data mining

Accurate identification of factors affecting product quality can lead to improved production processes, reduced waste, increased productivity, and ultimately improved customer satisfaction. This study aimed to identify factors affecting the quality of industrial products and analyze the relationships between these factors using data mining and clustering techniques. Data related to the industrial production process, including variables such as raw material purity, machine error rate, temperature, pressure, production speed, and human experience, were simulated and preprocessed. K-Means, hierarchical clustering, and DBSCAN algorithms were used to analyze the data. The results showed that raw material purity, machine error rate, and temperature are among the most important factors affecting product quality. Also, clusters with high raw material purity (>95%) and machine error rate less than 2% showed the best quality, while clusters with low purity and high production speed had poor quality. This research provides a comprehensive model for analyzing and optimizing the quality of industrial products that can be used in various industries such as automotive, pharmaceutical, and food industries

Natural Language Processing (NLP) is a vital subfield of artificial intelligence that helps computers understand and interpret human language. The purpose of this research was to identify and understand the sentiments expressed by football fans on Twitter, especially after special events such as the Premier League, and to present a model based on their team identification level.

The current research is applied research in terms of its purpose and descriptive-analytical research in terms of its nature. The statistical population included all the tweets generated on Twitter by the fans of the clubs present in the 2021/22 Iranian Premier Football League. Therefore, 5560 tweets were collected after 120 games of the second-half season of the Premier Football League. Special Lexicon and sentiment classifiers based on artificial intelligence (VADER dictionary) were used in a Python environment as a sentiment analysis tool. A Regression model was used to predict fans'' sentiments.

The results of natural language processing showed that positive sentiments were the most expressed emotions by fans. The regression model predicted fans'' sentiments based on factors such as the level of team identification and the result of winning and losing the game, and the conceptual model of the research was confirmed.

This study can be used by media and marketing managers of teams who seek a deeper understanding of the emotional dynamics of football fans.

Banks have complex and long-term processes for facilities, including many stages, control points and approvals. Continuous analysis of such processes is increasingly important for continuous improvement and gaining knowledge from them. The main goal of the present research is to provide a comprehensive methodological framework based on process mining and data mining regarding the analysis of fixed capital facility processes. The method used in the present research is derived from the techniques of process mining and data mining based on the event log of the facility system, an active bank in Iran. This method includes nine phases of initiation, preparation, inspection, exploration and analysis, evaluation, multi-dimensional analysis, prediction, review of results and improvement. Among the results of the present research is the extraction of the real process model, identification of bottlenecks, frequent activities in a case and all cases and process variant. In addition to this identification of branches and people with the most important roles and based on data features in reducing the time of payment of facilities, the analysis of the process from dimensions such as the province was one of the other findings. One of the initiatives of the present research was the use of data mining to predict the payment time of facilities. In the comparison of various methods, the decision tree algorithm had the best performance with 72% accuracy. In addition to identifying deviations, based on the creation of event log and its analysis, the improved process of extracting which showed a 67% improvement.

The most important objectives of this research are to evaluate and illustrate the knowledge map of data mining in covid-19 research, to identify the various components and relationships between data mining concepts in different fields of covid-19 studies. The research method is applied and descriptive. In this research, we utilize the content analysis and text mining techniques. One of the findings of the current research is that a group of researches was related to the "structure and human nature of the disease". Another group of research looked at the set of factors related to adulthood that could be the basis for the absorption of the Covid-19 virus. In the co-authoring section, the highest amount of cooperation was between America and India. The data mining map of Covid-19 is based on thematic modeling including the structure and human nature of the disease, the background and structure of the virus, pandemic prevention, and computer science and artificial intelligence. In this research, according to the use of information technology tools and text mining techniques, a large amount of research has been studied. The presented knowledge map deals with the thematic separation of the researches and information discovery in each collection, so it enables the possibility of comparison between specific thematic groups. In this research, the components of data mining concepts in different fields of Covid-19 studies were examined in six dimensions. In the sixth dimension, the data mining map of Covid-19 was presented based on thematic modeling.

