental innovations, and industry-academic contributions make him a top contender for the Best Researcher Award. Expanding his AI applications, increasing global collaborations, and enhancing industry-academia partnerships will further solidify his impact in environmental monitoring and AI-based sustainability solutions.

Mohammad Reza Nikpour | Artificial Intelligence | Best Researcher Award

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Assoc. Prof. Dr. Mohammad Reza Nikpour | Artificial Intelligence | Best Researcher Award

Mohammad Reza Nikpour at University of Mohaghegh Ardabili, Iranđź“–

Dr. Mohammad Reza Nikpour is an esteemed scholar in Water Engineering, currently serving as a faculty member at the University of Mohaghegh Ardabili, Iran. His expertise lies in hydrodynamics, river engineering, and water resource management, with extensive contributions to computational modeling and environmental sustainability.

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Education Background🎓

  • Ph.D. in Water Engineering, University of Mohaghegh Ardabili, Iran
  • M.Sc. in Water Engineering, University of Mohaghegh Ardabili, Iran
  • B.Sc. in Water Engineering, University of Mohaghegh Ardabili, Iran

Professional Experience🌱

Dr. Nikpour has been actively involved in academic research and teaching at the University of Mohaghegh Ardabili. His work focuses on computational hydrodynamics, groundwater quality assessment, and flood prediction modeling. He has collaborated with international researchers and contributed to innovative water management solutions through data-driven models.

Research Interests🔬

Her research interests include:

  • Hydrodynamics and River Engineering
  • Groundwater Quality Assessment
  • Soft Computing and AI Applications in Water Resource Management
  • Flood Prediction and Climate Change Impact Studies

Author Metrics

Dr. Mohammad Reza Nikpour has established a strong academic presence with numerous publications in high-impact journals, including River Research and Applications, Journal of Cleaner Production, and Stochastic Environmental Research and Risk Assessment. His research contributions have been widely recognized, earning him a growing citation count on Google Scholar and an impressive h-index on Scopus (to be verified). As a highly cited researcher in water engineering, his work has significantly influenced hydrodynamics, groundwater quality assessment, and computational water resource management. His ORCID ID is 0000-0003-4332-0525, and his research continues to shape innovative solutions in environmental sustainability and AI-driven water system modeling.

Awards and Honors
  • Recognized for outstanding contributions in hydrodynamic modeling and water resource sustainability.
  • Published multiple high-impact research papers in top-tier journals such as River Research and Applications, Journal of Cleaner Production, and Stochastic Environmental Research and Risk Assessment.
  • Recipient of research grants and funding for pioneering studies in environmental and computational water management.
Publications Top Notes đź“„

1. Estimation of daily pan evaporation using two different adaptive neuro-fuzzy computing techniques

  • Authors: H. Sanikhani, O. Kisi, M.R. Nikpour, Y. Dinpashoh
  • Journal: Water Resources Management
  • Volume: 26
  • Pages: 4347-4365
  • Year: 2012
  • Citations: 70
  • Summary: This study applies adaptive neuro-fuzzy inference system (ANFIS) models to estimate daily pan evaporation, comparing their accuracy and efficiency in hydrological forecasting.

2. Experimental and numerical simulation of water hammer

  • Authors: M.R. Nikpour, A.H. Nazemi, A.H. Dalir, F. Shoja, P. Varjavand
  • Journal: Arabian Journal for Science and Engineering
  • Volume: 39
  • Pages: 2669-2675
  • Year: 2014
  • Citations: 48
  • Summary: This paper investigates water hammer phenomena using both experimental methods and numerical simulations, providing insights into fluid dynamics and pipeline safety.

3. Exploring the application of soft computing techniques for spatial evaluation of groundwater quality variables

  • Authors: F. Esmaeilbeiki, M.R. Nikpour, V.K. Singh, O. Kisi, P. Sihag, H. Sanikhani
  • Journal: Journal of Cleaner Production
  • Volume: 276
  • Article: 124206
  • Year: 2020
  • Citations: 31
  • Summary: This research explores soft computing techniques, such as machine learning, for the spatial analysis of groundwater quality, enhancing environmental monitoring and sustainability.

