Loading...
Search for: nazari--erfan
0.19 seconds

    A Process Mining Approach to Analyze Customer Journeys to Improve Customer Experience

    , M.Sc. Thesis Sharif University of Technology Akhavan, Fatemeh (Author) ; Hassannayebi, Erfan (Supervisor)
    Abstract
    With the growth of the number of online service providers and the need to innovate in these services, in this study, the processes and the journeys taken by visitors of a website that provides employment services and employment insurance has been analyzed. In this research, process mining techniques and predictive process monitoring were implemented. With the use of a supervised and unsupervised learning algorithm, it attempted to identify the customer journeys' output and the existing patterns that lead to the complaint. In the first step, the website event log is extracted. Afterward, by using frequency-based encoding methods, the journeys traveled by users were clustered based on the... 

    Predictive Business Process Monitoring Using Machine Learning Algorithms

    , M.Sc. Thesis Sharif University of Technology Feiz, Roya (Author) ; Hassannayebi, Erfan (Supervisor)
    Abstract
    In order to survive in today's business world, which is changing at a very fast pace, organizations can detect deviations even before they occur, quickly and with a high percentage of confidence, by analyzing their processes, in order to prevent disruptions in the processes. by monitoring the information systems that automatically execute business processes, it is possible to ensure the correct implementation of the existing processes. For this purpose, various techniques for monitoring business processes have been presented so that managers have a comprehensive and real view of how implement processes and be able to identify possible deviations in the future and try to fix them because the... 

    Data-Driven Prediction for Monitoring Business Process Pperformances Based on Classification Algorithms

    , M.Sc. Thesis Sharif University of Technology Taheriyan, Zahra (Author) ; Hassannayebi, Erfan (Supervisor)
    Abstract
    In recent years, several studies have been conducted in the field of data mining techniques in the field of process mining with the aim of improving the performance of organizations. Predictive process monitoring is a data-driven approach that helps business managers to improve the status and conditions of their organization. In this approach, the event log, which includes a set of completed examples of a process, is received as input, and machine learning methods are used to predict the outcome and results of the organization's processes before the process is completed. This prediction can include the prediction of the final result, the next event, the time remaining until the completion of... 

    Operations Optimization in Supply Chain Systems using Simulation and Reinforcement Learning

    , M.Sc. Thesis Sharif University of Technology Mahmoudi, Farzaneh (Author) ; Hassan Nayebi, Erfan (Supervisor)
    Abstract
    The inventory costs constitute a significant portion of the supply chain costs. Therefore, choosing an optimal inventory policy for orders is of great importance. The aim of this research is to find the optimal inventory policy for a distribution center in a three-tier supply chain consisting of a manufacturer, a distribution center, and a retailer. This research simulates a supply chain in agent-based framework and optimizes it using reinforcement learning. The optimization KPI in this research is the mean daily cost of the supply chain. Finally, the result obtained from reinforcement learning is compared with the optimized result of AnyLogic and the mean daily cost in the model optimized... 

    Predictive Process Monitoring Based on Optimized Deep Learning Methods

    , M.Sc. Thesis Sharif University of Technology Alibakhshi, Alireza (Author) ; Hassannayebi, Erfan (Supervisor)
    Abstract
    Business processes are an essential part of every business as they provide insights on how to optimize and make them more efficient. Predictive Business Process Monitoring has garnered significant attention in recent years due to its capability to forecast process outcomes and predict the next activity within an ongoing process. In the last few years, there have been works that focused on deep learning and its applications in predicting the next activity. Some research used Long Term Short Memory, while others used Convolutional Neural Networks. However, long term short term memory models have the constraint of relatively slow training, while Convolutional Neural Networks are fast but may... 

