Mining Software Research Articles

Innovation Practice and Ability Cultivation of Software Engineering Professional Competition under the Background of Emerging Engineer

. In this paper, it briefly summarizes the source of emerging engineer and the ability that shall be equipped by graduates of emerging engineer; meanwhile, starting from the angle of the frontline teachers, it gives the guidance opinions for competition innovation practice under the background of emerging engineer for the major of software engineering, and highlights cross-disciplinary teams, creative mining, and practical software project development management process conforming to undergraduate students under the background of emerging engineer. Moreover, in this paper, it further analyzes and summarizes the cultivation effects of competition innovation practice on nine abilities which can be reached by the students who major in software engineering of the emerging engineer.

A Comparative Analysis of Patients' Quality of Life, Body Image and Self-confidence Before and After Aesthetic Rhinoplasty Surgery.

Having desired facial features creates a better mental image, results in increased self-confidence and boosts social activities to a more acceptable level. The goal of the current study is to investigate how rhinoplasty surgery affects patients admitted to Shahrekord surgical clinic. The study was conducted on 100 patients randomly selected and referring for rhinoplasty surgery. The required information was collected through demographic questionnaires of rhinoplasty outcomes F 36 and Rosenberg's self-esteem scale. The questionnaires were filled out by patients before surgery and 3months after surgery. The data were analyzed using SPSS and data mining software. Out of the hundred participants, 63% were female and 37% were male. The number of individuals in the studied groups decreased in older age groups so that most of the participants were in the 20-24-year group followed by 25-30 y/o group. Patients from other age groups were less frequent. In terms of educational attainment, the highest frequency was associated with a BSc and the lowest with a diploma. In terms of employment status, the highest frequency was among students and the unemployed and employees and the self-employed followed. Body image and self-esteem were significantly improved after surgery (p < 0.005). Although not statistically significant, quality of life was improved too after surgery. According to the results of this study, physicians and patients should measure the pros and cons of the surgery before making a treatment decision. It also showed that rhinoplasty leads to an increase in quality of life and self-esteem. Prior to surgery, specialist consultation should be performed, and rhinoplasty should be performed if a specialist physician allows it. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to Table of Contents or the Online Instructions to Authors www.springer.com/00266.

Expanding the Kendrick Mass Plot Toolbox in MZmine 2 to Enable Rapid Polymer Characterization in Liquid Chromatography-Mass Spectrometry Data Sets.

Technological advances in mass spectrometry (MS) toward more accurate and faster data acquisition result in highly informative but also more complex data sets. Especially the hyphenation of liquid chromatography (LC) and MS yields large data files containing a high amount of compound specific information. Using electrospray-ionization for compounds such as polymers enables highly sensitive detection, yet results in very complex spectra, containing multiply charged ions and adducts. Recent years have seen the development of novel or updated data mining strategies to reduce the MS spectra complexity and to ultimately simplify the data analysis workflow. Among other techniques, the Kendrick mass defect analysis, which graphically highlights compounds containing a given repeating unit, has been revitalized with applications in multiple fields of study, such as lipids and polymers. Especially for the latter, various data mining concepts have been developed, which extend regular Kendrick mass defect analysis to multiply charged ion series. The aim of this work is to collect and subsequently implement these concepts in one of the most popular open-source MS data mining software, i.e., MZmine 2, to make them rapidly available for different MS based measurement techniques and various vendor formats, with a special focus on hyphenated techniques such as LC-MS. In combination with already existing data mining modules, an example data set was processed and simplified, enabling an ever faster evaluation and polymer characterization.

Natural language processing in mining unstructured data from software repositories: a review

With the increasing popularity of open-source platforms, software data is easily available from various open-source tools like GitHub, CVS, SVN, etc. More than 80 percent of the data present in them is unstructured. Mining data from these repositories helps project managers, developers and businesses, in getting interesting insights. Most of the software artefacts present in these repositories are in the natural language form, which makes natural language processing (NLP) an important part of mining to get the useful results. The paper reviews the application of NLP techniques in the field of Mining Software Repositories (MSR). The paper mainly focuses on sentiment analysis, summarization, traceability, norms mining and mobile analytics. The paper presents the major NLP works performed in this area by surveying the research papers from 2000 to 2018. The paper firstly describes the major artefacts present in the software repositories where the NLP techniques have been applied. Next, the paper presents some popular open-source NLP tools that have been used to perform NLP tasks. Later the paper discusses, in brief, the research state of NLP in MSR field. The paper also lists down the various challenges along with the pointers for future work in this field of research and finally the conclusion.

