Alireza Rezvanian is an Assistant Professor in the Department of Computer Engineering, University of Science and Culture (USC), Tehran, Iran. Prior to his current position, he worked from 2016 to 2020 as a researcher in the School of Computer Science at Institute for Research in Fundamental Sciences (IPM), Tehran, Iran. He is an ACM professional member and member of the board of IEEE computer society Iran section. Also, He is associate editor of human-centric computing and information sciences, CAAI Transactions on Intelligence Technology (Wiley), The Journal of Engineering (Wiley), and Data in Brief (Elsevier). He was a lead guest editor of the special issue on new applications of learning automata-based techniques in real-world environments for the journal of computational science (Elsevier). His research activities include soft computing, Machine learning, learning automata, complex networks, social network analysis, data mining, and evolutionary algorithms.
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Engineering, Artificial Intelligence, Computer Networks and Communications
109
Scopus Publications
2541
Scholar Citations
29
Scholar h-index
70
Scholar i10-index
Scopus Publications
Maximum independent set in multiplex social networks and its application to influence maximization Mohammad Mehdi Daliri Khomami, Alireza Rezvanian, Mohammad Reza Meybodi Scientific Reports, 2025 Identifying the most influential spreaders as an influence maximization problem (IMP) has become one of the most compelling topics in social network analysis due to its successes in viral marketing. In this paper, we first assume the network model to be a multiplex network, consisting of layers where each layer represents a different type of association among users based on their activities. We then define the concept of the maximum independent set (MIS) problem within multiplex networks. Next, we propose MIS as a potential solution to the MIP for identifying the initial candidate set of spreaders. Finally, we develop a learning automaton framework to solve the MIS in multiplex networks and to demonstrate its applicability for influence maximization. Theoretical properties of the MIS in multiplex networks are provided, along with various experiments on both artificial and real networks to showcase the performance of the proposed algorithm.
Intelligent vehicle routing for stochastic service times: a grouping evolution strategy approach Sepinoud Rezvanian, Ali Husseinzadeh Kashan, Alireza Rezvanian, Alireza Sabzevari International Journal of Transportation Science and Technology, 2025 After-sales service companies often need to transport and distribute their products, as well as provide services like installation and setup. In this paper, we study a vehicle routing problem (VRP) that considers the possibility of on-site servicing by installers, with stochastic service times. We formulate the distribution of products to customers as a VRP and extract the route plan for installers by solving a traveling salesman-type problem. To model the stochastic service times, we use a chance constraint while minimizing the total cost for both vehicles and installers. We develop a Grouping Evolution Strategy (GES) algorithm equipped with various heuristics to generate efficient solutions for large-scale problems. Numerical results are reported from a case study using existing data for the problem. The computational experiments demonstrate that GES can produce near-optimal solutions for operational use within a reasonable amount of time. In summary, our study shows the effectiveness of GES in solving the VRP with on-site stochastic service times, which is relevant to many after-sales service companies.
Efficient identification of maximum independent sets in stochastic multilayer graphs with learning automata Mohammad Mehdi Daliri Khomami, Mohammad Reza Meybodi, Alireza Rezvanian Results in Engineering, 2024 Investigating the maximum independent set in stochastic multilayer graphs provides critical insights into the structural and dynamical properties of complex networks. Recently, stochastic multilayer graphs effectively model the intricate interactions and interdependencies inherent in real-world systems, including social, biological, and transportation networks. The identification of a maximum independent set -comprising nodes without direct connections- offers a significant understanding of phenomena such as information diffusion, resource allocation, and epidemic spread within complex social networks. For instance, independent sets play a crucial role in identifying influencers -individuals who profoundly impact their peers, propagating information or opinions widely. In this paper, we introduce the stochastic version of the maximum independent set and propose five algorithms based on learning automata to identify maximum independent sets in the stochastic multilayer graphs. Our approach utilizes learning automata to provide a guided sampling from candidate independent sets of the stochastic multilayer graph, aiming to identify the independent set with the maximum expected value while utilizing fewer vertex samples than standard methods that do not incorporate the learning. In addition to proving several mathematical properties of the proposed approach, simulations conducted across diverse stochastic multilayer graphs demonstrate that our learning automata-based algorithms outperform traditional approaches, achieving higher convergence rates and requiring fewer samples.
