Sharif Digital Repository

In this project, we intend to use intelligent and learning algorithms to forecast the U.S. presidential elections. First, we considered some economic and political variables in our model. Then by using stepwise regression, we found the most significant variables. After that, we used three data mining techniques on these data. In the next step, we used support vector regression and neural networks to predict the elections. Then we compared these two algorithms with each other. Eventually, we realized how strong and accurate these methods are to predict the U.S. presidential elections. We have, also, proved that using data mining techniques is beneficial to make models more accurate  

There are a variety of multivariate industrial processes in real world problems. It seems to be necessary to control them through strong tools such as control charts. One of the state-of-the-art methods to monitor processes is neural network. Neural networks are data processing systems inspired by human brain and they are capable of processing data with a variety of small processors working in parallel forming an integrated network to solve a problem. Chaotic models, one of the states of being out of control, are deterministic non-linear models which have extremely complex behavior under determined assumption. Researches have shown neural networks have excellent performance in such systems.... 

Water demand forecasting and modeling is very important and needful in water resource planning and management as well as water consumption forecasting. The forecasting helps the managers to design and operate various infrastructures of water supply such as tanks and other distribution equipments. Nowadays, intelligent systems are very efficient and practical tools because of their high ability in forecasting and independency from limitative assumptions in classic methods. In this thesis, one of the newest methods, called support vector regression method, is used to forecast monthly demands of water consumption in Tehran, Iran. To develop the method, data is first preprocessed through... 

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... 

In this work, effects of information aggregation on risk attitude of Iranian individuals is being studied through two experiments. In these experiments a risk-free asset with a guaranteed revenue and a risky asset is introduced to each individual. Then the individual has to allocate a certain amount of money between two assets. In both experiments three treatments of control, separation and aggregation are defined in a way that the degree of information aggregation increases respectively. Given the specific treatment assigned to each individual, complementary information is presented, in orderto finalize the decision. Next a financial market simulation for a five year horizon is conducted to... 

In this research, we consider the application of copulas in multivariate quality control problems. In particular, we consider two specific problems. The first problem concerns the situation where the normality assumption is rejected. In this case, copulas can be used as a flexible tool to define a broad range of multivariate distributions with different dependence structure as well as marginal distributions. The second problem concerns proposing control charts to monitor the dependence structure among quality characteristics. The proposed method not only produces an out-of-control signal when dependence structure among variables deviates from the specified one, but also can be used to... 

In modern industries, multivariate multistage auto-correlated processes are widely used to ensure productivity and product quality. Interconnections between work stations bring a challenging task in detecting various shifts and identifying their root causes. In addition, simultaneous monitoring process mean and variability with single control chart methods has gained considerable attention throughout these years. In this article, a double-max multivariate exponentially weighted moving average (DM-MEWMA) chart is proposed based on two novel statistics to monitor the parameters of multivariate multistage auto-correlated processes jointly. Prior knowledge of variation propagation has been used... 

One of the most critical problems in medical supply chain is tracking the pharmaceutical throughout the chain. The challenging solution is to use of barcodes which has numerous limits. Radio frequency identification (RFID) tools are the next generation of barcodes that provide more convenient and faster track of supplies. With automatic workplace, the precision of the system is increased and less personnel is needed. In this study, various medical supply chain management procedures were studied for a hospital and the most appropriate procedure was chosen. According to the hospital conditions, RFID over barcode was the procedure of choice. In order to validate this choice, the ARENA software... 

Blood is a very valuable material for the life of human beings and should be transfused to the patient as soon as possible in case of necessity. It is s perishable product and different blood products have different shelf lives. Therefore, it is important to manage properly blood product inventories in hospitals to avoid shortage and outdate as much as possible. In this research, a bi-objective mixed-integer programming model is developed which aims to simultaneously minimize the total cost of the supply chain network and the total environmental impacts of the activities of the supply chain network. Since the nature of the problem is uncertain, a robust possibilistic programming approach is... 

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,... 

Unstructured data is an important resource in data mining which In spite of their large volume, they haven’t been analyzed so much. Natural language data are a typical kind of unstructured data which humans can easily understand them but normally it is not possible for machines to process these kind of data. To make these data usable for prediction, pre-processing is required to prepare them for feeding into machine learning algorithms. Therefore, feature extraction is needed for texts in order to make presentative features from them that can unveil the hidden pattern. In this study, in addition to the variables that extracted from the technical indicators, the texts from telegram channels... 

