Assistant Professor at University of Science and Culture, Iran📖

Dr. Alireza Rezvanian is an accomplished academic and researcher, serving as an Assistant Professor at the University of Science and Culture (USC) in Tehran, Iran. He holds multiple editorial positions, including Associate Editor for journals such as CAAI Transactions on Intelligence Technology, Human-Centric Computing and Information Sciences, The Journal of Engineering, and Data in Brief. Dr. Rezvanian is actively involved in various professional and scientific activities, including serving as the Director of Information and Scientific Resources at USC and contributing to the IEEE Computer Society Iran Chapter.

Profile

Scopus Profile

Orcid Profile

Google Scholar Profile

Education Background🎓

Dr. Rezvanian completed his Ph.D. in Computer Engineering from Amirkabir University of Technology (Tehran Polytechnic) in 2016, under the guidance of Dr. Mohammad Reza Meybodi. His doctoral thesis focused on “Stochastic Graphs for Social Network Analysis.” He holds a Master’s degree in Computer Engineering from Islamic Azad University of Qazvin (2010), where he specialized in improving Artificial Immune System algorithms using Learning Automata for dynamic environments. He also earned a Bachelor’s degree in Computer Engineering from Bu-Ali Sina University of Hamedan (2007).

Professional Experience🌱

Dr. Rezvanian has extensive teaching and research experience across multiple prestigious institutions. Currently, he is an Assistant Professor at the University of Science and Culture, Tehran. He is also an Adjunct Professor at Amirkabir University of Technology, the University of Tehran, and Tarbiat Modares University. His leadership roles include serving as the Head of the Computer Engineering Department at USC (2021-2023) and as the Director of Information and Scientific Resources at USC since 2023. He has previously held research positions at the Institute for Research in Fundamental Sciences (IPM) and the Niroo Research Institute (NRI).

Research Interests🔬

Dr. Rezvanian’s research interests lie in the areas of complex networks, social network analysis, machine learning, learning automata, data mining, and soft computing. His work focuses on the application of evolutionary algorithms, image processing, and stochastic graphs for modeling social networks. His research aims to provide insights into real-world applications through innovative techniques in network analysis and machine learning.

Author Metrics

Dr. Rezvanian has a strong academic presence, with an H-index of 26 on Google Scholar (2024), 23 on Scopus, and 18 on Web of Science. He has authored and co-authored numerous research articles in renowned journals and conferences, contributing significantly to the fields of computer science, machine learning, and network analysis. His work has earned him recognition and a substantial citation count, further solidifying his impact in academia.

Publications Top Notes 📄

1. Robust Fall Detection Using Human Shape and Multi-Class Support Vector Machine

Authors: H. Foroughi, A. Rezvanian, A. Paziraee
Conference: Sixth Indian Conference on Computer Vision, Graphics & Image Processing (ICVGIP 2008)
Year: 2008
Summary: This paper focuses on a robust fall detection system utilizing human shape and a multi-class support vector machine (SVM) for classifying human body shapes and movements. The system aims to effectively detect falls, which is crucial in healthcare applications like elderly care.

2. Sampling from Complex Networks Using Distributed Learning Automata

Authors: A. Rezvanian, M. Rahmati, M.R. Meybodi
Journal: Physica A: Statistical Mechanics and its Applications
Volume: 396
Pages: 224–234
Year: 2014
Summary: This paper introduces a method for sampling complex networks using distributed learning automata (LA), a technique inspired by machine learning algorithms. The approach aims to enhance network analysis by efficiently exploring and sampling complex graph structures.

3. Minimum Positive Influence Dominating Set and Its Application in Influence Maximization: A Learning Automata Approach

Authors: M.M.D. Khomami, A. Rezvanian, N. Bagherpour, M.R. Meybodi
Journal: Applied Intelligence
Volume: 48 (3)
Pages: 570–593
Year: 2018
Summary: This paper presents a novel approach for solving the Minimum Positive Influence Dominating Set (MPIDS) problem, using learning automata for influence maximization in social networks. The proposed method addresses the optimization challenges in selecting influential nodes for spreading information effectively in network-based applications.

4. CDEPSO: A Bi-population Hybrid Approach for Dynamic Optimization Problems

Authors: J.K. Kordestani, A. Rezvanian, M.R. Meybodi
Journal: Applied Intelligence
Volume: 40 (4)
Pages: 682–694
Year: 2014
Summary: The paper introduces CDEPSO (Cognitive Dynamic Evolutionary Particle Swarm Optimization), a hybrid approach that integrates bi-population evolutionary algorithms to address dynamic optimization problems. The method aims to improve the solution quality and efficiency in environments where the optimization landscape changes over time.

