Sharif Digital Repository

Bankruptcy is an important issue in the economy that can have extensive financial and social consequences on individuals and society. Timely warning to managers and providing analysis may prevent bankruptcy. Many studies have been conducted on the application and implementation of machine learning techniques to predict bankruptcy. Many bankruptcy prediction models produce incomprehensible outputs for the user. Therefore, they are called black box algorithms. Implementation of advanced models inevitably requires interpretability for users to understand the result and trust. Since most machine learning methods are "black box", explainable AI, which aims to provide explanations to users, has... 

The main goal of reliability engineering is to improve systems’ dependability and responsiveness. In this prospect, utilization of mathematical and statisticalmeansto predict and evaluate the optimal performance and the reliability of the system is of immenseimportace. Traditional methods of reliability theory are based on two states: failure and health. Unlike the classical reliabilitytheory, performance of some systems have more than two modes,in fact the system could be defined in the widespectrumbetween the complete failures to full health. In addition,in real-world,exact parameters of the system may not be available. However in practical circumstances systems statistical data for... 

Developments in the field of data storage which is due to computers have led to an extraordinary increase in medical data just like the increase in all other fields. As a result, physicians are faced with the problem of using the stored data. Therefore, the traditional manual data analysis is inadequate due to the large amounts of data. Furthermore, the ability to use this data to extract useful information is critical for the quality of medical care. Therefore, data mining techniques arose so that we will be able to extract knowledge through applying them to the raw data and subsequently help the doctors in making decisions.
In this study, we are pursuing four goals. First, in order to... 

Control charts have always had an undeniable role in Statistical Control of processes in many fields. The growth of quality characteristics to be monitored, has led to the vast utilization of multivariate control charts. These variables are characterized by relatively high correlation between them. The complicated structure of measured variables has lessened the reliability of conventional control charts. Projection methods have been developed to address the problem of high correlated variables by transforming the correlated variables to an uncorrelated set of variables. Among them, Principal Component Analysis based control charts have been widely used to overcome the problem of correlated... 

Investors' sentiment toward a coin is very vital in cryptocurrencies' markets. If traders do not invest in a coin, its price will decline; therefore, it is essential for investors to take others' sentiment into account when they want to buy or sell a coin. Text mining on social media is one technique that can help traders understand others' opinions about the coin they are trading. Hence, in this research, we try to predict the top three cryptocurrencies, Bitcoin, Etheruem, and Litecoin, price movement for the next day based on Twitter posts and News title using text mining. In this research, we found out that considering all tweets can reduce our model's accuracy, and for better accuracy,... 

What people write about their experience on web pages or social media about a product they have used or a service they have received can influence the reputation and the popularity of a certain brand with a great deal. If the reviews that exist about a product or a service of a certain company are mainly positive, it can increase the profit and improve the image of the company. On the other hand, mostly negative reviews can decrease the profit and destroy a company's image irreversibly. Unfortunately, because of this great influence that online reviews have over general public's decision to use a a product or a service of a brand, some companies hire people to write undeserving positive... 

In this research, it has been tried to use deep learning methods and embedding vector, in addition to user-item data, from user side information such as age, gender, city, etc., and also for item information such as product name, product category, etc. can be used to better understand customer behavior patterns and provide a relatively rich recommender system. The proposed model in this research has two phases, the first phase tries to identify the user and item feature vector and form the user similarity matrix and the user-item correlation matrix. The outputs of phase one are used as inputs of phase two. In the second phase of the model, using these inputs, Top-N recommendation are... 

We consider finding some efficient control schemes for multivariate variability monitoring with capability of working with individual observations. To do this, the existing efficient control charts for multivariate variability monitoring are studied first and it is determined that the and control statistics, defined by individual observations, estimate the covariance matrix quite well. However, the control method that is based on monitoring the trace of these matrices is not necessarily the best. Thus, by applying the first and the second norm on these two statistics, four new control schemes, namely MEWMSL1, MEWMVL1, MEWMVL1 and MEWMVL2are proposed. Performance comparison results show... 

The multi-facility location-allocation problem is concerned with locating m facilities in the Euclidian plane and allocating n customers to them at minimum total cost. In this work, we focus on a probabilistic version of the problem, in which the locations of the customers and their arrivals are probabilistically distributed. We first formulate the problem as a continuous location-allocation problem. Then, we give an approximated discrete model in which facilities can be located on a set of candidate points. The proposed model has two objective functions which can be calculated only through simulation. Considering the NP-hard nature of the problem and the functions’ unique properties, we use... 