    Customer Journey Analytics using Process Mining Based on the Markov Model

    , M.Sc. Thesis Sharif University of Technology Torabi Ardekani, Saba (Author) ; Hassan Nayebi, Erfan (Supervisor)
    Abstract
    The analysis of customer journeys has gained significant attention due to the critical role of customer behavior data in enhancing business decision-making and formulating strategies for customer acquisition and retention. By segmenting customers based on their journey patterns, businesses can offer personalized recommendations, thereby improving customer engagement and loyalty. Additionally, predicting the next steps in a customer’s journey based on historical data allows for timely and appropriate interventions at various touchpoints. By understanding where customers are in their journey, businesses can provide targeted recommendations that increase the likelihood of converting potential... 

    A System Dynamic Simulation Approach to Investigate Economic And Environment Factors Based on VUCA framework: A Case Study in Petrochemical Industry

    , M.Sc. Thesis Sharif University of Technology Monfaredi Jafarbagi, Aoun (Author) ; Hassannayebi, Erfan (Supervisor)
    Abstract
    To maintain adaptability, businesses should anticipate changes in their environment. The commonly employed forecasting method within corporate circles is the bottom-up approach, which relies on historical data for projecting future trends. However, research suggests that this approach often falls short of accurately reflecting real-world events. This has led to the adoption of dynamic systems modeling, a technique grounded in the assumption of stable conditions. This method effectively replicates the system's current state, thereby assisting in predicting future behaviors over a longer timeframe. The dynamic systems modeling approach was employed in this study, underscoring the imperative... 

    Discovering and Improving the Processes of an Iranian Psychiatric Hospital Using Process Mining

    , M.Sc. Thesis Sharif University of Technology Roshan, Mohammad Amin (Author) ; Hassan Nayebi, Erfan (Supervisor)
    Abstract
    Providing quality hospital services depends on the efficient and correct implementation of processes. Therapeutic care processes are a set of activities that are carried out with the aim of diagnosing, treating and preventing any disease in order to improve and promote the patient's health. The purpose of this study is to use process mining techniques to discover and improve healthcare processes. The case study of this research is a psychiatric hospital in Shiraz. The approach implemented in this research consists of three main stages including data pre-processing, model discovery phase, and analysis phase. Three algorithms including Heuristic Miner, Inductive Miner, and ILP Miner were used... 

    Drop formation from a capillary tube: comparison of different bulk fluid on newtonian drops and formation of newtonian and non-newtonian drops in air using image processing

    , Article International Journal of Heat and Mass Transfer ; Volume 124 , 2018 , Pages 912-919 ; 00179310 (ISSN) Nazari, A ; Zadkazemi Derakhshi, A ; Nazari, A ; Firoozabadi, B ; Sharif University of Technology
    2018
    Abstract
    The formation of water drops as a Newtonian fluid and formation of a shear-thinning non-Newtonian fluid, Carboxyl Methyl Cellulose (CMC) from a capillary into different bulk fluids are experimentally investigated. A high speed camera is used to visualize the images of the drops and an image-processing code employed to determine the drop properties from each image. It was found that the properties of the water drops when they are drooped into the liquids bulk fluids such as toluene and n-hexane are almost the same while they differed substantially when they were drooped into the air bulk fluid. It is shown that during the formation of water drop in all three kinds of bulk fluids, the drop... 

    The investigation of natural super-cavitation flow behind three-dimensional cavitators: Full cavitation model

    , Article Applied Mathematical Modelling ; Volume 45 , 2016 , Pages 165-178 ; 0307904X (ISSN) Kadivar, E ; Kadivar, Erfan ; Javadi, K ; Javadpour, S. M ; Sharif University of Technology
    Elsevier Inc  2016
    Abstract
    In this study, natural super-cavitating flow around three different conical cavitators with wedge angles of 30°, 45° and 60° is investigated. We apply the k−ϵ turbulence model and the volume of fluid (VOF) technique to numerically study the three-dimensional cavitating flow around the cavitators. The turbulence approach is coupled with a mass transfer model which is implemented into the finite-volume package. Simulations are performed for different cavitation numbers. Finally, the effects of some important parameters such as the cavitation index, inlet velocity, Froude number and wedge angle of cavitators on the geometrical characteristics of the super-cavities are discussed. Our numerical... 

