Álvaro Figueira | Artificial Intelligence | Best Paper Award

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Assist. Prof. Dr. Álvaro Figueira | Artificial Intelligence | Best Paper Award

Professor Auxiliar, FCUP – Universidade do Porto, Portugal

Profile

Orcid

Álvaro Figueira is a distinguished academic and researcher in the field of Computer Science, currently serving as a Professor (Prof. Auxiliar) at Universidade do Porto, Faculdade de Ciências in Portugal. With a robust academic background and extensive experience, his research focuses on data mining, machine learning, social network analysis, and eLearning. Figueira’s passion for technology and innovation is evident in his contribution to various scientific fields, including data visualization and text mining, where his work aims to bridge theory with practical applications. With years of experience in teaching and leading research initiatives, Figueira is a prominent figure in his discipline. 📚💻

Education

Álvaro Figueira’s academic journey is distinguished by his advanced qualifications in Computer Science. He obtained his Bachelor’s (BSc) degree from Universidade do Porto, followed by a Master’s (MSc) from Imperial College London. He continued his academic excellence by completing a Ph.D. at Universidade do Porto in 2004, where he focused on Computer Science. Additionally, Figueira pursued Post-Graduation in Business Intelligence and Analytics at Porto Business School in 2017, further enhancing his expertise. 🎓📖

Experience

Throughout his career, Álvaro Figueira has amassed a wealth of academic and professional experience. He is currently a Professor at Universidade do Porto, where he teaches and supervises students in the field of Computer Science. He has also worked on a variety of research projects related to eLearning, data science, and machine learning, particularly focused on how these technologies can improve education and business practices. His previous experience includes a prestigious Master’s thesis position at Imperial College London. 🌍📊

Research Interests

Álvaro Figueira’s research interests span a wide array of cutting-edge fields within Computer Science. His primary focus areas include Data Mining, Text Mining, Machine Learning, Social Network Analysis, Data Visualization, and eLearning. Figueira’s work aims to apply computational techniques to improve the analysis of large datasets, making significant strides in understanding and enhancing social networks and educational systems. His research has contributed to the advancement of automated assessment systems and the optimization of learning processes. 📈🔍

Award

Álvaro Figueira’s contributions to computer science and education have been recognized with various awards and accolades. Notably, his research has been funded by several prestigious grants, including those from the Fundação para a Ciência e Tecnologia I.P. and Instituto de Engenharia de Sistemas e Computadores. His excellence in research is further highlighted by his numerous publications in top-tier journals, where he continues to make an impact in the fields of data science and machine learning. 🏆🎖️

Publications Top Notes

Álvaro Figueira’s publication record reflects his significant contributions to the fields of data science, machine learning, and eLearning. Some of his recent publications include:

“Topic Extraction: BERTopic’s Insight into the 117th Congress’s Twitterverse” – Informatics (2024).

“Clustering source code from automated assessment of programming assignments” – International Journal of Data Science and Analytics (2024).

“Comparing Semantic Graph Representations of Source Code: The Case of Automatic Feedback on Programming Assignments” – Computer Science and Information Systems (2024).

“GANs in the Panorama of Synthetic Data Generation Methods” – ACM Transactions on Multimedia Computing, Communications, and Applications (2024).

“On the Quality of Synthetic Generated Tabular Data” – Mathematics (2023).

“Bibliometric Analysis of Automated Assessment in Programming Education: A Deeper Insight into Feedback” – Electronics (2023).