Advanced fusion of MTM-LSTM and MLP models for time series forecasting: An application for forecasting the solar radiation Mahin Mohammadi, Saman Jamshidi, Alireza Rezvanian, Mehdi Gheisari, Ajay Kumar Measurement Sensors, 2024 Accurate time series forecasting has become increasingly important across various domains such as finance, energy, and medicine. This study introduces an innovative hybrid model that leverages the power of neural networks, precisely Many To Many LSTM (MTM LSTM) and Multilayer Perceptron (MLP), to improve time series forecasting accuracy. In this new combination, we trained network MTM LSTM to approximate the target at each step, and finally, we used network MLP to combine these approximations. To perform the evaluation, we made a forecast for the amount of solar energy radiation in the city of Mashhad, Iran. The experiment results concerning MSE and MAE showed that the proposed method with five lags outperforms the standard models. We hypothesize that MTM LSTM can effectively capture solar radiation's intricate temporal dependencies and nonlinearity. At the same time, MLP can enhance function approximation by modeling complex interactions, resulting in improved forecast accuracy. By employing the hybrid MTM LSTM and MLP model, we achieved improved accuracy in predicting solar energy radiation, which has significant implications for the renewable energy sector and its energy management and planning applications. This research advances time series forecasting techniques, highlighting the effectiveness of combining neural networks to address complex and dynamic patterns in time-dependent data. Overall, our findings underscore the potential and efficacy of the proposed hybrid model as a robust tool for accurate time series forecasting in various domains, supporting effective decision-making and planning processes.
Effective text classification using BERT, MTM LSTM, and DT Saman Jamshidi, Mahin Mohammadi, Saeed Bagheri, Hamid Esmaeili Najafabadi, Alireza Rezvanian, Mehdi Gheisari, Mustafa Ghaderzadeh, Amir Shahab Shahabi, Zongda Wu Data and Knowledge Engineering, 2024
An Efficient Approach to Detecting Lung Nodules Using Swin Transformer Saeed Shakuri, Alireza Rezvanian Icis 2024 19th Iranian Conference on Intelligent Systems, 2024 Lung cancer has the highest rate of cancer-caused deaths, and early-stage diagnosis could increase the survival rate. Lung nodules are one of the most common lung cancer signs, therefore, the development of lung nodule detection systems becomes significantly crucial. Different lung nodule detection models have been introduced, however, they haven't fully considered all aspects of efficiency. Hence, in this paper, we take a more efficient approach in introducing a lung nodule detection model while providing improvements in nodule detection. In this regard, we consider 2D slices of the CT scans which not only lead to lower computational load and complexity in training and inference phases but also, allow us to examine the potential of 2D models in lung nodule detection. Moreover, we utilize the tiny version of Swin Transformer to leverage the benefits of Vision Transformers (ViT) while having a much lower computational complexity. We also build a Feature Pyramid Network on top of our backbone to detect nodules of various sizes better, especially small nodules. Additionally, we consider Transfer Learning for the training procedure, resulting in significantly lower training iterations. The experimental results demonstrate the competitive performance of our model compared to the state-of-the-art models with even higher mAP and mAR for small nodules by 1.3% and 1.6% respectively. Also, our proposed model achieved the highest mAP in all size nodules with 94.7% and mAR of 94.9%.