Granting facilities is an important part of every bank's operations. This part of banking activities is economically important. With its operations, banks can provide the transfer of resources from those that have directly invested to those who need money, this repayment makes another people use these resources. Failure to repay facilities on time will cause the bank's resources to stagnate and in the long will cause the country's economic recession. It is important to monitor the correct allocation of resources because if bank resources are used and unfounded payments are made, the banks will not be able to pay the depositors and will become bankrupt. By examining the researches in the field of banking, it was found that most of the researches have focused on the optimal combination of the investment portfolio in the capital market, and less researchers have paid attention to the discussion of the optimal combination in the money market. Most of the researches that used operational and statistical research methods were related to industrial issues, and less in financial issues and operational research discussions were used. In the optimal portfolio of the combination of facilities or investments, mostly the genetic algorithm has been used and less than other fuzzy methods have been used in the conditions of uncertainty. Therefore, in this research, the improvement of facility payment modeling has been addressed by using convolutional neural networks and CNN-LSTM modeling.

Personal branding is an emerging area in the field of branding, where individuals can build a lasting personal brand by aligning their goals with effective strategies. Given the profound need for research in personal branding as well as the significant role of social networks in this process, this study aims to explore the factors influencing the personal branding of active musicians through a data mining approach on Instagram.

The research targets individuals who have established themselves as brands in specialized and scientific fields on Instagram, including experts and musicians. For this study, 10 prominent musicians on Instagram were selected for analysis. In total, 10 personal Instagram pages of musicians were analyzed. The sampling method used was judgment sampling, and the database consisted of an Excel file containing 1,150 records. According to previous studies and expert opinions, data were examined from these personal pages over one year, from December 1, 2019, to December 1, 2020, during which 1,150 Instagram posts were analyzed. The number of comments and likes is identified as a key factor in the development of the personal brand of the studied individuals, reflecting the level of awareness and interaction with the personal brand. Consequently, the study examines the effect of 11 different features on the likes and comments of the posts. After data collection, Naim software was used for data analysis and data mining operations, and the decision tree model was extracted, accordingly.

The decision tree analysis reveals that factors such as post type, content, context, tags, and hashtags in Persian and English, as well as the month and day of publication, significantly impact user engagement and personal brand awareness. In this study, user engagement on Instagram is measured by likes and comments, which lead to brand awareness. The findings identified eleven input features—followers, post type, post content, context, caption, tags, English hashtags, Persian hashtags, total hashtags, day of post, and month of post—that influence comments and likes. These findings provide musicians with insights into the factors influencing their brand on Instagram, allowing them to effectively manage, develop, and enhance their personal brands. This, in turn, can lead to increased user engagement and greater audience awareness.

The results indicate that musicians can improve their performance on Instagram by focusing on specific factors that influence their personal brands. Image posts receive the most likes, while videos garner the most comments i.e. to increase audience engagement and brand awareness, musicians should prioritize posting images and videos. Posts featuring personal content, memories, entertainment, and non-concert performances attract the most likes. It is recommended that non-concert performance posts feature movie-style backgrounds to boost likes and comments.

In response to the Covid-19 pandemic, governments around the world have imposed severe traffic restrictions and presented different scenarios to reduce emissions from traffic sources. By applying the traffic restrictions caused by the Covid-19 epidemic, it was expected to see changes in the concentrations of air pollutants. Therefore, it was decided that the changes of air pollutants as one of the subsets of the environmental index of sustainable urban development during the covid-19 epidemic will be investigated. For this purpose, the aforementioned data are first collected in the four metropolitan cities: Tehran, Karaj, Ahvaz and Tabriz, and then processed and cleaned. After that, a proposed algorithm based on machine learning methods is presented. Machine learning methods decision tree, random forest, support vector machine, Bayesian network and perceptron neural network are applied to the selected features. Investigations showed that the prediction model using decision tree and random forest had the best performance for both recall and accuracy criteria. The research results showed that the effect of restrictions on the concentration of pollutants in different cities is different. Also, the results show that, in general, the application of traffic restrictions during the epidemic period did not have a significant and noticeable effect in reducing the concentration of air pollutants. Also, the studies showed that the application of traffic restrictions and consequently the possible reduction of some air pollutants are not related to the deaths caused by Covid-19.

The aim of the research is predicting critical risks of the gas transmision network and choosing optimal corrective action in optimal time and cost. Risks were predicted with data-mining algorithms based on the CRISP methodology. K-Means, Kohnen, Two Step algorithm and Neural Network Algorithms, C.5 Tree, Nearest Neighbor and Support Vector have been used for clsutering and classification. Markov decision process is also used to select the optimal control action.Decision making problem is based on back down induction in stochastic dynamic programming in the limited time of modeling and simulation and sensitivity analysis and model validation. Based on results, in 97.56% of the agreed data, learning was created and the accuracy and validity of the data mining model was estimated at 86.92%. Also, 13 risks have been identified as critical, and the simulation results show a 92% improvement rate in the cost and 77% in the control action implementation time.

