University of QomJournal of Engineering Management and Soft Computing3116-01586220200922Speed-up Technique in Time-Varying Shortest Path Problems with Arbitrary Waiting Times-مساله تسریع در کوتاهترین مسیرهای متغیر زمانی با زمانهای انتظار دلخواه921106810.22091/jemsc.2018.1688.1049ENGholamhasanShirdelAssociate Prof., Department of Mathematics, Faculty of Basic Sciences, University of Qom, Qom, Iran.0000-0003-2759-4606HasanRezapourPh.D, Applied Mathematics, Faculty of Basic Sciences, University of Qom, Qom, Iran.Journal Article20170104Network flow problems are considered a vital branch of operations research. These problems are classified into static and time-varying classes. Network flow problems are time-varying in real application, because any flow must take a given amount of time to traverse an arc. Moreover, all the parameters in the network can be time-dependent. In this paper, the speed-up technique on time-varying shortest path problems is studied. First of all, the time-varying shortest path problem is explained. The problem is to find the shortest paths from a specific vertex (which is called a source) to other vertices, so that the total cost of the path is minimized and the total travel times and waiting times reach a maximum value of T, where T is a given positive integer. Then the speed-up technique is explained for a shortest path problem.https://jemsc.qom.ac.ir/article_1068_c81e51869e8e5eb621be93c818e5df13.pdfUniversity of QomJournal of Engineering Management and Soft Computing3116-01586220200922Segmentation of the Sensor Data Stream in Pervasive Smart Environments-قطعهبندی جریان داده حسگرها در محیطهای هوشمند فراگیر2339127310.22091/jemsc.2018.1273ENVahidGhasemiKermanshah University of Technology (KUT), Kermanshah, Iran.MohammadJavadianDepartment of Computer Engineering, Kermanshah University of Technology (KUT), Kermanshah, Iran.SajadHayatiDepartment of Mechanical Engineering, Kermanshah University of Technology (KUT), Kermanshah, Iran.Journal Article20190316Nowadays, pervasive environment development has garnered lots of attentions. In such environments, user-object interactions along time are recorded via several sensors, and sensor events are processed as a stream of data. In this process, user’s activities are recognized, and accordingly, essential services are provided. In many activity recognition approaches, firstly the input data stream is segmented, then the activity pertaining to each segment is induced. In such approaches, sensor data stream segmentation is a predominant phase. In this paper, this problem is investigated and a novel method, based on a difference of convex programming problem, is proposed to solve it. In the proposed method a feature vector is calculated for each sensor event in the data stream using a Bayesian approach, and the sequence of such vectors is hired in a difference of convex cost function. The cost function and feature vectors has been calculated by considering heuristics adopting to smart environments. Data segments are extracted by minimizing the cost function. The segmentation purity and conditional entropy have been calculated to measure the performance. Evaluations show that the proposed method has an acceptable performance comparing to some existing approaches.https://jemsc.qom.ac.ir/article_1273_d4ee9fa811f31019d8d1a6a702006b28.pdfUniversity of QomJournal of Engineering Management and Soft Computing3116-01586220200922Query-Based Extractive Multi-Document Summarization Using Paraphrasing and Textual Entailment-خلاصهسازی چندسندی استخراجی مبتنی بر پرسوجوی متن با استفاده از تفسیر و استلزام متنی183198127010.22091/jemsc.2018.1270ENAliNaserasadiComputerGroup,ZarnadIndustrialandMiningFaculty,ShahidBahonarUniversity,Kerman,IranJournal Article20190220One of the most common problems with computer networks is the amount of information in these networks. Meanwhile searching and getting inform about content of textual document, as the most widespread forms of information on such networks, is difficult and sometimes impossible. The goal of multi-document textual summarization is to produce a pre-defined length summary from input textual documents while maximizing documents’ content coverage. This paper presents a new approach for textual document summarization based on paraphrasing and textual entailment relations and