Prof. Dr. Mohammad Ali Balafar | Computer Science | Best Researcher Award

Prof at University of Tabriz, Iran

Prof. Dr. Mohammad Ali Balafar is a distinguished researcher in Artificial Intelligence and Multimedia Systems. With an h-index of 24 (Google Scholar) and inclusion in Stanford’s top 2% most-cited authors, his work is widely recognized for its impact. He leads the Intelligent Information Technology and Multimedia Research Laboratory at Tabriz University, focusing on deep learning, image processing, machine learning, and graph neural networks. His research projects address real-world problems, including image encryption, stock price prediction, and medical diagnosis through brain image segmentation. Dr. Balafar has authored numerous high-impact publications in reputable journals like IEEE Transactions and Chaos, Solitons & Fractals. Fluent in four languages, he fosters collaboration across diverse academic and cultural landscapes. His work blends innovation with application, making him a pioneer in intelligent systems. A strong advocate of interdisciplinary research, Dr. Balafar’s contributions exemplify excellence in both theoretical advancements and practical implementations.

Professional Profile

• Scopus Profile
• ORCID Profile
• Google scholar

Education

Prof. Dr. Mohammad Ali Balafar has a strong academic foundation, specializing in Artificial Intelligence and Multimedia Systems. He earned his Bachelor’s degree in Computer Engineering, laying the groundwork for his expertise in computational systems and programming. Pursuing advanced studies, he obtained a Master’s degree in Software Engineering, where he focused on algorithm development and software methodologies. Dr. Balafar then completed his Ph.D. in Computer Engineering, concentrating on cutting-edge technologies such as image processing, data mining, and deep learning. Throughout his educational journey, he honed his skills in machine learning, graph neural networks, and intelligent information systems, which later became central to his research. His academic excellence was complemented by multilingual proficiency (Azerbaijani, English, Farsi, and Turkish), facilitating collaboration in diverse research environments. These educational milestones have equipped Dr. Balafar with the theoretical knowledge and technical expertise essential for pioneering innovations in artificial intelligence and intelligent multimedia technologies.

Professional  Experience

Prof. Dr. Mohammad Ali Balafar is a seasoned academic and researcher with extensive experience in Artificial Intelligence and Multimedia Systems. Currently, he serves as a faculty member in the Department of Electrical and Computer Engineering at Tabriz University. He is the founder and head of the Intelligent Information Technology and Multimedia Research Laboratory, established in 1391 (2012), where he leads innovative projects in areas such as image processing, machine vision, and robotics. Dr. Balafar has been instrumental in advancing intelligent multimedia systems through diverse research initiatives, including expert recommendation systems, stock price prediction, and medical imaging for diagnosing diseases like MS. He has authored numerous high-impact publications and collaborated with leading scholars, contributing to advancements in fields such as deep learning and data mining. With fluency in multiple languages and a global academic network, his professional career reflects a blend of academic rigor, research innovation, and leadership in cutting-edge technology development.

Research Interests

Prof. Dr. Mohammad Ali Balafar’s research interests are deeply rooted in the fields of Artificial Intelligence, Machine Learning, and Multimedia Systems, with a focus on addressing complex computational challenges. His expertise spans a wide range of cutting-edge topics, including Deep Learning, Image Processing, Computer Vision, and Graph Neural Networks. He is particularly interested in developing intelligent systems that can process and analyze visual data, such as creating efficient algorithms for image encryption, clustering, and anomaly detection. Dr. Balafar’s work also delves into Data Mining, where he applies advanced techniques to uncover patterns and insights in domains such as medical diagnostics, stock price prediction, and emergency service optimization. His contributions aim to bridge the gap between theory and application, advancing technologies that enhance real-world decision-making. This interdisciplinary approach not only pushes the boundaries of innovation but also showcases his dedication to solving impactful societal and scientific problems.

Awards and Honors

Prof. Dr. Mohammad Ali Balafar is a highly acclaimed researcher whose contributions have been recognized through various awards and honors. Notably, he has been included in Stanford University’s list of the top 2% most-cited scientists worldwide, based on a one-year performance metric—a testament to his impactful research and global influence in Artificial Intelligence and Multimedia Systems. Dr. Balafar’s scholarly achievements, reflected in his impressive h-index of 24 (Google Scholar) and over 2,380 citations, underscore his standing as a leading researcher in fields like Deep Learning, Image Processing, and Graph Neural Networks. His role as the head of the Intelligent Information Technology and Multimedia Research Laboratory further highlights his leadership in advancing innovative solutions for complex technological challenges. These accolades, combined with his extensive publication record in top-tier journals, position Dr. Balafar as a pioneer in his domain, earning him well-deserved recognition in the academic and research communities.