The scope of technologies such as the Internet of Things has caused a large amount of data to be produced by humans and machines. In addition, the development of virtual space and Internet users also provides a platform so that the needs and opinions of customers and users of products and services can be easily recorded and exploited. This data is an opportunity to design products, services and systems in a data-oriented manner. The product design and development period behind the closed doors of the companies' research and development unit has ended. Companies must survive in the current turbulent era, take advantage of the knowledge available in the surrounding world, move towards open innovation, and create shared value. The customers and final consumers are the most important part of the value chain of any business, and one of the most important sources of information among them is their preferences and reviews. These resources are generally uploaded and stored on the Internet, online shopping and selling sites, social media, and other similar databases. Accordingly, it will be vital to use the reviews and experiences of customers in the process of developing new products. Considering the importance of this issue, this research has used bibliometric and systematic literature review.

IntroductionSustainability is one of the considerable challenges we widely face these days. Transformation into a more vibrant life provides a city for social, economic, and environmental interaction in which people can prosper, and hence sustainable transportation comes into play. Accordingly, public concerns about sustainable solutions in the urban transportation industry have increased, followed by discussions on public transportation, taxi apps, and ride-sharing. On the other hand, with the increasing emergence and development of social networks, customers can freely express their opinions without being bound by predefined issues. Moreover, social network analytics is an opportunity for organizations to understand the priorities of a large portion of their customers while spending less time and money compared to traditional research methods such as surveys. Twitter is now an accessible and valuable resource to access customers' feedback and what is on their minds. The present study has tried to identify the mentality of users of a transportation system by using data mining and text mining on Twitter so that the factors affecting the tendency of people to use any urban transportation mode have been investigated and prioritized. This article provides a model for improving the sustainability of urban transportation systems.MethodologyGiven that conducting field research or quantitative studies is very expensive, and the information obtained in this way is limited to certain customers, analyzing the contents produced by users on social media is a necessity for any organization. Compared to conventional methods of communication, social media have unique features. This study used Twitter since it has the highest growth rate among all social media platforms. Using the sentiment analysis, we determine what aspects of the organizations' management should improve to ensure their sustainable performance. Twitter sentiment analysis provides an easy and reliable way for businesses to monitor people's feelings towards their brands, businesses, and stake holders.This research is of a based type and tries to design and develop a model for the purpose of evaluating sustainable transportation. used as data related to transportation and collected all the required information sources using hashtags and with the help of text mining technique which is also known as text analysis and sought to extract high-quality and desired information in the form of Structured use and then with the help of sentiment analysis, we explored the recognition of positive, negative and neutral emotions about transportation issues in the texts. In fact, by analyzing emotions, opinions, feelings, behaviors, tendencies and emotions written with a written language, we analyze them and proceed to the final analysis. The result of the analysis provides sustainable transportation tools for use by policy makers and transportation network investors.Results and DiscussionThe results of this analysis show that the price factor, according to Twitter users' feedback, has the greatest impact on choosing the type of transportation while maintaining stability. Other main and important factors besides "price", according to the statistics published in the analysis, are "air pollution", "traffic", "vehicle fuel", "vehicle model", "driver age", "salary" and passenger age, employment and usage, routing, parking and driver gender, and vehicle monitoring are ranked next in terms of the number of Twitter users, respectively.ConclusionContrary to the idea, in addition to important issues such as price, pollution, traffic and parking, car model, driver's age and even driver's gender and car cleanliness are also important for passengers. On the other hand, the salary and age of the passenger are also important for drivers. The placement of these indicators together leads to the belief that in today's society, as the society grows older, the complexity and tastes also increase, but on the other hand, the exchange of information becomes easier with the advancement of technology. The results of this study can be provided to investors, policy makers and customers of taxi applications to improve the sustainable transportation system. With the help of these results, investors design and invest in sustainable transportation systems and marketing programs according to the characteristics expected by customers and advance their advertising. Customers' willingness to use such a sustainable system will guide policy makers to develop sound policies regarding investors, customers, and citizens, and identify the needs of transportation customers to increase their satisfaction. Knowing the indicators, tastes and priorities of people in using means of transportation can be an opportunity for entrepreneurs and investors for entrepreneurship and investment in the field of urban transportation.

