Camille Charette | Information Science | Best Researcher Award

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Ms. Camille Charette | Information Science | Best Researcher Award

California State Polytechnic University, Humboldt Library | United States

Camille Charette, MA, MLIS is an interdisciplinary researcher and emerging scholar in library and information science whose work bridges the fields of information retrieval, human–AI interaction, critical information literacy, and inclusive pedagogy. With a strong foundation in philosophy, literature, and information science, she focuses on creating accessible, user-centered information environments that promote equity, inclusion, and ethical engagement in digital ecosystems. Her academic and professional practice reflect a decade of experience in applied and theoretical research, instructional design, and the development of open educational resources that support diverse learners and communities. Camille’s research integrates human-centered design, critical theory, and evidence-based methodologies to examine how evolving technologies influence access to information and participation in knowledge systems. As a graduate researcher and instructor at San José State University’s School of Information, she has co-developed the Human-Centered Artificial Intelligence Certificate program, designed courses such as Responsible Human-AI Interaction and Introduction to Human-Centered Artificial Intelligence, and collaborated on the American Library Association’s eLearning Advanced eCourse Introduction to AI. Her contributions extend to authoring curricular materials, designing accessibility-first learning environments, and conducting user research to enhance digital literacy and usability. Through her work on projects such as Design Concepts in Information Retrieval: Creating User-Centered Systems, Search Engines, and Sites, she advances the understanding of how human values, learning psychology, and inclusive design shape information technologies. Camille’s commitment to critical information literacy and equitable learning underscores her vision of a future where digital systems and educational practices are both socially responsible and human-centered.

Profile: Orcid 

Featured Publications:

Bulent Koc | Digital Lean System | Best Researcher Award

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Dr. Bulent Koc | Digital Lean System | Best Researcher Award

Researcher | Istanbul Technical University | Turkey

Dr. Bulent Koc is a Ph.D. candidate in Textile Engineering at Istanbul Technical University with more than two decades of experience in the apparel and textile industry. His expertise lies in integrating lean production principles with digital transformation strategies to enhance efficiency and sustainability in garment manufacturing. Throughout his career, he has worked in diverse roles, from production planning and product management to certification and digital productivity systems. His current research focuses on designing sustainable digital lean models for ready-made garment enterprises, particularly in labor-intensive sewing operations. He has collaborated with multiple organizations, implementing projects on workflow optimization, efficiency enhancement, and the use of real-time Process Monitoring Devices (PMDs). By bridging academic research with industrial applications, Koc contributes significantly to advancing operational excellence and competitiveness in the textile and apparel sector. His work underscores the potential of digital lean transformation as a sustainable solution for future manufacturing systems.

Professional Profile

Scopus

Education

Dr. Bulent Koc pursued his academic journey entirely at Istanbul Technical University, specializing in Textile Engineering. He earned his B.Sc. in Textile Engineering, where he built a foundation in fabric production, apparel processes, and material technology. He then completed his M.Sc. in Textile Engineering, focusing on production management and optimization in knitted garment manufacturing. His master’s thesis explored methods to enhance efficiency, cost-effectiveness, and lean principles in textile production environments. Currently, he is a Ph.D. candidate in the same department, expected to complete. His doctoral research centers on lean production and the development of sustainable digital lean models tailored for the ready-made garment industry. This work combines advanced lean management techniques with Industry, including real-time production monitoring, digital line balancing, and sustainability frameworks. Through this academic progression, Koc has developed a strong balance of theoretical knowledge and practical industrial insights in textile engineering.

Experience

Dr. Bulent Koc has built extensive professional experience in textile and apparel manufacturing since. He began as Production Planning Manager at Serfil Yarn and Fabric Factory, where he led efficiency projects and factory setup operations. Later, as Product Group Leader at Tars International Trade Ltd., he managed men’s wear collections and coordinated procurement. At Koton Mensucat, he advanced as a Product Manager, overseeing procurement and R&D in fabric development. he worked at Certurk Certification and Inspection Services, managing professional qualification certifications and training in textiles. His latest role was as Productivity Management Specialist at ITM Techsoft, where he developed digital lean systems, real-time data integration, and line balancing algorithms. Across his career, Koc has successfully combined lean manufacturing principles with technology-driven innovations. His projects consistently targeted productivity, sustainability, and competitiveness, making him a key contributor to both industry practices and applied textile engineering research.

