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    External parameter orthogonalization-support vector machine for processing of attenuated total reflectance-mid-infrared spectra: A solution for saffron authenticity problem

    , Article Analytica Chimica Acta ; Volume 1154 , 2021 ; 00032670 (ISSN) Amirvaresi, A ; Parastar, H ; Sharif University of Technology
    Elsevier B.V  2021
    Abstract
    In the present work, a new approach based on external parameter orthogonalization combined with support vector machine (EPO-SVM) is proposed for processing of attenuated total reflectance-Fourier transform mid-infrared (ATR-FT-MIR) spectra with the goal of solving authentication problem in saffron, the most expensive spice in the world. First, one-hundred authentic saffron samples are clustered by principal component analysis (PCA) with EPO as the best preprocessing strategy. Then, EPO-SVM is used for the detection of four commonly used plant-derived adulterants (i.e. safflower, calendula, rubia, and style) in binary mixtures (saffron and each of plant adulterants) and its performance is... 

    An Integrated Multi-criteria Decision Making Approach for a Sustainable Supply Chain Network Design

    , M.Sc. Thesis Sharif University of Technology Arian, Ebrahim (Author) ; Shavandi, Hassan (Supervisor)
    Abstract
    Growing concerns towards social and environmental issues besides economic in supply chain causes that the sustainable supply chain become one of the most important concepts in a supply chain. Likewise, a supply chain network design which is profound influence on long-run economic, environmental, and social decisions is one of the key and strategic topics in a supply chain. In this study, we represent an integrated approach for a three-layer sustainable supply chain network design with routing and different transportation modes, the objectives of which are minimizing total costs, a minimizing 〖CO〗_2 emissions of transportation, and maximizing total values of social purchasing. In this... 

    Characterization of Plasma Etching Process Using Plasmonic Structures

    , M.Sc. Thesis Sharif University of Technology Arian, Kiarash (Author) ; Rashidian, Bizhan (Supervisor)
    Abstract
    plasma characterization has become an essential tool for characterization of etching process in fabrication of nano-electronic devices. The existing methods, such as Langmuir probe and interferometry, have shown drawbacks including disturbing the plasma and sensitivity to mechanical and thermal stability of the measuring systems. In recent years, due to the scale down of microelectronic devices and increase of their sensitivity to disturbance in the plasma etching process, a demand for measuring methods offering less disturbance has arisen. Plasmonic structures, owing to their unprecedented field enhancement and confinement, have been extensively studied. Their sub-wavelength dimensions,... 

    Chemometrics-assisted-liquid phase microextraction based on deep eutectic solvents followed by gas chromatography for determination of polycyclic aromatic hydrocarbons in aqueous and juice samples

    , Article Microchemical Journal ; Volume 191 , 2023 ; 0026265X (ISSN) Amirvaresi, A ; Keyvan, N ; Nikzad, N ; Parastar, H ; Sharif University of Technology
    Elsevier Inc  2023
    Abstract
    In the present study, an optimized liquid-phase microextraction technique based on the deep eutectic solvent (LPME-DES) followed by gas chromatography-flame ionization detector (GC-FID) is developed to determine 13 carcinogenic polycyclic aromatic hydrocarbons (PAHs) in water and juice samples. In this regard, central composite design (CCD) was used to design, multiple linear regression (MLR) to model, and Nelder-Mead simplex optimization to optimize the effective LPME-DES factors. The optimum values for DES volume, temperature, extraction time, and salt amount were 112 µL, 72 °C, 9.14 min, and 2.5% (w/v), respectively. Afterward, multivariate calibration based on partial least squares... 

    Pathwise grid valuation of fixed-income portfolios with applications to risk management

    , Article Heliyon ; Volume 8, Issue 7 , 2022 ; 24058440 (ISSN) Zamani, S ; Chaghazardi, A ; Arian, H ; Sharif University of Technology
    Elsevier Ltd  2022
    Abstract
    Numerical calculation of Value-at-Risk (VaR) for large-scale portfolios poses great challenges to financial institutions. The problem is even more daunting for large fixed-income portfolios as their underlying instruments have exposure to higher dimensions of risk factors. This article provides an efficient algorithm for calculating VaR using a historical grid-based approach with volatility updating and shows its efficiency in computational cost and accuracy. Our VaR computation algorithm is flexible and simple, while one can easily extend it to cover other nonlinear portfolios such as derivative portfolios on equities and FX securities. © 2022  