By applying the restrictions caused by the Covid-19 pandemic, it seems that changes in the concentrations of pollutants CO, O3, NO, NO2, SO2, PM2.5, PM10 and AQI can be seen in the periods before and after the epidemic. Therefore, the changes of air pollutants and traffic restrictions are investigated as one of the sub-categories of environmental indicators of sustainable urban development in the period of 2018/01/21 to 2022/03/20 in the stations under the supervision of Tehran city. First, the data is collected, processed and cleaned. Machine learning methods including decision tree, random forest, support vector machine, Bayesian network and perceptron neural network are applied to select the effective features using the particle swarm optimization method. Investigations showed that the prediction model using decision tree and random forest had the best performance for both precision and recall criteria. The results of the research showed that the concentration of pollutants in the period of Covid-19 compared to before, is increased in some stations and decreased in others, and also the application of traffic restrictions during the epidemic did not have a significant and noticeable effect in reducing the concentration of air pollutants. Also, by examining the trend of deaths during the epidemic period, it was found that the decrease or increase of pollutants has no significant relationship with the trend of deaths caused by Covid-19.

In recent years, the rapid growth of virtual space has made people devote more of their time in virtual space, especially to social networks, which can be attributed to the remarkable features of virtual space; including increasing the speed of information exchange, easy and free access to information and variety of knowledge topics. In this regard, the opinions recorded by users in virtual networks have grown day by day and have become very important, and extracting the opinions and feelings of users' opinions for more informed decision-making is of great help to businesses, on the other hand, virtual reality technology in the past few decades It has undergone technical changes and improved immersion and the feeling of remote presence; This technology is used in various fields such as education, tourism, health, sports, entertainment, architecture and construction, etc. The increasing progress of virtual reality technology has caused many businesses to operate in this field, but due to changes Continuous market and the need for timely information, companies should use differentiation and growth strategies, in this regard, they need to ask users' opinions and in line with that, try to grow and improve their business, considering that Users' comments are textual, and reading and summarizing them is time-consuming and difficult. Based on this, the aim of the current research was to categorize comments related to virtual reality technology using machine learning methods and a dictionary-based approach. Therefore, about one million tweets in the field of virtual reality technology were collected by the web crawler, and after data preprocessing, 480,432 samples remained in the data, then Dirichlet's hidden allocation topic modeling was implemented on the data. This modeling separated different topics by examining the distribution of words in tweets; The tweets whose distribution of words were similar were placed into a topic and the number of topics with the highest coherence score was selected, the number of topics 9 had higher coherence and the data were grouped into 9 topics, so once again the Dirichlet hidden allocation modeling was set to 9. The topic was done, with this the tweets were grouped into 9 different topics. To evaluate the model, considering that we had a probability distribution, the confusion criterion was used, the value of which was -9.44, and the coherence score was used for the degree of semantic similarity between words and the distinction between subjects, and the result was 0.47. The lower the confusion criterion and the higher the coherence score, the more efficient the model is. With the help of keyword weights obtained by Dirichlet hidden allocation modeling and examining at least 5 different tweets from each topic, 9 topics related to virtual reality technology were identified: "New Technology", "Creation and Make", "Technological Business", "Education", "Virtual Games", "Progress", "Gadget", "Metaverse", and "Indiegame", the topics were analyzed with the help of several graphs. We found that the number of neutral comments on topics such as "New Technology" and "Metaverse" is more than positive and negative comments, which indicates the lack of sufficient information or the lack of use of these technologies, and it is necessary for businesses in this field, to try more in this regard, in the same way, if we observe the graph of "Virtual Games" and "Technological Business", we can see that it changes almost with the same ratio in different years, in the sense that this The two graphs are related, in fact, businesses should keep in mind that the factors affecting these two issues are the same, but users pay more attention to the issue of "Virtual Games", as a result, if the creators of "Technological Business" Focus specifically on "Virtual Games", they will grow more due to the more attention of users, also the creators of games should consider that "Virtual Games" are a topic of more attention than "Indiegame". Is. In the subjects of "Education" and "Gadget", users lost their attention to these subjects in the field of virtual reality over time, in fact they showed their attention to other subjects, so it is better for businesses that operate in this field to take measures To advertise and attract users or change their user area if there is no growth.