Construction and optimization method of the open-pit mine DEM based on the oblique photogrammetry generated DSM

The digital surface model (DSM) of open-pit mines generated by oblique photogrammetry has a large amount of data and much interference information. Therefore, it is necessary to construct a 3D model with a small amount of data that can reflect the real terrain of open-pit mines. The model should adapt to the existing 3D mining software and provide a guarantee for the subsequent production planning and open-pit mine design. Taking three different complexity scenes in two open-pit mines with 1:500 mapping scale as experimental objects, we realized the DEM construction of the DSM in different scenarios in open-pit mines. The extraction completeness of mining equipment in the DSM using this method is 94.7%. The data can be compressed to approximately 0.02% of the original data. The accuracy of 210 check points in the model is consistent with the industry standard. The DEM model can be quickly imported into 3D mining software.

Unsupervised software repositories mining and its application to code search

SummarySoftware repositories are crucial resources for many software tasks, including code retrieval and annotation. Programming forums provide questions and answers (Q&amp;A) from software developers, containing abundant code‐description posts for exchanging knowledge about programming issues. However, most posts provide personal opinions of users that are often not adequately confirmed or outdated. Mining software repositories in such open and unrestricted forums is challenging. Since the posts can be arbitrary and noisy, it is difficult to get unified labels for supervised noise elimination. Different from existing mining approaches, this paper proposes Code‐Description Mining Framework (CodeMF), an unsupervised framework to eliminate noisy posts and extract high quality software repositories from programming forums. CodeMF treats all social features of the posts as discrete‐time signals for kernel principal component analysis and further performs wavelet transform feature fusion to find the delicate changes (noises in temporal signals). We conduct comprehensive experiments on StackOverflow. Experimental results demonstrate that CodeMF can effectively reduce running time and improve precision via mining high‐quality software repositories for various programming languages, especially for the large‐scale codebases. To further illustrate the effect of CodeMF applied in software tasks, we introduce it to improve the performance of query‐expansion code search. Meanwhile, for SQL and C# programs, compared to the state‐of‐the‐art query‐expansion method QECK, the improvement of QECKCodeMF is 2% and 6% on Recall@10, and 4% and 14% on mean reciprocal rank, respectively.

DATA MINING DALAM PENGELOMPOKAN NILAI IQ SISWA

Education is one aspect of life that should be owned by every human being. The most crucial times for education is during school. During school, students will start from kindergarten until finally graduating at the high school level. One aspect that is highly considered during school is the level of intelligence. Intelligence levels are often associated with IQ levels. According to research, a person's IQ level becomes a benchmark on the level of intelligence of that person. The level of intelligence can affect the lives of students in receiving lessons so well that the school should properly classify students. Grouping students based on IQ level aims that students can receive more appropriate and effective learning methods so that students can receive lessons well. At Yos Sudarso Highschool at Batam, there are a lot of students who enroll and grouping students will take a lot of time and is not efficient if done manually. Grouping students can be done quickly and precisely by utilizing data mining. One method in data mining is clustering by using K-means. The stages of this method begin with random selection K, K here is the number of clusters that you want to form. Then set K values ​​randomly, while the value becomes the center of the cluster or commonly referred to as centroid, mean or "means". Calculating the distance of each existing data on each centroid using the Euclidian formula to find the closest distance from each data with the centroid after the manual calculation is run on the data mining software, RapidMiner. The use of the K-means method for grouping is one of the right methods when viewed from the variables to be used, namely the value of the student's IQ level. This ¬K-means method will form clusters that classify students based on IQ levels. By applying data mining clustering with the K-means method it is hoped that it can help the school in classifying students appropriately so as to facilitate the school in ensuring students get the right learning method.

