Assoc. Prof. Dr. Suhail Odeh | Intelligent Systems | Research Excellence Award

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Assoc. Prof. Dr. Suhail Odeh | Intelligent Systems | Research Excellence Award

Assoc. Prof. Dr. Suhail Odeh | Intelligent Systems | Bethlehem University | Palestine

Assoc. Prof. Dr. Suhail Odeh is an accomplished academic and researcher from Bethlehem, Palestine, holding dual Palestinian and Jordanian nationality, with extensive expertise in software engineering, artificial intelligence, physics, and intelligent systems. He earned a PhD in Computer Engineering from Universidad de Granada in Spain, where his doctoral work focused on developing intelligent systems for medical classification and diagnosis, supported by a graduate diploma in computer engineering and a strong science foundation built through an MSc in Physics and a BSc in Physics and Electronic Technology from Al-Quds University. Throughout his career, Suhail Musa Odeh has held significant academic roles, including Associate Professor at Bethlehem University, Chair of the Software Engineering Department, Assistant Professor in Computer and Information Systems, visiting professor at the University of Cyprus, research visitor at the University of Sherbrooke, and post-doctoral researcher at L’Aquila University and Granada University, where he contributed to advancements in intelligent agents, multi-agent control environments, and autonomous behavior modeling. His earlier professional experience includes teaching at Palestine Technical College, the Palestine Polytechnic University, and Beit Sahour Secondary School, as well as conducting physics research at Al-Quds University.

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 Featured Publications


A Hybrid Fuzzy Genetic Algorithm for an Adaptive Traffic Signal System

– Advances in Fuzzy Systems, 2015 (49 citations)


Off-line Signature Verification and Recognition: Neural Network Approach

– International Symposium on Innovations in Intelligent Systems, 2011 (40 citations)


Management of an Intelligent Traffic Light System by Using Genetic Algorithm

– Journal of Image and Graphics, 2013 (37 citations)


A Comparison of Classification Methods as Diagnostic System: A Case Study on Skin Lesions

– Computer Methods and Programs in Biomedicine, 2016 (30 citations)

Dr. Seyedeh Tina Sefati | Reinforcement Learning | Best Researcher Award

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Dr. Seyedeh Tina Sefati | Reinforcement Learning | Best Researcher Award

Dr. Seyedeh Tina Sefati | Reinforcement Learning | University of Tabriz | Iran

Dr. Seyedeh Tina Sefati is a highly skilled and innovative Ph.D. candidate in Artificial Intelligence at the University of Tabriz, Iran, whose academic and professional trajectory reflects a strong commitment to advancing the fields of deep learning, generative adversarial networks, and game theory. Her doctoral research focuses on unsupervised multivariate time-series anomaly detection, contributing significantly to intelligent sensing and automated decision-making systems. Dr. Seyedeh Tina Sefati holds a Master’s degree in Artificial Intelligence from the University of Tabriz, where she explored spam filtering through game theory, an MBA from the Iran Technical and Vocational Training Organization, and a Bachelor’s degree in Computer Engineering from Seraj University with a thesis on solving optimization problems using ant colony algorithms. Professionally, Dr. Seyedeh Tina Sefati serves as the CEO and AI Architect at Saman Digital Eurasia, leading high-impact projects that integrate deep learning, natural language processing, and image analysis for clients across more than ten countries. Her prior experience as an AI Project Manager at Rayin Samaneh Arta and as a Programming Instructor at MFTabriz showcases her multifaceted expertise in both applied and academic contexts. Her research interests center around deep learning architectures, machine learning, NLP, image processing, and federated reinforcement learning for secure data transmission in wireless sensor networks. She has been involved in several international collaborations and industrial projects, including data-driven solutions for HepsiBurada and AndMe in Turkey, where she developed large-scale AI-based recommendation and forecasting systems. Dr. Seyedeh Tina Sefati’s technical skill set includes advanced proficiency in Python, TensorFlow, PyTorch, CNN, LSTM, GANs, and Transformers, demonstrating her ability to bridge theoretical concepts with real-world applications. Her research excellence is reflected in publications in Scopus and IEEE-indexed journals such as The Journal of Supercomputing and Mathematics. She is a recognized member of professional organizations such as IEEE and ACM and has received honors for her research contributions in deep learning and anomaly detection.

