Search for: multi-layer-perceptrons
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    Detecting and estimating the time of a single-step change in nonlinear profiles using artificial neural networks

    , Article International Journal of Systems Assurance Engineering and Management ; 2021 ; 09756809 (ISSN) Ghazizadeh, A ; Sarani, M ; Hamid, M ; Ghasemkhani, A ; Sharif University of Technology
    Springer  2021
    This effort attempts to study the change point problem in the area of non-linear profiles. A method based on Artificial Neural Networks (ANN) is proposed for estimating the real time of a single step change. The feature vector of the proposed Multi-Layer Perceptron (MLP) is based on Z and control chart statistics for nonlinear profiles. The merits of the proposed estimator are evaluated through simulation experiments. The results show that the estimator provides an accurate estimate of the single step change point in non-linear profiles in the selected case problem. © 2021, The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of... 

    Prediction of CO2 loading capacity of chemical absorbents using a multi-layer perceptron neural network

    , Article Fluid Phase Equilibria ; Volume 354 , September , 2013 , Pages 6-11 ; 03783812 (ISSN) Bastani, D ; Hamzehie, M. E ; Davardoost, F ; Mazinani, S ; Poorbashiri, A ; Sharif University of Technology
    A feed forward multi-layer perceptron neural network was developed to predict carbon dioxide loading capacity of chemical absorbents over wide ranges of temperature, pressure, and concentration based on the molecular weight of solution. To verify the suggested artificial neural network (ANN), regression analysis was conducted on the estimated and experimental values of CO2 solubility in various aqueous solutions. Furthermore, a comparison was performed between results of the proposed neural network and experimental data that were not previously used for network training, as well as a set of data for binary solutions. Comparison between the proposed multi-layer perceptron (MLP) network and... 

    A complexity-based approach in image compression using neural networks

    , Article World Academy of Science, Engineering and Technology ; Volume 35 , 2009 , Pages 684-694 ; 2010376X (ISSN) Veisi, H ; Jamzad, M ; Sharif University of Technology
    In this paper we present an adaptive method for image compression that is based on complexity level of the image. The basic compressor/de-compressor structure of this method is a multilayer perceptron artificial neural network. In adaptive approach different Back-Propagation artificial neural networks are used as compressor and de-compressor and this is done by dividing the image into blocks, computing the complexity of each block and then selecting one network for each block according to its complexity value. Three complexity measure methods, called Entropy, Activity and Pattern-based are used to determine the level of complexity in image blocks and their ability in complexity estimation... 

    Remote Sensing of Hyperspectral Images for Detection Surface Mines

    , M.Sc. Thesis Sharif University of Technology Motahari Kelarestaghi, Alireza (Author) ; Amini, Arash (Supervisor)
    Hyperspectral unmixing (HU) is a method used to estimate the fractional abundances corresponding to endmembers in each of the mixed pixels in the hyperspectral remote sensing image. In recent times, deep learning has been recognized as an effective technique for hyperspectral image classification. In this thesis, an end-to-end HU method is proposed based on the convolutional neural network (CNN) and multi-layer perceptron (MLP). which consists of two steps: the first stage extracts features from the input data along with the inverse learning of the spectral library matrix in the hyperspectral image where columns represent the pure spectral of endmembers and The second stage is to estimate... 

    Implementation of Optical Character Recognition with Deep Learning

    , M.Sc. Thesis Sharif University of Technology Samangouei, Mohammad (Author) ; Bagheri Shouraki, Saeed (Supervisor)
    Optical character recognition (OCR) method has been used in converting printed text into editable text. OCR is very usefuland popular method in various applications. Accuracy of OCRn can be dependent on text preprocessing and segmentation algorithms. Sometimes it is difficult to retrieve text from the image because of different size, style, orientation, complex background of image etc. and Deep learning is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using multiple processing layers, with complex structures or otherwise, composed of multiple non-linear transformations and a deep belief network (DBN) is a probabilistic ,... 

