Sharif Digital Repository

With the rapid development of the economy, more people have started investing in the stock market. Predicting price changes can reduce the risk of investing in stocks. Technical data such as price and volume in the stock market is usually used to predict stock prices, and less often other types of data such as market data or fundamental data are used. In this study, we want to determine what impact each of the available data types has on stock prices. For example, data of buy and sell for per capita, capital inflows and outflows for small and large natural and legal investors, information related to the stocks themselves, indicators, fundamental data such as earnings per share (EPS) and... 

Nowadays, finding suppliers of goods and materials is easier than the past, because of developments in communications technology. Therefore, some of supply chains have expanded their relations to global area and select international partners. With an increase in the scope of suppliers available, supplier management sections encounter a large number of suppliers to choose from, and loads of information to process. Therefore proper methods must be adopted in order to evaluate the suppliers. One way is to create credit levels and ranking the suppliers according to past cooperation. Implementation of this method requires the use of tools that provide Comprehensive analysis of the occult rules... 

Taxis provide a flexible and indispensable service to satisfy the urban travel demand of public commuters. Balancing supply and demand and minimizing the driving time before finding a customer would directly increase taxi drivers and system income. Availability of GPS traces and easy access to the Internet has enabled taxi companies to be aware of supply-demand level in every region and all taxi states in real time. In this research, we propose a novel fair recommender system to maximize the sum of taxi drivers’ income by recommending regions with the highest profitability due to each driver’s acceptance rate and each regions’ minimum supply availability. Experiment results on TAP30’s data... 

In this thesis the goal is to use data Mining techniques for finding features of different Partial discharges happen in power transformer insulation defect models in order to monitor the insulation condition of in-service power transformers continuously and on-line and to identify any type of insulation defect in power transformers at the early stage of formation.For this purpose, power transformer Insulation defects are modeled physically and partial discharge current pulses are recorded as Partial discharge data.Tow format of data are investigated, in first one, recorded data in one power frequency cycle are explored and in second one, individual PD pulses are considered for study.In... 

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... 

An object-oriented application framework, like Eclipse, not only provides a framework for designing and implementing new applications, but also decreases the time and the cost of developing new software applications. Moreover, theseframeworks increase the maintainability of software systems. Therefore, their popularity is on the rise. The main problem of using object-oriented application frameworks is the lack of proper documentations and guides. Thus, developers often try to learn how to implement their desired concepts (e.g., Context Menu) from available sample applications. This leads the programmers to another problem which is finding the sample applications. Finding a proper sample... 

Mobile and wireless services industry has been among the most attractive businesses recently and is progressing at a fast pace. In this huge industry various organizations and entities cooperate to deliver value to the end user. The mobile operator plays the pivotal role in this chain. Hence mobile operator’s market was chosen in this study as the representative of the industry. Market segmentation and customer satisfaction are the main topics of this study.
Market segmentation for MCI was done using data mining with two-step algorithm. Input data included the consumer behavior extracted from mobile post-paid bills. The results indicated five distinctive clusters. The next part included... 

The performance of the power plant decreases during its lifetime and deviates from its design and initial operation conditions; Maintenance issues, variety of operational patterns, market limitations and financial goals have been caused this situation. Knowing the appropriate actions and finding the optimal operation conditions of the power plant can support the system to restore its initial operational performance and bring it closer to its design condition. In this research, with historical data helps of a steam thermal power plant in Kermanshah, unsupervised machine learning techniques have been used to identify operational patterns, which lead to the identification of optimal operating... 

Nowadays the issue of recognizing customer’s needs and leading different activities of industrial units to obviate such needs are important concerns and are coined as “Customer Relationship Management” in business literature. What’ s more, with the development of information technology ,creating the possibility to gather, warehouse and analyze huge amount of data quickly, “Data Mining” subject is presented to analysts and experts as a powerful tool. Currently in many aspects of CRM, analysts are considering Data Mining techniques in order to identify customers’ behavior pattern, performance and predict customers’ behavior and to specify their desires based on this pattern. This Thesis is to... 

Today with the development of technology in medical science, there is a need to develop new methods to analyze and process the medical images. Furthermore, increasing use of machines and computers to accomplish prediction goals delineates that these tools had promising results. Because of all the above, this research focuses on processing and analyzing medical images with using data mining tools in order to identify MS and tumor disease which have been ubiquitous in last decades, fast and meticulous. To do so, we introduce a new clustering algorithm based on the modularity measure of graph networks as well as a new machine learning algorithm based on Kalman filter for Tensor-based data.... 

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... 