Social network sampling Alireza Rezvanian, Behnaz Moradabadi, Mina Ghavipour, Mohammad Mehdi Daliri Khomami, Mohammad Reza Meybodi Studies in Computational Intelligence, 2019
Social link prediction Alireza Rezvanian, Behnaz Moradabadi, Mina Ghavipour, Mohammad Mehdi Daliri Khomami, Mohammad Reza Meybodi Studies in Computational Intelligence, 2019
Social recommender systems Alireza Rezvanian, Behnaz Moradabadi, Mina Ghavipour, Mohammad Mehdi Daliri Khomami, Mohammad Reza Meybodi Studies in Computational Intelligence, 2019
Preface Mark Minas Studies in Computational Intelligence, 2019
Social trust management Alireza Rezvanian, Behnaz Moradabadi, Mina Ghavipour, Mohammad Mehdi Daliri Khomami, Mohammad Reza Meybodi Studies in Computational Intelligence, 2019
Social community detection Alireza Rezvanian, Behnaz Moradabadi, Mina Ghavipour, Mohammad Mehdi Daliri Khomami, Mohammad Reza Meybodi Studies in Computational Intelligence, 2019
Social influence maximization Alireza Rezvanian, Behnaz Moradabadi, Mina Ghavipour, Mohammad Mehdi Daliri Khomami, Mohammad Reza Meybodi Studies in Computational Intelligence, 2019
Introduction to learning automata models Alireza Rezvanian, Behnaz Moradabadi, Mina Ghavipour, Mohammad Mehdi Daliri Khomami, Mohammad Reza Meybodi Studies in Computational Intelligence, 2019
Summary and future directions Alireza Rezvanian, Ali Mohammad Saghiri, Seyed Mehdi Vahidipour, Mehdi Esnaashari, Mohammad Reza Meybodi Studies in Computational Intelligence, 2018
Preface Studies in Computational Intelligence, 2018
Recent advances in learning automata Alireza Rezvanian, Ali Mohammad Saghiri, Seyed Mehdi Vahidipour, Mehdi Esnaashari, Mohammad Reza Meybodi Studies in Computational Intelligence, 2018
Cellular learning automata Alireza Rezvanian, Ali Mohammad Saghiri, Seyed Mehdi Vahidipour, Mehdi Esnaashari, Mohammad Reza Meybodi Studies in Computational Intelligence, 2018
Learning automata theory Alireza Rezvanian, Ali Mohammad Saghiri, Seyed Mehdi Vahidipour, Mehdi Esnaashari, Mohammad Reza Meybodi Studies in Computational Intelligence, 2018
Learning automata for wireless sensor networks Alireza Rezvanian, Ali Mohammad Saghiri, Seyed Mehdi Vahidipour, Mehdi Esnaashari, Mohammad Reza Meybodi Studies in Computational Intelligence, 2018
Adaptive petri net based on learning automata Alireza Rezvanian, Ali Mohammad Saghiri, Seyed Mehdi Vahidipour, Mehdi Esnaashari, Mohammad Reza Meybodi Studies in Computational Intelligence, 2018
Learning automata for complex social networks Alireza Rezvanian, Ali Mohammad Saghiri, Seyed Mehdi Vahidipour, Mehdi Esnaashari, Mohammad Reza Meybodi Studies in Computational Intelligence, 2018
Irregular cellular automata based diffusion model for influence maximization Mohammad Mehdi Daliri Khomami, Alireza Rezvanian, Negin Bagherpour, Mohammad Reza Meybodi 5th Iranian Joint Congress on Fuzzy and Intelligent Systems 16th Conference on Fuzzy Systems and 14th Conference on Intelligent Systems Cfis 2017, 2017
A fast algorithm for overlapping community detection Mostafa Elyasi, Mohammadreza Meybodi, Alireza Rezvanian, Maryam Amir Haeri 2016 8th International Conference on Information and Knowledge Technology Ikt 2016, 2016
A two-phase sampling algorithm for social networks Zeinab S. Jalali, Alireza Rezvanian, Mohammad Reza Meybodi Conference Proceedings of 2015 2nd International Conference on Knowledge Based Engineering and Innovation Kbei 2015, 2016
Success rate group search optimiser Mohammad Hasanzadeh, Sana Sadeghi, Alireza Rezvanian, Mohammad Reza Meybodi Journal of Experimental and Theoretical Artificial Intelligence, 2016
Parallel sorting on linear cellular automata Proceedings of the 2008 International Conference on Parallel and Distributed Processing Techniques and Applications PDPTA 2008, 2008
RECENT SCHOLAR PUBLICATIONS