formulating the problem as an optimization problem. In this approach the sentences of input documents are clustered according to paraphrasing relation and then the entailment score and final score of a fraction of the header sentences of clusters which have the best score according to the user query is calculated. Finally, the optimization problem is solved via greedy and dynamic programming approaches and while selecting the best sentences, the final summary is generated. The results of implementing the proposed system on standard datasets and evaluation via ROUGE system show that the proposed system outperforms the state-of-the-art systems at least by 2.5% in average.https://jemsc.qom.ac.ir/article_1270_d874d641d3de3efad886d09ba7e820fa.pdfUniversity of QomJournal of Engineering Management and Soft Computing3116-01586220200922The Impact of Managers’ Attitude Towards Internet Marketing on Internet Marketing Adoption in Organizations (Case Study: Book-Publishing and Distribution Companies in Iran)-تأثیر گرایش مدیران به بازاریابی اینترنتی بر میزان بکارگیری آن در سازمانها (مورد مطالعه: شرکتهای نشر و توزیع کتاب در ایران)4168106710.22091/jemsc.2017.2353.1056ENHamidrezaRezvaniAssistant Prof., MBA Department, Management faculty, Mehralborz university, Tehran, Iran.NargesAghakhaniMSc, IT management, IT department, Faculty of Information Technology, University of Mehralborz, Tehran, Iran.Journal Article20170810In today’s turbulent and competitive market, organizations tend to use Internet marketing for achieving and maintaining optimal performance in order to gain competitive advantage. One of the most important success factors in Internet marketing is the support of top managers of the organization. Since there has been a limited number of researches investigating the relationship between "top managers’ supportive attitude" and "the extent to which Internet marketing is used", this paper seeks to identify, categorize, and provide indicators for measuring these two variables in book publishing and distribution companies. This study is a descriptive survey research and the data has been collected by sending electronic questionnaires to the top managers and marketing managers of under-research companies. The results indicate that there is a weak positive correlation between these two variables. In other words, the use of Internet marketing in under-research companies doesn't seem to have a strong relationship with managers' attitudes, and there are other factors involved. However, knowing the top managers’ attitude will lead to appropriate decisions on using resources for implementation of Internet marketing.https://jemsc.qom.ac.ir/article_1067_3bc9a375a2693fd2f122eeee1244c756.pdfUniversity of QomJournal of Engineering Management and Soft Computing3116-01586220200922Breast Cancer Prediction Using the Affinity Propagation Clustering with Regard to the Weights of Variables-پیش بینی سرطان سینه با استفاده از روش خوشهبندی انتشار وابستگی با در نظر گرفتن وزن متغیرها6981127410.22091/jemsc.2018.1274ENSinaDamiDept. Computer Engineering, West Tehran Branch, Islamic Azad University, IranZeinabHatamchuriDept. Computer Engineering, West Tehran Branch,Islamic Azad University, IranJournal Article20190316By using data mining tools in the field of medical diagnosis, some limitations such as the high cost of some tests or their timing will be addressed. In addition, the existence of errors in some experiments has led researchers to be welcomed by categorization methods. In this regard, the present study, based on the combination of clustering and categorization methods, has proposed a new method for the diagnosis of breast cancer. In this operation, the combination is performed using an iterative algorithm and a dependency propagation clustering algorithm. This method produces weights for variables using an innovative algorithm and forms cluster clusters based on the dependency propagation algorithm. Then the number of clusters as a new variable is added to the data, and in the next step, the block algorithm is implemented on the modified dataset containing the main data and the number of clusters. According to the accuracy index, the weights production continues to reach the highest possible precision. According to the numerical experiments conducted in this study, the combination of the