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Dimensional characterization of anesthesia dynamic in reconstructed embedding space
, Article 29th Annual International Conference of IEEE-EMBS, Engineering in Medicine and Biology Society, EMBC'07, Lyon, 23 August 2007 through 26 August 2007 ; 2007 , Pages 6483-6486 ; 05891019 (ISSN) ; 1424407885 (ISBN); 9781424407880 (ISBN) ; Rabiee, H. R ; Hashemi, M. R ; Ghanbari, M ; Sharif University of Technology
2007
Abstract
The depth of anesthesia quantification has been one of the most research interests in the field of EEG signal processing and nonlinear dynamical analysis has emerged as a novel method for the study of complex systems in the past few decades. In this investigation we use the concept of nonlinear time series analysis techniques to reconstruct the attractor of anesthesia from EEG signal which have been obtained from different hypnotic states during surgery to give a characterization of the dimensional complexity of EEG by Correlation Dimension estimation. The dimension of the anesthesia strange attractor can be thought of as a measure of the degrees of freedom or the 'complexity' of the...
Evaluating the Impact of Gasoline Price Change on the Passing Car Volume in the Provinces of Iran and Tehran and the Impact of CBD Entry Policy Change on the Passing Car Volume in Tehran
, M.Sc. Thesis Sharif University of Technology ; Amini, Zahra (Supervisor)
Abstract
Nowadays, various policies are adopted by transportation managers and planners. These policies aim to improve system performance, reduce user costs, control and reduce air pollution, reduce noise pollution, and ultimately reduce congestion. A set of these policies in the form of transportation demand management is presented in the literature. A common way to find the effect of a policy on user behavior is to use questionnaires. Other causal inference models have been proposed in disciplines such as statistics, political science, marketing science, epidemiology, and psychology. The purpose of these models is to find the causal effect of an intervention (treatment) on a system. These studies...
Short-term prediction of air pollution using TD-CMAC neural network model
, Article Soft Computing with Industrial Applications - International Symposium on Soft Computing for Industry, ISSCI - Sixth Biannual World Automation Congress, WAC 2004, Sevilla, 28 June 2004 through 1 July 2004 ; 2004 , Pages 357-362 ; 1889335231 (ISBN) ; Teshnehlab, M ; Abbaspour, M ; Setayeshi, S ; Sharif University of Technology
2004
Abstract
This paper presents a new model to short-term prediction of air pollution using a new structure is based on the intelligent neural networks. A new structure known as Time Delay Cerebellar Model Arithmetic Computer (TD-CMAC), an extension to the CMAC, it requires fewer memory sizes. The new model is demonstrated and validated with three primary air pollutants known as carbon monoxide (CO), sulfur dioxide (SO2), and nitrogen dioxide (NO 2). The simulation results for the half an hour ahead-prediction of the air pollutant data set show that the suggested new model is suitable for our purpose
Time Series Analysis Using Deep Neural Networks Based on DTW Kernels and its Application in Blood Pressure Estimation Using PPG Signals
, M.Sc. Thesis Sharif University of Technology ; Mohammadzadeh, Narjesolhoda (Supervisor)
Abstract
This work presents a modification of deep neural networks for time series analysis. We used kernel layer(s), as a novel approach, at the beginning of the common deep neural networks. These kernels learn based on dynamic time warping (DTW). In each kernel, DTW is calculated between the kernel value and a part of input time series or a part of last layer output (if the kernel is not in the first layer). DTW also gives an alignment path for the input series. This alignment path is used to defining a loss function with the goal of getting better alignment (lower DTW distance) between the kernel and the other input. Besides getting better accuracy on the examined datasets, the other achievement...
Forecasting Airline Demand by Using Hybric Bayesian Method and Time Series
, M.Sc. Thesis Sharif University of Technology ; Refie, Majid (Supervisor)
Abstract
Using revenue management in any industry can increase the profit. In aviation industries, due to the huge number of requests and travels for each airline, a revenue management system can lead to a good profit for the airlines. The first step in revenue management system is predicting the demand.In this article two models are developed using time series techniques, based on the information taken from one of the Iranian airlines in Tehran-Mashhad fly route.The first model is developed using ARIMA and seasonal-ARIMA models and the second one is based on the demand and price history, price in the day of prediction and the ARIMA model. The second model which is a combination of price, prior price...
Complex Dynamics of Epileptic Brain and Turbulence :From Time Series to Information Flow
, Ph.D. Dissertation Sharif University of Technology ; Rahimi Tabar, Mohammad Reza (Supervisor) ; Karimipour, Vahid (Supervisor)
Abstract
Complex systems are composed of a large number of subsystems behaving in a collective manner. In such systems, which are usually far from equilibrium, collective behavior arises due to self-organization and results in the formation of temporal, spatial, spatio-temporal and functional structures. The dynamics of order parameters in complex systems are generally non-stationary and can interact with each other in nonlinear manner. As a result, the analysis of the behavior of complex systems must be based on the assessment of the nonlinear interactions, as well as the determination of the characteristics and the strength of the fluctuating forces. This leads to the problem of retrieving a...