    Analysis and Improvement of Agile Software Development Process Using Process Mining and Machine Learning Algorithms

    , M.Sc. Thesis Sharif University of Technology Fardipour Asl, Mohsen (Author) ; Hassan Nayebi, Erfan (Supervisor)
    Abstract
    In the competitive world of software development, IT product development teams face numerous challenges and issues within business processes. In recent years, the combination of process mining and machine learning algorithms to optimize software development processes has gained attention. However, there is still a lack of a comprehensive model for identifying and conducting root-cause analysis of problems and providing solutions based on them in agile software development systems. The aim of this research is to find a solution for identifying and addressing issues in the software development process by modifying the change request workflow and examining various aspects of incidents. To... 

    Improve Performance of Process Mining Algorithms in Low-Level Event Log with Machine Learning Methods

    , M.Sc. Thesis Sharif University of Technology Choopannezhad, Mahsa (Author) ; Hassan Nayebi, Erfan (Supervisor)
    Abstract
    This thesis abstract addresses the use of process mining techniques when event data is stored at varying levels of granularity. While most techniques assume that events have the same level of granularity, real data is often stored differently. Pre-processing techniques allow for appropriate summarization of the data, which simplifies the output while retaining important process details. The goal is to ensure an interpretable output for stakeholders and different business teams without losing critical process points. However, adding new data as a feature to the dataset can be expensive, and at times, infeasible. Therefore, existing data is the only solution. To overcome this challenge, this... 

    Robust Markov Decision Processes and Applications in Mathematical Finance

    , M.Sc. Thesis Sharif University of Technology Soori, Mohammad (Author) ; Fotouhi Firouzabadi, Morteza (Supervisor) ; Salavati, Erfan (Supervisor)
    Abstract
    Dynamic portfolio optimization is one of the prominent problems in financial mathematics, for which numerous theories have been proposed to solve it. One of the solutions to this problem is the use of reinforcement learning. The main challenge with this method is that most reinforcement learning algorithms require a large amount of data, and therefore, the necessary data is often obtained not from the real world, but through simulations by estimating the parameters of a model. However, the approximation error of the parameters can propagate through the final solution, leading to inaccurate results. One approach to addressing this issue is the use of robust Markov processes and robust... 

    Simulation-Based Optimization for IoT-Enabled Epidemic Patients Care Systems

    , M.Sc. Thesis Sharif University of Technology Bisheh Niasar, Mohammad Amir (Author) ; Hassan Nayebi, Erfan (Supervisor)
    Abstract
    This research focuses on examining and improving healthcare systems, particularly during crises and pandemics. Following disasters such as natural calamities and pandemics like COVID-19, healthcare systems face significant challenges due to the increased demand for medical services, creating a substantial threat to the population in the affected regions. This study emphasizes the importance of utilizing modern technologies such as the Internet of Things (IoT) and telemedicine systems in alleviating the pressure on healthcare systems. A combined approach of prediction and multi-objective optimization based on simulation is proposed in this study to improve resource allocation and demand... 

    Business Processes Deviation Analysis Using Process Mining Algorithms

    , M.Sc. Thesis Sharif University of Technology Attarzadeh, Milad (Author) ; Akbari Jokar, Mohammad Reza (Supervisor) ; Hassannayebi, Erfan (Co-Supervisor)
    Abstract
    Deviations in business processes consistently impose significant financial and temporal costs on business owners and can lead to decreased customer satisfaction with organizations. Therefore, timely identification of deviations is a crucial and significant issue for business process managers. While extensive research has been conducted on the detection of antecedent deviations, predicting deviations before they occur—which could facilitate preemptive actions to prevent these deviations—has received less attention. In this context, the aim of this study is to predict two types of process deviations—temporal deviations and Rework deviations—using machine learning and deep learning algorithms,... 