Semantic Center-Oriented Loss Enhanced Decision-Based Heterogeneous Graph Attention Networks for Multi-Class Fake News Detection E Shokri, A Rezvanian 12th International Conference on Web Research (ICWR2026) , 2026 2026
Maximum independent set in multiplex social networks and its application to influence maximization MM Daliri Khomami, A Rezvanian, MR Meybodi Scientific Reports 15 (1), 16322 , 2025 2025 Citations: 6
Intelligent Vehicle Routing for Stochastic Service Times: A Grouping Evolution Strategy Approach S Rezvanian, AH Kashan, A Rezvanian, A Sabzevari International Journal of Transportation Science and Technology , 2025 2025 Citations: 2
Fall Detection Using Ensemble Deep Learning F Dehghani, A Rezvanian 11th International Conference on Web Research (ICWR2025) , 2025 2025
Enhancing Link Prediction Using Node Representation and Ensemble Learning S Dolati, A Rezvanian 11th International Conference on Web Research (ICWR2025) , 2025 2025
SEAL+: A Subgraph-Enhanced Framework for Link Prediction with Graph Neural Networks R Karami, SM Vahidipour, A Rezvanian Journal of Industrial Information Integration 44, 100802 , 2025 2025 Citations: 9
A cellular goore game-based algorithm for finding the shortest path in stochastic multi-layer graphs MMD Khomami, MR Meybodi, A Rezvanian The Journal of Supercomputing 81 (1), 322 , 2025 2025 Citations: 1
Efficient identification of maximum independent sets in stochastic multilayer graphs with learning automata MMD Khomami, MR Meybodi, A Rezvanian Results in Engineering 24, 103224 , 2024 2024 Citations: 5
An Efficient Approach to Detecting Lung Nodules Using Swin Transformer S Shakuri, A Rezvanian 2024 19th Iranian Conference on Intelligent Systems (ICIS), 1-5 , 2024 2024 Citations: 2
Advanced fusion of MTM-LSTM and MLP models for time series forecasting: An application for forecasting the solar radiation M Mohammadi, S Jamshidi, A Rezvanian, M Gheisari, A Kumar Measurement: Sensors 33, 101179 , 2024 2024 Citations: 77
A spanning tree approach to social network sampling with degree constraints A Rezvanian, SM Vahidipour, ZS Jalali Social Network Analysis and Mining 14 (1), 1-21 , 2024 2024 Citations: 2
Effective text classification using BERT, MTM LSTM, and DT S Jamshidi, M Mohammadi, S Bagheri, HE Najafabadi, A Rezvanian, ... Data & Knowledge Engineering 151, 102306 , 2024 2024 Citations: 80
Exploring social networks through stochastic multilayer graph modeling MMD Khomami, MR Meybodi, A Rezvanian Chaos, Solitons & Fractals 182, 114764 , 2024 2024 Citations: 6
Scalable Real-time Emotion Recognition using EfficientNetV2 and Resolution Scaling O Ghadami, A Rezvanian, S Shakuri 2024 10th International Conference on Web Research (ICWR), 7-12 , 2024 2024 Citations: 4
Improving the DeepWalk Algorithm for Link Prediction In Social Networks P Mahmoodzadeh, A Rezvanian 10th International Conference on Web Research (ICWR2024) , 2024 2024
A Scalable Method for Real-Time Facial Emotion Recognition using an Artificial Neural Network and Polynomial Equation O Ghadami, A Rezvanian International Journal of Web Research 7 (4), 39-49 , 2024 2024
Improving the Maximization of Information Diffusion in Social Networks Using User Similarity Metrics Z Sajdeh, A Rezvanian Computing Science Journal 9 (3), 25-38 , 2024 2024
CaAIS: Cellular Automata-Based Artificial Immune System for Dynamic Environments A Rezvanian, SM Vahidipour, AM Saghiri Algorithms 17 (1), 18 , 2023 2023 Citations: 8
A novel regularized weighted estimation method for information diffusion prediction in social networks Y Mashayekhi, A Rezvanian, SM Vahidipour Applied Network Science 8 (1), 81 , 2023 2023 Citations: 1
An Overview of Ant Colony Optimization Algorithms for Dynamic Optimization Problems A Rezvanian, SM Vahidipour, A Sadollah Ant Colony Optimization - Recent Variants, Application and Perspectives , 2023 2023 Citations: 33
