Yazar "Torkamanian-Afshar, Mahsa" seçeneğine göre listele

Listeleniyor 1 - 8 / 8
  • Küçük Resim Yok
    Öğe
    A bioinspired discrete heuristic algorithm to generate the effective structural model of a program source code
    (Elsevier, 2023) Arasteh, Bahman; Sadegi, Razieh; Arasteh, Keyvan; Gunes, Peri; Kiani, Farzad; Torkamanian-Afshar, Mahsa
    When the source code of a software is the only product available, program understanding has a substantial influence on software maintenance costs. The main goal in code comprehension is to extract information that is used in the software maintenance stage. Generating the structural model from the source code helps to alleviate the software maintenance cost. Software module clustering is thought to be a viable reverse engineering approach for building structural design models from source code. Finding the optimal clustering model is an NP-complete problem. The primary goals of this study are to minimize the number of connections between created clusters, enhance internal connections inside clusters, and enhance clustering quality. The previous approaches' main flaws were their poor success rates, instability, and inadequate modularization quality. The Olympiad optimization algorithm was introduced in this paper as a novel population-based and discrete heuristic algorithm for solving the software module clustering problem. This algorithm was inspired by the competition of a group of students to increase their knowledge and prepare for an Olympiad exam. The suggested algorithm employs a divide-and-conquer strategy, as well as local and global search methodologies. The effectiveness of the suggested Olympiad algorithm to solve the module clustering problem was evaluated using ten real-world and standard software benchmarks. According to the experimental results, on average, the modularization quality of the generated clustered models for the ten benchmarks is about 3.94 with 0.067 standard deviations. The proposed algorithm is superior to the prior algorithms in terms of modularization quality, convergence, and stability of results. Furthermore, the results of the experiments indicate that the proposed algorithm can be used to solve other discrete optimization problems efficiently. (c) 2023 The Author(s). Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
  • Küçük Resim Yok
    Öğe
    Detecting SQL injection attacks by binary gray wolf optimizer and machine learning algorithms
    (Springer London Ltd, 2024) Arasteh, Bahman; Aghaei, Babak; Farzad, Behnoud; Arasteh, Keyvan; Kiani, Farzad; Torkamanian-Afshar, Mahsa
    SQL injection is one of the important security issues in web applications because it allows an attacker to interact with the application's database. SQL injection attacks can be detected using machine learning algorithms. The effective features should be employed in the training stage to develop an optimal classifier with optimal accuracy. Identifying the most effective features is an NP-complete combinatorial optimization problem. Feature selection is the process of selecting the training dataset's smallest and most effective features. The main objective of this study is to enhance the accuracy, precision, and sensitivity of the SQLi detection method. In this study, an effective method to detect SQL injection attacks has been proposed. In the first stage, a specific training dataset consisting of 13 features was prepared. In the second stage, two different binary versions of the Gray-Wolf algorithm were developed to select the most effective features of the dataset. The created optimal datasets were used by different machine learning algorithms. Creating a new SQLi training dataset with 13 numeric features, developing two different binary versions of the gray wolf optimizer to optimally select the features of the dataset, and creating an effective and efficient classifier to detect SQLi attacks are the main contributions of this study. The results of the conducted tests indicate that the proposed SQL injection detector obtain 99.68% accuracy, 99.40% precision, and 98.72% sensitivity. The proposed method increases the efficiency of attack detection methods by selecting 20% of the most effective features.
  • Küçük Resim Yok
    Öğe
    A discrete heuristic algorithm with swarm and evolutionary features for data replication problem in distributed systems
    (Springer London Ltd, 2023) Arasteh, Bahman; Allahviranloo, Tofigh; Funes, Peri; Torkamanian-Afshar, Mahsa; Khari, Manju; Catak, Muammer
    Availability and accessibility of data objects in a reasonable time is a main issue in distributed systems like cloud computing services. As a result, the reduction of data-related operation times in distributed systems such as data read/write has become a major challenge in the development of these systems. In this regard, replicating the data objects on different servers is one commonly used technique. In general, replica placement plays an essential role in the efficiency of distributed systems and can be implemented statically or dynamically. Estimation of the minimum number of data replicas and the optimal placement of the replicas is an NP-complete optimization problem. Hence, different heuristic algorithms have been proposed for optimal replica placement in distributed systems. Reducing data processing costs as well as the number of replicas, and increasing the reliability of the replica placement algorithms are the main goals of this research. This paper presents a discrete and swarm-evolutionary method using a combination of shuffle-frog leaping and genetic algorithms to data-replica placement problems in distributed systems. The experiments on the standard dataset show that the proposed method reduces data access time by up to 30% with about 14 replicas; whereas the generated replicas by the GA and ACO are, respectively, 24 and 30. The average reduction in data access time by GA and ACO 21% and 18% which shows less efficiency than the SFLA-GA algorithm. Regarding the results, the SFLA-GA converges on the optimal solution before the 10th iteration, which shows the higher performance of the proposed method. Furthermore, the standard deviation among the results obtained by the proposed method on several runs is about 0.029, which is lower than other algorithms. Additionally, the proposed method has a higher success rate than other algorithms in the replica placement problem.