Privacy preserving data mining helps organizations and companies not only to deal with privacy concerns of customers and regular limitations, but also to benefit from collaborative data mining. Utilizing cryptographic techniques and secure multiparty computation (SMC) are among widely employed approaches for preserving privacy in distributed data mining. The general purpose of secure multiparty computation protocols to compute specific functions on private inputs of parties in a collaborative manner and without revealing their private inputs. Providing rigorous security proof of secure multiparty computation makes it a good choice for privacy preservation, despite of its cryptographic... 

As one of the most important agglomeration methods, pelletizing has a very important place in the iron and steel production chain. In the present study, the feasibility of improving FeO index in the final product of Pellet Factory No.1 of GolEGohar Mining and Industrial Company was investigated. The remaining FeO in the iron oxide pellet represents the amount of Magnetite that was not oxidized during the pelletizing process. The remaining FeO in the primary Magnetite is a disturbing structure for the direct reduction process and will cause a decrease in the compressive strength of the pellet (CCS) and as a result it will be crushed. Therefore, the percentage of the remaining FeO in the... 

Peer-to-peer (P2P) computing is a popular distributed computing paradigm for many applications which in-volve exchange of information among a large number of peers. In such applications, large amount of data is distributed among multiple dispersed sources. Therefore, data analysis is challenging due to processing, storage and transmission costs. Moreover, the data rarely remains static and frequent data changes, quickly out date previously extracted data mining models. Distributed data mining deals with the problem of data analysis in environments with distributed data and computing resources. In this dissertation, we explore distributed data mining in different structures of P2P systems. In... 

Nowadays we see competition between organizations to maintain excellence and survival in business. Organizations should focus on maintaining and satisfying their customers in their services and products, because it is the customers who, as buyers of services and products, provide the company's revenue stream. With the advancement of computer technology today, a large amount of customer data can be stored and maintained. This data is a valuable resource for analyzing customer behavior and making the right decisions for the organization. Organizations need to use customer behavioral data to make their marketing activities smart and data-driven. In fact, organizations need to move towards... 

Networked computers are expanding more and more around the world, and digital social networks becoming of great importance for many people's work and leisure. Emails, Weblogs and Instant Messengers are popular instances of social networks. In this thesis, the main target is to have an advertisement according to user favorites and interests by mining his/her interactions in digital social networks. Briefly, in our method social network users are categorized based on the topics exchanges between them in the network, these topics discovered by mining of flowing data in that environment, considering that these topics shows the user willing, finally relevant advertisements will be represented to... 

Prescribing and consuming drugs more than necessary is considered polypharmacy, which is both wasteful and harmful. In this study, an innovative data mining framework is developed for analyzing prescriptions regarding polypharmacy. The approach consists of three main steps: pre-modeling, modeling, and post-modeling. In the first step, after collecting and cleaning the raw data, several novel features are extracted for physicians and patients. In the modeling step, decision trees are applied to generate a set of If-Then rules to detect and describe physicians’ features or patients’ features associated with polypharmacy. A novel approach based on the response surface methodology (RSM) is... 

Since physical variables are almost dependent, uncertain and have significant time and spatial changes, nature of hydrological and environmental systems is so complicated, nonlinear and dynamic. Many models have been developed for studying and analyzing various phenomena in these systems. Recently data mining approaches have been used as new methods for modeling of complicated engineering systems. In this study, performance of several data mining tools in analyzing two phenomena in water quality management have been evaluated. These algorithms are Multilayer Perceptron Neural Network (MLP), Radial Basis Function Neural Network (RBFN), Support Vector Machines (SVMs) in the field of Artificial... 

Abnormality detection and prognosis of epileptic patients with artificial intelligence and machine learning techniques is still in its early experimental stages. Surgical candidacy determination for epilepsy depends on the clinical actions which involve an intracranial electrode implantation followed by prolonged electrographic monitoring (EEG phase II) .This invasive test is very costly, painful and time consuming. Here the goal is integration of the two following paradigms: 1-Non invasive multimodality data of epilepsy. 2- Artificial intelligence and machine learning techniques. We have used human brain multi-modality database system that includes patient’s demographics, clinical and EEG... 

Semantic relation extraction is one of the challenging information extraction’s steps. The main task of relation extraction is finding of relationships in text corpus using Machine Learning methods. There are plenty of usage for relation extraction such as providing information for Question Answering systems, protein interaction detection in biomedical corpora and ontology population. There are many research take placed in this area in which relation extraction task is defined as a classification problem and in most of them, this problem is solved by SVM method. Results of recent research imply that current state of relation extraction methods are far from appropriate state that is...