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  • دکتری (1390)

    مهندسی صنایع

    دانشگاه تربیت مدرس،

  • کارشناسی‌ارشد (1384)

    مهندسی صنایع، مدیریت سیستم وبهره وری

    دانشگاه تربیت مدرس،

  • کارشناسی (1374)

    مهندسی کامپیوتر ، نرم افزار

    دانشگاه علم و صنعت ایران،

  • مدلسازی و بهینه سازی
  • سلامت الکترونیکی و سیستمهای تشخیص بهینه ( ژنتیک و بهبود سیستمهای بیمارستانی)
  • مدلسازی هوشمند شبکه و برنامه ریزی حمل و نقل و زنجیره تامین با استفاده از فناوری اطلاعات
  • تحلیل داده های انبوه وهوش تجاری و پیش بینی در حوزهای (سلامت ، بانكداری ، صنعت ، حمل و نقل و كسب و كار)
  • طراحی سیستمهای توصیه گر در اکو سیستم نوآوری الکترونیکی( بانک و امنیت - تجارت - سلامت – برنامه ریزی تولید صنعتی )

    داده ای یافت نشد

    ارتباط

    رزومه

    GenHITS: A network science approach to driver gene detection in human regulatory network using gene’s influence evaluation

    M Akhavan-Safar, B Teimourpour, M Kargari
    Journal Papers , , {Pages }

    Abstract

    Visualizing Computer-Aided Diagnosis Systems Studies Based on Co-Word analysis in PubMed Database

    M Alavi, A Barghbani, M Kargari
    Journal Papers , , {Pages }

    Abstract

    A Network Science Approach to Driver Gene Detection In Human Regulatory Network Using Genes Influence Evaluation

    Mostafa Akhavan Safar, Babak Teimourpour, Mehrdad Kargari
    Journal PapersarXiv preprint arXiv:2001.09481 , 2020 January 26, {Pages }

    Abstract

    Cancer disease occurs because of a disorder in the cellular regulatory mechanism, Which causes cellular malformation. The genes that start the malformation are called Cancer driver genes (CDGs). Numerous computational methods have been introduced to identify cancer driver genes that use the concept of mutation. Regarding abnormalities spread in human cell and tumor development, CDGs are likely to be the potential types of gene with high influence in the network. This increases the importance of influence diffusion concept for the identification of CDGs. recently developed a method based on influence maximization for identifying cancer driver genes. One of the challenges in these types of networks is to find the power of regulatory interacti

    Multi-objective decision-macking model for distribution planning of goods and routing of vehicles in emergency

    Abdolhamid Zahedi, Mehrdad Kargari, Ali Husseinzadeh Kashan
    Journal PapersInternational Journal of Disaster Risk Reduction , 2020 April 7, {Pages 101587 }

    Abstract

    Emergencies are commonly recognized as conditions that should be addressed in the shortest possible time. The readiness to overcome emergency situations -in places where the necessary measures are anticipated-leads to a quicker response, and reduces the inflicted casualties as well as costs. In the occasion of an emergency, distant areas may be affected while having limited resources available. Hence, the main question here is how the available resources should be distributed among the affected areas. The answer to this question could be helpful in critical situations. In this study, an empirical investigation is conducted in order to develop an optimal resource and vehicle scheduling model to meet the needs of the incident areas whose dema

    Modeling Uncertainties based on Data Mining Approach in Emergency Service Resource Allocation

    Mostafa Najafi Zonouzi, Mehrdad Kargari
    Journal PapersComputers & Industrial Engineering , 2020 April 20, {Pages 106485 }

    Abstract

    Thousands of victims and millions of people are affected by natural and non-natural incidents every year. The Resource Allocation Problem can be often considered as part of a post-incident and pre-incident measure. In this paper, a new mathematical model is presented based on data mining methods to maximize the coverage of demand points by ambulances and rescue cars under conditions of uncertainty by rescue and relief (RAR) stations. Also, to increase the accuracy and quality of the results, data mining methods were used to estimate the critical parameter which is defined as “the minimum required number of each kind of equipment to cover demand points under certain conditions” based on historical data in Iran which this method is the or

