Sharif University of TechnologyScientia Iranica1026-3098311020240601A MapReduce-based big data clustering using swarm-inspired meta-heuristic algorithms7377492349210.24200/sci.2024.61178.7184ENR.TekiehFaculty of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad, IranZ.Beheshti- Faculty of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran.
- Big Data Research Center, Najafabad Branch, Islamic Azad University, Najafabad, Iran.Journal Article20220927Clustering is one of the important methods in data analysis. For big data, clustering is difficult due to the volume of data and the complexity of clustering algorithms. Therefore, methods that can handle a large amount of data clustering at the reasonable time are required. MapReduce is a powerful programming model that allows parallel algorithms to run in distributed computing environments. In this study, an improved artificial bee colony algorithm based on a MapReduce clustering model (MR-CWABC) is proposed. The weighted average without greedy selection of the results improves the local and global search of ABC. The improved algorithm is implemented in accordance with the MapReduce model on the Hadoop framework to allocate optimal samples to the clusters such that the compression and separation of the clusters are preserved. The proposed method is compared with some well-known bio-inspired algorithms such as particle swarm optimization (PSO), artificial bee colony (ABC) and gravitational search algorithm (GSA) implemented based on the MapReduce model on the Hadoop framework. The results showed that MR-CWABC is well-suited for big data, while maintaining clustering quality. The MR-CWABC demonstrates an improvement of 7.13%, 7.71% and 6.77% based on the average F-measure compared to MR-CABC, MR-CPSO, and MR-CGSA, respectively.https://scientiairanica.sharif.edu/article_23492_cf7bc5cea551ee29aaaa0b078e8d5fef.pdfSharif University of TechnologyScientia Iranica1026-3098311020240601CNUIML: Towards the automatic generation of enterprise-level rich internet applications using controlled natural user interface modeling language7507632346210.24200/sci.2023.62435.7839ENH.BahriDepartment of Computer Engineering, Sari Branch, Islamic Azad University, Sari, IranH.MotameniDepartment of Computer Engineering, Sari Branch, Islamic Azad University, Sari, IranB.BarzegarDepartment of Computer Engineering, Babol Branch, Islamic Azad University, Babol, IranJournal Article20230514The lack of qualified developers is the main reason for the shortage of software. One solution to overcome this problem is to leverage the end user for software development. Model-based approaches attempt to facilitate the involvement of the end user in the software development process. Various approaches have been explored to automatically transform the user interface model into the source code. However, the research community has focused less on describing the user interface with natural language. We used the MDA approach and the CAMELEON reference framework to develop a natural controlled modeling language (CNUIML) for modeling the user interface of web applications. The meta-model of the designed language is represented by the meta-meta-model and the grammar of the language is developed using EBNF. The usability of CNUIML has been evaluated through two case studies. The models described with this language are AUI-level models based on CRF and a PIM, based on the MDA approach. In this study, we have shown that the model designed with this language can be transformed into similar models such as task models or class diagrams using M2M. We have also discussed how the source code is obtained from the transformation of this model using M2T.https://scientiairanica.sharif.edu/article_23462_3694de51b80fcdb79fbea554f26d27bd.pdfSharif University of TechnologyScientia Iranica1026-3098311020240601Stance detection on social media, case study: Persian sentences using deep learning architecture7647732351310.24200/sci.2024.62504.7876ENS. M.MohammadiDepartment of Mathematics and Computer Science, Arak Branch, Islamic Azad University, Arak, Iran.S.FarziDepartment of Computer, K. N. Toosi University of Technology, Tehran, Iran.S. M.AlaviDepartment of Mathematics and Computer Science, Arak Branch, Islamic Azad University, Arak, Iran.Gh.Heydary JoonaghanyDepartment of Mathematics and Computer Science, Arak Branch, Islamic Azad University, Arak, Iran.Journal Article20230528In the paper, we need to identify the stance of persian language in social networks. While the data set for detecting the stance with persian content have applications. Therefore, with the aim of accurately identifying the stance in the post and extracting the stances of persian language, the user expressed a post that relation to one or more target entities a new method for the first time. Hybrid LSTM-CNN architecture was used and, unlike previous researches, rotational learning rate was used, and a new the method for processing data before entering the network is presented to improve the results, which can stance persian in the network. Identifying