Search for: regression-analysis
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    Meta modelling of job satisfaction effective factors for improvement policy making in organizations

    , Article Benchmarking ; Volume 23, Issue 2 , 2016 , Pages 388-405 ; 14635771 (ISSN) Fazlollahtabar, H ; Mahdavi, I ; Mahdavi Amiri, N ; Sharif University of Technology
    Emerald Group Publishing Ltd  2016
    Purpose – The purpose of this paper is to propose a Meta modeling based on regression, neural network, and clustering to analyze the job satisfaction factors and improvement policy making. Design/methodology/approach – Since any job satisfaction evaluation supposes to improve the status by prescribing specific strategies to be performed in the organization, proposing applicable strategies is decisively important. Task demand, social structure and leader-member exchange (LMX) are general applications easily conceptualized while proposing job satisfaction improvement strategies. Findings – On the basis of these empirical findings, the authors first aim to identify relationships between LMX,... 

    Correlation of specific ampere draw with rock brittleness indexes in rock sawing process

    , Article Archives of Mining Sciences ; Volume 56, Issue 4 , 2011 , Pages 777-788 ; 08607001 (ISSN) Mikaeil, R ; Ozcelik, Y ; Ataei, M ; Yousefi, R ; Sharif University of Technology
    The aim of this study is to investigate the effect of the rock brittleness indexes on the specific ampere draw of the circular diamond saws. In this study, twelve rock types belonging to granite and carbonate rock were cut with two different types of circular diamond saws on a fully instrumented laboratory-sawing rig at different feed rate and depths of cut. Rock samples were collected from factories from Iran for laboratory tests and uniaxial compressive strength and tensile strength were determined. The brittleness of B 1 (the ratio of compressive strength to tensile strength), B 2 (the ratio of compressive strength minus tensile strength to compressive strength plus tensile strength), and... 

    Application of neural networks in the estimation of two-dimensional target orientation

    , Article Applied Computational Electromagnetics Society Journal ; Volume 18, Issue 2 , 2003 , Pages 121-127 ; 10544887 (ISSN) Kabiri, A ; Sarshar, N ; Barkeshli, K ; Sharif University of Technology
    A new method for the robust estimation of target orientation using measured radar cross section is proposed. The method is based on a Generalized Regression Neural Network (GRNN) scheme. The network is trained by the FFT modulus of bistatic radar cross section data sampled at the receiver positions. The target value to be trained is the angle between a defined target orientation and the incident wave. Results based on actual measurements are presented  

    Use of self-training artificial neural networks in modeling of gas chromatographic relative retention times of a variety of organic compounds

    , Article Journal of Chromatography A ; Volume 945, Issue 1-2 , 2002 , Pages 173-184 ; 00219673 (ISSN) Jalali Heravi, M ; Garkani Nejad, Z ; Sharif University of Technology
    A quantitative structure-activity relationship study based on multiple linear regression (MLR), artificial neural network (ANN), and self-training artificial neural network (STANN) techniques was carried out for the prediction of gas chromatographic relative retention times of 13 different classes of organic compounds. The five descriptors appearing in the selected MLR model are molecular density, Winer number, boiling point, polarizability and square of polarizability. A 5-6-1 ANN and a 5-4-1 STANN were generated using the five descriptors appearing in the MLR model as inputs. Comparison of the standard errors and correlation coefficients shows the superiority of ANN and STANN over the MLR... 

    Correlation of production rate of ornamental stone with rock brittleness indexes

    , Article Arabian Journal of Geosciences ; Volume 6, Issue 1 , 2013 , Pages 115-121 ; 18667511 (ISSN) Mikaeil, R ; Ataei, M ; Yousefi, R ; Sharif University of Technology
    The prediction of production rate in ornamental stones sawing is very important in cost estimation and process planning of the rock sawing plants. The main aim of this paper is finding a mathematical correlation between production rate and rock brittleness indexes. The utilized data have been collected from several stone factories in Iran. Seventeen different granite and carbonate rocks have been experienced sawing conditions with large-diameter circular saws. The laboratory tests such as uniaxial compressive strength and tensile strength, were carried out on the rock samples which were collected from these factories. The ratio of compressive strength to tensile strength (B1), the ratio of... 