Crop Classification using Sentinel-Image Timeseries and Deep Learning
, M.Sc. Thesis Sharif University of Technology ; Manzuri, Mohammad Taghi (Supervisor)
Abstract
Crop classification is one of the most important applications of remote sensing in agriculture. Knowing what crops are on the farm is invaluable both on a micro and macro scale. For example, this information can be used to design and imple- ment agricultural policies, product management and ensure food security. Also, this information can be used as a prerequisite for implementing other programs at the farm scale, such as monitoring and detecting anomalies during the crop growth cycle. Most of the studies in this field are focused on the optical data of the Sentinel-2 satel- lite, but the optical data are vulnerable to atmospheric conditions, and on the other hand, there is valuable...
Estimation of Brain Connectivity Via Deep Neural Network
, M.Sc. Thesis Sharif University of Technology ; Shamsollahi, Mohammad Bagher (Supervisor)
Abstract
The human brain is one of the most complex and least understood systems in nature. In recent decades, numerous studies have been conducted to identify the behavior of this system. One of the areas of brain research is the investigation of the connections between different regions of the brain during a presumed process or in a resting state. Among various types of brain connections, effective connectivity provides researchers with higher-level information on brain behavior compared to other connections, but also entails greater computational complexity. In recent years, researchers have aimed to provide an estimator with the maximum desirable capabilities, and with the advent of (deep) neural...
An AI Based Cryptocurrency Trading System
, M.Sc. Thesis Sharif University of Technology ; Khayyat, Amir Ali Akbar (Supervisor)
Abstract
Cryptocurrencies are not only regarded as a trustworthy method of financial transaction validated by a decentralized cryptographic system as opposed to a centralized authority, but also as one of the most popular and lucrative forms of trade and investing. Predicting the price of a cryptocurrency is a challenging topic in time-series research. Its intricacy is due to the volatility and large swings of cryptocurrencies' price. The emergence of brand-new cryptocurrencies, which might present a profitable trading opportunity but lack sufficient historical data for technical analysis, prompted us to develop a trading strategy that could be applied universally. The forecast of the next timestep's...
Modeling the accuracy of traffic crash prediction models
, Article IATSS Research ; Volume 46, Issue 3 , 2022 , Pages 345-352 ; 03861112 (ISSN) ; Keshavarz, S ; Pazari, P ; Safahieh, N ; Samimi, A ; Sharif University of Technology
Elsevier B.V
2022
Abstract
Crash forecasting enables safety planners to take appropriate actions before casualty or loss occurs. Identifying and analyzing the attributes influencing forecasting accuracy is of great importance in road crash forecasting. This study aims to model the forecasting accuracy of 31 provinces using their macroeconomic variables and road traffic indicators. Iran's road crashes throughout 2011–2018 are calibrated and cross-validated using the Holt-Winters (HW) forecasting method. The sensitivity of crash forecast reliability is studied by a regression model. The results suggested that the root mean square error (RMSE) of crash prediction increased among the provinces with higher and more variant...
Deep long short-term memory (LSTM) networks for ultrasonic-based distributed damage assessment in concrete
, Article Cement and Concrete Research ; Volume 162 , 2022 ; 00088846 (ISSN) ; Toufigh, V ; Sharif University of Technology
Elsevier Ltd
2022
Abstract
This paper presented a comprehensive study on developing a deep learning approach for ultrasonic-based distributed damage assessment in concrete. In particular, two architectures of long short-term memory (LSTM) networks were proposed: (1) a classification model to evaluate the concrete's damage stage; (2) a regression model to predict the concrete's absorbed energy ratio. Two input configurations were considered and compared for both architectures: (1) the input was a single signal; (2) the inputs were four signals from four sides of the specimen. A comprehensive experimental study was designed and conducted on ground granulated blast furnace slag-based geopolymer concrete, providing a...
The Application of Deep Learning Models in Estimating the Energy of Residential Buildings
, M.Sc. Thesis Sharif University of Technology ; Rafiee, Majid (Supervisor) ; Shavandi, Hassan (Co-Supervisor)
Abstract
Electricity consumption has increased dramatically in recent decades, and this increase has severely affected electricity distribution. Therefore, forecasting electricity demand can provide a precondition for distributors. Predicting power consumption requires many parameters to be considered.In this research, machine learning, and deep learning methods such as recursive neural networks, long short-term memory networks, etc., as well as the ARIMA model will be used. These models have been tested on the London Smart Measurement Database. In order to evaluate the capability of the models in forecasting electricity consumption, each has been used to predict the electricity consumption of a...