    Managing Hospital medicine Costs in Healthcare Reform: Case Shari'ati Hospital

    Susan Sahranavard
    Journal PapersInternational Journal of Industrial Engineering & Production Research , Volume 31 , Issue 1, 2020 March 10, {Pages 115-130 }

    Abstract

    Background: The continuous growth of healthcare and medicine costs as a strategic commodity requires tools to identify high cost populations and cost control. After the implementation of the healthcare Reform plan in Iran, a huge share of hospital funding has been spent on undesirable costs due to changes in the use of medicines and instruments.Objective: The aim of this study was to compare the cost of medicines in both the pre and post period of health plan implementation to detect abnormalities and low frequency patterns in the medical prescriptive that account more than 30% of hospital budget funds.Method: Therefore a data mining model has been used. First, by forming incidence matrices on the cross-features; categorized prescriptions i

    GenHITS: A Network Science Approach to Driver Gene Detection in Human Regulatory Network Using Gene’s Influence Evaluation

    Mostafa Akhavan Safar, Babak Teimourpour, Mehrdad Kargari
    Journal PapersJournal of Biomedical Informatics , 2020 December 14, {Pages 103661 }

    Abstract

    Cancer is among the diseases causing death, in which, cells uncontrollably grow and reproduce beyond the cell regulatory mechanism. In this disease, some genes are initiators of abnormalities and then transmit them to other genes through protein interactions. Accordingly, these genes are known as cancer driver genes (CDGs). In this regard, several methods have been previously developed for identifying cancer driver genes. Most of these methods are computational-based, which use the concept of mutation to predict CDGs. In this research, a method has been proposed for identifying CDGs in the transcription regulatory network using the concept of influence diffusion and by modifying the Hyperlink-Induced Topic Search algorithm based on the diff

    A novel method for contrast enhancement of gray scale image based on shadowed sets

    Meysam Alavi, Mehrdad Kargari
    Conference Papers2020 6th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS) , 2020 December 23, {Pages 07-Jan }

    Abstract

    Image processing is considered to be one of the basic components in intelligent and decision support systems. On the other hand, improving image contrast is one of the most challenging issues in the field of image processing, pattern recognition and machine vision. This process improves understanding the information in the image for the human viewer. This is especially of great importance for medical imaging because low light intensity and high noise levels make it difficult to accurately diagnose specific diseases as well as to document and analyze them. The main motivation in image enhancement is to modify the image properties so that it is suitable for viewing and application, which can be done by removing noise, sharpening the edge and

    Routing relief teams by introducing new urban congestion parameter and solving using GACD-MDVRP clustering through genetic algorithm

    Mehrdad Kargari, Sayyed Amin Biabanaki
    Journal PapersAUT Journal of Modeling and Simulation , 2020 June 10, {Pages }

    Abstract

    Emergency disaster-relief activities could dramatically reduce injuries and casualties, while routing and scheduling of the relief teams is also considered an important factor in reducing the fatalities. For this reason, in this paper, a new model is proposed for routing rescue teams considering time windows, capacitated and multi-depot vehicles. In this model, additional factors such as availability of relief centers, congestion and service standard for the vehicles. A new parameter has been developed to denote the congestion of each path and id incorporated into the model using the concept of Social Network Analysis (SNA). Finally, the model is solved using a COREI5 8GB system. The model is also implemented using the data obtained from th

    Multi-objective decision-making model for distribution planning of goods and routing of vehicles in emergency multi-objective decision-making model for distribution planning of …

    A Zahedi, M Kargari, AH Kashan
    Journal Papers , , {Pages }

    Abstract

    Introducing a new method for the fusion of fraud evidence in banking transactions with regards to uncertainty

    Abdollah Eshghi, Mehrdad Kargari
    Journal PapersExpert Systems with Applications , Volume 121 , 2019 May 1, {Pages 382-392 }

    Abstract

    Detection of fraudulent transactions is a vital factor for financial institutions, and finding more effective and accurate methods is of tremendous importance. The use of supervised data mining techniques is not feasible in many cases due to the lack of access to labeled data. Fraud detection is a complex task, and unsupervised methods like clustering and outlier detection techniques employed alone do not yield satisfactory results. Another issue is epistemic uncertainty due to the absence of sufficient information on the behavioral aspects of different customers, which also leads to poorer results for fraud detection and makes the fraud detection system inapplicable in real world environment. In this paper, using multi criteria decision me

    Introducing a Method for Combining Supervised and Semi-Supervised Methods in Fraud Detection

    Abdollah Eshghi, Mehrdad Kargari
    Conference Papers2019 15th Iran International Industrial Engineering Conference (IIIEC) , 2019 January 23, {Pages 23-30 }

    Abstract

    As electronic transactions are growing, fraud cases are also growing drastically. Detection of frauds is a complicated task and limiting fraud detection systems to certain kinds of detection methods like supervised or unsupervised methods does not seem efficient. In this paper, a combination framework for fraud detection systems, consisting of both supervised and semi-supervised methods in three main components namely rule-based component, trend-analysis-based component and, a scenario-based component is proposed. The rule-based component is the supervised part of the framework and decision tree which is a cost-insensitive classification algorithm is used for this component. In the trend-analysis-based component, which is the semi-supervise

    Using RNN to Predict Customer Behavior in High Volume Transactional Data

    Hamed Mirashk, Amir Albadvi, Mehrdad Kargari, Mostafa Javide, Abdollah Eshghi, Ghazaleh Shahidi
    Conference PapersInternational Congress on High-Performance Computing and Big Data Analysis , 2019 April 23, {Pages 394-405 }

    Abstract

    Big data tools and techniques introduce new approaches based on distributed computing methods. When dealing with large data, one of these state-of-art approaches for analysing and predicting in the shortest possible time is the use of deep learning networks that provide real-time, accurate, and comprehensive analysis. This method has provided a new perspective to artificial intelligence with respect to increasing volume of data and complexity of real-world issues. The models used to predict customer behavior have mainly worked with limited features and dimensions. One of the applications of this method is to prevent customer churn, when predicting future behavior of customer transaction on point of sale (POS) devices.

    A disease outbreak prediction model using Bayesian inference: a case of influenza

    Atefeh Sadat Mirarabshahi, Mehrdad Kargari
    Journal PapersInternational Journal of Travel Medicine and Global Health , Volume 7 , Issue 3, 2019 September 10, {Pages 91-98 }

    Abstract

    Introduction: One major problem in analyzing epidemic data is the lack of data and high dependency among the available data, which is due to the fact that the epidemic process is not directly observable.Methods: One method for epidemic data analysis to estimate the desired epidemic parameters, such as disease transmission rate and recovery rate, is data intensification. In this method, unknown quantities are considered as additional parameters of the model and are extracted using other parameters. The Markov Chain Monte Carlo algorithm is extensively used in this field.Results: The current study presents a Bayesian statistical analysis of influenza outbreak data using Markov Chain Monte Carlo data intensification that is independent of prob

    Binary integer programming for Multiple Sequence Alignment

    Seyed Ali Lajevardy, Mehrdad Kargari
    Journal PapersbioRxiv , 2019 January 1, {Pages 854786 }

    Abstract

    Molecular biology advances in the past few decades have contributed to the rapid increase in genome sequencing of various organisms; sequence alignment is usually considered as the first step in understanding the molecular function of a sequence. An optimal alignment adjusts two or more sequences in a way that it could compare the maximum number of identical or similar residues. The two sequence alignments types are: Pairwise Sequence Alignment (PSA) and Multiple Sequence Alignment (MSA). While dynamic programming (DP) technique is used in PSA to provide the optimal method, it will lead to more complexity if used in MSA. So, the MSA mainly uses heuristic and approximation methods. This paper presents a mathematical model for MSA that can be

    Forecasting the Amount of Blood Ordered in the Obstetrics and Gynaecology Ward with the Data Mining Approach