5. Cellular Edge Detection: Combining Cellular Automata and Cellular Learning Automata

Authors: M. Hasanzadeh Mofrad, S. Sadeghi, A. Rezvanian, M.R. Meybodi
Journal: AEU-International Journal of Electronics and Communications
Volume: 69 (9)
Pages: 1282–1290
Year: 2015
Summary: This paper explores the combination of cellular automata (CA) and cellular learning automata (CLA) for edge detection in image processing. The approach leverages the computational power of CA and CLA to enhance the edge detection process in digital images, contributing to improvements in image recognition and processing tasks.

Conclusion

Dr. Alireza Rezvanian is highly deserving of the Network Science Excellence Award due to his pioneering contributions to the field of complex networks and social network analysis. His research not only provides innovative methods for understanding and optimizing networks but also demonstrates a strong academic leadership role in advancing network science. With his continued focus on interdisciplinary research and industry collaboration, Dr. Rezvanian is poised to make even greater contributions to the field of network science, making him a worthy recipient of this prestigious award.

Fahimeh Dabaghi Zarandi | Community Detection | Women Researcher Award

Posted on 27/11/202427/11/2024 by Leave a Comment

Assist. Prof. Dr. Fahimeh Dabaghi Zarandi | Community Detection | Women Researcher Award

Assistant Professor, at Vali-e-Asr University of Rafsanjan, Iran📖

Dr. Fahimeh Dabaghi-Zarandi is an accomplished researcher and academic in software engineering, specializing in data mining, green communication, and IoT. With a Ph.D. from the Iran University of Science and Technology, she brings a rich academic background and a passion for leveraging technology to address complex problems. As an Assistant Professor at Vali-e-Asr University, she continues to inspire students and contribute to the field through innovative research and collaboration.

Profile

Scopus Profile

Google Scholar Profile

Education Background🎓

Dr. Fahimeh Dabaghi-Zarandi holds a Ph.D. in Software Engineering from the Iran University of Science and Technology, Tehran, Iran, which she completed in September 2018. She earned her Master’s degree in Software Engineering from the prestigious Sharif University of Technology, Tehran, Iran, in August 2010, and her Bachelor’s degree in the same field from Ferdowsi University of Mashhad, Iran, in August 2008. Her academic journey reflects a consistent focus on software engineering, laying a strong foundation for her expertise in data mining, graph processing, and Internet of Things applications.

Professional Experience🌱

Dr. Fahimeh Dabaghi-Zarandi is an Assistant Professor at the Department of Engineering, Vali-e-Asr University of Rafsanjan, where she contributes to the advancement of computer engineering through teaching and research. She has actively participated in several national conferences on topics such as data mining and computational geometry, including the 16th CSI Computer Conference in Tehran (2011) and the Winter School on Computational Geometry at Amirkabir University (2009). Her involvement in these events reflects her commitment to staying at the forefront of developments in computer science and engineering.

Research Interests🔬

Dr. Dabaghi-Zarandi’s research focuses on:

Green Communication: Enhancing energy efficiency in communication systems.
Community Detection: Identifying clusters and patterns in large networks.
Data Mining: Extracting meaningful insights from large datasets.
Graph Processing: Algorithms and applications for analyzing graph structures.
Internet of Things (IoT): Developing intelligent solutions for interconnected systems.

Author Metrics

Dr. Dabaghi-Zarandi’s publications have made significant contributions to her fields of interest, with her work cited by researchers worldwide. Her expertise in graph processing and community detection has been recognized in peer-reviewed journals and conferences, where she has shared her findings on the applications of data mining and IoT in sustainable technology

Publications Top Notes 📄

1. A survey on green routing protocols using sleep-scheduling in wired networks

Authors: F. Dabaghi, Z. Movahedi, R. Langar
Journal: Journal of Network and Computer Applications
Volume: 77
Pages: 106-122
Year: 2017
Citations: 47
Abstract: This paper provides a detailed survey of green routing protocols in wired networks, focusing on energy-saving methods achieved through sleep-scheduling mechanisms. The study reviews various techniques and evaluates their effectiveness, contributing valuable insights to the field of green networking.

2. Community detection in complex networks based on an improved random algorithm using local and global network information

Authors: F. Dabaghi-Zarandi, P. KamaliPour
Journal: Journal of Network and Computer Applications
Volume: 206
Article: 103492
Year: 2022
Citations: 11
Abstract: This work presents an enhanced random algorithm for community detection in complex networks. By integrating both local and global network information, the proposed method achieves higher accuracy and robustness compared to traditional approaches.