  • Küçük Resim Yok
    Öğe
    Effective test-data generation using the modified black widow optimization algorithm
    (Springer, 2024) Arasteh, Bahman; Ghaffari, Ali; Khadir, Milad; Torkamanian-Afshar, Mahsa; Pirahesh, Sajad
    Software testing is one of the software development activities and is used to identify and remove software bugs. Most small-sized projects may be manually tested to find and fix any bugs. In large and real-world software products, manual testing is thought to be a time and money-consuming process. Finding a minimal subset of input data in the shortest amount of time (as test data) to obtain the maximal branch coverage is an NP-complete problem in the field. Different heuristic-based methods have been used to generate test data. In this paper, for addressing and solving the test data generation problem, the black widow optimization algorithm has been used. The branch coverage criterion was used as the fitness function to optimize the generated data. The obtained experimental results on the standard benchmarks show that the proposed method generates more effective test data than the simulated annealing, genetic algorithm, ant colony optimization, particle swarm optimization, and artificial bee colony algorithms. According to the results, with 99.98% average coverage, 99.96% success rate, and 9.36 required iteration, the method was able to outperform the other methods.
  • Küçük Resim
    Öğe
    High-throughput analysis of the interactions between viral proteins and host cell RNAs
    (Elsevier Ltd, 2021) Lanjanian, Hossein; Nematzadeh, Sajjad; Hosseini, Shadi; Torkamanian-Afshar, Mahsa; Kiani, Farzad; Moazzam-Jazi, Maryam; Aydin, Nizamettin; Masoudi-Nejad, Ali
    Indexed keywords Abstract RNA-protein interactions of a virus play a major role in the replication of RNA viruses. The replication and transcription of these viruses take place in the cytoplasm of the host cell; hence, there is a probability for the host RNA-viral protein and viral RNA-host protein interactions. The current study applies a high-throughput computational approach, including feature extraction and machine learning methods, to predict the affinity of protein sequences of ten viruses to three categories of RNA sequences. These categories include RNAs involved in the protein-RNA complexes stored in the RCSB database, the human miRNAs deposited at the mirBase database, and the lncRNA deposited in the LNCipedia database. The results show that evolution not only tries to conserve key viral proteins involved in the replication and transcription but also prunes their interaction capability. These proteins with specific interactions do not perturb the host cell through undesired interactions. On the other hand, the hypermutation rate of NSP3 is related to its affinity to host cell RNAs. The Gene Ontology (GO) analysis of the miRNA with affiliation to NSP3 suggests that these miRNAs show strongly significantly enriched GO terms related to the known symptoms of COVID-19. Docking and MD simulation study of the obtained miRNA through high-throughput analysis suggest a non-coding RNA (an RNA antitoxin, ToxI) as a natural aptamer drug candidate for NSP5 inhibition. Finally, a significant interplay of the host RNA-viral protein in the host cell can disrupt the host cell's system by influencing the RNA-dependent processes of the host cells, such as a differential expression in RNA. Furthermore, our results are useful to identify the side effects of mRNA-based vaccines, many of which are caused by the off-label interactions with the human lncRNAs.
  • Küçük Resim
    Öğe
    Impact of 5HydroxyMethylCytosine (5hmC) on reverse/direct association of cell-cycle, apoptosis, and extracellular matrix pathways in gastrointestinal cancers
    (Springer, 2022) Moravveji, Sayyed Sajjad; Khoshbakht, Samane; Mokhtari, Majid; Salimi, Mahdieh; Lanjanian, Hossein; Nematzadeh, Sajjad; Torkamanian-Afshar, Mahsa; Masoudi-Nejad, Ali
    Background: Aberrant levels of 5-hydroxymethylcytosine (5-hmC) can lead to cancer progression. Identifcation of 5-hmC-related biological pathways in cancer studies can produce better understanding of gastrointestinal (GI) cancers. We conducted a network-based analysis on 5-hmC levels extracted from circulating free DNAs (cfDNA) in GI cancers including colon, gastric, and pancreatic cancers, and from healthy donors. The co-5-hmC network was reconstructed using the weighted-gene co-expression network method. The cancer-related modules/subnetworks were detected. Preservation of three detected 5-hmC-related modules was assessed in an external dataset. The 5-hmC-related modules were functionally enriched, and biological pathways were identifed. The relationship between modules was assessed using the Pearson correlation coefcient (p-value<0.05). An elastic network classifer was used to assess the potential of the 5-hmC modules in distinguishing cancer patients from healthy individuals. To assess the efciency of the model, the Area Under the Curve (AUC) was computed using fve-fold cross-validation in an external dataset. Results: The main biological pathways were the cell cycle, apoptosis, and extracellular matrix (ECM) organization. Direct association between the cell cycle and apoptosis, inverse association between apoptosis and ECM organiza? tion, and inverse association between the cell cycle and ECM organization were detected for the 5-hmC modules in GI cancers. An AUC of 92% (0.73–1.00) was observed for the predictive model including 11 genes. Conclusion: The intricate association between biological pathways of identifed modules may reveal the hidden signifcance of 5-hmC in GI cancers. The identifed predictive model and new biomarkers may be benefcial in cancer detection and precision medicine using liquid biopsy in the early stages.
