Sharif Digital Repository

The rapid increase in the size of digital multimedia data collections has resulted in wide availability of multimedia contents to the general users. Effective and efficient management of these collections is an important task that has become a focus in the research of multimedia signal processing and pattern recognition. In this thesis, we address the problem of automatic classification of music, as one of the main multimedia signals. In this context, music genres are crucial descriptors that are widely used to organize the large music collections. The two main components of automatic music genre classification systems are feature extraction and classification. While features are a compact... 

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... 

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... 

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... 

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... 

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... 

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... 

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... 

Vibration is one of the most important factors in design, operation and lifetime of drill strings. There for, investigation of vibrations in drill string, like every other rotary machine, is very important. The main goal of this research is developing a nonlinear model for a drill string system dynamics in an inclined well. Effects of drilling mud flow rate, weight on bit, angular velocity along with viscous damping, on the stability and vibration of a drill string are studied. Dynamic equation of the model is developed considering axial displacement and lateral bending geometric nonlinear coupling. The effect of drilling mud flow is modeled using Paidoussis formulations. Equation of motion... 

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... 

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... 

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...