3. An energy‐efficient algorithm based on sleep‐scheduling in IP backbone networks

Authors: F. Dabaghi-Zarandi, Z. Movahedi
Journal: International Journal of Communication Systems
Volume: 30, Issue 13
Article: e3276
Year: 2017
Citations: 11
Abstract: This paper introduces an energy-efficient algorithm for IP backbone networks leveraging sleep-scheduling techniques. The algorithm optimizes energy consumption while maintaining network performance.

4. A dynamic traffic-aware energy-efficient algorithm based on sleep-scheduling for autonomous systems

Authors: F. Dabaghi-Zarandi, Z. Movahedi
Journal: Computing
Volume: 100, Issue 6
Pages: 645-665
Year: 2018
Citations: 7
Abstract: The study proposes a dynamic traffic-aware algorithm that enhances energy efficiency in autonomous systems by incorporating adaptive sleep-scheduling.

5. Local traffic-aware green algorithm based on sleep-scheduling in autonomous networks

Authors: F. Dabaghi-Zarandi
Journal: Simulation Modelling Practice and Theory
Volume: 114
Article: 102418
Year: 2022
Citations: 2
Abstract: This paper introduces a localized green algorithm tailored for autonomous networks. By integrating sleep-scheduling and traffic awareness, the proposed approach reduces energy consumption without compromising network performance.

Conclusion

Dr. Fahimeh Dabaghi-Zarandi is a highly deserving nominee for the Women Researcher Award due to her pioneering research in green communication, community detection, and energy-efficient algorithms. Her contributions address global challenges such as energy conservation and sustainable technology, making her work both impactful and timely.

With a clear trajectory of excellence and continuous innovation, Dr. Dabaghi-Zarandi exemplifies the qualities of a distinguished researcher. Addressing the identified areas for improvement would further amplify her achievements, but her existing body of work strongly supports her candidacy for this award.

Emerging Talent Award in Network Science and Graph Analytics

Posted on 16/11/202323/11/2023 by Leave a Comment

Introduction of Emerging Talent Award in Network Science and Graph Analytics

Welcome to the future of Network Science and Graph Analytics! The Emerging Talent Award celebrates individuals who showcase exceptional promise and potential in shaping the landscape of these dynamic fields. This prestigious award recognizes and honors the contributions of young professionals, researchers, and innovators who are making significant strides in advancing the realms of network science and graph analytics.

Eligibility:

The Emerging Talent Award is open to individuals under the age of 35, actively engaged in the fields of Network Science and Graph Analytics. Eligible candidates must hold a relevant academic degree, demonstrate a noteworthy record of publications, and exhibit a passion for pushing the boundaries of knowledge in these domains.

Evaluation Criteria:

Candidates will be evaluated based on the novelty and impact of their contributions, the depth of their research, and the potential for future growth and innovation in the field. The selection process considers academic achievements, research publications, industry impact, and a commitment to advancing the understanding and application of network science and graph analytics.

Submission Guidelines:

To be considered for the Emerging Talent Award, applicants must submit a comprehensive biography, an abstract outlining their work, and supporting files that highlight their contributions. Submissions should be sent via the online submission form, adhering to the specified format guidelines.

Recognition:

The recipient of the Emerging Talent Award will receive widespread recognition within the academic and industry communities, providing a platform to showcase their work on a global scale. The award includes a certificate of achievement, a monetary prize, and an opportunity to present their findings at a prominent industry event.

Community Impact:

Beyond individual recognition, the Emerging Talent Award aims to foster a sense of community among emerging talents. Winners will be encouraged to engage in knowledge-sharing activities, mentorship programs, and collaborative initiatives to further enrich the collective understanding of network science and graph analytics.

Innovation Excellence Award in Network Science and Graph Analytics

Introduction of Innovation Excellence Award in Network Science and Graph Analytics Welcome to the forefront of recognition in the realm of Network Science and Graph Analytics! The Innovation Excellence Award

Outstanding Research Achievement Award in Network Science and Graph Analytics

Introduction of Outstanding Research Achievement Award in Network Science and Graph Analytics Welcome to the pinnacle of excellence in the realm of Network Science and Graph Analytics! The "Outstanding Research

Academic Excellence Award in Network Science and Graph Analytics

Introduction of Academic Excellence Award in Network Science and Graph Analytics Welcome to the Academic Excellence Award in Network Science and Graph Analytics, recognizing outstanding achievements and contributions in the