  • Küçük Resim
    Öğe
    Maximizing coverage and maintaining connectivity in WSN and decentralized IoT: an efficient metaheuristic-based method for environment-aware node deployment
    (Springer Science and Business Media Deutschland GmbH, 2022) Nematzadeh, Sajjad; Torkamanian-Afshar, Mahsa; Seyyedabbasi, Amir; Kiani, Farzad
    The node deployment problem is a non-deterministic polynomial time (NP-hard). This study proposes a new and efficient method to solve this problem without the need for predefined circumstances about the environments independent of terrain. The proposed method is based on a metaheuristic algorithm and mimics the grey wolf optimizer (GWO) algorithm. In this study, we also suggested an enhanced version of the GWO algorithm to work adaptively in such problems and named it Mutant-GWO (MuGWO). Also, the suggested model ensures connectivity by generating topology graphs and potentially supports data transmission mechanisms. Therefore, the proposed method based on MuGWO can enhance resources utilization, such as reducing the number of nodes, by maximizing the coverage rate and maintaining the connectivity. While most studies assume classical rectangle uniform environments, this study also focuses on custom (environment-aware) maps in line with the importance and requirements of the real world. The motivation of supporting custom maps by this study is that environments can consist of custom shapes with prioritized and critical areas. In this way, environment awareness halts the deployment of nodes in undesired regions and averts resource waste. Besides, novel multi-purpose fitness functions of the proposed method satisfy a convenient approach to calculate costs instead of using complicated processes. Accordingly, this method is suitable for large-scale networks thanks to the capability of the distributed architecture and the metaheuristic-based approach. This study justifies the improvements in the suggested model by presenting comparisons with a Deterministic Grid-based approach and the Original GWO. Moreover, this method outperforms the fruit fly optimization algorithm, bat algorithm (BA), Optimized BA, harmony search, and improved dynamic deployment technique based on genetic algorithm methods in declared scenarios in literature, considering the results of simulations.
  • Küçük Resim Yok
    Öğe
    A Novel Metaheuristic Based Method for Software Mutation Test Using the Discretized and Modified Forrest Optimization Algorithm
    (Springer, 2023) Arasteh, Bahman; Gharehchopogh, Farhad Soleimanian; Gunes, Peri; Kiani, Farzad; Torkamanian-Afshar, Mahsa
    The number of detected bugs by software test data determines the efficacy of the test data. One of the most important topics in software engineering is software mutation testing, which is used to evaluate the efficiency of software test methods. The syntactical modifications are made to the program source code to make buggy (mutated) programs, and then the resulting mutants (buggy programs) along with the original programs are executed with the test data. Mutation testing has several drawbacks, one of which is its high computational cost. Higher execution time of mutation tests is a challenging problem in the software engineering field. The major goal of this work is to reduce the time and cost of mutation testing. Mutants are inserted in each instruction of a program using typical mutation procedures and tools. Meanwhile, in a real-world program, the likelihood of a bug occurrence in the simple and non-bug-prone sections of a program is quite low. According to the 80-20 rule, 80 percent of a program's bugs are discovered in 20% of its fault-prone code. The first stage of the suggested solution uses a discretized and modified version of the Forrest optimization algorithm to identify the program's most bug-prone paths; the second stage injects mutants just in the identified bug-prone instructions and data. In the second step, the mutation operators are only injected into the identified instructions and data that are bug-prone. Studies on standard benchmark programs have shown that the proposed method reduces about 27.63% of the created mutants when compared to existing techniques. If the number of produced mutants is decreased, the cost of mutation testing will also decrease. The proposed method is independent of the platform and testing tool. The results of the experiments confirm that the use of the proposed method in each testing tool such as Mujava, Muclipse, Jester, and Jumble makes a considerable mutant reduction.