Professional Profiles: Google Scholar

Featured Publications 

  1. Sefati, S. T., Razavi, S. N., & Salehpour, P. (2025). Enhancing autoencoder models for multivariate time series anomaly detection: The role of noise and data amount. The Journal of Supercomputing, 81(4), 559. (2 citations)

  2. Sefati, S. T., Feizi-Derakhshi, M. R., & Razavi, S. N. (2016). Improvement of Persian spam filtering by game theory. International Journal of Advanced Computer Science and Applications, 7(6). (1 citation)

  3. Sefati, S. S., Sefati, S. T., Nazir, S., Farkhady, R. Z., & Obreja, S. G. (2025). Federated reinforcement learning with hybrid optimization for secure and reliable data transmission in wireless sensor networks (WSNs). Mathematics, 13(19), 1–37.

  4. Sefati, S. T., Razavi, S. N. (2024). Hybrid deep learning approach for intelligent anomaly detection in IoT sensor data. IEEE Internet of Things Journal. (3 citations)

  5. Sefati, S. T., Salehpour, P. (2023). GAN-based synthetic data generation for anomaly detection in multivariate time series. Expert Systems with Applications. (4 citations)

  6. Sefati, S. T., Feizi-Derakhshi, M. R. (2022). Game-theoretic optimization in distributed deep learning systems. Applied Intelligence. (2 citations)

  7. Sefati, S. T., Nazir, S. (2021). Deep learning-based adaptive framework for real-time sensor data analysis. IEEE Access. (3 citations)

Gabriel Danciu | Intelligent sensing | Excellence in Research

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Mr. Gabriel Danciu | Intelligent sensing | Excellence in Research

Lecturer at Transilvania University, Romania

Danciu Gabriel is a prominent researcher and educator from Romania, specializing in electrical engineering and computer science. Currently serving as a Şef lucrări at the University of Transilvania in Brașov, he combines his academic role with practical experience as an engineer and manager at Siemens. With a robust publication record exceeding 50 papers, Gabriel is recognized for his contributions to artificial intelligence, image processing, and software architecture. He is an active member of the IEEE and has presented at numerous international conferences. His commitment to education is reflected in his mentoring roles and project coordination, making him a vital part of the academic community. Gabriel’s expertise in developing algorithms for RGB-D cameras and his innovative research approaches have earned him respect in the field. He aims to bridge theoretical knowledge with practical applications, enhancing technological advancements and shaping the next generation of engineers.

Profile:

Google Scholar Profile

Strengths for the Award:

  1. Extensive Experience: Gabriel has over 15 years of experience in academia and industry, demonstrating a strong commitment to both education and research.
  2. Publication Record: With over 50 published works, he shows a robust contribution to fields such as AI, image processing, and software architectures, indicating high productivity and impact in his research area.
  3. Diverse Skill Set: His competencies in various programming languages (C++, C#, Python) and expertise in software architecture showcase his technical proficiency, which is critical for modern research.
  4. Leadership Roles: As a Şef lucrări (Head of Department) and an engineer at Siemens, he has proven leadership capabilities, indicating his ability to manage projects and mentor others effectively.
  5. International Engagement: Participation in over 5 European projects and presentations at numerous conferences reflects his active engagement with the global research community.
  6. Research Innovation: His focus on cutting-edge topics like AI and image processing highlights his relevance and adaptability to current technological trends.

Areas for Improvement:

  1. Language Proficiency: While he is proficient in English, improving his German skills could enhance his collaboration opportunities in Europe, particularly in multilingual environments.
  2. Broader Collaboration: Expanding his research network beyond existing affiliations could lead to more interdisciplinary projects and greater innovation.
  3. Public Engagement: Increasing visibility through popular science publications or community outreach could enhance his impact beyond the academic sphere.
  4. Mentoring: Actively seeking to mentor younger researchers or students could foster new talent in the field and enhance his leadership profile.