Conclusion

Publications Top Noted

• Review of brain MRI image segmentation methods
  • Authors: MA Balafar, AR Ramli, MI Saripan, S Mashohor
  • Year: 2010
  • Citations: 643
• Gene selection for microarray cancer classification using a new evolutionary method employing artificial intelligence concepts
  • Authors: M Dashtban, M Balafar
  • Year: 2017
  • Citations: 167
• A hybrid algorithm using a genetic algorithm and multiagent reinforcement learning heuristic to solve the traveling salesman problem
  • Authors: MM Alipour, SN Razavi, MR Feizi Derakhshi, MA Balafar
  • Year: 2018
  • Citations: 134
• A novel image encryption algorithm based on polynomial combination of chaotic maps and dynamic function generation
  • Authors: M Asgari-Chenaghlu, MA Balafar, MR Feizi-Derakhshi
  • Year: 2019
  • Citations: 131
• Gene selection for tumor classification using a novel bio-inspired multi-objective approach
  • Authors: M Dashtban, M Balafar, P Suravajhala
  • Year: 2018
  • Citations: 104
• Gaussian mixture model based segmentation methods for brain MRI images
  • Authors: MA Balafar
  • Year: 2014
  • Citations: 95
• The state-of-the-art in expert recommendation systems
  • Authors: N Nikzad–Khasmakhi, MA Balafar, MR Feizi–Derakhshi
  • Year: 2019
  • Citations: 89
• Fuzzy C-mean based brain MRI segmentation algorithms
  • Authors: MA Balafar
  • Year: 2014
  • Citations: 85
• CGFFCM: Cluster-weight and Group-local Feature-weight learning in Fuzzy C-Means clustering algorithm for color image segmentation
  • Authors: AG Oskouei, M Hashemzadeh, B Asheghi, MA Balafar
  • Year: 2021
  • Citations: 70
• CWI: A multimodal deep learning approach for named entity recognition from social media using character, word and image features
  • Authors: M Asgari-Chenaghlu, MR Feizi-Derakhshi, L Farzinvash, MA Balafar
  • Year: 2022
  • Citations: 48
• Cy: Chaotic yolo for user intended image encryption and sharing in social media
  • Authors: M Asgari-Chenaghlu, MR Feizi-Derakhshi, N Nikzad-Khasmakhi
  • Year: 2021
  • Citations: 36
• A new method for MR grayscale inhomogeneity correction
  • Authors: MA Balafar, AR Ramli, S Mashohor
  • Year: 2010
  • Citations: 36

Mr. Humam Kourani | Computer Science | Best Researcher Award

Research Associate at Fraunhofer FIT, Germany

Mr. Humam Kourani is a dedicated and highly skilled researcher with a strong background in Data Science and Computer Science. He holds both a Master’s and Bachelor’s degree from RWTH Aachen University, specializing in process mining, artificial intelligence, and data-driven decision-making. He has gained valuable experience working in research institutions and industry settings, most notably at the Fraunhofer Institute for Applied Information Technology and Fondazione Bruno Kessler in Italy. His research focuses on improving data science methodologies, particularly in process mining and workflow language models. With a solid academic foundation, practical experience, and significant contributions to his field, Humam has proven himself to be a promising and impactful researcher.

Professional Profile

• Scopus Profile
• ORCID Profile
• Google scholar

Education

Humam Kourani completed his Master of Science in Data Science from RWTH Aachen University in 2022, with a focus on Computer Science. His master’s thesis explored the improvement of the Hybrid Miner by utilizing causal graph metrics, an area critical for process mining. Prior to that, he earned his Bachelor of Science degree in Computer Science from the same institution in 2019. His Bachelor’s thesis involved the development of a scalable interactive event data visualization tool in Python, further showcasing his technical skills. Humam’s academic journey reflects his dedication to mastering complex data science concepts and his drive to contribute to the field’s advancement through academic research and innovation.

Professional  Experience

Mr. Kourani’s professional experience spans key positions in research and data science. Since May 2022, he has been working as a Research Associate at the Fraunhofer Institute for Applied Information Technology, specializing in Data Science and Artificial Intelligence. In this role, he contributes to research on process mining, artificial intelligence, and data-driven decision-making. Earlier, he held student assistant roles at RWTH Aachen University, including positions at the Chair of Process and Data Science and the Chair of Process and Data Science in 2021. Humam also completed an Erasmus+ internship at Fondazione Bruno Kessler in Italy, where he gained hands-on experience in process and data intelligence. His professional experience reflects a consistent focus on leveraging data science and AI for practical problem-solving and research innovation.

Research Interests

Humam Kourani’s research interests lie primarily in data science, artificial intelligence, and process mining. He is particularly focused on enhancing data-driven methods for analyzing and improving business processes, with an emphasis on process modeling and workflow languages. His recent work has explored innovative approaches, such as large language models for process modeling, and improving existing hybrid mining techniques using causal graph metrics. Through his work, Humam aims to bridge the gap between advanced computational techniques and practical business process applications, enabling more efficient decision-making. His research also delves into the intersection of data science and AI, with a strong interest in developing scalable models that address real-world challenges across various industries.

Awards and Honors

Humam Kourani has received several prestigious awards in recognition of his outstanding research contributions. He won the Best Paper Award at the EMMSAD 2024 conference for his paper on “Process Modeling with Large Language Models”. Additionally, he received the Best Paper Award at the BPM 2023 conference for his work on the “POWL: Partially Ordered Workflow Language”. These awards highlight the significance of his research in the fields of process mining and business process management. Humam was also honored with membership in the PADS Excellence Honors Class at RWTH Aachen University in 2022, further underscoring his academic excellence. These honors attest to his innovative contributions to the research community and his growing influence in the fields of data science and AI.

Conclusion

Publications Top Noted

• Title: Process Modeling With Large Language Models
  Authors: H. Kourani, A. Berti, D. Schuster, W.M.P. van der Aalst
  Year: 2024
  Citations: 21
• Title: Evaluating Large Language Models in Process Mining: Capabilities, Benchmarks, Evaluation Strategies, and Future Challenges
  Authors: A. Berti, H. Kourani, H. Hafke, C.Y. Li, D. Schuster
  Year: 2024
  Citations: 8
• Title: POWL: Partially Ordered Workflow Language
  Authors: H. Kourani, S.J. van Zelst
  Year: 2023
  Citations: 7
• Title: ProMoAI: Process Modeling with Generative AI
  Authors: H. Kourani, A. Berti, D. Schuster, W.M.P. van der Aalst
  Year: 2024
  Citations: 5
• Title: PM4KNIME: Process Mining Meets the KNIME Analytics Platform
  Authors: H. Kourani, S.J. van Zelst, B.D. Lehmann, G. Einsdorf, S. Helfrich, F. Liße
  Year: 2022
  Citations: 5
• Title: Scalable Discovery of Partially Ordered Workflow Models with Formal Guarantees
  Authors: H. Kourani, D. Schuster, W. Van Der Aalst
  Year: 2023
  Citations: 4
• Title: PM-LLM-Benchmark: Evaluating Large Language Models on Process Mining Tasks
  Authors: A. Berti, H. Kourani, W.M.P. van der Aalst
  Year: 2024
  Citations: 3
• Title: Discovering Hybrid Process Models with Bounds on Time and Complexity: When to be Formal and When Not?
  Authors: W.M.P. van der Aalst, R. De Masellis, C. Di Francescomarino, C. Ghidini, H. Kourani
  Year: 2023
  Citations: 3
• Title: Evaluating Large Language Models in Process Mining: Capabilities, Benchmarks, and Evaluation Strategies
  Authors: A. Berti, H. Kourani, H. Häfke, C.Y. Li, D. Schuster
  Year: 2024
  Citations: 2
• Title: Mining for Long-Term Dependencies in Causal Graphs
  Authors: H. Kourani, C. Di Francescomarino, C. Ghidini, W. van der Aalst, S. van Zelst
  Year: 2022
  Citations: 2
• Title: Bridging Domain Knowledge and Process Discovery Using Large Language Models
  Authors: A. Norouzifar, H. Kourani, M. Dees, W. van der Aalst
  Year: 2024
  Citations: 0 (preprint)
• Title: Leveraging Large Language Models for Enhanced Process Model Comprehension
  Authors: H. Kourani, A. Berti, J. Hennrich, W. Kratsch, R. Weidlich, C.Y. Li, A. Arslan, et al.
  Year: 2024
  Citations: 0 (preprint)
• Title: Discovering Hybrid Process Models with Bounds on Time and Complexity: When to be Formal and When Not?
  Authors: W. van der Aalst, R. De Masellis, C. Di Francescomarino, C. Ghidini, H. Kourani
  Year: 2023
  Citations: 0