    Rotated general regression neural network

    , Article 2007 International Joint Conference on Neural Networks, IJCNN 2007, Orlando, FL, 12 August 2007 through 17 August 2007 ; 2007 , Pages 1959-1964 ; 10987576 (ISSN) ; 142441380X (ISBN); 9781424413805 (ISBN) Gholamrezaei, M ; Ghorbanian, K ; Sharif University of Technology
    A rotated general regression neural network is presented as an enhancement to the general regression neural network. A variable kernel estimate for multivariate densities is considered. A coordinate transformation is adopted which circumvent the difficulty of predicting multimodal distribution with large variance differences between modes which is associated with the general regression neural network. The proposed technique trains the network in a way that the variance differences between modes is kept small and in the same order. Further, the technique reduces the number of indispensable training parameters to two parameters and lowers the load of the computation as well as the time for... 

    A genetic algorithm approach to find the best regression/econometric model among the candidates

    , Article Applied Mathematics and Computation ; Volume 183, Issue 1 , 2006 , Pages 337-349 ; 00963003 (ISSN) Hasheminia, H ; Akhavan Niaki, T ; Sharif University of Technology
    Although statistical modeling is a common task in different fields of science, it is still difficult to estimate the best model that can accurately describe inherent characteristics of a system for which historical or experimental data are available. Since we may classify estimating techniques as optimizations, we can model this problem as an optimization problem and solve it by a new heuristic algorithm like neural networks, genetic algorithms, and tabu search or by classic ones such as regression and econometric models. In this paper, we propose a new type of genetic algorithm to find the best regression model among all suggested and evaluate its performances by an economical case study. ©... 

    Optimal synthesis of planar and spatial mechanism for path generation using regression deviation

    , Article Scientia Iranica ; Volume 12, Issue 2 , 2005 , Pages 190-198 ; 10263098 (ISSN) Zohoor, H ; Tavakoli Nia, H ; Sharif University of Technology
    Sharif University of Technology  2005
    This method introduces the structural error of regression deviation, which is an effective method for the path generation of a vast type of planar and spatial mechanism. The proposed method avoids point-by-point comparison and requirement of timing and reflects the difference between the two curves very effectively in the objective function. By decreasing the number of the design variables, this method would help considerably in decreasing CPU time. The objective function that is based on regression error would converge to a global minimum by a genetic algorithm. At the end, the effectiveness of the method is shown by two numerical examples. © Sharif University of Technology  

    Prediction of electrophoretic mobilities of alkyl- and alkenylpyridines in capillary electrophoresis using artificial neural networks

    , Article Journal of Chromatography A ; Volume 971, Issue 1-2 , 2002 , Pages 207-215 ; 00219673 (ISSN) Jalali Heravi, M ; Garkani Nejad, Z ; Sharif University of Technology
    The electrophoretic mobilities of 31 isomeric alkyl- and alkenylpyridines in capillary electrophoresis were predicted using an artificial neural network (ANN). The multiple linear regression (MLR) technique was used to select the descriptors as inputs for the artificial neural network. The neural network is a fully connected back-propagation model with a 3-6-1 architecture. The results obtained using the neural network were compared with those obtained using the MLR technique. Standard error of training and standard error of prediction were 6.28 and 5.11%, respectively, for the MLR model and 1.03 and 1.20%, respectively, for the ANN model. Two geometric parameters and one electronic... 

    Develop a Fuzzy System Based on Evolutionary Algorithms To Predict Stock Market

    , M.Sc. Thesis Sharif University of Technology Kazemi, Mohammad Reza (Author) ; Kianfar, Farhad (Supervisor)
    today's financial markets such as stock market are more attractive and important position and wealth are considered income and therefore attracts many people have. But the other hand, activity in these markets requires a high risk of admission. The point that is important is that the risk of investing in these markets can be predicted to some extent with the trend of stocks and securities can be controlled. Time series trend of stock prices and non-static characters is excited. But analysis of such behavior is impossible, i.e., reliance on sophisticated tools and of course accept the possibility of an error can be predicted price to pay. Synthetic models of artificial intelligence today, due... 