Industry Impact Award in Network Science and Graph Analytics

Introduction of Industry Impact Award in Network Science and Graph Analytics Welcome to the Industry Impact Award in Network Science and Graph Analytics—an accolade honoring pioneers shaping the future of

Leadership in Business Applications Award in Network Science and Graph Analytics

Introduction of Leadership in Business Applications Award in Network Science and Graph Analytics Welcome to the forefront of innovation! The Leadership in Business Applications Award in Network Science and Graph

Collaborative Achievement Award in Network Science and Graph Analytics

Introduction of Collaborative Achievement Award in Network Science and Graph Analytics Welcome to the Collaborative Achievement Award in Network Science and Graph Analytics—a prestigious recognition celebrating innovation and collaboration in

Emerging Talent Award in Network Science and Graph Analytics

Introduction of Emerging Talent Award in Network Science and Graph Analytics Welcome to the future of Network Science and Graph Analytics! The Emerging Talent Award celebrates individuals who showcase exceptional

Strategic Implementation Award in Network Science and Graph Analytics

Introduction of Strategic Implementation Award in Network Science and Graph Analytics Welcome to the Strategic Implementation Award in Network Science and Graph Analytics—an accolade designed to recognize and honor outstanding

Pioneering Contribution Award in Network Science and Graph Analytics

Introduction of Pioneering Contribution Award in Network Science and Graph Analytics Welcome to the Pioneering Contribution Award in Network Science and Graph Analytics, celebrating the trailblazers shaping the future of

Outstanding Contribution to Graph Analytics in Business Award

Introduction of Outstanding Contribution to Graph Analytics in Business Award Welcome to the pinnacle of recognition for leaders shaping the future of Graph Analytics in the business realm. The Outstanding

1 2 3 4 Next »

Outstanding Research Achievement Award in Network Science and Graph Analytics

Posted on 16/11/202323/11/2023 by Leave a Comment

Introduction of Outstanding Research Achievement Award in Network Science and Graph Analytics

Welcome to the pinnacle of excellence in the realm of Network Science and Graph Analytics! The "Outstanding Research Achievement Award in Network Science and Graph Analytics" is a distinguished accolade that celebrates and honors groundbreaking research endeavors contributing to the advancement of these dynamic fields.

Award Eligibility:

This award is open to researchers and scholars worldwide who have demonstrated exceptional achievements in the domains of Network Science and Graph Analytics. There are no age limits, and applicants should hold a relevant academic qualification. Submissions must showcase outstanding publications and contributions to the field.

Evaluation Criteria:

Submissions will be evaluated based on the significance and impact of the research, innovation, methodological rigor, and the potential to advance the understanding and application of Network Science and Graph Analytics.

Submission Guidelines:

Eligible candidates are invited to submit their research papers, along with a comprehensive biography.
Abstracts should concisely summarize the research and its implications.
Supporting files, such as graphs, charts, or supplementary materials, must accompany the submission.

Recognition:

The recipient of the "Outstanding Research Achievement Award in Network Science and Graph Analytics" will receive global recognition for their groundbreaking contributions. The award aims to elevate the profile of exceptional researchers and their impactful work.

Community Impact:

This award not only recognizes individual excellence but also emphasizes the broader impact on the Network Science and Graph Analytics community. The recipient's work should demonstrate potential applications and advancements that benefit the wider community.

Biography:

Applicants should provide a detailed biography outlining their academic background, research experience, and notable contributions to Network Science and Graph Analytics.

Abstract and Supporting Files:

The abstract should be a concise summary of the research, and supporting files should complement the submission, providing additional context and data.

Innovation Excellence Award in Network Science and Graph Analytics

Outstanding Research Achievement Award in Network Science and Graph Analytics

Academic Excellence Award in Network Science and Graph Analytics

Industry Impact Award in Network Science and Graph Analytics

Leadership in Business Applications Award in Network Science and Graph Analytics

Collaborative Achievement Award in Network Science and Graph Analytics

Emerging Talent Award in Network Science and Graph Analytics

Strategic Implementation Award in Network Science and Graph Analytics

Pioneering Contribution Award in Network Science and Graph Analytics

Outstanding Contribution to Graph Analytics in Business Award

1 2 3 4 Next »

Innovation Excellence Award in Network Science and Graph Analytics

Posted on 16/11/202323/11/2023 by Leave a Comment

Introduction of Innovation Excellence Award in Network Science and Graph Analytics

Welcome to the forefront of recognition in the realm of Network Science and Graph Analytics! The Innovation Excellence Award celebrates pioneers, visionaries, and groundbreaking contributors who have significantly advanced the fields of network science and graph analytics. This prestigious award is a testament to the relentless pursuit of innovation that shapes the future of interconnected systems.