dependency emission clustering algorithm with an average accuracy of 36.98 was the most accurate. In addition, the Wilcoxon assumption test confirmed the superiority of the combined neural network compared to other methods.https://jemsc.qom.ac.ir/article_1274_2dc216d915e4337aa3fc8ebac5df5dc5.pdfUniversity of QomJournal of Engineering Management and Soft Computing3116-01586220200922Vehicle Detection in Different Environments-تشخیص وسایل نقلیه در محیطهای ترکیبی217231126910.22091/jemsc.2018.1269ENMohsenValizadehaslDepartment of engineering,. Faculty of Electrical and Computer Engineering. Kharazmi University. Tehran,0000-0001-7849-7668MohammadBadpeimaMalek Ashtar University of Technology, Tehran, IranSaharKhosraviyan ZahedaniPhd student of Artificial intelligence , Islamic Azad University, Lahijan, IranJournal Article20190220In this paper, we presented a vehicle detection method based on RGB color space components analysis. The proposed approach is mainly focused on designing the system which is applicable in the case of different weather conditions (rainy, snowy, misty etc), different times during the day and night (daylight, night, noon, afternoon), heavy traffics, the existence of the shadows and different road conditions. Most of the vehicle detection methods utilized background model generation. Since even slight changing in the brightness could decrease the detection quality, in these kinds of methods the background image needs to continuously be updated. In this paper, we presented the method in which the vehicle detection process is performed without any need to generate and update the background model. In the presented approach, we utilized the histogram normalization in order to alleviate the problems caused by brightness change in the case of different weather conditions. We also extracted moving objects using optical flow. Finally, we utilized the HOG descriptor and SVM classifier in order to detect vehicle objects. The performance of the proposed method is tested using VDTD dataset and the results illustrate that the proposed method provides acceptable results specially in heavy traffics and different weather conditions.https://jemsc.qom.ac.ir/article_1269_916974c16d14eeb0c12be63a98fd446a.pdfUniversity of QomJournal of Engineering Management and Soft Computing3116-01586220200922The Management and Evaluation of Independent Project Portfolios under Uncertainty and Projects Incompatibility-مدیریت و ارزیابی سبد پروژههای مستقل در شرایط عدم قطعیت و سازگاری پروژهها83112107210.22091/jemsc.2018.2451.1060ENHadiMokhtariAssistant Professor, Department of Industrial Engineering, Faculty of Engineering, University of Kashan, Kashan, Iran.ZeinabHabibiBSc Student of Industrial Engineering, Department of Industrial Engineering, Faculty of Engineering, University of Kashan, Kashan, Iran.Journal Article20170914This research focuses on evaluating and proposing some approaches in order to choose the most economic projects under risk and uncertainty. In this investigation, the considered projects are independent and naturally selecting multiple options, as a project portfolio, is possible. The restrictive criterion for the investor in selecting large-scale portfolios is the limited available budget and capital that determine which projects are economic and can be selected. However, variations and inconsistencies in the economic utility of projects, which is caused by external uncertainties, is an important factor that should be considered in such evaluations. In this research, two different approaches are proposed for the economic evaluation of project portfolio under risk and uncertainty. The first approach is designed based on a normal distribution curve and the minimum coefficient of variation (CV), while the second one acts based on a corrected available budget and the maximum expected value. Finally, the results of the proposed approaches are evaluated and analyzed considering the presented sample problems. https://jemsc.qom.ac.ir/article_1072_250c67c68105ae063878c3d22e7f54a6.pdfUniversity of QomJournal of Engineering Management and Soft Computing3116-01586220200922Proposed Method for Note Detection and Automatic Identification of the Melody Models (Gusheh) in Iranian Traditional Music with Micro Approach-روش پیشنهادی برای شناسایی خودکار گوشهها در ردیف موسیقی سنتی