نشریه مدیریت مهندسی و رایانش نرم
سال نهم شماره 2 (پیاپی 17، پاییز و زمستان 1402)

The purpose of this study is to investigate the effect of innovation tendency on the quality of production of new products. The present study is a descriptive-survey method. Also, the statistical population is the research of Saipa Company employees. Available sampling was used to select the sample group, so that finally 382 samples were collected and analyzed. The tool for measuring research variables is a questionnaire that has validity and validity. Confirmatory factor analysis was used for inferential analysis of the data and structural equation modeling was used to test the hypotheses. The results showed that the tendency to innovation has a significant impact on market-oriented, design and development of innovation resources. Market-oriented is effective on organizational creativity and quality of new products. Design affects organizational creativity and the performance of new products. Organizational creativity has a significant effect on the quality of new products. Also, the development of innovation resources has a significant effect on the quality of new products.

The present paper deals with modeling and solving a two-objective layout problem based on uncertainty conditions by neutrosophistic fuzzy programming method.The main goal in this model is to reduce the total cost of a robust layout and increase the number of equipment in each section of the hall. Therefore, due to the uncertainty of the mathematical model, a robust fuzzy method is used for uncertainty controls.The computational results of the problem solving show that due to inconsistencies in the objective functions, the total costs increase as the number of equipment used increases.The results also show that with the increase in the rate of uncertainty, due to the increase in the flow of materials, the costs associated with transportation have increased.On the other hand, in order to balance the costs and the level of equipment used, a lower level of equipment has been used.

Despite the advantages of massive multi-input multi-output (MIMO) technology, traditional multi-antenna detection algorithms are not suitable for systems with large-scale antennas, and the use of this technology requires a significant increase in computational costs. In this paper, a low-complexity receiver is proposed using a Teaching-Learning based optimization (TLBO) heuristic algorithm for a large-scale system. The TLBO algorithm, as one of the advanced methods of intelligence, is very useful for large-scale problems. Therefore, we use this method to search for the optimal solution vector in the modulation alphabet. In order to prove the accuracy and efficiency of the detector, it was suggested to simulate the system with different dimensions from 64×64 to 1024×1024. The proposed TLBO detector, in a limited time, achieves a bit error rate (BER) 10^(-5) in the average signal-to-noise ratio of 12 dB, which is approximately equal to the optimal detector performance, and maximum likelihood. As a result, the proposed detector is very efficient for use in massive MIMO systems.

In distributed population systems, cohesive structures prevail, playing a crucial role in the evolution of species across different sites and fostering diversity. These structures employ local selection and reproduction methods to enhance the evolution process. Alterations in migration rules on certain sites, coupled with the execution of search operations, have led to a significant improvement in discovering classification rules. Ultimately, information sharing is employed to mitigate the complexity of the identified rule set. This study evaluates the effectiveness of the Distributed Memetic Algorithm in discovering classification rules in data mining. The algorithm is analyzed based on results obtained from five datasets collected from UCI and KEEL repositories. The findings indicate that the Distributed Memetic Algorithm outperforms the traditional Memetic Algorithm in precision for predicting and discovering classification rules in data mining. This research underscores the observable impact of migration operations and search execution in the process of discovering classification rules in data mining.

One of the issues that cause problems for industries today, especially in the field of sales, is the cost of repairing equipment breakdowns and, as a result, out of service. Specific to that organization, therefore, it should be done by the managers and researchers of that group. Conducting the research "Designing a dynamic intelligent model of preventive maintenance and repairs using the methodology of artificial nervous system - fuzzy logic and in interaction with the production" of the documents of textile and clothing industries in Borujerd, where the project is implemented and on a sample of 2000 of them, the simulation is Vansim's help and then using the artificial intelligence environment of MATLAB software can be claimed. that if (If); situation "Technology factor in Net" should be good, that is 0.9129; and the situation"employees in the net" is good (the upper limit of the membership function is good), that is, 0.9239; And the status of "working environment factor on the net" is relatively good, that is, exactly 0.8859; And the status of should be completel

Given the competitive market of the insurance industry, customer retention is one of the most important goals of insurance brokers. As a matter of fact, attracting a new customer as well as establishing a relationship with the insurer and gaining his trust requires a lot of money. However, the cost of attracting new customers is much more than retaining existing customers. Accordingly, marketing strategies have shifted from product-oriented and many companies have turned to customer relationship management.Companies and organizations have found that retaining their current customers as their most valuable asset is highly important. Therefore, the strategy of insurance companies is to first retain existing customers and then attract new customers. In this regard, identifying the effective factors in customer turnover is essential. In this paper, data mining methods are used to predict the factors affecting customer dissatisfaction. Based on the empirical results, it has been determined that the customer attraction channel, purchase history and place of insurer are important factors affecting customers dissatisfaction in the insurance industry, respectively.