Research Focus

Dr. Bulent Kocs research is centered on the integration of lean production systems with digital transformation in apparel manufacturing. His work focuses particularly on labor-intensive sewing operations, where workflow optimization and productivity are critical. He explores how real-time Process Monitoring Devices (PMDs) can track lean metrics, improve line balancing, and reduce inefficiencies. By combining lean principles with Industry such as digital data management and automation, his research offers scalable frameworks for sustainable production. He also examines the role of digital lean models in enhancing overall equipment effectiveness (OEE), minimizing waste, and promoting eco-friendly manufacturing practices. Field-based studies conducted in collaboration with Turkish textile companies validate his approaches and demonstrate measurable improvements in efficiency and sustainability. Kocs research bridges theory and practice, offering both academic contributions and real-world industrial solutions. His goal is to transform digital lean systems into a long-term driver of competitiveness in the apparel sector.

Awards and Honors

Throughout his career, Bulent Koc has been recognized for his contributions to lean manufacturing and digital transformation in apparel production. His applied research has been acknowledged at academic and industrial platforms, particularly in the field of textile engineering innovation. He has collaborated on projects supported, which emphasize efficiency, sustainability, and competitiveness in textile SMEs. His industry-driven lean transformation projects were recognized for advancing operational excellence, including notable work in digital line balancing and real-time production monitoring. He has been invited to share his expertise at professional seminars and academic discussions on lean systems in apparel manufacturing. In addition, his involvement in mechanics-related awards and conferences reflects his interdisciplinary contributions to engineering-focused production methodologies. These honors highlight his role as a bridge between academic research and industrial practice, reinforcing his reputation as an innovator in digital lean textile systems.

Publication Top Notes

Conclusion

Dr. Bulent Koc demonstrates potential as a researcher in lean production systems and digital transformation in apparel manufacturing, with a strong practical background and research focus. His industry projects and contributions to operational excellence are notable, and his research has the potential to make a significant impact in the industry. With further development of his publication record and international collaboration, he could become a strong candidate for the Best Researcher Award.

Zicheng Xin | intelligentialization | Best Researcher Award

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Dr. Zicheng Xin | intelligentialization | Best Researcher Award

postdoctor, University of Science and Technology Beijing, China

Zicheng Xin is a renowned researcher and visiting professor at the Korea Invention Academy. He is affiliated with the University of Science and Technology Beijing (USTB) and has made significant contributions to the field of metallurgical engineering. His research focuses on metallurgical process engineering, intelligence, and simulation.

Profile

scopus

Education 🎓

Ph.D. in Metallurgical Engineering, State Key Laboratory of Advanced Metallurgy, University of Science and Technology Beijing (USTB) (2018-2022)

Experience 🧪

Visiting Professor, Korea Invention Academy (current)  Researcher, State Key Laboratory of Advanced Metallurgy, USTB (current)

Awards & Honors🏆

“Multiscale modeling and collaborative manufacturing for steelmaking plants”, the 10th World Scientist Grand Award — Golden Scientist Grand Award (Second Place, International Federation of Inventors’ Associations, 2023) “Multiscale modeling and collaborative manufacturing for steelmaking plants”, the 10th World Scientist Grand Award— Science & Technology Grand

Research Focus 🔍

Metallurgical process engineering and intelligence  Simulation and optimization of metallurgical process

Publications📚

1. Analysis of multi-zone reaction mechanisms in BOF steelmaking and comprehensive simulation [J]. Materials, 2025, 18(5): 1038. – Zicheng Xin, Qing Liu, Jiangshan Zhang, et al.
2. Modeling of LF refining process: a review [J]. Journal of Iron and Steel Research International, 2024, 31(2): 289-317. – Zicheng Xin, Jiangshan Zhang, Kaixiang Peng, et al.
3. Explainable machine learning model for predicting molten steel temperature in LF refining process [J]. International Journal of Minerals, Metallurgy and Materials, 2024, 31(12): 2657-2669. – Zicheng Xin, Jiangshan Zhang, Kaixiang Peng, et al.
4. Predicting temperature of molten steel in LF refining process using IF-ZCA-DNN model [J]. Metallurgical and Materials Transactions B, 2023, 54(3): 1181-1194. – Zicheng Xin, Jiangshan Zhang, Junguo Zhang, et al.
5. Predicting the alloying element yield in a ladle furnace using principal component analysis [J]. … – Zicheng Xin, Jiangshan Zhang, Yu Jin, et al.

Conclusion

Zicheng Xin’s academic excellence, research focus, and international recognition make him a strong candidate for the Best Researcher Award. While there are areas for improvement, his strengths and achievements demonstrate his potential to make significant contributions to the field of metallurgy.