The Powder Diffraction File: a quality materials characterization database

The ICDD's Powder Diffraction File™ (PDF®) is a database of inorganic and organic diffraction data used for phase identification and materials characterization by powder diffraction. The PDF has been available for over 75 years and finds application in X-ray, synchrotron, electron, and neutron diffraction analyses. With entries based on powder and single crystal data, the PDF is the only crystallographic database where every entry is editorially reviewed and marked with a quality mark that alerts the user to the reliability/quality of the submitted data. The editorial processes of ICDD's quality management system are unique in that they are ISO 9001:2015 certified. Initially offered as text on paper cards and books, the PDF evolved to a computer-readable database in the 1960s and today is both computer and web accessible. With data mining and phase identification software available in PDF products, and the databases’ compatibility with vendor (third party) software, the 1 000 000+ published PDF entries serve a wide range of disciplines covering academic, industrial, and government laboratories. Details describing the content of database entries are presented to enhance the use of the PDF.

Analysis of Agricultural Credit Performance of Turkey using K-means Clustering Algorithm

Agriculture is a significant sector that supplies raw materials to many sectors as well as providing nutrients to humans and animals and ensures employment. The economic crises, rapid population growth, the rise in demand for food products have increased importance and necessity of agriculture. For this reason, agriculture must be supported in order not to be affected by adverse conditions and effects. Thus, agricultural credit is an important factor in the development of the production and investment structure of the agricultural sector. In this study, agricultural credit performance of 81 provinces in Turkey in 2018 was compared by taking into consideration the value of total agricultural production, total cultivated area and the amount of agricultural credit used. The data used in this study were collected from the Banking Regulation and Supervision Agency (BRSA) and the Turkish Statistical Institute. In order to determine relationships between the 81 provinces of Turkey, one of the nonhierarchical clustering method, i.e. the K-means clustering method was applied using SPSS Clementine data mining software. As a result, the credit performance of provinces was evaluated and similarities and differences were revealed using agricultural production value, total cultivated land, agricultural credit volume data.

483. Case–control Study Evaluating the Risk of Stenotrophomonas maltophilia Pneumonia in Patients with Previous Exposure to Meropenem

Background Stenotrophomonas maltophilia (SM) is a growing cause of opportunistic nosocomial infections with a mortality rate of 23–77%. Previous studies have identified the use of broad-spectrum antibiotics, specifically carbapenems, as a risk factor for SM infection, but these findings were limited to secondary endpoints. Meropenem’s overall broad-spectrum activity but limited SM activity may favor SM colonization and infection.MethodsAdult patients admitted between January 2016 and July 2018 with available culture data were identified using data mining software. Cases were defined by a positive SM respiratory culture between days 2 and 60 of admission and receipt of antibiotic treatment. Controls were defined by a respiratory culture negative for SM during the same period. The primary endpoint was to evaluate the exposure to at least 48 hours of meropenem between cases and controls, with exposure defined as at least 48 hours of meropenem treatment with the last dose given within 15 days of respiratory culture. Secondary endpoints were to evaluate exposure to at least 7 days of meropenem or other antipseudomonal antibiotics.ResultsA total of 225 patients were included, 106 as cases and 119 as controls. Baseline demographics and age-adjusted Charlson comorbidity index score were similar between groups. Twenty-one cases (19.8%) and 5 controls (4.2%) were exposed to at least 48 hours of meropenem before developing SM pneumonia. The odds of meropenem exposure was 5 times greater in cases than controls (OR = 5.6, P < 0.001). After adjusting for a longer length of stay before culture collection as a potential confounding variable, meropenem use was still associated with SM pneumonia (adjusted OR = 4.6, 95% CI 1.7–14.7). Significant associations were also found with exposure to at least 7 days of meropenem (OR = 4.7, 95% CI 1.52–14.77, P = 0.004) or other antipseudomonal antibiotics (OR = 3.0, 95% CI 1.59–5.71, P < 0.001).ConclusionThis is the first study to evaluate meropenem as a risk factor for developing SM pneumonia. Even as little as 48 hours of meropenem exposure increases the risk of developing SM pneumonia. As one of the few modifiable risk factors for SM infection, judicious use of meropenem may reduce the incidence of SM infection and associated mortality. Disclosures All authors: No reported disclosures.

Utilization of Augmented and Virtual Reality Technologies in Geoscience and Mining

This study reviewed application cases of augmented reality (AR) and virtual reality (VR) in the fields of geoscience and mining and evaluated their future prospects. Numerous papers and reports were reviewed closely. AR has been applied in the field of geoscience for the purposes of geological surveying, geoscience education, underground facility management, and geotourism. There were relatively few cases in which AR was used in the mining sector, but some cases could be classified as mine development enhancement and mine reclamation support. Conversely, VR has been applied to education and geotourism in the field of geoscience. VR has been applied in the mining sector since the 1980s for mining development simulation, training for worker safety, and mining software control. These technologies will be useful for geological surveying and stability improvement in the fields of geoscience and mining.