Josiah Lebakeng | Intelligence | Best Research Article Award

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Prof. Josiah Lebakeng | Intelligence | Best Research Article Award 

Professor, Thabo Mbeki African School of Public and International Affairs, South Africa

Teboho Josiah Lebakeng is a distinguished Professor at the Thabo Mbeki African School of Public and International Affairs, UNISA, in South Africa. He specializes in Strategic Intelligence and Human Security, imparting knowledge on statecraft, intelligence gathering, analysis, and governance. Prior to his current role, he lectured in sociology and research methodology at the University of Limpopo and had teaching appointments at both Vista University (now University of Johannesburg) and the University of Pretoria. 🌍📚

Publication Profile

ORCID

Education

Professor Lebakeng holds a PhD in Sociology from the University of Limpopo, an MSc in Human Services from Springfield College, an MA in Sociology from the University of Dar es Salaam, and a BA in Sociology from The American University in Cairo. His academic journey reflects a strong foundation in social sciences and research methodologies. 🎓

Experience

His professional journey includes significant roles in the South African State Security Agency, where he served as Manager of Production and Strategic Research and Analysis, and as a Senior Regional Analyst. Additionally, he was seconded as Political Counsellor at South Africa’s Permanent Mission to the UN in New York and led the SADC Preventative Mission in Lesotho. He has extensive experience engaging with international intelligence services. 🔍

Research Focus

Professor Lebakeng’s research interests encompass Strategic Intelligence, Legislative Oversight, and African Epistemology. He is committed to advancing knowledge in these fields, emphasizing the importance of human security and good governance in contemporary society. 📖🔐

Awards and Honors

He has received recognition for his contributions to academia and intelligence, including various awards related to his scholarly work and his autobiography, “They Blunted Us: June 16, Exile and Homecoming,” published by Ziable Publisher in 2021. 🏆📖

Publication Top Notes

Policy sciences and the quest for human security in Africa (Journal of Public Administration, 2023)

The nature and issues in intelligence, with reference to the South African civilian intelligence services (African Security Review, 2023)

Lebakeng, T.J. & Matebese-Notshulwana, K.M. (2023). Doctoral Reflections of Journeys in the Humanities and Social Sciences in South African Universities (Ziable Publishers).

Repurposing the university in Africa in the context of the tenacity of an explicitly racist epistemology in Ndofirepi, A. P., Vurayai, S., and Erima, G. (Eds.). Unyoking African University Knowledge: A Pursuit of the Decolonial Agenda (Brill, 2023).

 

 

Jing Wang | Artificial Intelligence | Best Researcher Award

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Dr. Jing Wang | Artificial Intelligence | Best Researcher Award

Assistant Professor, Southeast University, China

Jing Wang is an assistant researcher at the School of Computer Science and Engineering, Southeast University, China. With a Ph.D. from Southeast University under Prof. Xin Geng, Jing has made significant strides in machine learning, focusing on multi-label learning and explainable machine learning. Jing is a recognized contributor to multiple esteemed journals and conferences, with impactful research on label distribution learning.

Publication Profile

ORCID

Strengths for the Award:

  1. Solid Academic Background: The candidate has pursued advanced degrees in Computer Science from reputable institutions, including a Ph.D. from Southeast University under the supervision of renowned professors.
  2. Focused Research Interests: The candidate’s research concentrates on machine learning, with a particular emphasis on multi-label learning and explainable machine learning—fields of significant current interest.
  3. Prolific Publication Record: The candidate has authored numerous high-quality journal and conference papers, many in well-regarded venues such as IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Pattern Analysis and Machine Intelligence, and AAAI Conference on Artificial Intelligence.
  4. Academic Service and Leadership: The candidate has served as a lead guest editor and guest editor for special issues in reputable journals and has been a program committee member and reviewer for major conferences and journals, showcasing their commitment to advancing their field.
  5. Collaboration and Recognition: The candidate’s work involves collaboration with other established researchers, and they have published in leading journals and conferences, reflecting their recognition and influence in the research community.

Areas for Improvement:

  1. Research Impact and Application: While the candidate has published extensively, there is limited information on the real-world impact and applications of their research. Emphasizing how their work has been applied or can be applied to solve practical problems in industry or society could strengthen their profile.
  2. Awards and Honors: Although the candidate has made notable academic contributions, there is no mention of individual awards or recognitions, which could further validate their research impact and excellence.
  3. International Collaboration and Diversity of Research Areas: Expanding collaborations beyond their current network, potentially with international researchers from diverse fields, could enhance their research’s global reach and interdisciplinary impact.