A common decision in project management is the selection of contractors to simultaneously optimize three objectives of the project’s triangle. This issue becomes more important if there is more monetary value involved or there is limited number of capable contractors. In project planning, there is numerous choices for tasks; each with specific time, cost and quality. There is no trade-off problem, if one of the choices has the best time, cost, and quality, simultaneously. However, as these criteria do not generally work in a unique direction, it makes the selection decision difficult. A contractor who performs the projects on time may have higher cost and lower quality. Given that there is a... 

There are many cases in industrial and non-industrial sections where the quality characteristics are in the form of profiles. A profile is the functional relationship between a response variable and one or more predictor variables used to describe the quality of a process. Profile monitoring is the implementation of statistical process control techniques for this purpose. According to the type of relationship between response variable and predictor variables, profiles are classified into many categories such as: simple linear profiles, multiple linear profiles, nonlinear profiles and generalized linear profiles. Most of the research efforts in the area of profile monitoring have been... 

In recent years, with the expansion of the use of information technology and information systems, data mining has attracted the attention of organizations. Functional analysis is one of the domains in data mining. This analysis can provide important information for companies in various fields. Functional analysis involves the development of statistical approaches for functional data. Studies in this analysis have covered a wide range of areas, including finance. Financial analysis is very important in business analysis. One of the tools used to analyze companies is the analysis of financial ratios. In fact, financial ratios reveal important realities in relation to the operations and... 

One of the most trivial factors for every service company is to fulfill customer demands and to satisfy them. For this purpose, it is needed to explore the business fundamentals. Transportation companies, however seem to be more important, because in one hand, the demand for their services is too many. In the other hand due to the intense competition between rivals, every business tries to stand in the crowd; thus, it is of high importance to correctly predict the travel time as well as fare. The importance of time is not only because of having a fare pricing system, but also it is important for scheduling, assigning drivers, and covering city or a particular district. Travel time... 

Intensity-modulated radiation therapy is a well-known technique to treat cancer patients worldwide. A treatment plan in this technique requires decision-making for three main problems: selection of beam angles, intensity map calculation, and leaf sequencing. Previous works have investigated these problems sequentially. In this research, we present a new integrated framework for simultaneous decision-making of directions, intensities, and apertures shape, called direct angle and aperture optimization, and develop a mixed-integer nonlinear mathematical model for the problem. At first, the problem's single-objective model is established using the quadratic dose penalty function. After that,... 

Control charts are the most important tools of statistical quality control. The problem that exists is that control charts do not show the real time the shift in a process started. The real time a change occurs in a process is called the change point. In this research, the monotonic change point in a multivariate normal process is estimated using the maximum likelihood estimation approach, where the process is monitored by a multivariate exponentially weighted moving average scheme. Mote Carlo simulation studies are performed to evaluate the performance of the proposed approach  

Integrated modeling of learning makes happen simultaneous investigation and analysis of the interaction among different scales, abstraction levels and contexts within an organization. Enquiry of the organization, organizational unit and individual employee scales at levels of low and high abstraction within temporal, spatial and event-driven contexts may have conspicuous impact on perspicuousness of learning concept, as well as the creation of productive policy making opportunities which improves system performance measurements and their influencing causes. By development in multi-method approach to system modeling, integrated representation of learning has got recently feasible. Thus, a... 

With the development of online two-sided platforms and increasing competition between these companies, issues such as customer targeting or recommendation systems have become more important to organizations. So far, various tools have been used for this purpose, but one of the most effective methods is the data analytics based on the stored data, through which personalized promotions can be automatically sent to the customers by implementing optimization models and algorithms. In this research, we present a model that re-adjust the commissions received from drivers based on detecting hidden patterns in their behavior in order to maximize the company's profit and then offer a suitable... 

In the last decade, emergency department (ED) overcrowding has become a national crisis for the US healthcare system. Increasing mortality rates, decreasing quality of care, financial losses due to walkouts, and ambulance diversion are some of the consequences of ED overcrowding. Given the increasing demand in terms of ambulance utilization which we can see an instance of it in the COVID-19 pandemic, being able to allocate service requests to EDs efficiently, becomes a key function of emergency medical services. in this investigation, an algorithm of deep reinforcement learning called deep Q-learning is used to address this problem and to assign ambulances to ED's appropriately. under...