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Total 159 records

    Modeling the accuracy of traffic crash prediction models

    , Article IATSS Research ; Volume 46, Issue 3 , 2022 , Pages 345-352 ; 03861112 (ISSN) Rashidi, M. H ; 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) Ranjbar, I ; 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... 

    Impact of mobility on COVID-19 spread – A time series analysis

    , Article Transportation Research Interdisciplinary Perspectives ; Volume 13 , 2022 ; 25901982 (ISSN) Zargari, F ; 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... 

    A high-accuracy hybrid method for short-term wind power forecasting

    , Article Energy ; Volume 238 , 2022 ; 03605442 (ISSN) Khazaei, S ; 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... 

    Pattern recognition in financial surveillance with the ARMA-GARCH time series model using support vector machine

    , Article Expert Systems with Applications ; Volume 182 , 2021 ; 09574174 (ISSN) Doroudyan, M. H ; Akhavan Niaki, S. T ; Sharif University of Technology
    Elsevier Ltd  2021
    Abstract
    As the intersection of finance and statistics, financial surveillance is a new interdisciplinary field of research. In this field, statistical process control methods are applied to monitor financial indices. The final aim is to detect out-of-control conditions and trigger a signal as soon as possible. These early signals can help practitioners in making on-time decisions. In this paper, a new method based on a support vector machine is proposed to detect upward and downward shifts with step and trend patterns in auto-correlated financial processes. These processes are modeled by the autoregressive moving average (ARMA) and generalized autoregressive conditional heteroskedasticity (GARCH)... 

    Time series analysis framework for forecasting the construction labor costs

    , Article KSCE Journal of Civil Engineering ; Volume 25, Issue 8 , 2021 , Pages 2809-2823 ; 12267988 (ISSN) Faghih, S. A. M ; Gholipour, Y ; Kashani, H ; Sharif University of Technology
    Springer Verlag  2021
    Abstract
    This manuscript presents a framework to develop vector error correction (VEC) models applicable to forecasting the short- and long-run movements of the average hourly earnings of construction labor, which is an essential predictor of the construction labor costs. These models characterize the relationship between average hourly earnings and a set of explanatory variables. The framework is applied to develop VEC forecasting models for the average hourly earnings of construction labor in the USA based on the identified variables that govern its movements, such as Global Energy Price Index, Gross Domestic Product, and Personal Consumption Expenditures. More than 150 candidate VEC models were... 

    Dynamic time warping-based features with class-specific joint importance maps for action recognition using kinect depth sensor

    , Article IEEE Sensors Journal ; Volume 21, Issue 7 , 2021 , Pages 9300-9313 ; 1530437X (ISSN) Mohammadzade, H ; Hosseini, S ; Rezaei Dastjerdehei, M. R ; Tabejamaat, M ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2021
    Abstract
    This paper proposes a novel 3D action recognition technique that uses time-series information extracted from depth image sequences for use in systems of human daily activity monitoring. To this end, each action is represented as a multi-dimensional time series, where each dimension represents the position variation of one skeleton joint over time. The time series is then mapped onto a vector space using Dynamic Time Warping (DTW) distance. Furthermore, to employ the correlation-distinctiveness relationship of the sequences in recognition, this vector space is remapped onto a discriminative space using the regularized Fisher method, where final decisions about the actions are made. Unlike... 

    fMRI functional connectivity analysis via kernel graph in Alzheimer’s disease

    , Article Signal, Image and Video Processing ; Volume 15, Issue 4 , 2021 , Pages 715-723 ; 18631703 (ISSN) Ahmadi, H ; Fatemizadeh, E ; Motie Nasrabadi, A ; Sharif University of Technology
    Springer Science and Business Media Deutschland GmbH  2021
    Abstract
    Functional magnetic resonance imaging (fMRI) is an imaging tool that is used to analyze the brain’s functions. Brain functional connectivity analysis based on fMRI signals often calculated correlations among time series in different areas of the brain. For FC analysis most prior research works generate the brain graphs based on linear correlations, however, the nonlinear behavior of the brain can lower the accuracy of such graphs. Usually, the Pearson correlation coefficient is used which has limitations in revealing nonlinear relationships. One of the proper methods for nonlinear analysis is the Kernel trick. This method maps the data into a high dimensional space and calculates the linear... 

    fMRI functional connectivity analysis via kernel graph in Alzheimer’s disease

    , Article Signal, Image and Video Processing ; Volume 15, Issue 4 , 2021 , Pages 715-723 ; 18631703 (ISSN) Ahmadi, H ; Fatemizadeh, E ; Motie-Nasrabadi, A ; Sharif University of Technology
    Springer Science and Business Media Deutschland GmbH  2021
    Abstract
    Functional magnetic resonance imaging (fMRI) is an imaging tool that is used to analyze the brain’s functions. Brain functional connectivity analysis based on fMRI signals often calculated correlations among time series in different areas of the brain. For FC analysis most prior research works generate the brain graphs based on linear correlations, however, the nonlinear behavior of the brain can lower the accuracy of such graphs. Usually, the Pearson correlation coefficient is used which has limitations in revealing nonlinear relationships. One of the proper methods for nonlinear analysis is the Kernel trick. This method maps the data into a high dimensional space and calculates the linear... 

    Multiclass classification of patients during different stages of Alzheimer's disease using fMRI time-series

    , Article Biomedical Physics and Engineering Express ; Volume 6, Issue 5 , 2020 Ahmadi, H ; Fatemizadeh, E ; Motie Nasrabadi, A ; Sharif University of Technology
    IOP Publishing Ltd  2020
    Abstract
    Alzheimer's Disease (AD) begins several years before the symptoms develop. It starts with Mild Cognitive Impairment (MCI) which can be separated into Early MCI and Late MCI (EMCI and LMCI). Functional connectivity analysis and classification are done among the different stages of illness with Functional Magnetic Resonance Imaging (fMRI). In this study, in addition to the four stages including healthy, EMCI, LMCI, and AD, the patients have been tracked for a year. Indeed, the classification has been done among 7 groups to analyze the functional connectivity changes in one year in different stages. After generating the functional connectivity graphs for eliminating the weak links, three... 

    fMRI functional connectivity analysis via kernel graph in Alzheimer’s disease

    , Article Signal, Image and Video Processing ; 2020 Ahmadi, H ; Fatemizadeh, E ; Motie-Nasrabadi, A ; Sharif University of Technology
    Springer Science and Business Media Deutschland GmbH  2020
    Abstract
    Functional magnetic resonance imaging (fMRI) is an imaging tool that is used to analyze the brain’s functions. Brain functional connectivity analysis based on fMRI signals often calculated correlations among time series in different areas of the brain. For FC analysis most prior research works generate the brain graphs based on linear correlations, however, the nonlinear behavior of the brain can lower the accuracy of such graphs. Usually, the Pearson correlation coefficient is used which has limitations in revealing nonlinear relationships. One of the proper methods for nonlinear analysis is the Kernel trick. This method maps the data into a high dimensional space and calculates the linear... 

    Investigating time-varying functional connectivity derived from the Jackknife Correlation method for distinguishing between emotions in fMRI data

    , Article Cognitive Neurodynamics ; Volume 14, Issue 4 , 2020 , Pages 457-471 Ghahari, S ; Farahani, N ; Fatemizadeh, E ; Motie Nasrabadi, A ; Sharif University of Technology
    Springer  2020
    Abstract
    Investigating human brain activity during expressing emotional states provides deep insight into complex cognitive functions and neurological correlations inside the brain. To be able to resemble the brain function in the best manner, a complex and natural stimulus should be applied as well, the method used for data analysis should have fewer assumptions, simplifications, and parameter adjustment. In this study, we examined a functional magnetic resonance imaging dataset obtained during an emotional audio-movie stimulus associated with human life. We used Jackknife Correlation (JC) method to derive a representation of time-varying functional connectivity. We applied different binary measures... 

    Sparsness embedding in bending of space and time; a case study on unsupervised 3D action recognition

    , Article Journal of Visual Communication and Image Representation ; Volume 66 , January , 2020 Mohammadzade, H ; Tabejamaat, M ; Sharif University of Technology
    Academic Press Inc  2020
    Abstract
    Human action recognition from skeletal data is one of the most popular topics in computer vision which has been widely studied in the literature, occasionally with some very promising results. However, being supervised, most of the existing methods suffer from two major drawbacks; (1) too much reliance on massive labeled data and (2) high sensitivity to outliers, which in turn hinder their applications in such real-world scenarios as recognizing long-term and complex movements. In this paper, we propose a novel unsupervised 3D action recognition method called Sparseness Embedding in which the spatiotemporal representation of action sequences is nonlinearly projected into an unwarped feature... 

    Time series forecasting of bitcoin price based on autoregressive integrated moving average and machine learning approaches

    , Article International Journal of Engineering, Transactions A: Basics ; Volume 33, Issue 7 , 2020 , Pages 1293-1303 Khedmati, M ; Seifi, F ; Azizi, M. J ; Sharif University of Technology
    Materials and Energy Research Center  2020
    Abstract
    Bitcoin as the current leader in cryptocurrencies is a new asset class receiving significant attention in the financial and investment community and presents an interesting time series prediction problem. In this paper, some forecasting models based on classical like ARIMA and machine learning approaches including Kriging, Artificial Neural Network (ANN), Bayesian method, Support Vector Machine (SVM) and Random Forest (RF) are proposed and analyzed for modelling and forecasting the Bitcoin price. While some of the proposed models are univariate, the other models are multivariate and as a result, the maximum, minimum and the opening daily price of Bitcoin are also used in these models. The... 