Sabum Jung | Smart factory | Best Researcher Award

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Mr. Sabum Jung | Smart factory | Best Researcher Award

Research engineer, Lg energy solution,South Korea

Sabum Jung is a seasoned Data Scientist and Machine Learning Engineer with over 23 years of expertise in predictive modeling, deep learning, and AI-driven optimization. His career spans LG Energy Solution, SK Holdings, and LG Production Engineering Research Institute, where he pioneered AI applications in high-tech manufacturing, including semiconductor, battery, and display industries. A former Military Intelligence Analyst for the U.S. Army, he has authored research papers and books on AI, machine learning, and Industry 4.0. Fluent in English, Korean, and Japanese, he continues to drive AI innovations in industrial applications.

Profile

🎓 Education

Sabum Jung holds a B.A. (3.9/4.5) and an M.S. (4.2/4.5) in Industrial Engineering from Sung Kyun Kwan University, South Korea. His academic journey focused on advanced analytics, AI-driven optimization, and industrial process improvements. His research contributions in artificial intelligence, reliability engineering, and digital transformation have shaped his expertise in machine learning, deep learning, and predictive modeling, positioning him as a leader in AI applications for manufacturing and industrial systems.

💼 Experience

Currently a Data Scientist at LG Energy Solution, Sabum Jung leads AI-driven innovations in virtual metrology, predictive maintenance, and defect analysis. Previously at SK Holdings, he optimized renewable energy predictions, semiconductor material discovery, and AI-powered industrial operations. His 20-year tenure at LG Production Engineering Research Institute saw groundbreaking work in machine learning for smart appliances, battery systems, and industrial automation. His early career as a Military Intelligence Analyst in the U.S. Army honed his analytical prowess, setting the foundation for his AI-driven problem-solving approach.

🏆 Awards & Honors

Sabum Jung has been recognized for his contributions to AI, machine learning, and industrial automation. His accolades include leadership in AI-driven manufacturing optimization, predictive maintenance, and reinforcement learning applications. He has received industry recognition for his research and innovation in deep learning, active learning, and process optimization in high-tech sectors, further cementing his influence in AI-driven industrial advancements.

🔬 Research Focus:

Sabum Jung specializes in AI applications for high-tech manufacturing, focusing on predictive maintenance, virtual metrology, and defect detection. His research spans deep learning, reinforcement learning, and AI-driven industrial process optimization. Notable contributions include renewable energy prediction, semiconductor material discovery, and advanced statistical modeling. His expertise in machine learning has been instrumental in developing AI solutions for smart manufacturing, Industry 4.0, and digital transformation.

Publications

Recent progress of LG PDP: High efficiency & productivity technologies Citations1

Conclusion

Sabum Jung is a strong candidate for the Best Researcher Award, given his vast industry experience, research excellence, and technological contributions to AI and machine learning in manufacturing. Enhancing academic collaborations and increasing research dissemination could further elevate his impact and recognition.

Manar Hamza | Computer Science Data mining | Best Researcher Award

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Dr. Manar Hamza | Computer Science Data mining | Best Researcher Award

professor at  Prince Sattam bin Abd El Aziz University, China

👩‍🏫 Experienced Computer Science Lecturer since 2005 with expertise in data mining, text mining, and information security. 💻 Holds a strong track record in research and academia, leveraging innovation and teamwork. Aims to thrive in challenging, dynamic, and team-oriented environments that foster growth. 🌍 Based in Sudan and Saudi Arabia, dedicated to academic excellence and community impact.

Professional Profiles:

scopus

Education 🎓

Ph.D. in Computer Science from Omdurman Islamic University, Sudan (2018–2021). 🎓 Master’s Degree in Computer Science from Sudan University of Science and Technology (2003–2005). 🎓 B.Sc. in Computer Science from Omdurman Islamic University, Sudan (1995–1999). 📚 Comprehensive training in research skills, academic advising, and IT tools like Mendeley, Latex, and iThenticate.

Experience 🖥️

Lecturer in Computer Science at Prince Sattam bin Abdul-Aziz University, Saudi Arabia (2013–present). 👩‍💼 Supervisor and Coordinator roles in quality, academic advising, and measurement (2014–2020). 🇸🇩 Lecturer at Omdurman Islamic University, Sudan (2005–2012). 👩‍🔬 E-teaching and training specialist with Arab Board experience (2023).