the social in addition, to solve the problems related to the lack of data, the BERT model was investigated to detect the persian stance. On the based on the results obtained, tagged data was collected, and after many surveys and numerous meetings. Taged data analyzed from the telegram social network in the field of business sport for a limited time frame, and show how the presented model has achieved higher accuracy than Competitors. At the end of the training course, the proposed model improves results by 11 .3% in terms of accuracy.https://scientiairanica.sharif.edu/article_23513_eab72a4beae1cdaaa07338b7e1c021f5.pdfSharif University of TechnologyScientia Iranica1026-3098311020240601Fault-tolerant capability analysis of six-phase induction motor with distributed, concentrated, and pseudo-concentrated windings7757892276910.24200/sci.2022.58674.5844ENM.Alemi-RostamiDepartment of Electrical Engineering, Khayam Research Institute (KRI), Ministry of Science, Research, and Technology, Tehran,
Iran.G.RezazadehSchool of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.F.TahamiDepartment of Electrical Engineering, Sharif University of Technology, Tehran, Iran.H. R.Akbari ResketiDepartment of Electrical and Computer Engineering, Babol Noshirvani University of Technology, Babol, Iran.Journal Article20210707Six-phase motors are becoming more popular because of their advantages such as lower torque ripple, better power distribution per phase, higher efficiency, and fault-tolerant capability compared to the three-phase ones. This paper presents the fault-tolerant capability analysis of a symmetrical six-phase induction motor equipped with distributed, conventional concentrated, and pseudo-concentrated windings under open-circuit fault scenarios. For further investigation, different load types such as constant-speed, constant-torque, and constant-power are applied to the motor. Two concepts of magnetic and physical phase separations are introduced as factors affecting the motor reliability. Analytically, these factors give an insight into how the pseudo-concentrated winding could be a fault-tolerant alternative. Moreover, five parameters such as the change of output power, power loss, power factor, efficiency, and expected load loss are considered as the fault-tolerant capability parameters to evaluate the windings reliability. The aforementioned parameters are reported using the finite element analysis for different fault scenarios and different load types. Although the baseline motor dimensions are not optimized for applying the pseudo-concentrated winding, the pseudo-concentrated shows a promising performance with high fault-tolerant capability.https://scientiairanica.sharif.edu/article_22769_bb242e09fcdbdbf9abbc9c15c847ba54.pdfSharif University of TechnologyScientia Iranica1026-3098311020240601Performance improvement of the hybrid switch reluctance motor by notching method7907972276510.24200/sci.2022.59347.6190ENS.HasanzadehDepartment of Electrical and Computer Engineering, Qom University of Technology, Qom, Iran.H.RezaeiDepartment of Electrical and Computer Engineering, Babol Noshirvani University of Technology, Babol, Iran & Mazandaran Regional Electric Company, Sari, Iran.H.TaheriDepartment of Electrical and Computer Engineering, Qom University of Technology, Qom, Iran.Journal Article20211104Hybrid switch reluctance motors are the family of switch reluctance motors (SRMs) that attenuate the magnetic saturation and increase the air gap magnetic flux by exploiting permanent magnets. The permanent magnet auxiliary air gap flux can affect the average torque and ripple. Commonly, the torque ripples reduction comes with the average torque drop. In this paper, the torque ripple reduction and average torque improvement are achieved for a 6/10 pole hybrid switch reluctance motor by inserting two symmetrical notches on its rotor. Also, the lengths of magnet and rectangular notches are optimized by the finite element method and sensitivity analysis. The comparison of the optimized design with the initial one carried out by finite element proves the efficiency of the proposed model.https://scientiairanica.sharif.edu/article_22765_816b7ea204ce6294f297f03b4823fed0.pdfSharif University of TechnologyScientia Iranica1026-3098311020240601Corrections to \On the global practical stabilization of discrete-time switched affine systems: Application to switching power converters" [Scientia Iranica (2021) 28(3), 1621-1642] and \Global practical stabilization of discrete-time switched affine systems via switched Lyapunov functions and state-dependent switching functions", [Scientia Iranica (2021) 28(3), 1606-1620]7988032357610.24200/sci.2024.61264.7230ENM.HejriDepartment of Electrical Engineering, Sahand University of Technology, P.O. Box 51335-1996, Sahand New Town, Tabriz, IranJournal Article20221013This note corrects the proof of Theorem 1 in [1], the statement and proof of Lemma 1, and a part of proofs in Theorems 1 and 2 of [2].https://scientiairanica.sharif.edu/article_23576_34462b428fa73a72719ec1c27c4bb7b0.pdf