 

🎓 Education

Ph.D. in Computer Science from Southeast University, China, supervised by Prof. Xin Geng. M.Sc. in Computer Science from Northeast University, China, supervised by Prof. Xingwei Wang. B.Sc. in Computer Science from Suzhou University of Science and Technology, China.

🏆 Experience

Jing Wang serves as an assistant researcher at the School of Computer Science and Engineering, Southeast University, China. Jing actively contributes to the academic community as a guest editor for renowned journals and as a program committee (PC) member and reviewer for prestigious conferences, including AAAI, UAI, and ECML.

🔍 Research Focus

Jing Wang’s research delves into machine learning, with a particular emphasis on multi-label learning and explainable machine learning. Jing’s work is notable for pioneering approaches in label distribution learning, leveraging common and label-specific feature fusion spaces, and developing innovative methodologies for driver distraction detection and open-world few-shot learning.

🏅 Awards and Honors

Lead Guest Editor for IEEE Transactions on Consumer Electronics on “When Consumer Electronics Meet Large Models: Opportunities and Challenges.” Guest Editor for the International Journal of Machine Learning and Cybernetics on “Reliable and Interpretable Machine Learning: Theory, Methodologies, Applications, and Beyond.” Program Committee Member for AAAI-23, UAI-24, and ECML-24.Reviewer for several high-impact journals, including IEEE TNNLS, IEEE TMM, IEEE TAI, IEEE JBHI, and Medical Image Analysis (MIA).

📚 Publications Top Notes

Jing Wang has authored numerous high-impact papers in top-tier journals and conferences. Key publications include works on label distribution learning in Pattern Recognition and IEEE Transactions on Neural Networks and Learning Systems, contributing to the understanding of label-specific feature fusion and fuzzy label correlation in machine learning. Jing’s research on “Driver Distraction Detection Using Semi-supervised Lightweight Vision Transformer” has been recognized for its innovative application in Engineering Applications of Artificial Intelligence.

Jing Wang, Fu Feng, Jianhui Lv, and Xin Geng. “Residual k-Nearest Neighbors Label Distribution Learning.” Pattern Recognition (PR), 2024, in press.

Zhiyun Zhang, Jing Wang†, and Xin Geng. “Label Distribution Learning by Utilizing Common and Label-Specific Feature Fusion Space.” International Journal of Machine Learning and Cybernetics, 2024, in press.

Jing Wang, Zhiqiang Kou, Yuheng Jia, Jianhui Lv, and Xin Geng. “Label Distribution Learning by Exploiting Fuzzy Label Correlation.” IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS), 2024, in press.

Zhiqiang Kou, Jing Wang, Yuheng Jia, and Xin Geng.* “Inaccurate Label Distribution Learning.” IEEE Transactions on Circuits and Systems for Video Technology (IEEE TCSVT), 2024, in press.

Jing Wang and Xin Geng. “Explaining the Better Generalization of Label Distribution Learning for Classification.” SCIENCE CHINA Information Sciences (SCIS), 2024, in press.

Conclusion:

The candidate demonstrates a strong research profile with a solid foundation in machine learning, a prolific publication record, and active involvement in the academic community. Their focused research in multi-label learning and explainable AI aligns well with contemporary challenges and advancements in artificial intelligence. To strengthen their candidacy for the Best Researcher Award, they could emphasize the practical impact of their research, seek additional recognitions or awards, and pursue more diverse and international collaborations. Overall, the candidate is highly suitable for the award, with a promising future in their research career.