Awards and Honors 🏆

Certificates of Appreciation from PSAU for contributions to quality, development, and academic planning. 🙌 Recognized for voluntary services, including extracurricular activities and technical support for students and staff. ⭐ Esteemed arbitrator in scientific and innovation conferences. 📜 Active contributor to enhancing the learning environment with innovative solutions.

Research Focus 🔍

Data mining, text mining, and information security are core research areas. 📊 Interested in qualitative research, outcome-based education, and e-learning systems. 🌐 Advocates for advancing academic IT tools like Prezi, Mendeley, and iThenticate. 🛡️ Exploring cybersecurity methods and their application in education and industry.

✍️Publications Top Note :

1. Robust Tweets Classification Using Arithmetic Optimization with Deep Learning for Sustainable Urban Living

Published in: SN Computer Science, 2024, 5(5), 549

Summary: This paper proposes a novel classification model for urban-related tweets using arithmetic optimization integrated with deep learning to support sustainable urban living solutions.

2. Enhancing Traffic Flow Prediction in Intelligent Cyber-Physical Systems

Published in: IEEE Transactions on Consumer Electronics, 2024, 70(1), pp. 1889–1902

Summary: Introduces a Bi-LSTM approach enhanced with a Kalman filter for accurate traffic flow prediction, addressing challenges in intelligent cyber-physical systems.

Citations: 5

3. Deer Hunting Optimization with Deep Learning-Driven Automated Fabric Defect Detection and Classification

Published in: Mobile Networks and Applications, 2024, 29(1), pp. 176–186

Summary: Utilizes the Deer Hunting Optimization algorithm with deep learning to achieve high accuracy in detecting and classifying fabric defects.

Citations: 1

4. Automatic Recognition of Cyberbullying in the Web of Things and Social Media Using Deep Learning Framework

Published in: IEEE Transactions on Big Data, 2024

Summary: Develops a deep learning-based framework to detect and prevent cyberbullying within social media and IoT environments.

5. Artificial Rabbit Optimizer with Deep Learning for Fall Detection in IoT Environment

Published in: AIMS Mathematics, 2024, 9(6), pp. 15486–15504

Summary: Introduces the Artificial Rabbit Optimizer combined with deep learning to enhance fall detection systems for disabled individuals in IoT environments.

Citations: 1

6. Computational Linguistics-Based Arabic Poem Classification and Dictarization Model

Published in: Computer Systems Science and Engineering, 2024, 48(1), pp. 98–114

Summary: Proposes a computational linguistics model to classify Arabic poems and enhance their dictarization process.

7. Abstractive Arabic Text Summarization Using Hyperparameter Tuned Denoising Deep Neural Network

Published in: Intelligent Automation and Soft Computing, 2024, 38(2), pp. 153–168

Summary: Develops a deep neural network with hyperparameter tuning for effective abstractive summarization of Arabic texts.

Citations: 1

8. Chaotic Equilibrium Optimizer-Based Green Communication With Deep Learning Enabled Load Prediction in IoT Environment

Published in: IEEE Access, 2024, 12, pp. 258–267

Summary: Presents a Chaotic Equilibrium Optimizer combined with deep learning to improve green communication and load prediction in IoT systems.

Citations: 2

9. Land Use and Land Cover Classification Using River Formation Dynamics Algorithm With Deep Learning on Remote Sensing Images

Published in: IEEE Access, 2024, 12, pp. 11147–11156

Summary: Leverages the River Formation Dynamics algorithm integrated with deep learning for efficient land use and land cover classification using remote sensing data.

Citations: 4

10. Prediction of Sleep Quality Using Wearable-Assisted Smart Health Monitoring Systems

Published in: Journal of King Saud University – Science, 2023, 35(9), 102927

Summary: Utilizes wearable technology and statistical data to predict sleep quality, providing insights into personalized smart health monitoring systems.

Citations: 1

Conclusion

The candidate’s extensive experience, academic qualifications, and contributions to computer science, particularly in data mining and information security, make them a strong contender for the Research for Best Researcher Award. With some strategic enhancements to highlight impactful research and global contributions, their profile could exemplify the qualities of an award-winning researcher in computer science.

Prof. Yang Zhao | Meteorology Artificial Intelligence | Young Scientist Award

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Prof. Yang Zhao | Meteorology Artificial Intelligenc | Young Scientist Award

Prof. Yang Zhao, Ocean University of China, China

Prof. Yang Zhao is academic and researcher in the field of renewable energy, holds a PhD in Bio systems Engineering from Kangwon National University, South Korea. His academic journey has been marked by a profound dedication to advancing solar energy technologies, specifically in solar thermal harvesting and its integration into agricultural and architectural applications.