The research in this study aims to analyze data in healthcare, especially the diagnosis of several diseases caused by heart failure. Analyzing and analyzing this data can lead to the discovery of relationships and patterns that can play an important role in the decision-making process of relevant officials in any field. Today, medical data around the world is stored in large volumes for future research. Various infrastructures and software have been set up in many health centers and research centers affiliated with those organizations.In this research, the general process of work is such that the data related to the heart sounds, which are in the four broad categories of S1 to S4, are... 

Over the past few decades, with the exponential increase in data volume, scientists and researchers have tried to discover relationships and algorithms for productivity and find useful information from this amount of data in various fields. Their efforts in data analysis have led to the development of algorithms in the big data field. The result of researchers' working in multiple fields has come to aid the people of science and technology. Among the most important of these areas, we can mention the health and medical sectors, financial sectors, services, manufacturing sectors, etc. The purpose of this study is to enter the financial industry and use data mining tools. One of the newest and... 

Today, with the development of financial and economic sciences and the increasing volume of financial data, it is necessary to process and analyze this field more accurately with up-to-date tools. On the other hand, by the significant growth of the use of machines and computers for analysis and forecasting purposes, their importance and application have been well defined. Therefore, this research is considered to provide a more efficient method by processing historical data and analyzing them using data mining techniques. The results of this study can be provided to experts in this field as an effective method. Therefore, in this research, a new method based on the selection of required... 

Identifying anomalous events is one of the vital topics in research as it often leads to the detection of actionable and critical information such as intrusions, faults, and system failures. With its importance, there has been a substantial body of work for network anomaly detection using supervised and unsupervised machine learning techniques with their own strengths and weaknesses. In this work, we take advantage of both worlds of unsupervised and supervised learning methods. The basic process model we present in this paper includes (i) clustering the training data set to create referential labels, (ii) building a supervised learning model with the automatically produced labels, and (iii)... 

Ⅾue to the absenⅽe of the ⅼabeⅼs or so−ⅽaⅼⅼeⅾ target variabⅼe، ⅽⅼustering vaⅼiⅾation، ⅾespite ⅽⅼassifiⅽation, is not that straightforward. So، the ⅽⅼuster evaⅼuation is a ⅽhaⅼⅼenging task both in researⅽh projeⅽts anⅾ appⅼiⅽations. Whiⅼe ⅿany ⅽⅼustering vaⅼiⅾity inⅾiⅽes are aⅾⅾresseⅾ in the ⅼiterature, ⅿost of theⅿ, even those wiⅾeⅼy useⅾ in the appⅼiⅽation, ⅽannot hanⅾⅼe arbitrary shapes. In this paper, a noveⅼ ⅽⅼustering vaⅼiⅾity inⅾex is proposeⅾ, whiⅽh is ⅿuⅽh ⅿore powerfuⅼ in ⅽapturing the ⅾata’s reaⅼ struⅽture anⅾ ⅾeaⅼing with arbitrary shapes. An aⅼⅿost noveⅼ separation ⅿeasure is proposeⅾ to represent the signifiⅽant or insignifiⅽant separation regarⅾing the ⅽⅼuster’s struⅽture... 

Today, machine learning has many applications in various industries, and healthcare is not an exception. Machine learning algorithms are used for medical diagnosis, make predictions about patients’ future health, newly-discovered treatment effect on patients prediction, drug recommendation system, build risk models and survival estimators and health risk prediction models. One of the topics that has received less attention in the world, especially in Iran, is the prediction of the surgery duration. This is very important because operating rooms in hospitals are the primary source of hospital revenue; We also need to predict the duration of surgery as accurately as possible in order to... 

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... 

This study proposes a deep reinforcement learning approach to solve a perishable inventory allocation problem in a two-echelon supply chain. The inventory allocation problem is studied considering the stochastic nature of demand and supply. The examined supply chain includes two retailers and one distribution center (DC) under a vendor-managed inventory (VMI) system. This research aims to minimize the wastages and shortages occurring at the retailer's sites in the examined supply chain. With regard to continuous action space in the considered inventory allocation problem, the Advantage Actor-Critic algorithm is implemented to solve the problem. Numerical experiments are implemented on... 

The Vehicle Routing Problem (VRP) is one of the most widely studied topics in the field of Operations Research and has received tremendous attention, especially in recent years. This attention is driven by today’s customer expectations with respect to fast and reliable service. In (classic) static VRPs, all information is known at the time of decision making. However, the world is more dynamic now – and (naturally) so are the optimization problems. the advance of information and communication technologies, Global interconnectedness, urbanization, and increased service orientation raise the need for anticipatory real-time decision making. A striking example is Logistic Service Providers...