    A new distributed uplink packet scheduling algorithm in WiMAX newtorks

    , Article Proceedings of the 2010 2nd International Conference on Future Computer and Communication, ICFCC 2010, 21 May 2010 through 24 May 2010 ; Volume 2 , May , 2010 , Pages V2232-V2236 ; 9781424458226 (ISBN) Nazari, S ; Beigy, H ; Sharif University of Technology
    2010
    Abstract
    Worldwide interoperability for Microwave Access (WiMAX) is designed to support a wide range of Quality of Service (QoS) requirements for different applications. Unlike the single service networks, a priority based mechanism is needed for specifying the transmission order of packets belonging to different traffic sources according to their QoS characteristics, which is called packet scheduling. In WiMAX standard, packet scheduling algorithm is not defined and its design is left for researchers. In this work, we propose a distributed uplink packet scheduling algorithm. In the proposed algorithm, when uplink capacity cannot satisfy the required resource of connections, according to traffic... 

    , M.Sc. Thesis Sharif University of Technology Nazari, Fatemeh (Author) ; Shamsaei, Abolfazl (Supervisor)
    Abstract
    Entrance of air to close water conduits occurs in many ways. This phenomenon makes displeasing effects on water transport structures. Two-phase flows in closed conduits are studied experimentally, numerically and theoretically. Even after decades, due to the complexity of the subject, these studies still require development and reformation. Numerical and theoretical two-phase models are often limited to a special regime of flow."Volume Of Fluid"(VOF) is a model for non-mixing two-phase flows. "Fluent" software uses this model in this field of flows. Volume of fluid is used successfully in some problems like dam break or rising bubbles. Main disadvantage of this model is distribution of the... 

    Synthesis of Graphene Oxide-based Polymer Nanocomposites and Investigation of Their Ability in Dye Adsorption

    , Ph.D. Dissertation Sharif University of Technology Nazari, Mojtaba (Author) ; Pourjavadi, Ali (Supervisor)
    Abstract
    In this project, polymer nanocomposites containing graphene oxide were prepared and used for adsorption of several cationic and anionic dyes. In most cases, these nanocomposites were prepared as three-dimentional polymer networks which contain graphene oxide. Nanocomposites with direct grafting of polymer chains onto the surface of graphene oxide were also prepared. Graphene oxide is an oxidized carbonaceous material with unique properties. This material has high surface area, good mechanical strength and various functional groups. Polymeric compounds with different functional groups, structures and properties can be prepared and used. By preparing composites of graphene oxide and polymers,... 

    Design and Implementation of a Master Robot for a Fracture Reduction Robotic Telesurgery System

    , M.Sc. Thesis Sharif University of Technology Nazari, Kianoosh (Author) ; Farahmand, Farzam (Supervisor)
    Abstract
    Common fracture treatments include open reduction and intramedullary nailing technology. However, these methods have disadvantages such as intraoperative X-ray radiation, delayed union or nonunion and postoperative rotation. Robots provide a novel solution to the aforementioned problems while having new challenges. The robotic system used in the study by providing more precise motion and exerting higher amount of forces makes the operation procedure more straightforward. In a normal operation 300-400 N of extension force would be needed which is fully provided by the surgen which leads to physical his/her (khastegi), by using the robotic system however, this force is simply provided without... 

    Study on Optimization of the EDC Model for Highly Preheated and Diluted Condition

    , M.Sc. Thesis Sharif University of Technology Nazari, Aslan (Author) ; Mardani, Amir (Supervisor)
    Abstract
    Moderate and Intense Low-oxygen Dilution(MILD) is the new member of combustion field. Highly preheated reactants and lowering the oxygen level in MILD combustion has some promising advantages. In this study the MILD burner, Jet-in-Hot-Coflow(JHC), is taken as the main test case. In this Research, Eddy Dissipation Concept combustion model ,introduced by Magnussen et al,is investigated in detail and governing equations are re-extracted. EDC combsution model due to moderate compuational cost in comparison with other combustion model and well prediction ability has drawn attention. Simulations on the Jet-in-Hot-Coflow(JHC) has shown the promising performance of the EDC combustion model. It seems...