    The Relationship Between Reading Comprehension Ability and Critical Thinking: Can it Predict Reading Comprehension Success?

    , M.Sc. Thesis Sharif University of Technology Movasagh, Hossein (Author) ; Barzabadi, Davoud (Supervisor) ; Khosravizade, Parvane (Supervisor)
    The present study was aimed at investigating the relationship between reading comprehension ability as probably the most vital language skill especially in academic contexts (Farrell, 2009) and critical thinking as one of the most debated issues among scholars in modern education (Ku, 2009; Rudd, 2006). To this end, 200 male and female university students studying English Translation and Literature were chosen as the participants who sat for two tests. The first was a reading section of a retired TOEFL test provided by educational testing service in which the participants' scores were regarded as their reading comprehension ability. The second was the California Critical Thinking Skills... 

    , M.Sc. Thesis Sharif University of Technology Solaymanian, Miremad (Author) ; Mahlooji, Hashem (Supervisor)
    In some statistical process control applications, quality of a process or product 1s characterized by a relationship between a response variable and one or more explanatory variables which is referred to as profile by researchers. In some applications such as calibration, this relationship is characterized by a simple linear regression. However, in some situations, more complicated models are needed. It seems that there is a little attention to monitoring of profiles with binary response variables. Furthermore, the extensive applications of binary response variables in real industrial worlds make it necessary to concentrate on this kind of profiles. In this ... 

    Modeling & Control of HIV by Computational Intelligence Techniques

    , M.Sc. Thesis Sharif University of Technology Bazyar Shourabi, Neda (Author) ; Kazemzadeh Hannani, Siamk (Supervisor) ; Seyfipour, Navid (Supervisor)
    An event must be modeled in a way that either reflects a comprehensive perspective for the event or acts in an especial part of that event, roperly. The main aim of this thesis is to control. Therefore the models will be accepted whenever the suggested mathematical models can move towards controller final target, desirably.This thesis proposes a linear mathematical model, five nonlinear models and a simple model based upon Convolution, Fuzzy Regression and Neural Networks techniques for Acquired Immune Deficiency Syndrome (AIDS), respectively. The proposed models were achieved through studying 300 HIV+ Patients who were under Highly Active Antiretroviral Therapy (HAART) approach in Iranian... 

    Performance assessment and optimization of a humidification dehumidification (HDH) system driven by absorption-compression heat pump cycle

    , Article Desalination ; Volume 447 , 2018 , Pages 84-101 ; 00119164 (ISSN) Rostamzadeh, H ; Shekari Namin, A ; Ghaebi, H ; Amidpour, M ; Sharif University of Technology
    Elsevier B.V  2018
    Feasibility investigation of a humidification dehumidification (HDH) desalination system driven by an absorption-compression heat pump cycle (ACHPC) is carried out in this paper. The proposed hybrid desalination system uses both heating capacity of the discharged brine as a waste heat for the ACHPC and mechanical power of the ACHPC for the HDH system. The system's performance is investigated under different optimal design modes, using genetic algorithm (GA) as the most robust tool for optimization. A steady-state mathematical model based on the mass, energy, exergy, and exergoeconomic balance equations for components of the proposed system is developed and the predicted results are validated... 

    Axial compressor performance map prediction using artificial neural network

    , Article 2007 ASME Turbo Expo, Montreal, Que., 14 May 2007 through 17 May 2007 ; Volume 6 PART B , 2007 , Pages 1199-1208 ; 079184790X (ISBN); 9780791847909 (ISBN) Ghorbanian, K ; Gholamrezaei, M ; Sharif University of Technology
    The application of artificial neural network to compressor performance map prediction is investigated. Different types of artificial neural network such as multilayer perceptron network, radial basis function network, general regression neural network, and a rotated general regression neural network proposed by the authors are considered. Two different models are utilized in simulating the performance map. The results indicate that while the rotated general regression neural network has the least mean error and best agreement to the experimental data, it is however limited to curve fitting application. On the other hand, if one considers a tool for curve fitting as well as for interpolation... 