Award Eligibility:

This award is open to individuals and teams across academia, industries, and research institutions who have demonstrated exceptional innovation in the field of Network Science and Graph Analytics.

Age Limits:

There are no age restrictions; this award recognizes excellence regardless of age.

Qualification:

Open to individuals and teams with a proven track record of innovative contributions in Network Science and Graph Analytics.

Publications:

Candidates should have notable publications that showcase their impactful work in the field.

Requirements:

Demonstration of groundbreaking innovation.
A record of significant contributions to Network Science and Graph Analytics.
Noteworthy publications showcasing advancements.

Evaluation Criteria:

Entries will be evaluated based on the originality, impact, and relevance of the innovation in Network Science and Graph Analytics.

Submission Guidelines:

Submit a comprehensive biography highlighting relevant achievements.
Include an abstract summarizing the innovative contribution.
Attach supporting files showcasing the impact of the work.

Recognition:

The awardee will receive public recognition, a trophy, and the opportunity to present their work at a prominent industry event.

Community Impact:

The award aims to foster a collaborative community by recognizing and promoting impactful contributions that benefit the broader network science and graph analytics community.

Biography:

Provide a brief but comprehensive biography highlighting your journey, achievements, and contributions to the field.

Abstract and Supporting Files:

Include a concise abstract summarizing the innovation and supporting files that demonstrate the impact of the work.

Innovation Excellence Award in Network Science and Graph Analytics

Outstanding Research Achievement Award in Network Science and Graph Analytics

Academic Excellence Award in Network Science and Graph Analytics

Industry Impact Award in Network Science and Graph Analytics

Leadership in Business Applications Award in Network Science and Graph Analytics

Collaborative Achievement Award in Network Science and Graph Analytics

Emerging Talent Award in Network Science and Graph Analytics

Strategic Implementation Award in Network Science and Graph Analytics

Pioneering Contribution Award in Network Science and Graph Analytics

Outstanding Contribution to Graph Analytics in Business Award

1 2 3 4 Next »

Profile

Fahimeh Dabaghi Zarandi | Community Detection | Women Researcher Award

Assist. Prof. Dr. Fahimeh Dabaghi Zarandi | Community Detection | Women Researcher Award

Education Background🎓

Research Interests🔬

Author Metrics

Publications Top Notes 📄

Conclusion

Emerging Talent Award in Network Science and Graph Analytics

Introduction of Emerging Talent Award in Network Science and Graph Analytics

Eligibility:

Evaluation Criteria:

Submission Guidelines:

Recognition:

Community Impact:

Outstanding Research Achievement Award in Network Science and Graph Analytics

Introduction of Outstanding Research Achievement Award in Network Science and Graph Analytics

Award Eligibility:

Evaluation Criteria:

Submission Guidelines:

Recognition:

Community Impact:

Biography:

Abstract and Supporting Files:

Innovation Excellence Award in Network Science and Graph Analytics

Introduction of Innovation Excellence Award in Network Science and Graph Analytics

Award Eligibility:

Age Limits:

Qualification:

Publications:

Requirements:

Evaluation Criteria:

Submission Guidelines:

Recognition:

Community Impact:

Biography:

Abstract and Supporting Files:

Latest News

RECOMMENDED

Mail us

Assist. Prof. Dr. Alireza Rezvanian | Complex Social Networks | Network Science Excellence Award

Profile

Education Background🎓

Author Metrics

Assist. Prof. Dr. Fahimeh Dabaghi Zarandi | Community Detection | Women Researcher Award

Education Background🎓

Research Interests🔬

Author Metrics

Publications Top Notes 📄

Conclusion

Introduction of Emerging Talent Award in Network Science and Graph Analytics

Eligibility:

Evaluation Criteria:

Submission Guidelines:

Recognition:

Community Impact:

Introduction of Outstanding Research Achievement Award in Network Science and Graph Analytics

Award Eligibility:

Evaluation Criteria:

Submission Guidelines:

Recognition:

Community Impact:

Biography:

Abstract and Supporting Files:

Introduction of Innovation Excellence Award in Network Science and Graph Analytics

Award Eligibility:

Age Limits:

Qualification:

Publications:

Requirements:

Evaluation Criteria:

Submission Guidelines:

Recognition:

Community Impact:

Biography:

Abstract and Supporting Files: