Sharif Digital Repository

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... 

According to the rise of turnover and pace of trading, accelerating of analysis and making decision is unavoidable. Humans are unable to analyze big data quickly without behavioral biases so, using machines to analyze big data seems critical. Hence, financial markets tend to apply algorithmic trading in which some techniques like data mining and machine learning are notable. OLPS which sequentially allocates capital among a set of assets aiming to maximize the final return of investment in the long run, is the core problem in algorithmic trading. This article presents an online portfolio selection algorithm. The online portfolio selection sequentially selects a portfolio over a set of assets... 

Obtaining high profit is the ultimate goal of an investor in the financial market. The key to achieve high profits in stock trading is to find the right time to trade with minimum business risk. However, it is difficult, often, to make decision about the best time to buy or sell some stocks due to the extremely dynamic and volatile behavior of the stock market. In order to resolve these problems, two steps have been followed in this research:1) Create a model to predict the final price of the stock with small error rate, and 2) Suggest the best stocks for trading to the trader. In order to achieve the goals of the first step, the stock price data of Hcltech, Maruti, Axisbank is selected and... 

Heart disease is one of the major causes of death in all countries, especially developing countries. At the moment, using Image Processing methods as well as analysis of electrocardiographic signals, heart disease is diagnosed with the help of specialists. Applying artificial intelligence and machine learning methods, many studies attempted to provide models that are used to diagnose automatically the heart disease without the need for a specialist and only relying on the past data. But less is done on CTA images of the heart. Hence, in this thesis, a new method for image processing and a Multi Support Vector Machine (MSVM) classification for coronary artery disease detection based on CTA... 

This paper presents some approaches based on data mining techniques to solve the prediction task of Computational Intelligence and Learning (CoIL) Challenge 2000. The prediction task of the contest is a direct mailing problem and the goal is to improve its response rate. The main issue in this competition is the incompatibility of the dataset in which the distribution of the classes of the target attribute is highly unbalanced. This in turn causes high error rate in identifying the minority class samples. Three different level methods including data-level, algorithm-level, and hybrid method are used to overcome this issue. The specificity and sensitivity criteria are employed to compare the... 

By considering the environmental factors, outpatient scheduling has become more efficient, and the closer these factors are to the actual situation, the more accurate the scheduling is. This study aims to schedule outpatients in such a way that in addition to bringing the hypotheses of the problem closer to the real world, its implementation can be done more easily. On the other hand, medical centers, due to their nature, do not consider the income in their objective function. Therefore, another contribution of this study is considering the income of medical centers in such a way that not only will it increase patient satisfaction, but also it maximizes the revenue of the medical center.... 

Nowadays, researchers working on classifiers which are designed to predict minority class. In this work, we attempt to improve fraud detection performance, with minimum possible complexity. In this regard, by incrementing model sensitivity to minority class samples, we solve the problem of model ignorance to these instances. Moreover, by using clustering, we cluster similar inputs based on their features, and split each class to smaller bins. Then with considering the fact that, prediction probability threshold influences the final performance, we define statistical hypothesis testing exclusively for each cluster to evaluate predictions with expected range. In this method, model is not... 

Recommending routes to city taxis in order to improve their performance in finding passengers can reduce traffic, pollution and waiting time for passengers, as well as this, increasing the efficiency of taxis can increase their income. In this study, by analyzing trajectory data obtained from 536 taxis in San Francisco over a period of one month, we generated networks of cells, each of which has time-dependent characteristics such as the probability of finding a passenger, the capacity of each cell, or the amount of demand exists is in the cell and the average speed of passing through that cell. also, the connections between these cells in order to make more use of the experiences of taxi... 

The purpose of the present study is to develop neural network estimation method for hybrid or panel data which have a combination of two cross-sectional and time series structures and because of the features of both structures, their use in different sciences offers many advantages; and the analytical methods for this data structure are also different from other one-dimensional structures, so different and specific regression models are presented for this data structure. However, in the artificial neural network method, modeling the development for this data structure is neglected, so in the present study, using the concepts of panel regression methods and their application to the... 

The outpatient clinics have some features that make them different from other types of the clinic. Something that must be paid attention is adherence to appointments by outpatients, which have an undeniable effect on the productivity of the Outpatient Appointment System. In most of the outpatient clinics, outpatients are supposed to schedule their appointment in advance. As a result, if patients do not come to the health center based on their scheduled appointment, and they do not inform the health center about their absence, not only will the clinic become deprived of the opportunity to utilize its resources efficiently, but also healthcare access for other patients will reduce. The rate of... 

Today, many real-world datasets such as social network data and web pages can be shown as graphs. Community detection and clustering of these big graphs has many applications in different fields like recommender systems in social networks and Diag- nosis of diseases in communication networks among proteins. A cluster in a graph is a sub-graph with many internal and few external edges. A new method for local cluster detection around an existing vertex is introduced in this paper. This method applies random walk algorithm for cluster detection. The time complexity of this algorithm based on the graph size is polynomial. Therefore, it can be used for clustering of big graphs. The experimental... 

Making money and maintaining the value of assets has always been one of the most important concerns of people. Real estate is one of the essential human needs, but it is also considered an investment tool for individuals. In addition to individuals in a family, various groups and organizations such as policymakers, analysts, banks and financial institutions, taxpayers, and real estate investors are directly or indirectly affected by the dynamic characteristic of the housing market. Therefore, forecasting the exact amount of housing value in the future is very important. Factors that can improve this forecasting's accuracy include considering the relationship between housing value and... 

Nowadays, capital markets play a key role in the economies of countries. Hence, this market is expanding more and more every day. In such circumstances, traditional analysis methods such as fundamental analysis and technical analysis have lost their position due to low speed and accuracy. In recent years, automated trading systems have been proposed as a solution to these problems. The online portfolio selection, which sequentially allocates capital among a set of assets aiming to maximize the final return of investment in the long run, is the core problem in algorithmic trading. In this research, we present an online portfolio selection algorithm based on pattern matching principle.... 

In this thesis, a hybrid method is proposed to estimate the change point in the parameters of simple linear profiles in multistage processes under monotonic changes. In monotonic changes, the type of change is not known a priori, and the only assumption is the changes are of non-decreasing (isotonic) or non-increasing (monotonic) type. In the proposed method, at first, the stages and the parameters experiencing the change are identified and then, the changes occurred in these stages and parameters are identified and examined based on the moving window approach and support vector machine (SVM) algorithm. Finally, the maximum likelihood estimator of the change point is proposed. The... 

Since the start of Covid-19 pandemic lately in 2019 from Wuhan in China, a lot of countries encountered it. Healthcare sysytems are the most important system against pandemics so it is needed to measure the efficiency of healthcare systems against Covid-19 in order to find best practices. In this research, a 3-phased method is proposed to evaluate the performance of the healthcare systems. In the first phase, countries are clustered, in the second phase the DEA model is applied in 2 separate parts, in one part with considering clusters and in another without it. In the third phase resilience is introduced for Covid-19 and then it is used as a criterion beside two other criteria, DEA result... 

Nowadays, investigating the factors affecting traffic models from different aspects such as metropolitan planning according to the present conditions can help high-level decision-makers and also, at the micro-level, help the travelers to make appropriate decisions for scheduling affairs, route selection, and vehicle type selection. Given the importance of this topic, a framework will be presented in this study that will evaluate the impact of some identified factors such as travel distance, climate, and urban events, and then all these factors will be presented in mathematical formulas. In the end, based on the model, the travel time will be predicted. In this framework, gene expression... 

Algorithmic trading in financial markets with machine learning is a developing and promising field of research. Financial markets have a complex, uncertain, and dynamic nature, making them challenging for algorithmic trading. To cope with the challenges of algorithmic trading in financial markets, we propose a multi-agent deep reinforcement learning framework trained by Deep Q-learning (DQN) algorithm to perform financial trading. This framework consists of multiple cooperative agents, each of which trained on a specific timeframe, to perform financial trading on the collective intelligence of the agents. Numerical experiments are conducted on historical data of the EUR/USD currency pair.... 

Performing analysis and forecasting is crucial. Better forecasting will lead to better decisions. One method for predicting the future is time series analysis. In reality, it is common for an intervention to occur and alter the characteristics of a time series. In recent years, interrupted time series analysis has been receiving a lot of attention. A new forecasting method for interrupted time series has been developed in this study. This is a system dynamics-based approach. At every stage of the approach, system thinking is incorporated. In order to model the effects of a given intervention, common modes of behavior in dynamic systems are used. Furthermore, control theory has been used to... 

In recent years, requirement engineering and goal modeling have gained more attention among different organisations. Therefore, different methods have been designed to discover stakeholders’ requirements and translate them into process and organizational goals. Various goal models have been introduced to represent these goals and their relations towards each other. Still, these methods are manual representations of different process scenarios and do not relate to the actual behavior. In 2014, Map Miner Method (MMM) was introduced to discover intentional process models from event logs. MMM uses Hidden Markov Model (HMM) as a means to represent the process as a map model of strategies and... 

Graphs are a language to describe and analyze connections and relations. Recent developments have increased graphs' applications in real-world problems such as social networks, researchers' collaborations, and chemical compounds. Now that we can extract graphs from real life, how can we model and generate graphs similar to a set of known graphs or that are very likely to exist but haven't been discovered yet? Therefore, this research will focus on the problem of graph generation. In graph generation, a set of graphs is a training dataset, and the goal of the thesis is to present an improved deep generative model to learn the training data's distribution, structure, and features.Identifying...