WordNet-like Lexical Databases (WLDs) group English words into sets of synonyms called “synsets.” Synsets are utilized for several applications in the field of text mining. However, they were also open to criticism because although, in theory, not all the members (i.e. word senses) of a synset represent the meaning of that synset with the same degree, in practice, in WLDs they are considered as members of the synset identically. Correspondingly, the fuzzy version of synonym sets, called fuzzy-synsets were proposed. But, to the best or our knowledge. In this study, we present an algorithm for constructing fuzzy version of WLDs of any language, given a corpus of documents and a word-sense-disambiguation system of that language. A theoretical proof is also proposed for the validity of results of the proposed algorithm. Then, inputting the open-American-online-corpus (OANC) and UKB word-sense-disambiguation to the algorithm, we construct and publish online the fuzzified version English WordNet (FWN), and apply them in a Sentiment Analysis problem.

Customer churn is a financial term that refers to the loss of a customer; Today, due the large number of banks , the loss of customers from one bank to another has become a serious concern for different banks. Therefore, in this article, which has been compiled for the customers of a bank , it is possible to identify customers who have a high probability of falling by considering the behavior and characteristics of the customers before the fall occurs and to keep them by providing suggestions. In marketing, everyone agrees that keeping a customer is much less expensive than attracting a new customer, this article introduces the different phases of the approach of predicting customer churn with the help of machine learning. The proposed method is a combination of random forest algorithms and Jaya optimization, and customer dropout is based on different characteristics. Customer like age, Gender, graphs and cases It predicts more . The results of model in the article are 91.41%, 95.66% and 93.35% respectively in Precision , Recall and Accuracy criteria.

Cyber security issues have become a complex challenge for companies that obligating to Industry 4.0 paradigm. On the other hand, the concept of cybersecurity in the context of Industry 4.0 proved to be an emerging topic in recent literature. Therefore, in the present study, for the first time, a hybrid FMEA model developed based on multi-criteria decision-making methods in uncertain environments with the Bowtie method in four phases has been used to evaluate cyber security in Industry 4.0. First, based on the literature, 16 cybersecurity risks in the fourth industrial revolution are identified based on the FMEA model and the determinants of RPN are quantified. Then PIPRECIA Fuzzy method was used for weighting the factors and Z-EDAS method for prioritization and critical identification. Finally, Bowtie analysis has been used to analyze these analyses. The result of the proposed implementation has shown its capability and superiority compared to other methods of traditional results such as FMEA and Fuzzy EDAS.

Until now, various methods have been presented for storing and retrieving information of semi-structured documents, most of them are placed in two groups with batch and incremental approach. In the batch or cluster approach, it is assumed that all the documents can be accessed and clustered, and the documents can be processed several times, which increases the execution time of such algorithms. In the incremental approach, all the documents do not exist in one place, but over time, they are provided to the classification method, and from this point of view, the execution time of such algorithms is less compared to the batch method, and as a result, their execution speed is faster. In this research, our proposed method was compared with XCLS and XCLS+ methods in three evaluation criteria: Entropy, Purity and Fscore. The results showed that the proposed method is preferable to the XCLS and XCLS+ methods in terms of Entropy, Purity and Fscore, and it is slightly less efficient than the XCLS+ method only in the Fscore criterion.

This paper aims at examining entrepreneurship in both service and manufacturing firms. To fulfill this goal, SAMAN bank, as the third private bank in Iran, and Iranian auto part manufacturing firms were chosen as the representatives of service and manufacturing firms. First, some entrepreneurial attributes and their effectual factors were educed from the relevant literature to be measured for SAMAN bank employees to perceive whether they impart these attributes or not. Second, some of the Iranian auto part manufacturing entrepreneurs who have the ownership of the respective firms were selected to procure a better recognition of manufacturing environments and the significant variables impress entrepreneurship in this milieu. Then a comprehensive model is presented for the process of starting entrepreneurial activities in these firms. Finally, the comparison is made between these two segments and their similarities and differences are posed.