ایرانی با رویکرد میکرو113138126810.22091/jemsc.2018.1268ENAmirVafaeianKnowledge and Information Studies, Kharazmi University, Tehran, IranKeivanBornaAssistant Prof., Computer Sciences, Faculty of Mathematical Sciences and Computer, Kharazmi University, Tehran, IranHamedSajediAssistant Prof., Electronic Engineering, Faculty of Engineering, Shahed University, Tehran, IranDariushAlimohammadiAssistant Prof., Knowledge and Information Studies, Faculty of Psychology and Education, Kharazmi University, Tehran, IranPouyaSarai5. Assistant Prof., Music Department, Faculty of Art, Islamic Azad University Central Tehran Branch, Tehran, IranJournal Article20190315One of the most common problems with computer networks is the amount of information in these networks. Meanwhile searching and getting inform about content of textual document, as the most widespread forms of information on such networks, is difficult and sometimes impossible. The goal of multi-document textual summarization is to produce a pre-defined length summary from input textual documents while maximizing documents’ content coverage. This paper presents a new approach for textual document summarization based on paraphrasing and textual entailment relations and formulating the problem as an optimization problem. In this approach the sentences of input documents are clustered according to paraphrasing relation and then the entailment score and final score of a fraction of the header sentences of clusters which have the best score according to the user query is calculated. Finally, the optimization problem is solved via greedy and dynamic programming approaches and while selecting the best sentences, the final summary is generated. The results of implementing the proposed system on standard datasets and evaluation via ROUGE system show that the proposed system outperforms the state-of-the-art systems at least by 2.5% in average.https://jemsc.qom.ac.ir/article_1268_9ef849dfdbd07c11a6361504799c29e2.pdfUniversity of QomJournal of Engineering Management and Soft Computing3116-01586220200922Optimization of Job Scheduling in the Cloud Computing Environment Using the Fuzzy Particle Swarm Optimization Algorithm-بهینهسازی زمانبندی وظایف در محیط ابر با استفاده از ویرایش فازی الگوریتم بهینهسازی اجتماع ذرات199215127110.22091/jemsc.2018.1271ENShabnamGharaeianDepartment of Computer Engineering, Garmsar Branch, Islamic Azad University,Garmsar,IranKhosrowAmirizadehDepartment of Computer Engineering, Garmsar Branch, Islamic Azad University,Garmsar, IranJournal Article20190220Nowadays, along with the constant increase of using cloud environment by companies and organizations, scheduling jobs in this environment in an optimum way is of prime importance. Therefore, different algorithms have been suggested for assigning tasks to resources in cloud environments; however, most of which do not consider criteria such as balanced load, and reduction of the task completion time. In this work, using the meta-heuristic algorithm of swarm particles optimization (PSO) and fuzzy logic, task completion time is reduced, and, as a result of which, efficiency of using resources is increased. Generally, in a distributed system like cloud environment, tasks are assigned randomly to resources. Hence, total load on the cloud environment could become imbalanced, which reduces system’s efficiency. In this research, PSO and fuzzy logic is used for job scheduling. In addition, the use of simulated annealing (SA) to improve the initial solutions, which are generated randomly, is suggested. Results show that the suggested optimization method can effectively improve criteria like makespan once compared with results of algorithms without optimization, like Ron-robin, and even in comparison to other optimization algorithms, like genetic algorithm. https://jemsc.qom.ac.ir/article_1271_ede13aaab1e7ff67858848c5cc505740.pdfUniversity of QomJournal of Engineering Management and Soft Computing3116-01586220200922Identifying the Effective Factors on Neuropathic Diseases in Patients with Chronic pain Using Deep Neural Networks-تعیین ویژگی های موثر برای بیماری نوروپاتیک در بیماران دارای درد مزمن با استفاده از شبکه عصبی عمیق139150127610.22091/jemsc.2018.1276ENMobinShaterianDepartment of Computer Engineering, Islamic Azad University Tehran Science and Research