    Comparison of near-infrared (NIR) and mid-infrared (MIR) spectroscopy based on chemometrics for saffron authentication and adulteration detection

    , Article Food Chemistry ; Volume 344 , 2021 ; 03088146 (ISSN) Amirvaresi, A ; Nikounezhad, N ; Amirahmadi, M ; Daraei, B ; Parastar, H ; Sharif University of Technology
    Elsevier Ltd  2021
    Abstract
    In this work, the potential of near-infrared (NIR) and mid-infrared (MIR) spectroscopy along with chemometrics was investigated for authentication and adulteration detection of Iranian saffron samples. First, authentication of one-hundred saffron samples was examined by principal component analysis (PCA). The results showed the NIR spectroscopy can better predict the origin of samples than the MIR. Next, partial least squares-discriminant analysis (PLS-DA) was developed to detect four common plant-derived adulterants (i.e., saffron style, calendula, safflower, and rubia). In all cases, PLS-DA classification figures of merit in terms of sensitivity, specificity, error rate and accuracy were... 

    Combining multivariate image analysis with high-performance thin-layer chromatography for development of a reliable tool for saffron authentication and adulteration detection

    , Article Journal of Chromatography A ; Volume 1628 , 2020 Amirvaresi, A ; Rashidi, M ; Kamyar, M ; Amirahmadi, M ; Daraei, B ; Parastar, H ; Sharif University of Technology
    Elsevier B.V  2020
    Abstract
    In this work, high-performance thin-layer chromatography (HPTLC) coupled with multivariate image analysis (MIA) is proposed as a fast and reliable tool for authentication and adulteration detection of Iranian saffron samples based on their HPTLC fingerprints. At first, the secondary metabolites of saffron were extracted using ultrasonic-assisted solvent extraction (UASE) which was optimized using central composite design (CCD). Next, the RGB coordinates of HPTLC images were used for estimation of saffron origin based on principal component analysis (PCA). The PCA scores plot showed that saffron samples were clustered into two clear-cut groups which was 92% matched with the geographical... 

    Portfolio Value-at-Risk and expected-shortfall using an efficient simulation approach based on Gaussian Mixture Model

    , Article Mathematics and Computers in Simulation ; Volume 190 , 2021 , Pages 1056-1079 ; 03784754 (ISSN) Seyfi, S. M. S ; Sharifi, A ; Arian, H ; Sharif University of Technology
    Elsevier B.V  2021
    Abstract
    Monte Carlo Approaches for calculating Value-at-Risk (VaR) are powerful tools widely used by financial risk managers across the globe. However, they are time consuming and sometimes inaccurate. In this paper, a fast and accurate Monte Carlo algorithm for calculating VaR and ES based on Gaussian Mixture Models is introduced. Gaussian Mixture Models are able to cluster input data with respect to market's conditions and therefore no correlation matrices are needed for risk computation. Sampling from each cluster with respect to their weights and then calculating the volatility-adjusted stock returns leads to possible scenarios for prices of assets. Our results on a sample of US stocks show that... 

    Customer Churn Prediction in Therapy Policy in Iran

    , M.Sc. Thesis Sharif University of Technology Khosravi, Mahnaz (Author) ; Aslani, Shirin (Supervisor) ; Arian, Hamidreza (Supervisor)
    Abstract
    Using customer relationship management systems has led to the formation of databases containing businesses customer information. Increased competition in the markets, limited resources and high costs of attracting new customers compared to retaining existing customers have made customer retention an inevitable subject for business owners. Meanwhile, insurance companies, which due to their long history have suitable databases of product information and their customers, have resorted to using their databases to manage the relationship with their customers. In this research, while predicting customers churn in health insurance, as one of the most widely used products of domestic insurance... 

    Comparative evaluation of advanced gas turbine cycles with modified blade cooling models [electronic resource]

    , Article Proceedings of the ASME Turbo Expo ; Volume 4, Pages 537-546 , 2006 Tabari, A ; Sharif University of Technology
    2006
    Abstract
    Advanced gas turbine cycles use advanced blade cooling technologies to reach high turbine inlet temperature. Accurate modeling and optimization of these cycles depend on blade cooling model. In this study, different models have been used to simulate gas turbine performance. The first model is the continuous model and the second is stage-by-stage model with alternative methods for calculating coolant, stagnation pressure loss and SPR. Variation of specific heat and enthalpy with temperature are included in both models. The composition of gas stream in turbine is changed step by step due to air cooling. These models are validated by two case study gas turbine results, which show good agreement... 