Ritu Tanwar | Artificial intelligence | Best Researcher Award

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Ms. Ritu Tanwar | Artificial intelligence | Best Researcher Award

Research Scholar | NIT Uttarakhand | India

Best Researcher Award

Strengths for the Award

Innovative Research Focus: Ms. Tanwar’s research is at the cutting edge of deep learning, artificial intelligence, and stress recognition. Her focus on multimodal physiological signals for affective state recognition and wearable technology is highly relevant and forward-looking.

Strong Academic Foundation: She is pursuing a PhD at the National Institute of Technology, Uttarakhand, with a well-defined thesis on deep learning frameworks for affective state recognition. Her previous education, including an M.Tech. in Emotion Recognition and a B.Tech. in Electronics & Communication Engineering, complements her current research focus.

Quality Publications: Ms. Tanwar has a strong publication record with peer-reviewed journal articles in high-impact journals like Engineering Applications of Artificial Intelligence and Computers in Biology and Medicine. Her conference papers and book chapters also demonstrate her active engagement with the academic community.

Recognition and Support: She has received a Senior Research Fellow Scholarship, highlighting her recognized potential in her field. Her involvement in teaching and supervision further indicates her commitment to academic excellence and leadership.

Technical Skills: Proficiency in Python, MATLAB, and deep learning frameworks enhances her ability to conduct high-quality research. Her experience with various software tools and programming languages supports her research in data analysis and machine learning.

Areas for Improvement

Broader Impact and Application: While Ms. Tanwar’s work is innovative, expanding the application of her research to practical, real-world scenarios could enhance its impact. Exploring collaborations with industry partners could provide valuable insights into the practical applications of her research findings.

Interdisciplinary Integration: Integrating her research with other disciplines, such as psychology or healthcare, could provide a more comprehensive understanding of stress recognition and its applications. This interdisciplinary approach might strengthen her research outcomes and broaden her impact.

Public Engagement and Outreach: Increasing her presence in public forums and engaging with broader audiences could amplify the reach of her research. Participating in outreach activities and science communication initiatives might help in translating her research for non-specialist audiences.

Conclusion

Ms. Ritu Tanwar demonstrates significant promise as a researcher, with a strong foundation in innovative areas of deep learning and stress recognition. Her research contributions are noteworthy, and she has established a solid track record with quality publications and academic achievements.

For the “Best Researcher Award,” Ms. Tanwar’s strengths in cutting-edge research, quality publications, and technical expertise make her a strong candidate. Addressing the suggested areas for improvement could further enhance her research impact and recognition in the field.

Short Bio

👩‍🔬 Ms. Ritu Tanwar is a dedicated Research Scholar in Electronics Engineering at the National Institute of Technology, Uttarakhand, India. With a focus on stress and emotion recognition through innovative technologies, she is pursuing a PhD under the supervision of Dr. Pankaj Kumar Pal and Dr. Ghanapriya Singh. Her extensive background in deep learning and artificial intelligence positions her as a notable contributor to the field of physiological signal analysis.

Profile

Orcid

Education

🎓 PhD (pursuing) – April 2021-present
Department of Electronics Engineering, National Institute of Technology, Uttarakhand, India
Thesis: A deep learning framework for affective state recognition using multimodal physiological signals
Thesis Supervisors: Dr. Pankaj Kumar Pal and Dr. Ghanapriya Singh

🎓 M. Tech. – July 2018
Department of Electronics & Communication Engineering, University Institute of Engineering & Technology, Kurukshetra, India
Thesis: Emotion Recognition from Audio Signals
Thesis Supervisor: Dr. Deepti Chaudhary

🎓 B. Tech. – July 2013
Department of Electronics & Communication Engineering, University Institute of Engineering & Technology, Kurukshetra, India

Experience

📚 Teaching Assistant
Department of Electronics Engineering, National Institute of Technology, Uttarakhand, India

  • Microcontroller and Interfacing (Jan–May 2024)
  • Digital Signal Processing (July–Dec 2021, July–Dec 2023)
  • Speech Signal Processing (July–Dec 2022)
  • Image Processing (Jan–July 2022)

📝 Supervision Experience
National Institute of Technology, Uttarakhand, India

  • Undergraduate Supervision: Kunal Kavi and Shivam Purwal (Completion year: 2024)

Research Interests

🔬 Stress and Emotion Recognition: Focused on understanding and analyzing stress and emotional states through physiological signals.
🧠 Data Analysis and Deep Learning: Leveraging advanced data analysis techniques and deep learning models to enhance emotion and stress recognition.
🤖 Artificial Intelligence and Machine Learning: Applying AI and ML technologies to improve the accuracy and effectiveness of stress recognition systems.