4. Hydrodynamics of river-channel confluence: toward modeling separation zone using GEP, MARS, M5 Tree, and DENFIS techniques

  • Authors: O. Kisi, P. Khosravinia, M.R. Nikpour, H. Sanikhani
  • Journal: Stochastic Environmental Research and Risk Assessment
  • Volume: 33 (4-6)
  • Pages: 1089-1107
  • Year: 2019
  • Citations: 28
  • Summary: The study applies various data-driven models, including gene expression programming (GEP) and M5 Tree, to model separation zones in river confluences, improving hydrodynamic predictions.

5. Application of novel data mining algorithms in prediction of discharge and end depth in trapezoidal sections

  • Authors: P. Khosravinia, M.R. Nikpour, O. Kisi, Z.M. Yaseen
  • Journal: Computers and Electronics in Agriculture
  • Volume: 170
  • Article: 105283
  • Year: 2020
  • Citations: 16
  • Summary: This paper investigates the use of advanced data mining techniques to predict discharge and end depth in trapezoidal channels, optimizing water resource management and agricultural planning.

Conclusion

Dr. Mohammad Reza Nikpour is an exceptional researcher in AI-driven water resource management, making him a strong candidate for the Best Researcher Award. His pioneering work in soft computing and AI applications for hydrology and environmental sustainability sets him apart in his field. Expanding into deep learning, increasing industry collaborations, and engaging in AI conferences could further solidify his leadership in AI for water engineering.

Zhiwei Si | Communication Engineering | Best Researcher Award

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Dr. Zhiwei Si | Communication Engineering | Best Researcher Award

Scientific Research Personnel at China Telecom Beijing Research Institute, Chinađź“–

Zhiwei Si is a dedicated researcher specializing in wireless communications, 5G/6G networks, and resource allocation. He is currently a scientific research personnel at the 6G Research Centre, China Telecom Beijing Research Institute. With a strong academic background in information and communication engineering, he has made significant contributions to ultra-dense networks, UAV communications, and millimeter-wave technologies. His research focuses on optimizing network efficiency, energy consumption, and backhaul bandwidth allocation.

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Education Background🎓

  • Ph.D. in Information and Communication Engineering, Beijing University of Posts and Telecommunications (BUPT), China (2017–2023)
  • Mathematics Studies, Beijing University of Posts and Telecommunications (2016–2017)
  • B.Sc. in Applied Mathematics, Shandong University of Science and Technology (SDUST), China (2012–2016)

Professional Experience🌱

Zhiwei Si has been contributing to cutting-edge research at the 6G Research Centre, China Telecom Beijing Research Institute since 2023. He has been involved in key national projects, including the development of 5G high-speed wide-area coverage technology and next-generation broadband wireless mobile communication networks. His experience spans industry collaborations with companies such as Huawei and Beacon Technology, where he worked on simulation platforms for LTE-R/M systems and base station antenna optimization. His technical expertise includes C/C++, Python, Latex, and MATLAB for network simulations and algorithm development.

Research Interests🔬

Her research interests include:

  • 6G Networks and Wireless Communications
  • Ultra-Dense Networks and mmWave Communications
  • User Association and Resource Allocation
  • UAV-Based Network Optimization
  • AI-Driven Wireless Network Solutions

Author Metrics

Zhiwei Si has published multiple research papers in IEEE conferences and journals such as Drones, Entropy, and EURASIP Journal on Wireless Communications and Networking. His work addresses key challenges in backhaul bandwidth allocation, energy efficiency, and user association in ultra-dense mmWave networks.

Awards and Honors
  • Five-Class Scholarship, BUPT (2016–2020)
  • Four-Class Scholarship, SDUST (2012–2015)
  • Second Prize, National University Mathematical Modeling Competition (Shandong Region, 2014)
Publications Top Notes đź“„

1. Energy-Efficient Joint User Association, Backhaul Bandwidth Allocation, and Power Allocation in Cell-Free mmWave UAV Networks

  • Authors: Zhiwei Si, Zheng Jiang, Kaisa Zhang, Qian Liu, Jianchi Zhu, Xiaoming She, Peng Chen
  • Journal: Drones
  • Volume/Issue: 9(2), 88
  • Publication Year: 2023
  • Summary: This paper explores an energy-efficient framework for joint user association, backhaul bandwidth allocation, and power allocation in cell-free mmWave UAV networks. The proposed method optimizes system efficiency while maintaining service quality.