    Estimation of a Portfolio's Value-at-Risk Using Variational Auto-Encoders

    , M.Sc. Thesis Sharif University of Technology Moghimi, Mehrdad (Author) ; Arian, Hamidreza (Supervisor) ; Talebian, Masoud (Supervisor)
    Abstract
    One of the most crucial aspects of financial risk management is risk measurement. Advanced AI-based solutions can provide the proper tools for assessing global markets, given the complexity of the global economy and the violation of typical modeling assumptions. A new strategy for quantifying stock portfolio risk based on one of the machine learning models known as Variational Autoencoders is provided in this dissertation. The suggested method is a generative model that can learn the stocks' dependency structure without relying on assumptions about stock return covariance and produce various market scenarios using cross-sectional stock return data with a higher signal-to-noise ratio. We... 

    Development and Application of Pattern Recognition Methods Combined with FT-IR Spectroscopy and HPTLC Techniques to Detect the Type and Amount of Fraud in Iranian Saffron Sample

    , M.Sc. Thesis Sharif University of Technology Amirvaresi, Arian (Author) ; Parastar, Hadi (Supervisor) ; Daraei, Bahram (Supervisor) ; Amir Ahmadi, Maryam (Co-Supervisor)
    Abstract
    Nowadays, with increasing concerns about health of food and its influence on human health,food authenticity has become a vital issue and a major challenge for consumers and regulatory agencies. It's especially remarkable about food with high nutritional and economical value. On the other hand, solving these challenges necessitates the use of fast and reliable techniques.Among the various foods, saffron is the main candidate for food adulteration due to its low production and high economic value and from this viewpoint it's in fourth place. Since the scope of fraud in this food is vast and also Iran is the major producer of saffron in the world,the present project has highlighted the... 

    Portfolio Management: Combining Hierarchical Models with Prior Hierarchical Structure

    , M.Sc. Thesis Sharif University of Technology Shahryarpoor, Farhad (Author) ; Arian, Hamid Reza (Supervisor) ; Zamani, Shiva (Supervisor)
    Abstract
    I investigate methods of integrating prior hierarchical structure into hierarchical portfolio optimization methods. My contributions to the literature are forming a prior hierarchical structure based on investors' priorities and generating a unique representative distance matrix, which can be used as an input to other portfolio optimization methods too. In addition, I use SIC and GICs industry classifications as priory information for S&P500 companies and use them as a complementary input to the Hierarchical Risk Parity model and Hierarchical Equal Risk Contribution and compare the resultant portfolios' performance with (López de Prado, 2019)’s method of integrating prior information and... 

    Integrating Supervised and Unsupervised Machine Learning Algorithms for Profit-based Credit Scoring

    , M.Sc. Thesis Sharif University of Technology Mehrabi, Amir (Author) ; Arian, Hamid Reza (Supervisor) ; Zamani, Shiva (Supervisor)
    Abstract
    In this study, we combined supervised and unsupervised machine learning algorithms, included the benefits of true identification of good borrowers and costs of false identification of bad borrowers, and then proposed a model for predicting the default of loan applicants with a profit-based approach. The results show that our proposed model has the best performance in profit measure in comparison with individual supervised models. In fact, we first divided the data into two train sets and one test set. We have constructed our model by training unsupervised models on the first train set and supervised models on the second train set. The results of implementing the model on the Australian and... 

    Investor Experience in Chabahar Free Zone: Scale Generation

    , M.Sc. Thesis Sharif University of Technology Pourvaziri, Rouhollah (Author) ; Najmi, Manoochehr (Supervisor) ; Arian, Hamidreza (Supervisor)
    Abstract
    Free economic zones in Iran have been established with the goal of developing infrastructure, improving living conditions, fostering economic growth, attracting capital and increasing income, productive job creation, regulating job and goods market, maintaining active presence in international and regional markets, manufacturing and exporting of goods and providing public services. Yet many free economic zones have fallen short of these goals. Considering the government's inability to fund the development of the free economic zones with its limited financial capacity and shortcomings in terms of management system, it is imperative to the development of these regions that private sector... 

