@article { author = {Alinezhad, Alireza and Yasi, Sahar}, title = {An Adaptive Neuro-Fuzzy System to Analyze the Cost of Quality}, journal = {Journal of Applied Intelligent Systems and Information Sciences}, volume = {1}, number = {1}, pages = {1-10}, year = {2020}, publisher = {FANAP Research Center}, issn = {2821-1987}, eissn = {2717-039X}, doi = {10.22034/jaisis.2020.99358}, abstract = {Many companies consider the cost of quality as a core value for promoting customers satisfaction to steer towards competitive advantages. A balance must be provided between the features of a product and its resultant quality on the one hand and the return on investment on the other hand. The desirable level of quality is assessable in terms of maintenance costs and thus pave the way for the balance to be achieved. In the industrial environment, regardless of information availability, the reasonable classification of such costs is vital to optimize the subsequent maintenance expenses which are the primary concern of our research. Here, an Adaptive Neuro-fuzzy Inference System (ANFIS) is presented to analyze the cost of quality in order to investigate the effectiveness of investment in the different types of costs. To implement the proposed methodology and demonstrate its applicability, a simulation in industrial enterprises is studied and the results are analyzed. The results show that the input component combinations that cause the minimum amount of error have proven effectiveness in the output.}, keywords = {adaptive neuro-fuzzy inference system,Neural Networks,Cost of quality}, url = {https://journal.research.fanap.com/article_99358.html}, eprint = {https://journal.research.fanap.com/article_99358_8b92d949a38b5c3fe63535ed7905a4b9.pdf} } @article { author = {Oveisi, Shahrzad and Farsi, Mohammad Ali and Kamandi, Ali}, title = {Design Safe Software via UML-based SFTA in Cyber Physical Systems}, journal = {Journal of Applied Intelligent Systems and Information Sciences}, volume = {1}, number = {1}, pages = {11-23}, year = {2020}, publisher = {FANAP Research Center}, issn = {2821-1987}, eissn = {2717-039X}, doi = {10.22034/jaisis.2020.102415}, abstract = {In cyber physical systems (CPSs), hazards can lead to injuries, deaths, destructions or loss of vital equipment or environmental damages. In these systems, software controls the behavior of mechanical and electronic components as well as their interactions; therefore, it plays a special role in creating system hazards and its safety plays a crucial role in a risk management process in cyber-physical systems. Many methods can be used to establish safety in software components of these systems and the software fault tree analysis (SFTA) is among the main methods. The main purpose of SFTA is to identify possible deficiencies in software requirements, design or implementation, which may result in undesirable events in software. On the other hand, unified modeling language (UML) is among the methods used for assurance the construction of object-oriented software. In this paper, a sequence diagram generated in the software production process and the SFTA are used to evaluate safety. The proposed method can play a major role in designing safe systems. The proposed method for designing safe software is implemented in a real CPS and due to the use of uncertain data the reliability of the system is calculated using SFTA-based Fuzzy.}, keywords = {Software safety,SFTA,UML,Cyber Physical Systems,Fuzzy}, url = {https://journal.research.fanap.com/article_102415.html}, eprint = {https://journal.research.fanap.com/article_102415_8589d56750fc0b7217efd9ac3fc23ce6.pdf} } @article { author = {Ilbeygi, Mahdi and Kangavari, Mohammad Reza}, title = {Mutual Information based Fuzzy Inference System for Classification Problems}, journal = {Journal of Applied Intelligent Systems and Information Sciences}, volume = {1}, number = {1}, pages = {24-34}, year = {2020}, publisher = {FANAP Research Center}, issn = {2821-1987}, eissn = {2717-039X}, doi = {10.22034/jaisis.2020.102462}, abstract = {Fuzzy inference system (FIS) is one of the most powerful inference systems that is widely used in the field of classification. Indeed, in this approach, FIS is engaged to create a mapping from features (inputs) onto classes (outputs) using fuzzy set theory. So far, many efforts have been made to improve classification accuracy performed by FIS. Generally, these efforts have been put in the following areas: efficient fuzzy rule generation, fuzzy membership function tuning, fuzzy rule weight tuning, feature selection for the antecedent part of fuzzy rules, and so on. In this paper, we consider this issue and propose a method based on mutual information for applying the impact factor of input parameters on the fuzzy inference process for improving the accuracy of fuzzy classification. Finally, we test our proposed method for boosting classification on six different problems using manual and auto-generated FIS. The method provided promising classification results confirming its correctness.