Constant changes in the market and the need for timely information force companies to use differentiation and growth strategies appropriate to the needs of customers. (Sánchez, Folgado-Fernández, & Sánchez, 2022). Companies can check and analyze their customers' opinions through microblogging sites (Facebook, Twitter, etc.) and finally improve the desired products or services (Ahmad, Aftab, Bashir, & Hameed, 2018). Today, users express their opinions and feelings and review products in online social networks. Therefore, user comments and the analysis of these comments have become a valuable resource for businesses (Kim et al., 2015; Loureiro et al., 2019).
Virtual reality and augmented reality have undergone technical developments in the past few decades and have improved immersion and the feeling of remote presence. Several examples of applications of such techniques can be found in stores, the tourism industry, hotels, restaurants, etc. (Loureiro, Guerreiro, & Ali, 2020). Due to the constant changes in the market and the need for timely information, companies should use differentiation and growth strategies, nowadays, due to the rapid evolution of the Internet, instead of collecting their opinions through time-consuming and expensive methods such as questionnaires and interviews, etc., they express in the context of social networks, which is very useful for businesses in their development, and they can measure the feelings of customers towards products and services, and understand the needs of users, and finally make appropriate and appropriate decisions in the direction of adopt growth, but in order to use the produced content correctly, text mining and sentiment analysis techniques should be used, which has not been researched in Iran so far. Analysis of users' opinions and feelings about virtual reality technology can help businesses that operate in the field of metaverse, virtual game production, virtual education, virtual tourism, etc., to make better decisions and plans.

Social media generates a large amount of real-time social signals that can provide new insights into human behavior and emotions. People around the world are constantly engaged with social media. (Al-Samarraie, Sarsam, & Alzahrani, 2023).
On the other hand, the amount of data is increasing day by day. Almost all institutions, organizations and business industries store their data electronically. A huge amount of text is circulating on the Internet in the form of digital libraries, repositories, and other textual information such as blogs, social media networks, and emails (Sagayam, Srinivasan, & Roshni, 2012).
Topic modeling is one of the most powerful techniques in text mining for data mining, discovering hidden data and finding relationships between data and textual documents (Jelodar et al., 2017).
The technological advances of the last century have confronted societies with new realities that have indisputably improved daily life, making it more convenient and interesting. In recent decades, technology using virtual reality and wearable devices have had a significant impact in the fields of education, tourism, health, sports, entertainment, architecture and construction, etc. (Kosti et al., 2023).
Virtual reality is a technology that allows a user to interact with a computer-simulated environment, whether that environment is a simulation of the real world or an imaginary one. With virtual reality, we can experience the most frightening and overwhelming situations with safe play and a learning perspective (Mandal, 2013). Most people are curious about the possibilities and future of new technologies, considering the various applications it is supposed to offer such as virtual meetings, learning environments and many others, however, there are also concerns about potential negative effects. because real world signals can be transmitted in the virtual world. In this regard, people express their feelings in different social networks (Bhattacharyya et al., 2023).

According to the main goal of the research, which is to classify comments related to virtual reality technology using machine learning methods and a dictionary-based approach, therefore, about one million tweets in the field of virtual reality technology were collected by the web crawler and After data preprocessing, 480,432 samples remained in the data, then Dirichlet hidden allocation thematic modeling was implemented on the data. By examining the distribution of words in tweets, this modeling tries to separate different topics by detecting the distribution of words; The tweets whose distribution of words are similar were put into a topic, and the number of topics with the highest score was selected, the number of topics 9 has higher coherence, and the data was grouped into 9 topics, so once again, Dirichlet hidden allocation modeling was applied 9 topics were done, whereby the tweets were grouped into 9 different topics. Considering that we have a probability distribution, the confusion criterion was used to evaluate the model. The lower the confusion criterion and the higher the coherence score, the more efficient the model is. With the help of keyword weights obtained by Dirichlet hidden allocation modeling and examining at least 5 different tweets from each topic, 9 topics related to virtual reality technology were identified: "New Technologies", "Creation and Make", "Technological Business", "Education", "Virtual Games", "Progress", "Gadget", "Metaverse" and "Indiegame" were named.