Awards

🏆 Senior Research Fellow Scholarship – 2021-present
Awarded for exceptional research potential and academic performance in the field of Electronics Engineering.

Publications

  1. Tanwar, R., Phukan, O. C., Singh, G., Pal, P. K., & Tiwari, S. (2024). Attention based hybrid deep learning model for wearable based stress recognition. Engineering Applications of Artificial Intelligence, 127, 107391.
  2. Tanwar, R., Singh, G., & Pal, P. K. (2024). A Hybrid Transposed Attention Based Deep Learning Model for Wearable and Explainable Stress Recognition. Computers and Electrical Engineering (Accepted).
  3. Tanwar, R., Singh, G., & Pal, P. K. (2024). Explainable Artificial Intelligence System For Stress Recognition Using Multimodal Physiological Signals. Computers in Biology and Medicine (under review).
  4. Tanwar, R., Singh, G., & Pal, P. K. (2024). Stress-Wed: Stress recognition autoencoder using Wearables Data. Second International Conference on Artificial Intelligence: Towards Sustainable Intelligence, Springer (Accepted).
  5. Tanwar, R., Singh, G., & Pal, P. K. (2024, July). Wearables Based Personalised Stress Recognition Using Signal Processing and Hybrid Deep learning Model. 2024 2nd International Conference on Computer, Electronics, Electrical Engineering and their Applications (IC2E3), IEEE.
  6. Tanwar, R., Singh, G., & Pal, P. K. (2023, July). FuSeR: Fusion of wearables data for StrEss Recognition using explainable artificial intelligence models. 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT), IEEE.
  7. Tanwar, R., Phukan, O. C., Singh, G., & Tiwari, S. (2022). CNN-LSTM Based Stress Recognition Using Wearables. CEUR Workshop Proceedings, Springer.

Ritu Tanwar | Artificial intelligence | Best Researcher Award

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Ms. Ritu Tanwar | Artificial intelligence | Best Researcher Award

Research Scholar, NIT Uttarakhand, India

Ms. Ritu Tanwar is a dedicated Research Scholar at the National Institute of Technology, Uttarakhand, India, specializing in stress and emotion recognition through advanced machine learning techniques. Her innovative research harnesses deep learning and artificial intelligence to interpret physiological signals, contributing significantly to the field of affective computing. Ritu’s academic journey and teaching roles underline her commitment to advancing both theoretical and practical aspects of her research.

Profile

Scopus

Research for “Best Researcher Award” for Ms. Ritu Tanwar

Strengths for the Award

Ms. Ritu Tanwar, currently pursuing her PhD at the National Institute of Technology, Uttarakhand, has demonstrated exceptional strengths in her field of research. Her primary area of focus—stress and emotion recognition through physiological signals—highlights her deep engagement with cutting-edge technology and data analysis. Ritu’s work utilizes advanced techniques in deep learning and machine learning to address significant challenges in affective state recognition.

Innovative Research Contributions: Ritu’s research integrates multimodal physiological signals to enhance stress recognition, showcasing her ability to develop and implement novel frameworks. Her attention-based hybrid deep learning models for wearable stress recognition, published in prestigious journals like Engineering Applications of Artificial Intelligence and Computers and Electrical Engineering, underline her proficiency in blending theory with practical application.