    Calculating Value at Risk for Bond Portfolios by Selecting Basic Scenarios in the Historical Simulation Method

    , M.Sc. Thesis Sharif University of Technology Chaghazardi, Ali (Author) ; Zamani, Shiva (Supervisor) ; Arian, Hamid Reza (Supervisor)
    Abstract
    In many methods of calculating Value-at-Risk (VaR), we need to calculate the value of the portfolio several times for different scenarios. Because an explicit formula is not available to calculate the value of some fixed income assets, calculating VaR for portfolios containing these assets imposes a heavy computational burden. In this study, we introduce a new method for calculating VaR for such portfolios. In this method, some of the existing scenarios are selected as basic scenarios and the value of the portfolio is calculated only for each of them. Next, using the calculated values, the portfolio values for other scenarios are estimated by interpolation (or extrapolation). Finally, by... 

    Solving High Dimensional PDEs with Machine Learning Methods and Its Application in Option Pricing

    , M.Sc. Thesis Sharif University of Technology Aghapour, Ahmad (Author) ; Arian, Hamid Reza (Supervisor) ; Zamani, Shiva (Supervisor)
    Abstract
    differential equations. These methods have extensive applications in the financial domain, including in risk hedging and derivative pricing. One of the main advantages of using deep learning methods in this area is their high capability to solve high-dimensional problems. Despite the introduction of these new methods, some of them have not performed well in certain problems. In this research, efforts have been made to improve the efficiency of these methods by enhancing the neural network architecture and modifying the learning process  

    Patch testing in Iranian children with allergic contact dermatitis

    , Article BMC Dermatology ; Volume 16, Issue 1 , 2016 ; 14715945 (ISSN) Mortazavi, H ; Ehsani, A ; Sajjadi, S. S ; Aghazadeh, N ; Arian, E ; Sharif University of Technology
    BioMed Central Ltd  2016
    Abstract
    Background: Allergic contact dermatitis is a common disorder in adults and children alike and appears to be on the increase. The purpose of this study was to determine the sensitization trends in Iranian children with contact dermatitis. Methods: The result of 109 patch tests performed using the 24 allergens of the European Standard Series in patients below 18 years old from September 2007 to March 2009 were recorded and analyzed. The tests were evaluated at 48 and 72 h after performing. Results: The study population consisted of 72 (66.1 %) females and 37 (33.9 %) males. Hands were the most commonly affected anatomic site. In the final evaluation of the tests on day three, 51 (46.8 %)... 

    Optimal Distance Calculation Method for Portfolio Optimization using
    Nested Cluster Optimization

    , M.Sc. Thesis Sharif University of Technology Rafatnezhad, Ramtin (Author) ; Arian, Hamid Reza (Supervisor) ; Zamani, Shiva (Supervisor)
    Abstract
    In the basic model of this thesis, which is called nested cluster optimization, only one distance function is used for clustering to form clusters with similar characteristics, while depending on whether the optimization model is long-only or long-short, different functions can be used. The aim of this thesis is to find the optimal distance function between assets in the simple nested cluster optimization so that during three different and separate strategies, based on three criteria of the lowest risk, the highest Sharpe ratio, and the highest return, the optimal distance function of assets is selected and clustering and finally weighting the portfolio to be done. The optimal distance... 

    Bitcoin Price Prediction based on Artificial Intelligence Models

    , M.Sc. Thesis Sharif University of Technology Shadkam, Mohammad Saeed (Author) ; Arian, Hamid Reza (Supervisor) ; Talebian, Masoud (Supervisor)
    Abstract
    Cryptocurrencies (cryptos), as a new type of money, are considered a medium of exchange, an investment asset, and a hedging tool in today's world. In 2008, bitcoin as the first cryptocurrency was introduced, which has survived through recent years and has gained more and more popularity every day. Cryptos are one of the first applications of blockchain, the technology that many expect to revolutionize the future world in different ways. We aim to investigate what affects the bitcoin price, based on artificial intelligence and, in particular, machine learning. First, we find features impacting bitcoin price via a thorough investigation of the literature. Then, applying machine learning and...