2. Backhaul Capacity-Limited Joint User Association and Power Allocation Scheme in Ultra-Dense Millimeter-Wave Networks

  • Authors: Zhiwei Si, Gang Chuai, Kaisa Zhang, Weidong Gao, Xiangyu Chen, Xuewen Liu
  • Journal: Entropy
  • Volume/Issue: 25(3), 409
  • Publication Year: 2023
  • Summary: This study proposes a user association and power allocation scheme in ultra-dense mmWave networks with backhaul capacity constraints. It employs optimization techniques to improve resource allocation while considering network limitations.

3. A QoS-Based Joint User Association and Resource Allocation Scheme in Ultra-Dense Networks

  • Authors: Zhiwei Si, Gang Chuai, Weidong Gao, Jinxi Zhang, Xiangyu Chen, Kaisa Zhang
  • Journal: EURASIP Journal on Wireless Communications and Networking
  • Volume/Issue: 2020(1), 2
  • Publication Year: 2020
  • Summary: The paper presents a novel Quality of Service (QoS)-based joint user association and resource allocation strategy for ultra-dense networks, aiming to enhance user experience and system performance.

4. A Low-Complexity Algorithm for the Joint Antenna Selection and User Scheduling in Multi-Cell Multi-User Downlink Massive MIMO Systems

  • Authors: Maimaiti, S., Gang Chuai, Weidong Gao, Xuewen Liu, Zhiwei Si
  • Journal: EURASIP Journal on Wireless Communications and Networking
  • Volume/Issue: 2019(1), 208
  • Publication Year: 2019
  • Summary: This research develops a low-complexity algorithm to jointly optimize antenna selection and user scheduling in multi-cell multi-user massive MIMO downlink networks.

5. A New Method for Traffic Forecasting in Urban Wireless Communication Network

  • Authors: Kaisa Zhang, Gang Chuai, Weidong Gao, Maimaiti S., Zhiwei Si
  • Journal: EURASIP Journal on Wireless Communications and Networking
  • Volume/Issue: 2019(1), 66
  • Publication Year: 2019
  • Summary: A traffic forecasting model for urban wireless networks based on deep learning techniques, providing insights into network planning and optimization.

6. DIC-ST: A Hybrid Prediction Framework Based on Causal Structure Learning for Cellular Traffic and Its Application in Urban Computing

  • Authors: Kaisa Zhang, Gang Chuai, Zhang J., Zhiwei Si, Maimaiti S.
  • Journal: Remote Sensing
  • Volume/Issue: 14(6), 1439
  • Publication Year: 2022
  • Summary: This paper introduces a hybrid prediction framework combining causal structure learning and machine learning models to improve cellular traffic forecasting for urban applications.

Conclusion

Dr. Zhiwei Si is a highly deserving candidate for the Best Researcher Award in Communication Engineering. His cutting-edge work in 5G/6G, UAV networks, and mmWave communications, coupled with strong academic and industry collaborations, makes him an outstanding researcher in wireless communications. Strengthening his research impact through patents, mentorship, and global collaborations would further solidify his position as a leader in the field.

Hasan Cagatay Ciftci | Artificial Neural Networks | Best Researcher Award

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Mr. Hasan Cagatay Ciftci | Artificial Neural Networks | Best Researcher Award

Hasan Cagatay Ciftci at Erciyes University, Turkeyđź“–

Hasan Çağatay Çiftçi, born on March 16, 1997, in Kayseri, Turkey, is a dedicated researcher in the field of Surveying Engineering. He has a strong academic background and has contributed to various studies focusing on environmental analysis, land use, and climate change impacts. Currently, he is pursuing his PhD at Erciyes University, aiming to further his research in geospatial analysis and its applications.

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Education Background🎓

  • Bachelor’s Degree: Surveying Engineering, Erciyes University, 2019
  • Master’s Degree: Surveying Engineering, NiÄźde Ă–mer Halisdemir University, 2023
  • PhD: Surveying Engineering, Erciyes University, Ongoing

Professional Experience🌱

Throughout his academic journey, Çiftçi has engaged in extensive research, contributing to both national and international scientific communities. His work includes analyzing the impacts of land use and climate change, as well as conducting spatial adequacy and accessibility analyses for urban green spaces. He has presented his findings at various international conferences and has publications in esteemed journals.