High-Impact Publications: Her publications in high-impact journals and conferences, including Computers in Biology and Medicine and the International Conference on Artificial Intelligence, reflect the substantial impact of her work on the field. Her innovative models, such as the CNN-LSTM based stress recognition system, are well-received and contribute to advancing the state of the art in affective computing.

Diverse Expertise: Ritu’s skill set spans various domains, from deep learning and artificial intelligence to data analysis and signal processing. Her ability to apply these skills effectively in her research demonstrates a well-rounded expertise that is crucial for a leading researcher.

Areas for Improvement

While Ms. Tanwar’s achievements are commendable, there are areas where she could further enhance her profile:

Broader Research Collaboration: Expanding her collaborative network with researchers from diverse fields could provide new insights and foster interdisciplinary approaches. Engaging in more collaborative projects could also increase the visibility and applicability of her research outcomes.

Broadened Publication Scope: Although Ritu has published extensively, diversifying her publication portfolio to include more interdisciplinary journals or higher-impact venues could further amplify the reach and influence of her research.

Enhanced Outreach: Increasing her participation in academic and industry conferences, workshops, and seminars could boost her professional network and provide more platforms to showcase her research. Additionally, contributing to review articles or special issues in her field could enhance her visibility as a thought leader.

Education 🎓

Ms. Tanwar is currently pursuing a PhD in Electronics Engineering at the National Institute of Technology, Uttarakhand, India, focusing on developing a deep learning framework for affective state recognition using multimodal physiological signals (April 2021-present). She earned her M.Tech. in Electronics & Communication Engineering from the University Institute of Engineering & Technology, Kurukshetra, India, with a thesis on emotion recognition from audio signals (July 2018). Her foundational B.Tech. in Electronics & Communication Engineering was also completed at the same institute (July 2013).

Experience 💼

Ms. Tanwar has a robust academic background, having worked as a Teaching Assistant at the National Institute of Technology, Uttarakhand, where she taught courses on Microcontroller and Interfacing, Digital Signal Processing, and Speech & Image Processing. Her research experience includes contributions as an Assistant/Associate Supervisor for undergraduate students and active participation in administrative and outreach activities, including her roles as Session Coordinator and Reviewer for the IC2E3 IEEE Conference.

Research Interests 🔬

Ms. Tanwar’s research interests are centered around stress and emotion recognition, physiological signals, and advanced data analysis techniques. She specializes in applying deep learning, machine learning, and artificial intelligence to improve the accuracy and applicability of affective state recognition systems.

Awards 🏆

Senior Research Fellow Scholarship (2021-present): Awarded for her exceptional research capabilities and contributions to her field.

Publication Recognition: Her work has been accepted and recognized in leading journals and conferences, reflecting her significant contributions to the field of artificial intelligence and machine learning.

Publications Top Notes

Tanwar, R., Phukan, O. C., Singh, G., Pal, P. K., & Tiwari, S. (2024). Attention based hybrid deep learning model for wearable based stress recognition. Engineering Applications of Artificial Intelligence, 127, 107391.

Tanwar, R., Singh, G., & Pal, P. K. (2024). A Hybrid Transposed Attention Based Deep Learning Model for Wearable and Explainable Stress Recognition. Computers and Electrical Engineering (Accepted).

Tanwar, R., Singh, G., & Pal, P. K. (2024). Explainable Artificial Intelligence System For Stress Recognition Using Multimodal Physiological Signals. Computers in Biology and Medicine (under review).

Tanwar, R., Singh, G., & Pal, P. K. (2024). Stress-Wed: Stress recognition autoencoder using Wearables Data. In Second International Conference on Artificial Intelligence: Towards Sustainable Intelligence. Springer (Accepted).

Conclusion

Ms. Ritu Tanwar’s research on stress and emotion recognition using physiological signals is both innovative and impactful, making her a strong candidate for the “Best Researcher Award.” Her contributions to deep learning and machine learning in affective computing are significant, and her academic and teaching experiences add to her profile as a dedicated and knowledgeable researcher. By addressing areas for improvement, such as expanding collaboration and publication scope, Ritu can further strengthen her position as a leading researcher in her field. Her ongoing research promises to make substantial contributions to both theoretical and applied aspects of artificial intelligence and emotion recognition.