Professional Profiles:

Google scholar

Orcid

Educational Background🎓

2016.09 – 2019.06: Ph.D. in Meteorology Chinese Academy of Meteorological Sciences, China & Nanjing University of  Science & Technology, Ch Supervisor: Prof. Xiangde Xu 2013.09 – 2016.06: Master of Science in Meteorology Chinese Academy of Meteorological Sciences, China Supervisor: Prof. Xiangde Xu 2009.09 – 2013.06: Bachelor of Science in Atmospheric Science Chengdu University of  Technology, China

Honors and Major Awards🏆

Outstanding Graduate Student, Chinese Academy of Meteorological Sciences (2019)Outstanding Graduate Student, Nanjing University of  Science & Technology (2019)Presidential Scholarship, Nanjing University of Science & Technology (2018)
National Scholarship, Nanjing University of  Science & Technology (2018) First Class Scholarship for Ph.D. Student, Nanjing Universityof  Science & Technology (2018) The First Prize of Outstanding Graduate Student Award, China Meteorological Administration (2017) Excellent Organization Award of Summer School, Chinese Academy of Meteorology (2015)

🔬 Research Area: 

Synoptic-scale Atmospheric Dynamics (Jet, Front, Storm Tracks, Cyclones, Rossby waves)  Atmospheric Water Cycle (Moisture sources, Moisture channel, Atmospheric Rivers) Machine Learning and Deep Learning (Atmospheric Rivers) Climate Dynamics; Future precipitation prediction (ENSO-Volcano; CMIP6)

📖 Publications  Top Note :

The third atmospheric scientific experiment for understanding the earth–atmosphere coupled system over the Tibetan Plateau and its effects

Authors: P Zhao, X Xu, F Chen, X Guo, X Zheng, L Liu, Y Hong, Y Li, Z La, H Peng, …

Bulletin of the American Meteorological Society, 99(4), 757-776, 2018

Spatiotemporal variation in the impact of meteorological conditions on PM2.5 pollution in China from 2000 to 2017

Authors: Yanlin Xu, Wenbo Xue, Yi Lei, Qing Huang, Yang Zhao, Shuiyuan Cheng, Zhenhai …

Atmospheric Environment, 77, 2020

Impact of Meteorological Conditions on PM2.5 Pollution in China during Winter

Authors: Y Xu, W Xue, Y Lei, Y Zhao, S Cheng, Z Ren, Q Huang

Atmosphere, 9(11), 429, 2018

Effect of the Asian Water Tower over the Qinghai-Tibet Plateau and the characteristics of atmospheric water circulation

Authors: X Xu, L Dong, Y Zhao, Y Wang

Chin. Sci. Bull, 64(27), 2830-2841, 2019

Vertical structures of dust aerosols over East Asia based on CALIPSO retrievals

Authors: D Liu, T Zhao, R Boiyo, S Chen, Z Lu, Y Wu, Y Zhao

Remote Sensing, 11(6), 701, 2019

Trends in observed mean and extreme precipitation within the Yellow River Basin, China

Authors: Y Zhao, X Xu, W Huang, Y Wang, Y Xu, H Chen, Z Kang

Theoretical and applied climatology, 136, 1387-1396, 2019

Enhancement of the summer extreme precipitation over North China by interactions between moisture convergence and topographic settings

Authors: Yang Zhao, Deliang Chen, Jiao Li, Dandan Chen, Yi Chang, Juan Li, Rui Qin

Climate Dynamics, 38, 2020

Extreme precipitation events in East China and associated moisture transport pathways

Authors: Y Zhao, XD Xu, TL Zhao, HX Xu, F Mao, H Sun, YH Wang

Science China Earth Sciences, 59, 1854-1872, 2016

The large‐scale circulation patterns responsible for extreme precipitation over the North China plain in midsummer

Authors: Y Zhao, X Xu, J Li, R Zhang, Y Kang, W Huang, Y Xia, D Liu, X Sun

Journal of Geophysical Research: Atmospheres, 124(23), 12794-12809, 2019

Are precipitation anomalies associated with aerosol variations over eastern China?

Authors: X Xu, X Guo, T Zhao, X An, Y Zhao, J Quan, F Mao, Y Gao, X Cheng, …

Atmospheric Chemistry and Physics, 17(12), 8011-8019, 2017