In this research, by examining topics in different years, we observed that the topic of "Progress" was the most popular topic among users from 2017 to the end of 2021, in early 2022, this topic gave way to "Metaverse", currently "Metaverse" is one of the most popular topics being discussed by users. Businesses in the field of virtual reality should strive for the attractiveness of "Metaverse" and attract users. Likewise, if we observe the "Virtual Games" and "Technological Business" graphs, we can see that they change with almost the same ratio in different years, meaning that these graphs are related to each other, in fact, business and keep in mind that the factors affecting these two issues are the same, but in the case of "Virtual Games" it has more effects, and if "Technological Businesses" specifically focus on virtual games, they will grow more due to the greater attention of users. had Similarly, "Indiegame" which have had a series of changes but in recent years have had a declining trend and then no change, now the creators of these games should check, and in general "Virtual Games" are a more interesting topic than "Indiegame". In the subjects of "Education" and "Gadget" it has been decreasing since the beginning of 2017, which shows that users lost their attention to these subjects in the field of virtual reality over time, in fact to other topics showed their attention, so it is better for businesses that are active in this field to take measures to advertise and attract users, or change their user field if there is no growth.

The purpose of this research is to investigate the effective factors in predicting the academic performance of undergraduate students in the classification of four classes. To achieve this goal, the study follows the CRISP data mining method. The data set was extracted from the NAD educational system for the bachelor's degree in Shahed University for the entry of the years 2011 to 2021. 1468 records were used in data mining. First, the effective features on students' academic performance were extracted. Modeling was done using Rapidminer9.9 tool. To improve classification performance and satisfactory prediction accuracy, we use a combination of principal component analysis combined with machine learning algorithms and feature selection techniques and optimization algorithms. The performance of the prediction models is verified using 10-fold cross-validation. The results showed that the decision tree algorithm is the best algorithm in predicting students' performance with an accuracy of 84.71%. This algorithm correctly predicted the graduation of 77.88% of excellent students, 85.26% of good students, 84.69% of medium students, and 85.96% of weak students based on the final GPA.

With the advent of digital technologies and the emergence of VOD media services, the landscape of cultural and creative industries has undergone a dramatic transformation. In particular, the market of these media in Iran has witnessed significant growth and offers a diverse range of content to meet the preferences of Persian-speaking audiences around the world. This research deals with the social assessment of Iranian VOD media and their opinion analysis among Persian Twitter users. The purpose of this study is to measure the influence and dynamics of Iranian VODs in the framework of Persian Twitter using data mining methods, computational social sciences, sentiment analysis and theme analysis. Through thematic analysis, six overarching themes were extracted: 1. Dark strains of VOD, 2. Broadcasting monitoring of VOD, 3. Popularity of VOD and its effective factors, 4. Filter and closure of VOD, 5. Actors appearing on VOD after riots in 1401, and 6. Purchasing VOD subscriptions. These themes reflect diverse views and conversations about Iranian VODs.Using social science computational methods, sentiment analysis, and user surveys, a deeper insight into the social measurement of Iranian VODs is obtained. Sentiment analysis of tweets showed that negative sentiments towards these media among Persian Twitter users were 59%, positive sentiments 39%, and neutral sentiments 2%. In addition, data mining techniques were used to investigate the percentage of attention towards Iranian VODs and the feelings of users towards each of them.

Today, most industries and factories in the country use statistical quality control tools to improve product quality, but due to the high volume of data, now there is a need for a more powerful tool that can control statistical quality control processes, given the extent Data Mining Algorithms and Its Ability to Discover Rules In this research, data mining tools have been used to improve the quality control process and increase it. The method is that first the failure database is formed and after collecting quality control data, the accuracy of predicting the quality of parts is determined using different decision tree algorithms and in the next step using Modeling, Coverage Analysis, Data Each of the rules is evaluated, and finally the workstations are evaluated using the rules that apply to each workstation. Accordingly, in this study, the statistical population of all Tiba surge arresters in 1398. The attributes consist of 9 workstations. Based on the results, the best algorithm in predicting C5 failure is and the most important attributes selected by it are determined as the most important attributes, which are: Cooling quality, hole quality and cutting quality. Also, the evaluation of the rules has been done using the model of cover analysis, data and the most important rules have been extracted. Finally, based on solving the model, the devices that will be in the corrective priority for the current year are: Rowling , Solder and cutting