Text mining for clinical support.

BackgroundIn 2013, the Dahlgren Memorial Library (DML) at the Georgetown University Medical Center began using text mining software to enable its clinical informationists to quickly retrieve specific, relevant information from MEDLINE abstracts while on patient rounds.DescriptionIn 2013, DML licensed the use of the Linguamatics I2E text-mining program, and DML’s clinical informationist began using it to text mine MEDLINE abstracts on patient rounds. In 2015, DML installed I2E on a server at Georgetown and negotiated with Elsevier to obtain the right to download and text mine the full text of clinical journals in ScienceDirect to support clinical decision making. In 2016, the license agreements for the New England Journal of Medicine and the BMJ platform were modified to allow text mining. In 2018, PubMed Central open access content was added to the Linguamatics license.ResultsDML’s informationists found that they were able to quickly find useful information that was not retrievable by traditional methods, and clinicians reported the information was valuable.ConclusionThe ability to text mine MEDLINE abstracts and selected journal articles on patient rounds has allowed DML’s clinical informationists to quickly search large amounts of medical literature that can be used to answer physicians’ clinical questions. DML plans to acquire additional journal articles from selected publishers in the future, which should increase the usefulness of the project.

Aplikasi Web Usage Mining Menggunakan Metode Association Rule Dengan Algoritma Fp-Growth Untuk Mengetahui Pola Browsing Pengunjung Website

As a way to improve the promotion of institutions via the web, there is a need for a method to view browsing patterns of visitors on the site unilak.ac.id, thereby showing the user's interest in the links he visits. Data mining or knowledge discovery is a process of extracting valuable information by analyzing the existence of certain patterns or relationships. To find visitor patterns in the form of association rules is to use the association rule method. FP-Growth is an alternative algorithm that can be used to determine the most frequent set of data in a set of data. FP-Growth is applied to get a pattern of visitors, about what links are frequently visited and seen by visitors on the site unilak.ac.id. This pattern is used to help web administrators in developing the site unilak.ac.id by utilizing knowledge from the association pattern to regulate the layout / layout design of the categories available on the site unilak.ac.id. From the results of processing the dataset with FP-Growth algorithm and processing data processed using data mining software, namely Rapidminer 6.5. It was found that the minimum value of support was 1% and the minimum confidence value of 50% resulted in 124 rules of association.

Machine Learning Approaches to Develop Pedotransfer Functions for Tropical Sri Lankan Soils

Poor data availability on soil hydraulic properties in tropical regions hampers many studies, including crop and environmental modeling. The high cost and effort of measurement and the increasing demand for such data have driven researchers to search for alternative approaches. Pedotransfer functions (PTFs) are predictive functions used to estimate soil properties by easily measurable soil parameters. PTFs are popular in temperate regions, but few attempts have been made to develop PTFs in tropical regions. Regression approaches are widely used to develop PTFs worldwide, and recently a few attempts were made using machine learning methods. PTFs for tropical Sri Lankan soils have already been developed using classical multiple linear regression approaches. However, no attempts were made to use machine learning approaches. This study aimed to determine the applicability of machine learning algorithms in developing PTFs for tropical Sri Lankan soils. We tested three machine learning algorithms (artificial neural networks (ANN), k-nearest neighbor (KNN), and random forest (RF)) with three different input combination (sand, silt, and clay (SSC) percentages; SSC and bulk density (BD); SSC, BD, and organic carbon (OC)) to estimate volumetric water content (VWC) at −10 kPa, −33 kPa (representing field capacity (FC); however, most studies in Sri Lanka use −33 kPa as the FC) and −1500 kPa (representing the permanent wilting point (PWP)) of Sri Lankan soils. This analysis used the open-source data mining software in the Waikato Environment for Knowledge Analysis. Using a wrapper approach and best-first search method, we selected the most appropriate inputs to develop PTFs using different machine learning algorithms and input levels. We developed PTFs to estimate FC and PWP and compared them with the previously reported PTFs for tropical Sri Lankan soils. We found that RF was the best algorithm to develop PTFs for tropical Sri Lankan soils. We tried to further the development of PTFs by adding volumetric water content at −10 kPa as an input variable because it is quite an easily measurable parameter compared to the other targeted VWCs. With the addition of VWC at −10 kPa, all machine learning algorithms boosted the performance. However, RF was the best. We studied the functionality of finetuned PTFs and found that they can estimate the available water content of Sri Lankan soils as well as measurements-based calculations. We identified RF as a robust alternative to linear regression methods in developing PTFs to estimate field capacity and the permanent wilting point of tropical Sri Lankan soils. With those findings, we recommended that PTFs be developed using the RF algorithm in the related software to make up for the data gaps present in tropical regions.