    Kinetic investigation of NDMA to UDMH hydrogenation on a pd/C catalyst

    , Article Iranian Journal of Science and Technology, Transaction B: Engineering ; Volume 30, Issue 5 , 2006 , Pages 581-593 ; 03601307 (ISSN) Gorji, M ; Kazemeini, M ; Bozorgmehri, R ; Sharif University of Technology
    Unsymmetrical dimethyl hydrazine (UDMH) is a strong propellant, which due to its very good physical properties and high power of repellency has been utilized as a liquid fuel for fighter jet engines for so many years. There are different methods for production of this material. One of the more efficient ones which results in higher yields compared to others is the catalytic hydrogenation of Nitroso Dimethylamine (NDMA). In this work hydrogenation of NDMA to UDMH on a 5% Pd/C in aqueous solution of NDMA was studied experimentally. Experiments were carried out in a Semi-batch three phase STR reactor under constant pressure and temperature in the range of 40 to 70°C, pressures of up to 15 bar... 

    Prediction of relative response factors for flame ionization and photoionization detection using self-training artificial neural networks

    , Article Journal of Chromatography A ; Volume 950, Issue 1-2 , 2002 , Pages 183-194 ; 00219673 (ISSN) Jalali Heravi, M ; Garkani Nejad, Z ; Sharif University of Technology
    The relative response factors (RRFs) of a flame ionization detection (FID) system and two pulsed discharge photoionization detection (PID) systems with different discharge gases are predicted for a set of organic compounds containing various functional groups. As a first step, numerical descriptors were calculated based on the molecular structures of compounds. Then, multiple linear regression (MLR) was employed to find informative subsets of descriptors that can predict the RRFs of these compounds. The selected MLR model for the FID system includes seven descriptors and two selected MLR models for the PID systems with argon- and krypton-doped helium as the discharge gases, respectively,... 

    Investigating the Relationship Between Measured Parameters by Satellite and Ground-Level Concentrations of PM

    , M.Sc. Thesis Sharif University of Technology Sotoudeheian, Saeed (Author) ; Arhami, Mohammad (Supervisor)
    Obtaining particulate matter (PM) concentration is very important in epidemiological studies. Measurement at the ground levels has been used as an accurate method to obtain PM levels. However, these measurements are more indicative of a small area around the stations than a whole region. Usually, limited space coverage and irregular distribution of air quality stations at the ground level is a restriction in the studies of air pollution and its effect on human health and environment. In this regard satellite measurements have been used for indirect estimation of PM concentration at ground levels. However, the correlation between satellite measurements and ground based data is affected by... 

    Long-Term Water Demand Forecasting for the Tehran City under Uncertainties

    , M.Sc. Thesis Sharif University of Technology Miraki, Ghasem (Author) ; Abrishamchi, Ahmad (Supervisor)
    Forecasting model of water consumption amounts could be used in order to manage water resources for future condition of city. In this thesis, a model for forecasting water demand for Tehran has been presented by evaluating regression models and intelligent models. In this study, uncertainties which are connected to climate and population changes are taken into account. The considered variables include minimum, maximum and medium temperature, precipitation and solar radiation. Considering objectives of this thesis and various forecasting methods and their advantages and regional conditions of Tehran, in addition to regression analysis, perceptron neural network, probabilistic neural network... 

    Predictive Process Control Using a Hierachical Method Based on Regression Analysis and Artificial Neural Networks (case study: Spray Drying in Tile Industry)

    , M.Sc. Thesis Sharif University of Technology Neshat, Najmeh (Author) ; Mahlooji, Hashem (Supervisor)
    This is the first attempt at process modeling in terms of predictive control using a hierachical method based on regression analysis and artificial neural networks(ANNs).This hierachical use leads to the reliability improvement of neural model of process in prediction (extrapolation and interpolation) of process output. such an outlook makes it possible to predict the proper input settings which achieved a desired process output by designing various senarios for process set up. This approach was applied in Tile industry for spray dring process and in order to indicate the achieved improvement,three models:(i) regression model of process using multiple linear regression,(ii)Neural model of...