Education:

Danciu Gabriel pursued his academic journey at the University of Transilvania in Brașov, where he obtained his Bachelor’s degree in Automatică și Informatică Industrială in 2004. He continued his studies at the same institution, completing a Master’s degree in Electrical Engineering and Telecommunications in 2006. Gabriel then earned his Ph.D. in 2014, focusing on developing algorithms for image processing using RGB-D cameras. His educational background laid a solid foundation for his future roles in academia and industry. As an Asistent universitar from 2007 to 2022, he dedicated himself to teaching and research, culminating in his current position as Șef lucrări, where he engages in educational leadership, research activities, and administrative duties. Gabriel’s academic achievements are complemented by ongoing professional development, ensuring that he stays at the forefront of technological advancements and educational methodologies in his field.

Experience:

Danciu Gabriel boasts extensive professional experience spanning over 15 years in both academia and industry. He began his career as a Software Engineer at Dynamic Ventures from 2005 to 2017, where he focused on research, mentorship, and software development. In 2018, he transitioned to Siemens as an Engineer, Researcher, and Manager, where he continues to work on innovative research projects while mentoring emerging talent. Concurrently, he has held various academic positions at the University of Transilvania, serving as an Asistent universitar for 15 years before advancing to Şef lucrări in 2022. His dual role allows him to integrate theoretical knowledge with practical applications, contributing to the growth of his students and the advancement of technology. Gabriel’s experience is characterized by a commitment to education, research innovation, and leadership, positioning him as a key figure in the fields of electrical engineering and computer science.

Research Focus:

Danciu Gabriel’s research primarily revolves around artificial intelligence, image processing, and software architecture, with a specific emphasis on RGB-D cameras. His work in developing innovative algorithms for depth image analysis has significantly contributed to advancements in computer vision and signal processing. Gabriel has published over 50 papers in renowned journals and conferences, exploring various topics, including noise pollution monitoring, functional verification in digital designs, and object tracking methods. He actively participates in European projects, collaborating with interdisciplinary teams to address real-world challenges through technology. Gabriel is passionate about integrating theoretical concepts with practical applications, aiming to improve the efficiency and accuracy of image processing techniques. His ongoing research endeavors focus on enhancing machine learning models and exploring new avenues in automated systems, positioning him at the cutting edge of technological innovation in the fields of engineering and computer science.

Publication Top Notes:

  • Shadow removal in depth images morphology-based for Kinect cameras 🌌
  • Objective erythema assessment of Psoriasis lesions for PASI evaluation 🌿
  • A novel approach for face expressions recognition 😊
  • Improved contours for ToF cameras based on vicinity logic operations 🖼️
  • Cost-efficient approaches for fulfillment of functional coverage during verification of digital designs 💻
  • Coverage fulfillment automation in hardware functional verification using genetic algorithms 🔍
  • Extended control-value emotional agent based on fuzzy logic approach 🤖
  • Scale and rotation-invariant feature extraction for color images of iris melanoma 🌈
  • Level up in verification: Learning from functional snapshots 📊
  • Noise pollution monitoring using mobile crowd sensing and SAP analytics 📱
  • Debugging FPGA projects using artificial intelligence 🧩
  • Debug FPGA projects using machine learning 📈
  • Efficient analysis of digital systems’ supplied data ⚙️
  • Method proposal for blob separation in segmented images 🔍
  • Solutions for Roaming and Interoperability Problems Between LTE and 2G or 3G Networks 📶
  • Methods of Object Tracking 🕵️‍♂️
  • Adaptive Scaling for Image Sensors in Embedded Security Applications 🔒
  • A method proposal of scene recognition for RGB-D cameras 🌍
  • Genetic algorithm for depth images in RGB-D cameras 🔧
  • Hierarchical contours based on depth images 🗺️

Conclusion:

Gabriel Danciu demonstrates a strong profile as a candidate for the Best Researcher Award, with a solid foundation in research, a wealth of experience, and a proven track record of publications and collaborations. By addressing the suggested areas for improvement, particularly in broader engagement and mentorship, he could further strengthen his candidacy and impact in the research community.