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    Data Mining Using Extended LASSO-based Factor Selection Algorithms

    , M.Sc. Thesis Sharif University of Technology Javadi Narab, Nahid (Author) ; Akhavan Niaki, Taghi (Supervisor)
    Abstract
    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... 

    Prediction of Surgery Duration with Data Mining Techniques

    , M.Sc. Thesis Sharif University of Technology Ardehkhani, Pegah (Author) ; Akhavan Niaki, Taghi (Supervisor)
    Abstract
    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... 

    How People's Sentiment and Attention Affect the Return of Bitcoin?

    , M.Sc. Thesis Sharif University of Technology Dolatzadeh, Hirad (Author) ; Aslani, Shirin (Supervisor) ; Talebian, Masoud (Co-Supervisor)
    Abstract
    With the huge growth of cryptocurrencies in recent years, the attention of investors has been drawn to predict and invest in it. Because of the high volatility of this market, which includes a large share of Bitcoin, there is a need for good forecasting in it. Although past studies have been able to accurately predict the price of Bitcoin using fundamental variables and variables related to the blockchain network, less attention has been paid to the use of variables related to investor sentiments in this market. In this research, variables widely used in the literature that show the emotions and attention of investors, such as sentiment analysis of Twitter texts, Google search index,... 

    Using of Statistical and Machine Learning Methods in Financial Markets

    , M.Sc. Thesis Sharif University of Technology Rostamzadeh, Mehrdad (Author) ; Kianfar, Farhad (Supervisor)
    Abstract
    The problem of stock price direction prediction is of great value among investors and researchers in the past decades. Even the smallest improvement in the performance of forecasting methods can lead to noticeable profit for investors. In this regard, in this research, a new method for filling the literature gap in the field of stock price direction forecasting is proposed. In the proposed method, two concepts of dynamics and model selection in dealing with data is investigated. Finally a predictive model is developed according to the two abovementioned concepts. Moreover, in this work, using a meta-learning approach one step towards making the prediction process automatic is taken. The... 

    Developing a New Prediction Method for Grid Environments

    , M.Sc. Thesis Sharif University of Technology Naddaf Sichany, Babak (Author) ; Habibi, Jafar (Supervisor)
    Abstract
    In this project we have worked on the new architecture of the auction based resource scheduling, from the bidders point of view. The performance of different bidding strategies for the resources which participate in reverse auction system has been investigated; our main parameter for evaluating different bidding strategies is the amount of the profit gained by resources which follow such strategies. The main historical bidding strategies are created based on two famous predictors ES and AUTO-REGRESSION. In addition a game theory approach has been proposed. We have shown that our bidding algorithm (based on the sequential game model) reaches to an equilibrium point if all the bidders follow... 

    Using Bump Modeling in Brain Wave Analysis

    , M.Sc. Thesis Sharif University of Technology Ghanbari Garakani, Zahra (Author) ; Shamsollahi, Mohammad Bagher (Supervisor)
    Abstract
    In this thesis, the efficiency of bump modeling has been investigated on brain signals, in a variety of aspects including analysis, detection, classification and prediction. The aim of bump modeling is to provide an optimized representation of the signal in time-frequency domain. This would be done by discriminating oscillatory bursts from background signal and then showing them by half-ellipsoid functions called bump. Consequently, the problem of dealing with large numbers of parameters and hence complicated calculations, which are serious concerns in similar methods, can be overcome. This is in addition to the benefits of using time-frequency representation of the signal.The aim of bump... 

    A new Temporal Locality Method for Multi-Core Processor data Cache

    , M.Sc. Thesis Sharif University of Technology Banihashemi, Borzoo (Author) ; Jahangir, AmirHossein (Supervisor)
    Abstract
    By increasing speed gap between microprocessors and off-chip Last Level Cache, Optimization in Last Level Cache makes improvement in system performance. With development of new generation of multi-core processors and sharing LLC between these cores, the so called issue of Memory Wall has caused an incremental effect of LLC on system performance. There are three approaches to use this memory more efficiently:
    1. Increasing cache capacity
    2. Making cache hierarchical and adding different layers to hierarchy
    3. Improvement of replacement algorithms in cache memory
    The first approach has not been used in regard with limitation of technology and growth of access time due to... 