Branch,Tehran, IranMohammadTeshnehlabElectrical Eng. Department of K. N. Toosi University of Technology, Tehran, Iran.Journal Article20190316<strong> </strong>The main purpose of this research is finding major characteristics of clinical signs in the diagnosis of neuropathic disease in patients with chronic long-term pain. This type of disease is caused by various factors such as war, accidents and sports events. In this research, pain questionnaire of Shafa Neuroscience Research Center in Khatam-ol-Anbia Hospital in Tehran is study. By using the deep neural network and the nearest neighbor and the genetic algorithm and the auto encoder, the list of features was obtained with a precision measurement of 75 percentage. The McGill questionnaire was designated as the best effective feature for Neuropathic Pain.https://jemsc.qom.ac.ir/article_1276_0c943ca8aa651abebc2d53745c2f0aff.pdfUniversity of QomJournal of Engineering Management and Soft Computing3116-01586220200922Diagnosis of Coronary Heart Disease by Using Hybrid Intelligent Systems Based on the Whale Optimization Algorithm, Simulated Annealing and Support Vector Machine-تشخیص بیماری قلبی عروق کرونر با سیستم هوشمند ترکیبی براساس الگوریتم نهنگ، شبیه ساز تبرید و ماشین بردار پشتیبان167181127710.22091/jemsc.2018.1277ENZeinabHassaniEngineering Faculty, Kosar University of Bojnord, Bojnurd, IranMahinKhosraviEngineering Faculty, Kosar University of Bojnord, Bojnurd, IranJournal Article20190316<em> </em>In recent years, machine learning algorithms are widely used for diagnosis and timely treatment of diseases. Moreover, diagnosis of disease on early stages is very effective in improving the disease and in reducing the cost of treatment for the patient. Heart disease is one of the main causes of death in the world. Several studies have been conducted to diagnose of disease and to design an intelligent and efficient system. In this paper, a hybrid algorithm of Whale Optimization Algorithm and simulated annealing are presented to identify the effective factors in the diagnosis of the disease. The support vector machine algorithm is considered for effective classification of the disease. The proposed approach is evaluated using the Cleveland Heart Disease Data Collection in the UCI database. The proposed algorithm has obtained with an accuracy of 87.78% which is able to diagnose of disease with fewer attributes. The results exhibition the superiority of the proposed method which the proposed approach can help physicians to diagnose and to improve disease in the early stageshttps://jemsc.qom.ac.ir/article_1277_0034b961ec78c93b97ef0ba0714e9a8a.pdfUniversity of QomJournal of Engineering Management and Soft Computing3116-01586220200922Improvement of CRF-Based Saliency Detection Algorithm Using Matrix Decomposition Based Features-بهبود الگوریتم تشخیص نقشه برجستگی مبتنی بر CRF با استفاده از ویژگیهای مبتنی بر تجزیه ماتریس151166127510.22091/jemsc.2018.1275ENMohammadShouryabiElectrical and Computer Engineering Faculty, Semnan University,Semnan, IranMohammad JavadFadaeieslamElectrical and Computer Engineering Department, Semnan University Semnan, IranJournal Article20190316One of the most important processing steps in the human vision system is the detection of a scene saliency map. Since saliency map can be applied to algorithms such as segmentation, compression and image retrieval, Researchers have focused on providing an efficient model to recognize it. Although a lot of works have been done in this area, the obtained saliency maps are still not satisfying enough. For this purpose, we propose a simple and supervised algorithm to identify the saliency map using a conditional random field (CRF) and saliency cues. In the proposed method, local contrast, center-bias, and backgroundness features have been used for CRF training. Additionally, a new feature based on matrix decomposition has been employed to improve the performance. In the following, CRF has been trained according to the features of 20 images close to the input image. Finally, input image saliency is estimated according to calculated weights in the training phase, input image saliency cues, and ground truths. The proposed method outperforms other methods in terms of algorithm implementation accuracy and speed.https://jemsc.qom.ac.ir/article_1275_1f6cd23b3430eaba3d8b821d80f59dba.pdf