    Network-based direction of movement prediction in financial markets

    , Article Engineering Applications of Artificial Intelligence ; Volume 88 , February , 2020 Kia, A. N ; Haratizadeh, S ; Shouraki, S. B ; Sharif University of Technology
    Elsevier Ltd  2020
    Abstract
    Market prediction has been an important research problem for decades. Having better predictive models that are both more accurate and faster has been attractive for both researchers and traders. Among many approaches, semi-supervised graph-based prediction has been used as a solution in recent researches. Based on this approach, we present two prediction models. In the first model, a new network structure is introduced that can capture more information about markets’ direction of movements compared to the previous state of the art methods. Based on this novel network, a new algorithm for semi-supervised label propagation is designed that is able to prediction the direction of movement faster... 

    Scaling behavior in measured keystroke time series from patients with Parkinson’s disease

    , Article European Physical Journal B ; Volume 93, Issue 7 , July , 2020 Madanchi, A ; Taghavi Shahri, F ; Taghavi Shahri, S. M ; Rahimi Tabar, M. R ; Sharif University of Technology
    Springer  2020
    Abstract
    Abstract: Parkinson has remained as one of the most difficult diseases to diagnose, as there are no biomarkers to be measured, and this requires one patient to do neurological and physical examinations. As Parkinson is a progressive disease, accurate detection of its symptoms is a crucial factor for therapeutic reasons. In this study, we perform Multifractal Detrended Fluctuation Analysis (MFDFA) on measured keystroke time series for three different categories of subjects: healthy, early-PD, and De-Novo patients. We have observed different scaling behavior in terms of multifractality of the measured time series, which can be used as a practical tool for diagnosis purposes. Additionally, the... 

    Stockwell transform of time-series of fMRI data for diagnoses of attention deficit hyperactive disorder

    , Article Applied Soft Computing Journal ; Volume 86 , 2020 Sartipi, S ; Kalbkhani, H ; Ghasemzadeh, P ; Shayesteh, M. G ; Sharif University of Technology
    Elsevier Ltd  2020
    Abstract
    Attention deficit hyperactivity disorder (ADHD) is a common brain disorder among children. It presents various symptoms, hence, utilizing the information obtained from functional magnetic resonance imaging (fMRI) time-series data can be useful. Finding functional connections in typically developed control (TDC) and ADHD patients can be helpful in classification. The aim of this paper is to present a multifold method for the study of fMRI data to diagnose ADHD patients. In the proposed method, first, by applying the Stockwell transform (ST), we obtain detailed information about the time-series of the region of interests (ROIs) in the time and frequency domains. ST provides information about... 

    Analysis and data-driven reconstruction of bivariate jump-diffusion processes

    , Article Physical Review E ; Volume 100, Issue 6 , 2019 ; 24700045 (ISSN) Rydin Gorjao, L ; Heysel, J ; Lehnertz, K ; Rahimi Tabar, M. R ; Sharif University of Technology
    American Physical Society  2019
    Abstract
    We introduce the bivariate jump-diffusion process, consisting of two-dimensional diffusion and two-dimensional jumps, that can be coupled to one another. We present a data-driven, nonparametric estimation procedure of higher-order (up to 8) Kramers-Moyal coefficients that allows one to reconstruct relevant aspects of the underlying jump-diffusion processes and to recover the underlying parameters. The procedure is validated with numerically integrated data using synthetic bivariate time series from continuous and discontinuous processes. We further evaluate the possibility of estimating the parameters of the jump-diffusion model via data-driven analyses of the higher-order Kramers-Moyal... 

    Comparative study of application of different supervised learning methods in forecasting future states of NPPs operating parameters

    , Article Annals of Nuclear Energy ; Volume 132 , 2019 , Pages 87-99 ; 03064549 (ISSN) Moshkbar Bakhshayesh, K ; Sharif University of Technology
    Elsevier Ltd  2019
    Abstract
    In this paper, some important operating parameters of nuclear power plants (NPPs) transients are forecasted using different supervised learning methods including feed-forward back propagation (FFBP) neural networks such as cascade feed-forward neural network (CFFNN), statistical methods such as support vector regression (SVR), and localized networks such as radial basis network (RBN). Different learning algorithms, including gradient descent (GD), gradient descent with momentum (GDM), scaled conjugate gradient (SCG), Levenberg-Marquardt (LM), and Bayesian regularization (BR) are used in CFFNN method. SVR method is used with different kernel functions including Gaussian, polynomial, and... 

    On the existence of proper stochastic Markov models for statistical reconstruction and prediction of chaotic time series

    , Article Chaos, Solitons and Fractals ; Volume 123 , 2019 , Pages 373-382 ; 09600779 (ISSN) Jokar, M ; Salarieh, H ; Alasty, A ; Sharif University of Technology
    Elsevier Ltd  2019
    Abstract
    In this paper, the problem of statistical reconstruction and prediction of chaotic systems with unknown governing equations using stochastic Markov models is investigated. Using the time series of only one measurable state, an algorithm is proposed to design any orders of Markov models and the approach is state transition matrix extraction. Using this modeling, two goals are followed: first, using the time series, statistical reconstruction is performed through which the probability density and conditional probability density functions are reconstructed; and second, prediction is performed. For this problem, some estimators are required and here the maximum likelihood and the conditional...