Research Interests🔬

Her research interests include:

  • Land Use and Climate Change Impacts
  • Geospatial Analysis
  • Environmental Monitoring
  • Urban Planning
  • Remote Sensing and GIS

Author Metrics

Çiftçi has authored several publications, including articles in journals indexed by SCI, SSCI, and AHCI. Notably, his article titled “Analyzing Land Use and Climate Change Impacts of SuÄźla Water Storage in Turkey” was published in Theoretical and Applied Climatology in 2024. He has also contributed to national refereed journals and presented papers at international scientific meetings.

Awards and Honors
  • UAV-1 Commercial Pilot Certificate
  • Basic Radio Communication (R\T) Training Certificate

These certifications complement his research, particularly in areas involving remote sensing and geospatial data collection.

Publications Top Notes đź“„

1. Buffer ve Network Analiz Teknikleri Kullanılarak Kentsel Aktif Yeşil Alanlar için Mekânsal Yeterlilik ve Erişilebilirlik Analizi

  • Authors: MG GĂĽmĂĽĹź, HÇ Çiftçi, K GĂĽmĂĽĹź
  • Journal: Dokuz EylĂĽl Ăśniversitesi MĂĽhendislik FakĂĽltesi Fen ve MĂĽhendislik Dergisi
  • Volume/Issue: 26
  • Year: 2024
  • Citations: 3*
  • Summary: This study evaluates the spatial adequacy and accessibility of urban active green spaces using buffer and network analysis techniques.

2. Analyzing Land Use and Climate Change Impacts of SuÄźla Water Storage in Turkey

  • Authors: HÇ Çiftçi, K GĂĽmĂĽĹź, MG GĂĽmĂĽĹź
  • Journal: Theoretical and Applied Climatology
  • Volume: 155
  • Pages: 6797–6814
  • Year: 2024
  • Citations: 2
  • Summary: This paper examines the impact of land use and climate change on the SuÄźla water storage system, providing critical insights into environmental changes in the region.

3. Niğde ili rüzgâr karakteristiğinin belirlenmesi

  • Authors: HÇ Çiftçi, K GĂĽmĂĽĹź
  • Conference: IV. International Turkic World Congress on Science and Engineering
  • Pages: 1050-1063
  • Year: 2022
  • Citations: 1*
  • Summary: The study determines the wind characteristics of NiÄźde province, analyzing its potential for renewable energy applications.

4. Determination of the Performance of Training Algorithms and Activation Functions in Meteorological Drought Index Prediction with Nonlinear Autoregressive Neural Network

  • Authors: MG GĂĽmĂĽĹź, HÇ Çiftçi, K GĂĽmĂĽĹź
  • Journal: Earth Science Informatics
  • Volume: 18 (2)
  • Page: 197
  • Year: 2025
  • Citations: N/A
  • Summary: This research evaluates various training algorithms and activation functions in predicting meteorological drought indices using a nonlinear autoregressive neural network approach.

5. Ardışık Özellik Seçiminin Hiper Optimize Edilmiş Sınıflandırıcıların Performansına Etkisi

  • Authors: HÇ Çiftçi, ĂśH Atasever
  • Conference: IX. Uzaktan Algılama ve CoÄźrafi Bilgi Sistemleri Sempozyumu
  • Year: 2024
  • Citations: N/A
  • Summary: The paper explores the effect of sequential feature selection on the performance of hyper-optimized classifiers in remote sensing and geographic information systems applications.

Conclusion

Hasan Çağatay Çiftçi is a strong candidate for the Best Researcher Award, given his impressive research background, technical expertise, and contributions to environmental and geospatial sciences. His strengths lie in interdisciplinary research, practical applications of GIS and remote sensing, and a growing publication record.

Recommendation: To enhance his candidacy further, Çiftçi should focus on expanding his research collaborations, increasing his leadership in funded projects, and boosting his publication impact through international partnerships.

Overall, his achievements and potential make him a deserving contender for recognition as an emerging leader in the field of geospatial and environmental research.

Julius Derghe Cham | Technological Networks | Best Researcher Award

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Mr. Julius Derghe Cham | Technological Networks | Best Researcher Award

Teacher at University of Douala, Cameroonđź“–

Dr. Julius Derghe Cham is an experienced academic and professional in the field of Electrical Engineering, specializing in electrical power systems, electrotechnics, and renewable energy solutions. He has extensive teaching and research experience in various institutions across Cameroon, contributing to the training of future engineers and advancing research in electrical systems. With a strong background in project management, electrical installations, and MATLAB SIMULINK simulation, Dr. Cham is committed to driving innovation in power systems and establishing a consultancy firm.