Ragp: Pipeline for mining of plant hydroxyproline-rich glycoproteins with implementation in R.

Hydroxyproline-rich glycoproteins (HRGPs) are one of the most complex families of macromolecules found in plants, due to the diversity of glycans decorating the protein backbone, as well as the heterogeneity of the protein backbones. While this diversity is responsible for a wide array of physiological functions associated with HRGPs, it hinders attempts for homology based identification. Current approaches, based on identifying sequences with characteristic motifs and biased amino acid composition, are limited to prototypical sequences. Ragp is an R package for mining and analysis of HRGPs, with emphasis on arabinogalactan proteins. The ragp filtering pipeline exploits one of the HRGPs key features, the presence of hydroxyprolines which represent glycosylation sites. Main package features include prediction of proline hydroxylation sites, amino acid motif and bias analyses, efficient communication with web servers for prediction of N-terminal signal peptides, glycosylphosphatidylinositol modification sites and disordered regions and the ability to annotate sequences through hmmscan and subsequent GO enrichment, based on predicted Pfam domains. As such, ragp extends R's rich ecosystem for high-throughput sequence data analyses. The ragp R package is available under the MIT Open Source license and is freely available to download from GitHub at: https://github.com/missuse/ragp.

The Magnificent VWF SNPs and Where to Find Them: A Journey in Exon 28 of the Hispanic Population

Abstract von Willebrand disease (VWD) is a common coagulation disorder with a prevalence of 0.1% to 1.0% manifesting as a wide spectrum of bleeding symptoms. Type 1 is diagnosed by confirming a quantitative decrease in VWF level, while type 3 has undetectable levels of VWF. Type 2 VWD variants are characterized by functional and binding defects. Initial diagnosis and follow-up depend mainly on measuring VWF protein and VWF activity and calculating the activity to protein ratio. Ristocetin cofactor activity (VWF:RCo) is the most widely used and gold standard activity assay. In our institute, we use a cutoff of VWF:RCo/VWF:Ag &lt;0.7 to screen for possible type 2 VWD. However, the use of this ratio is flawed by the presence of specific SNPs (I1380V, N1435S, and D1472H) in the A1 domain, especially in African Americans. These SNPs lead to a decrease in ristocetin binding to VWF and hence decreased VWF:RCo/VWF:Ag ratio. In this retrospective study, we analyzed the levels of VWF:AG and VWF:RCo based on patients’ ethnicity using an in-house data mining software from 2011 to 2016. Then, we validated several exon 28 primers, kindly provided by Dr. Montgomery (Blood Center of Wisconsin), used to detect type 2 mutations and SNPs in African Americans. We excluded cases diagnosed as positive for VWD and included only cases rendered nondiagnostic of VWD following a comprehensive panel including multimers, collagen binding, and molecular studies when indicated. In our Hispanic population (n = 936), VWF:Ag was 138.9% and 117% (average and median) and VWF:RCo was 110.5% and 93.0% (average and median); 43.2% of Hispanics had VWF:RCo/VWF:Ag &lt;0.7. In our African American population (n = 664), VWF:Ag was 163.0% and 138.5% (average and median) and VWF:RCo was 108.5% and 92.0% (average and median); 50.6% of the African American patients had VWF:RCo/VWF:Ag &lt;0.7. Patients from Caucasian origins (n = 242) had VWF:Ag of 149.6% and 113.0% (average and median) and VWF:RCo of 137.6% and 116.0% (average and median), respectively; 30.9% of Caucasian patients had VWF:RCo/VWF:Ag of less than 0.7. We then selected four random Hispanic cases with VWF:RCo/VWF:Ag &lt;0.5 as part of the validation study of exon 28 sequencing. Using a Sanger sequence assay, we found multiple benign/likely benign single-nucleotide polymorphisms (SNPs) at exon 28 that code for VWF antigen A1 domain. All four cases showed P.Thr1381Ala and P.Thr1547 = [OC1] polymorphism, three showed p.Val1565Leu polymorphism, and two showed p.Ala1555 = polymorphism. The statistical analysis of VWF:Ag/VWF:RCo levels from Hispanics shows a similar trend to African Americans with a high rate of cases with VWF:RCo/VWF:Ag &lt;0.7 in comparison to Caucasians. However, the finding of SNPs and absence of known African American polymorphisms suggest that these SNPs may be the cause of decreased ristocetin binding in Hispanics. This study calls for ethnic-based considerations in VWD workflows.