Portfolio Formation Using Deep Learning
, M.Sc. Thesis Sharif University of Technology ; Manzuri, Mohammad Taghi (Supervisor)
Abstract
Throughout history, forming an optimal asset portfolio has been the primary goal of capital owners and managers of investment funds in any economic activity. Achieving this goal is equivalent to trying to minimize the risk caused by the inevitable fluctuations in the capital market and maximizing the overall investment return during the expected period. Investors can operate in various financial markets where there are different stocks and asset classes in each of these markets. The main goal of investors is to identify profitable stocks and form an optimal asset portfolio based on them.Based on this, during the past decades, many studies have been conducted to form and optimize the stock...
A Machine Learning and Time-Frequency Domain Combined Approach for Improving Stock Portfolio Management
, Ph.D. Dissertation Sharif University of Technology ; Manzuri, Mohammad Taghi (Supervisor)
Abstract
Price prediction in financial markets is an exciting problem for a vast majority of groups and people; however, investment portfolio managers and owners are always looking for holistic predic-tion approaches and tools having high functional accurate metrics. Strictly speaking, players in fi-nancial markets are always in search of methods and toolboxes since they need to overcome the un-certainty of their buy, sell, or hold decisions in order to reduce the investment risk. In this research, we have tried to deal with the stock price prediction problem as an asset pricing problem and find a novel approach to push forward the state-of-the-art of the problem based on the fundamental pric-ing...
Feature Extraction for Financial Markets’ Transactions
, M.Sc. Thesis Sharif University of Technology ; Manzuri Shalmani, Mohammad Taghi (Supervisor)
Abstract
The use of machine learning and deep learning tools to predict the future behavior of trends in massive data requires the extraction and creation of the eigenvector for the chosen model in the problem. It should be noted that simply by increasing the number of features, it cannot be expected that the learning model will have a higher efficiency. Rather, the quality and importance of the features in the field under study should be carefully considered. Topics such as data redundancy, data correlation, the amount of information in the data, distorted data, outliers, etc. are important steps in improving the dataset and creating a feature vector for training the learning model. In the realm of...
Development of Macro-Level Crash Prediction Models, using Advanced Statistical and Machine Learning Methods
, Ph.D. Dissertation Sharif University of Technology ; Nassiri, Habibollah (Supervisor)
Abstract
Road casualty is the fifth leading cause of death in Iran. To adopt proper countermeasures there is a need to evaluate the consequences of the implemented policies. Despite the development of crash time series models, these methods have not been in accordance with the multivariate, seasonal, and non-linear nature of crash data. On the other hand, the interpretable crash causal analysis frameworks are descriptive and they lack predictive power. Moreover, the unobserved homogeneity between observations has been widely overlooked in the crash causal analysis literature. This thesis introduces a novel causal analysis methodology by combining the interpretability and prediction power of the...
Impact of mobility on COVID-19 spread – A time series analysis
, Article Transportation Research Interdisciplinary Perspectives ; Volume 13 , 2022 ; 25901982 (ISSN) ; Aminpour, N ; Ahmadian, M. A ; Samimi, A ; Saidi, S ; Sharif University of Technology
Elsevier Ltd
2022
Abstract
In this paper, we investigate the impact of mobility on the spread of COVID-19 in Tehran, Iran. We have performed a time series analysis between the indicators of public transit use and inter-city trips on the number of infected people. Our results showed a significant relationship between the number of infected people and mobility variables with both short-term and long-term lags. The long-term effect of mobility showed to have a consistent lag correlation with the weekly number of new COVID-19 positive cases. In our statistical analysis, we also investigated key non-transportation variables. For instance, the mandatory use of masks in public transit resulted in observing a 10% decrease in...
Stock Price Prediction Based on Shareholders Trading Behavior
, M.Sc. Thesis Sharif University of Technology ; Habibi, Jafar (Supervisor)
Abstract
Nowadays, the capital market has a significant impact on the economy of a country and causes economic dynamism and growth in gross production. Among the important phenomena in the stock market is stock pricing, the correctness or incorrectness of which has a significant role in the performance of the stock market and the value of companies. The stock price in the stock exchange represents the stock market value and usually represents the investment value of the shareholders. Forecasting the trend of the stock market is considered an important and necessary thing and has been given much attention, because the successful forecasting of the stock price may lead to attractive profits by making...
A high-accuracy hybrid method for short-term wind power forecasting
, Article Energy ; Volume 238 , 2022 ; 03605442 (ISSN) ; Ehsan, M ; Soleymani, S ; Mohammadnezhad Shourkaei, H ; Sharif University of Technology
Elsevier Ltd
2022
Abstract
In this article, a high-accuracy hybrid approach for short-term wind power forecasting is proposed using historical data of wind farm and Numerical Weather Prediction (NWP) data. The power forecasting is carried out in three stages: wind direction forecasting, wind speed forecasting, and wind power forecasting. In all three phases, the same hybrid method is used, and the only difference is in the input data set. The main steps of the proposed method are constituted of outlier detection, decomposition of time series using wavelet transform, effective feature selection and prediction of each time series decomposed using Multilayer Perceptron (MLP) neural network. The combination of automatic...