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Education Background🎓

  • Ph.D. (Ongoing): Electrical Power Systems, University of Douala
  • Master of Engineering: Electrical Power Systems, University of Buea (2018–2021)
  • Master of Research: Electrotechnics, University of Douala (2017–2020)
  • DIPET II & I: Electrotechnics, University of Bamenda (2010–2015)
  • Baccalaureate Technique F3 (Electrical Technology): Government Technical High School Wum (2008)
  • Probatoire Technique F3 (Electrical Technology): Government Technical High School Wum (2007)
  • CAP Industrial in Electrical Equipment: Government Technical College Wum (2005)

Professional Experience🌱

Dr. Cham has extensive experience as both an academic and industry professional. He has served as a part-time lecturer at the University of Bamenda, IUC Douala, and Insam Bafoussam, where he has taught subjects such as Electrical Machines and Drives, Electrical Installations, and Power Systems. In addition, he has worked as an electrotechnician for MV/LV lines and domestic installations at CENELEC Ets and WELL Service Enterprise Company in Douala. His expertise spans project development, cost analysis, and renewable energy system design.

Research Interests🔬

Her research interests include:

  • Electrical power systems and renewable energy
  • Advanced electrotechnics and electrical installations
  • Energy efficiency and smart grid technology
  • Simulation and modeling using MATLAB SIMULINK

Author Metrics

Dr. Cham has contributed to research in electrical engineering, focusing on power systems, electrotechnics, and renewable energy solutions. His publications in various academic forums highlight his commitment to advancing sustainable electrical solutions and innovative teaching methodologies.

Awards and Honors
  • Recognized for excellence in teaching and research at multiple institutions
  • Active participant in pedagogical seminars and technical training workshops
  • Contributor to capacity-building programs for aspiring electrical engineers
Publications Top Notes đź“„

1. Robust Adaptive Integral Sliding Mode Control of a Half-Bridge Bidirectional DC-DC Converter

  • Authors: JD Cham, FLD Koffi, AT Boum, A Harrison
  • Journal: International Journal of Electrical & Computer Engineering (2088-8708)
  • Volume/Issue: 15 (1)
  • Year: 2025
  • Key Focus: This paper presents a robust adaptive integral sliding mode control (RAISMC) approach for a half-bridge bidirectional DC-DC converter, ensuring stability and improved dynamic response under varying load conditions.

2. Accurate and Optimal Control of a Bidirectional DC-DC Converter: A Robust Adaptive Approach Enhanced by Particle Swarm Optimization

  • Authors: JD Cham, FLD Koffi, AT Boum, A Harrison, PMD Zemgue, NH Alombah
  • Journal: e-Prime – Advances in Electrical Engineering, Electronics and Energy
  • Article Number: 100899
  • Year: 2025
  • Key Focus: This study introduces a robust adaptive control strategy combined with Particle Swarm Optimization (PSO) to enhance the performance and efficiency of bidirectional DC-DC converters, optimizing control parameters for dynamic load conditions.

3. Robust Adaptive Sliding Mode Control of a Bidirectional DC-DC Converter Feeding a Resistive and CPL Based on PSO

  • Authors: JD Cham, FLD Koffi, AT Boum, A Harrison
  • Journal: International Journal of Power Electronics and Drive Systems (IJPEDS)
  • Volume/Issue: 15 (4)
  • Year: 2024
  • Key Focus: The paper explores the application of robust adaptive sliding mode control (RASMC) to a bidirectional DC-DC converter, ensuring stability while feeding both resistive and constant power loads (CPL), with PSO employed to optimize control performance.

Conclusion

Dr. Julius Derghe Cham is undoubtedly a deserving candidate for the Best Researcher Award. His substantial contributions to electrical engineering research, particularly in power systems, renewable energy, and advanced control techniques, position him as a leader in his field. With his solid academic background, research excellence, and dedication to teaching, Dr. Cham’s future in advancing innovative electrical solutions looks promising. Focusing on increasing his global research visibility, engaging in industry partnerships, and expanding the interdisciplinary scope of his work could further solidify his reputation as an influential researcher in the field. His passion for advancing technological networks and sustainability through research makes him an exemplary figure in the scientific community.