Yusuf KARADEDE | Artificial Intelligence | Best Researcher Award

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Assist Prof Dr. Yusuf KARADEDE | Artificial Intelligence | Best Researcher Award

Doctor, Gaziantep Islam Science and Technology University, Faculty of Engineering and Natural Sciences, Department of Industrial Engineering, 27010 Gaziantep, Turkey

Profile

Scopus

Strengths for the Award

Dr. Yusuf Karadede’s research in stochastic processes, heuristic algorithms, and stochastic optimization underscores his deep expertise and innovative approach in industrial engineering. His doctoral thesis and subsequent work have made significant contributions to the fields of simulation and stochastic modeling. Notably, his publications in esteemed journals like Soft Computing and Energy highlight his ability to tackle complex problems with advanced computational techniques.

Dr. Karadede’s diverse range of scientific activities demonstrates his commitment to advancing both theoretical and applied aspects of his field. His development of novel models such as the ProFiVaS model for financial indicators, showcased in his recent publication in Expert Systems with Applications, exemplifies his forward-thinking approach and impact on financial modeling.

Areas for Improvement

While Dr. Karadede’s research is highly impactful, expanding the scope of his research to include interdisciplinary approaches could further enhance the applicability of his work. For instance, integrating his stochastic models with emerging technologies like machine learning could offer new insights and broaden the impact of his research. Additionally, increasing collaboration with international research groups might provide new perspectives and enhance the global reach of his contributions.

Academic Background:

  • Bachelor’s Degree: Mathematics, Suleyman Demirel University, 2006-2010
  • Master’s Degree: Industrial Engineering, Suleyman Demirel University, 2011-2014
  • Doctorate (Ph.D.): Industrial Engineering, Suleyman Demirel University, 2015-2020

Professional Experience:

  • Kafkas University: Faculty of Engineering and Architecture, Department of Industrial Engineering (2014-2015)
  • Suleyman Demirel University: Faculty of Engineering, Department of Industrial Engineering (2015-2020)
  • Gaziantep Islam Science and Technology University: Department of Industrial Engineering (2020-Present)

Research Interests:

  • Stochastic Processes and Models
  • Simulation
  • Heuristic Algorithms
  • Stochastic Optimization

 Awards and Scholarships:

  • TÜBİTAK 2210-C Program Scholarship (2013-2014)
  • TÜBİTAK 2211-C Program Scholarship (2018-2020)

Publications Top Notes:

Karadede, Y., Özdemir, G. (2018). A hierarchical soft computing model for parameter estimation of curve-fitting problems. Soft Computing, 22(20), 6937-6964.

Karadede, Y., Ozdemir, G., Aydemir, E. (2017). Breeder Hybrid Algorithm Approach for Natural Gas Demand Forecasting Model. Energy, 141, 1269-1284.

Akdeniz, F., Biçil, M., Karadede, Y., Özbek, F. E., Özdemir, G. (2018). Application of real valued genetic algorithm on prediction of higher heating values of various lignocellulosic materials. Energy, 160, 1047-1054.

Karadede, Y. (2024). A novel stochastic ProFiVaS model based on decomposition of stochastic Vasicek differential equation for modeling and simulating financial indicators. Expert Systems with Applications

Conclusion

Dr. Yusuf Karadede’s distinguished research in stochastic processes and optimization positions him as a strong candidate for the Best Researcher Award. His innovative contributions, including high-impact publications and successful research projects funded by prestigious institutions like TÜBİTAK, highlight his significant achievements and potential for future breakthroughs. His work not only advances theoretical understanding but also offers practical solutions to real-world problems, making him a deserving nominee for this esteemed accolade.