    Tahmineh Aldaghi, Ghasemi H Morteza, Mehrdad Kargari
    Journal PapersIndian Journal of Hematology and Blood Transfusion , 2019 January , {Pages 07-Jan }

    Abstract

    Preoperative blood ordering is frequently used in the obstetrics and gynecology ward of university hospitals in Iran, even for surgeries that rarely require blood transfusions. This routine procedure is an inefficient use of resources and rising costs, wasting time and cause shortage for essential patients. So this study was carried out to propose a new optimal system based on data mining techniques for ordering blood. This cross-sectional study examined the number of units cross-matched and transfused during surgery in the obstetrics and gynecology ward from 2013 to 2015. Data was collected for 1097 patients. Statistical analyzing was applied on data to prove that; the current blood ordering was not optimal. So with use of blood indices, C

    Toilet in transportation vehicles: a topic for consideration on travel sanitation in travel medicine

    Viroj Wiwanitkit
    Journal PapersInternational Journal of Travel Medicine and Global Health , Volume 7 , Issue 3, 2019 January , {Pages 111-112 }

    Abstract

    Toileting is a basic human activity. Human beings have to urinate and defecate as a regular daily activity to maintain a normal physiological life. During travel, urination and defecation are required, and this is usually a forgotten issue in medicine. In travel medicine, long travel and the lack of a good toilet for toileting activities are considered problematic. For example, waiting for long periods of time without the chance to go to the toilet while passing through the immigration process might be a problem for the elderly traveler. 1 During long travel, the toilet in the vehicle is also an important issue. Toilets are seen in several types of vehicles, such as the bus and the airplane. They are considered as public toilets, and concer

    A new framework for combining supervised and semi-supervised methods in fraud detection

    Abdollah Eshghi, MEHRDAD KARGARI
    Journal Papers , Volume 16 , Issue 2, 2019 January 1, {Pages 13-Jan }

    Abstract

    Every year a large amount of money is lost due to fraud in financial institutions. Detecting frauds is a complicated task and limiting fraud detection systems to certain kinds of detection methods like supervised or unsupervised methods does not seem efficient. In this paper, a combined framework for fraud detection systems, consisting of both supervised and semi-supervised methods in three main components namely rule-based component, trend-analysis-based component and, a scenario-based component is proposed. The rule-based component is the supervised part of the framework and a decision tree, which is a cost-insensitive classification algorithm, is used for this component. In the trend-analysis-based component, which is the semi-supervised

    Binary integer programming for Multiple Sequence Alignment

    M Kargari
    Journal Papers , , {Pages }

    Abstract

    Detecting frauds using customer behavior trend analysis and known scenarios

    Abdollah Eshghi, Mehrdad Kargari
    Journal PapersInternational Journal of Industrial Engineering & Production Research , Volume 29 , Issue 1, 2018 March 15, {Pages 91-101 }

    Abstract

    The prese user beha known as componen customers functions. transactio the basis f not. An o estimated and alarm complicate according

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    دروس نیمسال جاری

    • دكتري
      امنيت فضاي سايبر و شبكه هاي پيشرفته ( واحد)
      دانشکده مهندسی صنایع و سیستم‌ها، گروه مهندسي فناوري اطلاعات
    • كارشناسي ارشد
      مباني تجارت الكترونيكي ( واحد)
    • كارشناسي ارشد
      امنيت سيستم هاي اطلاعات ( واحد)

    دروس نیمسال قبل

    • كارشناسي ارشد
      تكنولوژي ها ومدلهاي كسب و كار تجارت الكترو نيكي ( واحد)
      دانشکده مهندسی صنایع و سیستم‌ها، گروه مهندسي فناوري اطلاعات
    • كارشناسي ارشد
      مدل هاي كسب و كار و خلق ارزش ( واحد)
    • 1400
      عباس نژاد, محمدرضا
      ارائه سيستم معاملاتي روي سبد دارايي با استفاده از يادگيري تقويتي عميق
    • 1400
      كوهستانيان نيك, رضا
    • عضو غیرثابت شورای راهبری جشنواره ملی فن آفرینی شیخ بهایی
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