Yazar "Rouhi, Alireza" seçeneğine göre listele

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    A software defect prediction method using binary gray wolf optimizer and machine learning algorithms
    (Pergamon-Elsevier Science, 2024) Wang, Hao; Arasteh, Bahman; Arasteh, Keyvan; Gharehchopogh, Farhad Soleimanian; Rouhi, Alireza
    Context: Software defect prediction means finding defect-prone modules before the testing process which will reduce testing cost and time. Machine learning methods can provide valuable models for developers to classify software faulty modules. Problem: The inherent problem of the classification is the large volume of the training dataset's features, which reduces the accuracy and precision of the classification results. The selection of the effective features of the training dataset for classification is an NP-hard problem that can be solved using heuristic algorithms. Method: In this study, a binary version of the Gray Wolf optimizer (bGWO) was developed to select the most effective features of the training dataset. By selecting the most influential features in the classification, the precision and accuracy of the software module classifiers can be increased. Contribution: Developing a binary version of the gray wolf optimization algorithm to optimally select the effective features and creating an effective defect predictor are the main contributions of this study. To evaluate the effectiveness of the proposed method, five real-world and standard datasets have been used for the training and testing stages of the classifier. Results: The results indicate that among the 21 features of the train datasets, the basic complexity, sum of operators and operands, lines of codes, number of lines containing code and comments, and sum of operands have the greatest effect in predicting software defects. In this research, by combining the bGWO method and machine learning algorithms, accuracy, precision, recall, and F1 criteria have been considerably increased.
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    DATA REPLICATION IN DISTRIBUTED SYSTEMS USING OLYMPIAD OPTIMIZATION ALGORITHM
    (Univ Nis, 2023) Arasteh, Bahman; Bouyer, Asgarali; Ghanbarzadeh, Reza; Rouhi, Alireza; Mehrabani, Mahsa Nazeri; Tirkolaee, Erfan Babaee
    Achieving timely access to data objects is a major challenge in big distributed systems like the Internet of Things (IoT) platforms. Therefore, minimizing the data read and write operation time in distributed systems has elevated to a higher priority for system designers and mechanical engineers. Replication and the appropriate placement of the replicas on the most accessible data servers is a problem of NP-complete optimization. The key objectives of the current study are minimizing the data access time, reducing the quantity of replicas, and improving the data availability. The current paper employs the Olympiad Optimization Algorithm (OOA) as a novel population-based and discrete heuristic algorithm to solve the replica placement problem which is also applicable to other fields such as mechanical and computer engineering design problems. This discrete algorithm was inspired by the learning process of student groups who are preparing for the Olympiad exams. The proposed algorithm, which is divide-and-conquer-based with local and global search strategies, was used in solving the replica placement problem in a standard simulated distributed system. The 'European Union Database' (EUData) was employed to evaluate the proposed algorithm, which contains 28 nodes as servers and a network architecture in the format of a complete graph. It was revealed that the proposed technique reduces data access time by 39% with around six replicas, which is vastly superior to the earlier methods. Moreover, the standard deviation of the results of the algorithm's different executions is approximately 0.0062, which is lower than the other techniques' standard deviation within the same experiments.
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    A fast module identification and filtering approach for influence maximization problem in social networks
    (Elsevier Science Inc, 2023) Beni, Hamid Ahmadi; Bouyer, Asgarali; Azimi, Sevda; Rouhi, Alireza; Arasteh, Bahman
    In this paper, we explore influence maximization, one of the most widely studied problems in social network analysis. However, developing an effective algorithm for influence maximization is still a challenging task given its NP-hard nature. To tackle this issue, we propose the CSP (Combined modules for Seed Processing) algorithm, which aim to identify influential nodes. In CSP, graph modules are initially identified by a combination of criteria such as the clustering coefficient, degree, and common neighbors of nodes. Nodes with the same label are then clustered together into modules using label diffusion. Subsequently, only the most influential modules are selected using a filtering method based on their diffusion capacity. The algorithm then merges neighboring modules into distinct modules and extracts a candidate set of influential nodes using a new metric to quickly select seed sets. The number of selected nodes for the candidate set is restricted by a defined limit measure. Finally, seed nodes are chosen from the candidate set using a novel node scoring measure. We evaluated the proposed algorithm on both real-world and synthetic networks, and our experimental results indicate that the CSP algorithm outperforms other competitive algorithms in terms of solution quality and speedup on tested networks.
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    A hybrid chaos-based algorithm for data object replication in distributed systems
    (Taylor & Francis Ltd, 2024) Arasteh, Bahman; Gunes, Peri; Bouyer, Asgarali; Rouhi, Alireza; Ghanbarzadeh, Reza
    One of the primary challenges in distributed systems, such as cloud computing, lies in ensuring that data objects are accessible within a reasonable timeframe. To address this challenge, the data objects are replicated across multiple servers. Estimating the minimum quantity of data replicas and their optimal placement is considered an NP-complete optimization problem. The primary objectives of the current research include minimizing data processing costs, reducing the quantity of replicas, and maximizing the applied algorithms' reliability in replica placement. This paper introduces a hybrid chaos-based swarm approach using the modified shuffle-frog leaping algorithm with a new local search strategy for replicating data in distributed systems. Taking into account the algorithm's performance in static settings, the introduced method reduces the expenses associated with replica placement. The results of the experiment conducted on a standard data set indicate that the proposed approach can decrease data access time by about 33% when using approximately seven replicas. When executed several times, the suggested method yields a standard deviation of approximately 0.012 for the results, which is lower than the result existing algorithms produce. Additionally, the new approach's success rate is higher in comparison with existing algorithms used in addressing the problem of replica placement.
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    Two-pronged feature reduction in spectral clustering with optimized landmark selection
    (Elsevier, 2024) Rouhi, Alireza; Bouyer, Asgarali; Arasteh, Bahman; Liu, Xiaoyang
    Spectral clustering is widely employed for clustering data points, particularly for non-linear and non-convex structures in high-dimensional spaces. However, it faces challenges due to the high computational cost of eigen decomposition operations and the performance limitations with high-dimensional data. In this paper, we introduce BVA_LSC, a novel spectral clustering algorithm designed to address these challenges. Firstly, we incorporate an advanced feature reduction stage utilizing Barnes-Hut t-SNE and a deep Variational Autoencoder (VAE) to efficiently reduce the dimensionality of the data, thereby accelerating eigen decomposition. Secondly, we propose an adaptive landmark selection strategy that combines the Grey Wolf Optimizer (GWO) with a novel objective function and K-harmonic means clustering. This strategy dynamically determines an optimal number of landmarks, enhancing the representativeness of the data and reducing the size of the similarity matrix. We assess the performance of our algorithm on various standard datasets, demonstrating its superiority over state-of-the-art methods in terms of accuracy and efficiency.