MOST CITED SCHOLAR PUBLICATIONS
Robust fall detection using human shape and multi-class support vector machine H Foroughi, A Rezvanian, A Paziraee Sixth Indian Conference on Computer Vision, Graphics & Image Processing … , 2008 2008 Citations: 143
Sampling from complex networks using distributed learning automata A Rezvanian, M Rahmati, MR Meybodi Physica A: Statistical Mechanics and its Applications 396, 224-234 , 2014 2014 Citations: 95
Minimum positive influence dominating set and its application in influence maximization: a learning automata approach MMD Khomami, A Rezvanian, N Bagherpour, MR Meybodi Applied Intelligence 48 (3), 570–593 , 2018 2018 Citations: 92
CDEPSO: A Bi-population Hybrid Approach for Dynamic Optimization Problems JK Kordestani, A Rezvanian, MR Meybodi Applied Intelligence 40 (4), 682-694 , 2014 2014 Citations: 83
Effective text classification using BERT, MTM LSTM, and DT S Jamshidi, M Mohammadi, S Bagheri, HE Najafabadi, A Rezvanian, ... Data & Knowledge Engineering 151, 102306 , 2024 2024 Citations: 80
Advanced fusion of MTM-LSTM and MLP models for time series forecasting: An application for forecasting the solar radiation M Mohammadi, S Jamshidi, A Rezvanian, M Gheisari, A Kumar Measurement: Sensors 33, 101179 , 2024 2024 Citations: 77
A New Cellular Learning Automata-based Algorithm for Community Detection in Complex Social Networks MMD Khomami, A Rezvanian, MR Meybodi Journal of Computational Science 24, 413-426 , 2018 2018 Citations: 68
Stochastic graph as a model for social networks A Rezvanian, MR Meybodi Computers in Human Behavior 64, 621-640 , 2016 2016 Citations: 65
Cellular Edge Detection: Combining Cellular Automata and Cellular Learning Automata M Hasanzadeh Mofrad, S Sadeghi, A Rezvanian, MR Meybodi AEU-International Journal of Electronics and Communications 69 (9), 1282–1290 , 2015 2015 Citations: 65
Sampling social networks using shortest paths A Rezvanian, MR Meybodi Physica A: Statistical Mechanics and its Applications 424, 254–268 , 2015 2015 Citations: 65
Learning Automata Clustering M Hasanzadeh-Mofrad, A Rezvanian Journal of Computational Science 24, 379-388 , 2018 2018 Citations: 64
Distributed learning automata-based algorithm for community detection in complex networks MMD Khomami, A Rezvanian, MR Meybodi International Journal of Modern Physics B 30, 1650042 , 2016 2016 Citations: 64
CFIN: A community‑based algorithm for finding influential nodes in complex social networks MMD Khomami, A Rezvanian, MR Meybodi, A Bagheri The Journal of Supercomputing , 2020 2020 Citations: 62
Recent Advances in Learning Automata A Rezvanian, AM Saghiri, SM Vahidipour, M Esnaashari, MR Meybodi Springer , 2018 2018 Citations: 58
An efficient method for impulse noise reduction from images using fuzzy cellular automata S Sadeghi, A Rezvanian, E Kamrani AEU-International Journal of Electronics and Communications 66 (9), 772-779 , 2012 2012 Citations: 52
A new learning automata based sampling algorithm for social networks A Rezvanian, MR Meybodi International Journal of Communication Systems 30 (5), e3091 , 2017 2017 Citations: 50
AntLP: ant-based label propagation algorithm for community detection in social networks R Hosseini, A Rezvanian CAAI Transactions on Intelligence Technology 5 (1), 34-41 , 2020 2020 Citations: 46
Tracking Extrema in Dynamic Environments Using a Learning Automata-Based Immune Algorithm A Rezvanian, MR Meybodi Grid and Distributed Computing, Control and Automation 12, 216-225 , 2010 2010 Citations: 44
Learning Automata Approach for Social Networks A Rezvanian, B Moradabadi, M Ghavipour, MMD Khomami, MR Meybodi Springer , 2019 2019 Citations: 41
Finding Maximum Clique in Stochastic Graphs Using Distributed Learning Automata A Rezvanian, MR Meybodi International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems … , 2015 2015 Citations: 38