    Fault Growth Forecasting of Rotatory Systems Using Wavelet Transform and Artificial Neural Network Algorithm

    , M.Sc. Thesis Sharif University of Technology Sohrabi, Ahmad (Author) ; Behzad, Mahdi (Supervisor) ; Mahdigholi, Hamid (Supervisor)
    Abstract
    Failure of mechanical parts in the industry lead to a larger system downtime and even imposing economic losses to the factory. For this Purpose, for many years, researchers have been trying to find ways to predict early failure and to prevent losses from occurring. Creation of new sciences like artificial intelligence, helped researchers in this field.In the current study, using experimental data of a set of bearings that have been tested and recorded in the Intelligent Systems Research Center, A new approach with sufficient accuracy is presented for the prediction algorithm. Among the features extracted, three features of entropy, root mean square and maximum are the most appropriate... 

    A Thesis Submitted in Partial Fulfillment of the Requirement for the Degree of Master of Science in Electrical Engineering

    , M.Sc. Thesis Sharif University of Technology Mohammadi, Mahsa (Author) ; Jahed, Mehran (Supervisor) ; Motahhari, Abolfazl (Co-Advisor)
    Abstract
    Dnase I Hypersensitive Sites (DHSs) are known as comprehensive markers of DNA regulatory elements. The main function of regulatory elements is repressing or enhancing transcription of genes. Hence, the recruitment of the data is prevalent in many studies of genome. One of the applications of this data is to utilize it to predict active regulatory regions (Transcription Factor Binding Sites).There are different means to do this, divided in three major groups: first, the methods only use the number of DNase-seq reads that surround a candidate binding site. While robust, these methods do not reflect the shape of the signal. A second strategy uses a variety of approaches to model and identify... 

    Study of Statistical Behavior of Chaotic Maps and Design of Stochastic Models for Reconstruction and Prediction of Behavioral Patterns of Chaotic Systems

    , M.Sc. Thesis Sharif University of Technology Jokar, Meysam (Author) ; Salarieh, Hassan (Supervisor) ; Alasty, Aria (Supervisor)
    Abstract
    Chaotic time series analysis, study of statistical behavior of chaotic maps and eventually an attempt to reconstruction and prediction of dynamical and statistical properties of output data of chaotic systems using stochastic models such as Markov models and autoregressive-moving average models are the main purposes of the present research. Examples of chaotic time series abound in the output of economics, engineering systems, the natural sciences (especially geophysics and meteorology) and social sciences. An intrinsic feature of an output time series of a dynamic system is that, adjacent observations are dependent. Time series analysis is concerned with techniques for the analysis of this... 

    Use and Evaluation of Predictive and Speculative Techniques in Software-Defined Networks (SDN) processor

    , M.Sc. Thesis Sharif University of Technology Dorosti, Zahra (Author) ; Jahangir, Amir Hossein (Supervisor)
    Abstract
    Software Defined Networking (SDN) is an emerging paradigm which makes the network programmable by separating the control plane from data plane and makes both planes to work independently. There is a centralized controller and a programmable data plane in these networks architecture and forwarding data packets is realized by programming the data plane via an open interface called OpenFlow. OpenFlow is a communication protocol between control and data planes. The centralized architecture of these networks provides a global view of the underlying network to upper applications and brings numerous advantages such as routing, traffic engineering and QoS control. Despite these advantages there are... 

    Town Trip Analysis With Data Mining and Statistical Techniques

    , M.Sc. Thesis Sharif University of Technology Fili, Mohammad (Author) ; Khedmati, Majid (Supervisor) ; Akhavan Niaki, Taghi ($item.subfieldsMap.e)
    Abstract
    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... 

    Stock Market Prediction Based on Analysis of Textual and Numerical Data

    , M.Sc. Thesis Sharif University of Technology Taleb, Mohsen (Author) ; Akhavan Niaki, Taghi (Supervisor)
    Abstract
    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...