Assoc. Prof. Dr.Shahbaz Gul Hassan | Computer Science | Best Researcher Award

Associat professor at Zhongkai University of Agriculture and Engineering, China

Dr. Shahbaz Gul Hassan is an accomplished Associate Professor at Zhongkai University of Agriculture and Engineering, specializing in agricultural information technology and computer science. With a strong academic background, including a Ph.D. from China Agricultural University, he focuses on machine learning, image processing, and predictive modeling in the context of agricultural and environmental systems. His work has earned significant recognition, including awards for research and innovation in agricultural technology. Dr. Hassan’s numerous high-impact publications in top-tier journals demonstrate his ability to integrate advanced computational techniques into real-world applications in agriculture.

Professional Profile

• Scopus Profile
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• Google scholar

Education

Dr. Shahbaz Gul Hassan completed his Ph.D. in Agricultural Information Technology at China Agricultural University, Beijing, in 2017. His research during his Ph.D. focused on the integration of information technology with agriculture, particularly in areas such as machine learning and predictive modeling. Prior to his Ph.D., he earned a Master’s in Computer Science from PMAS Arid Agriculture University, Rawalpindi, in 2011, where he developed a deep understanding of computer science applications in agriculture. He completed his Bachelor’s degree in Science from the University of Punjab, Lahore, in 2007. These educational milestones have equipped Dr. Hassan with a solid foundation in both computer science and agricultural technology, enabling him to innovate at the intersection of these two fields. His academic journey reflects a consistent focus on enhancing agricultural practices through advanced technologies, positioning him as a leading figure in agricultural information systems and technology research.

Experience

Dr. Shahbaz Gul Hassan has extensive experience in both academia and industry. He is currently an Associate Professor at Zhongkai University of Agriculture and Engineering, Guangzhou, China, where he has been teaching since 2019. Prior to this, he served as a Postdoctoral Researcher in Agricultural Engineering at South China Agricultural University, Guangzhou, from 2017 to 2019. In this role, he applied his expertise in machine learning and image processing to agricultural engineering projects. Dr. Hassan also worked as a Ph.D. Research Scholar at China Agricultural University, Beijing, from 2013 to 2017, where he focused on applying technology to solve critical problems in agriculture. Earlier, he worked as a Software Engineer at MTBC in Rawalpindi from 2011 to 2012. His diverse professional experience blends research, teaching, and practical applications of technology in agriculture, with a focus on using advanced computing to optimize agricultural processes.

Research Interests

Dr. Shahbaz Gul Hassan’s research focuses on the application of machine learning, image processing, and predictive modeling to solve agricultural challenges. He is particularly interested in developing smart technologies for precision farming and environmental monitoring. One of his key areas of research involves computer vision and machine learning techniques for detecting and predicting behaviors and conditions in agricultural environments, such as water quality and animal health. His work aims to enhance automation in agriculture and improve sustainability by leveraging data-driven technologies. Dr. Hassan also focuses on predictive modeling for environmental variables such as humidity, temperature, and dissolved oxygen levels in aquaculture. These models help optimize farming processes and ensure better resource management. His research not only pushes the boundaries of agricultural technology but also contributes to the development of sustainable practices in farming and aquaculture. Dr. Hassan’s interdisciplinary approach integrates computer science and engineering with practical agricultural needs to drive innovation.

Awards and Honors

Dr. Shahbaz Gul Hassan has received numerous prestigious awards for his outstanding contributions to agricultural research. In December 2023, he was honored with the First Prize in the Guangdong Province Agricultural Technology Promotion Award. He also received the Third Prize from the Guangdong Provincial Science and Technology Department in January 2024. Dr. Hassan’s work on a microservice-based agricultural app earned him the Second Prize in the 16th China University Computer Design Competition in the Guangdong-Hong Kong-Macao Greater Bay Area. Additionally, he was awarded the Excellent Instructor Award in the 13th Blue Bridge Cup Provincial Competition. His work has been recognized by the Guangdong Computer Society, where he received the Second Prize for Outstanding Paper. These awards reflect Dr. Hassan’s innovative approach to integrating advanced technologies in agriculture, as well as his ability to drive real-world impact with his research. His accolades highlight his leadership and dedication to improving agricultural technologies globally.

Conclusion

Publications Top Noted