Automatic Stop Word Generation for Mining Software Artifact Using Topic Model with Pointwise Mutual Information

Automatic Stop Word Generation for Mining Software Artifact Using Topic Model with Pointwise Mutual Information

Mining Software Repositories for Software Metrics (MSR-SM): Conceptual Framework

A number of software metrics estimate the complexity of software program by a couple of substantial software attributes and trends. Metric for measuring the reliability is extraordinary among the presently available complexity metrics by contemplating a non-physical attributes i.e. readability. Readability may be a key quality attribute for managing software source codes. Readability of the source code is mainly concerned with code maintainability which is a significant characteristic of software quality, mainly from the developers’ perspective. As the code is readable, the easier it is to modify, lesser errors, copious maintainable, easy to reuse and even more reliable. Readability is employed to enhance source codes for subsequent maintenance and extensibility. But code readability is not simply computable while dealing with open source software as contributors access the code and modify its structure according to his/her style of coding. This nature of development begins problems for the new contributors to understand the code structure. To enhance code readability, In this paper, we proposed a conceptual model of mining software repositories for software metrics in which we proposed a set of metrics for readability of the code that is easier to use and helpful to understand the code structure. We also mention a mechanism to validate the proposed metrics by the data extracted from the mining software repositories and comparing it with a survey conducting from experts working in industries.

SAÜ Bilgisayar Mühendisliği Karma Eğitim Öğrencileri Arasında Yapılan Anketin Açık Kaynak Kodlu Veri Madenciliği Yazılımları ile Değerlendirilmesi

Veri tabanlarındaki veriler arasında bulunan ilişkiler, örüntüler, sapma ve eğilimler gibi bilgilerin ortaya çıkarılması ve keşfi veri madenciliğinin temelini oluşturur. Ortaya çıkarılan bu bilgiler faaliyete yönelik karar destek sistemleri için gerekli temel verileri sağlar. Bu çalışmada Sakarya Üniversitesi Bilgisayar ve Bilişim Sistemleri Fakültesi Bilgisayar Mühendisliği bölümünde uygulanmakta olan Karma Eğitim Programı öğrencileri arasında yapılan bir anket veri madenciliği birliktelik analizi yöntemiyle incelenerek öğrencilerin memnuniyet ve başarılarını etkileyen etmenler belirlenmeye çalışılmıştır. Çalışmada açık kaynak kodlu veri madenciliği yazılımları kullanılmıştır.

Employing Data Mining Techniques for Predicting Opioid Withdrawal in Applicants of Health Centers

&#x0D; Addiction to narcotics is one of the greatest health challenges in today’s world which has become a serious threat for social, economic, and cultural structures and has ruined a part of an active force of the society and it is one of the main factors of growth of diseases such as HIV and hepatitis. Today, addiction is known as a disease and welfare organization, and many of the dependent centers try to help the addicts treat this disease. In this study, using data mining algorithms and based on data collected from opioid withdrawal applicants referring to welfare organization, a prediction model is proposed to predict the success of opioid withdrawal applicants. In this study, the statistical population is comprised opioid withdrawal applicants in a welfare organization. This statistical population includes 26 features of 793 instances including men and women. The proposed model is a combination of meta-learning algorithms (decorate and bagging) and J48 decision tree implemented in Weka data mining software. The efficiency of the proposed model is evaluated in terms of precision, recall, Kappa, and root mean squared error and the results are compared with algorithms such as multilayer perceptron neural network, Naive Bayes, and Random Forest. The results of various experiments showed that the precision of the proposed model is 71.3% which is superior over the other compared algorithms.