}, keywords = {Fuzzy inference system,Fuzzy classification,Mutual Information,Fusion Operator,Auto Generated FIS}, url = {https://journal.research.fanap.com/article_102462.html}, eprint = {https://journal.research.fanap.com/article_102462_830fb6cef9ded27cd572fdcc14a6a4cc.pdf} } @article { author = {Maraghi, Mohsen and Adibi, Mohammad Amin and Mehdizadeh, Esmaeil}, title = {Using RFM Model and Market Basket Analysis for Segmenting Customers and Assigning Marketing Strategies to Resulted Segments}, journal = {Journal of Applied Intelligent Systems and Information Sciences}, volume = {1}, number = {1}, pages = {35-43}, year = {2020}, publisher = {FANAP Research Center}, issn = {2821-1987}, eissn = {2717-039X}, doi = {10.22034/jaisis.2020.102488}, abstract = {Customer relationship management (CRM) at supermarkets is willing to interact with customers appropriately with the aim of making strong relationship and resultantly gaining maximum profits. Customers consist of various groups of people and have different needs, styles and expectations. Marketing management of a supermarket segments customers to respond their different demands correctly. Another important concern of supermarket managers is to detect profitable customers. These customers supply main profit of company and saving them guarantees existence of the supermarket. This research presents a model completing CRM process from understanding customers to assigning marketing strategies. Profitable customers will be distinced as a result of correct understanding of all customers. Present research is comprised of two phases. At phase one, dataset with recency, frequency and monetary (RFM) measures is constructed and clustered using K-means algorithm. Six segments of customers are detected based on the results of clustering. All segments are comprehensively analyzed and marketing strategies for them are described in phase two. Transactions of every segment of customers are separated and association rules are extracted using market basket analysis and Apriori algorithm. Consequent and also antecedent product items are proposed to customers who purchased antecedent product iems. So, dedicated marketing proposals are developed for some special customers.}, keywords = {RFM model,Segmentation,Association rules,k-means,Market Basket Analysis,Machine Learning}, url = {https://journal.research.fanap.com/article_102488.html}, eprint = {https://journal.research.fanap.com/article_102488_0d2cfe9411d072c81caab1fbd25e4d40.pdf} } @article { author = {Parsanejad, Abozar and Nayebi, Mohammad Amin}, title = {An Applied Intelligent Fuzzy Assignment Approach for Supply Chain Facilities}, journal = {Journal of Applied Intelligent Systems and Information Sciences}, volume = {1}, number = {1}, pages = {44-53}, year = {2020}, publisher = {FANAP Research Center}, issn = {2821-1987}, eissn = {2717-039X}, doi = {10.22034/jaisis.2020.103706}, abstract = {Nowadays, all industrial private and governmental sectors are making the effort to finde suitable location for their new facilities under practical restrictions such as uncertain traveling time/distance. In the throes of these restrictions, the applied intelligent-based approaches such as fuzzy logic are indispensable for dealing with the uncertainties. This paper extends beyond the previous studies by introducing the fuzzy logic into the facility assignment problem in which the uncertain traveling factors are handled by means of a distance matrix. To aid the managerial judgement on operational research decisions, here a fuzzy assignment model is used. Thus, the model determines which one of the production facilities have to be moved in light of making a better stream of transportation. The stream is optimized by minimizing the total traveling time/distance to cover the customers. Using the creditably theory, the cession of the most appropriate fuzzy assignment would be selected. The numerical results shows the effectiveness of the proposed approach in finding the appropriate location for facilities.}, keywords = {Applied Intelligent System,Fuzzy Assignment, Supply Chain, Credibility Theory}, url = {https://journal.research.fanap.com/article_103706.html}, eprint = {https://journal.research.fanap.com/article_103706_9502001773e52dfe3e99b045b4b88c0e.pdf} } @article { author = {Aminian, Mohammad and Eskandari, Mahdi}, title = {Authorship Clustering using Homogeneous Feature Space and Two-stepped Automatic Fuzzy Cmeans Clustering}, journal = {Journal of Applied Intelligent Systems and Information Sciences}, volume = {1}, number = {1}, pages = {54-63}, year = {2020}, publisher = {FANAP Research Center}, issn = {2821-1987}, eissn = {2717-039X}, doi = {10.22034/jaisis.2020.219089.1006}, abstract = {Identifying the authorship either of an anonymous or a doubtful document constitutes a cornerstone for automatic forensic applications.  Moreover, it is a challenging task for both humans and computers considering complex content of document with variety of backgrounds. Due to nature of task it is always considered as an unsupervised task. Clustering documents according to the linguistic style of the authors who wrote them has been a task little studied by the research community. In order to address this problem, PAN Evaluation Framework has become the first effort to promote the development of the author clustering. There are different approaches to address the task and this article proposes a method based on a set of homogeneous features and two-stepped automatic FCM clustering. We use word Ngram, part-of-speech tagging and some other context free features, then using document similarity graph (DSG) estimating number of clusters; finally we use FCM to cluster corpus. We have done the task in very short amount of time and our performance results is comparable with leaderboard competitors in PAN CLEF 2017 challenge.}, keywords = {authorship clustering,homogeneous features,word Ngram,part-of-speech,fuzzy Cmeans}, url = {https://journal.research.fanap.com/article_104113.html}, eprint = {https://journal.research.fanap.com/article_104113_be73a35e26715ce487231aadac5f89ba.pdf} }