    Two new methods for DNA splice site prediction based on neuro-fuzzy network and clustering

    , Article Neural Computing and Applications ; Volume 23, Issue SUPPL1 , 2013 , Pages 407-414 ; 09410643 (ISSN) Moghimi, F ; Manzuri Shalmani, M. T ; Khaki Sedigh, A ; Kia, M ; Sharif University of Technology
    Nowadays, genetic disorders, like cancer and birth defects, are a great threat to human life. Since the first noticing of these types of diseases, many efforts have been made and researches performed in order to recognize them and find a cure for them. These disorders affect genes and they appear as abnormal traits in a genetic organism. In order to recognize abnormal genes, we need to predict splice sites in a DNA signal; then, we can process the genetic codes between two continuous splice sites and analyze the trait that it represents. In addition to abnormal genes and their consequent disorders, we can also identify other normal human traits like physical and mental features. So the... 

    Removing undesired effects of mass/inertia on transparency using artificial neural networks in a haptic mechanism

    , Article ICCAS 2010 - International Conference on Control, Automation and Systems, 27 October 2010 through 30 October 2010, Gyeonggi-do ; 2010 , Pages 156-161 ; 9781424474530 (ISBN) Khodabakhsh, M ; Boroushaki, M ; Vossoughi, G ; Sharif University of Technology
    In this paper, Artificial Neural Networks (ANN) has been used to identify the dynamics of robots used in haptic and master slave devices in order to improve transparency. In haptic and master slave devices, transparency depends on some factors such as robot's mass and inertia, gravitational forces and friction [1]. In such systems, mass and inertia of the robot has an undesirable effect on the system outputs, which should be neutralized for improved transparency. The main purpose of this paper introducting a method to neutralize the undesirable effects of mass and inertia of the robot. A recurrent multilayer perceptron (RMLP) is used in a way that the inputs and outputs of the neural network... 

    Reduced complexity enhancement of steganalysis of LSB-matching image steganography

    , Article 7th IEEE/ACS International Conference on Computer Systems and Applications, AICCSA-2009, Rabat, 10 May 2009 through 13 May 2009 ; 2009 , Pages 1013-1017 ; 9781424438068 (ISBN) Malekmohamadi, H ; Ghaemmaghami, S ; Sharif University of Technology
    We propose a method for steganalysis of still, grayscale images using a novel set of features that are extracted from images. This feature set employs the Gabor filter coefficients to train a multi-layer perceptron neural network and a support vector machine classifier. We show that incorporation of the Gabor filter coefficients to the feature sets of images could have a significant role in discrimination between clean and altered images. Experimental results show that the proposed method outperforms previous methods, introduced for steganalysis of LSB-matching image steganography, in terms of both discrimination accuracy and feature set dimensionality. © 2009 IEEE  

    Cerebrovascular Attack Detection Using Artificial Intelligent Neural Network

    , M.Sc. Thesis Sharif University of Technology Bagheri, Mahdi (Author) ; Bagheri Shouraki, Saeed (Supervisor) ; Haj Sadeghi, Khosrow (Co-Advisor)
    Cerebrovascular Attack has been ranked the second or third of top 10 death causes in Taiwan. It has caused about 13,000 deaths every year since 1986. Once Cerebrovascular Attack (CVA) occurs, it not only leads to the huge cost of medical care, but even death. All developed countries in the world put CVA prevention and treatment in high priority. However, it is necessary to build a detective model to enhance the accurate diagnosis of CVA. From this detective model, CVA classification rules were extracted and used to improve the diagnosis and detection of CVA. This study acquired 2449 valid samples from this CVA prevention and treatment program, and adopted three classification algorithms,... 

    Intelligent Diagnosis of Cardiovascular Disease using ECG Signals

    , M.Sc. Thesis Sharif University of Technology Baghdadi, Fatemeh (Author) ; Haj Sadeghi, Khosrow (Supervisor)
    Cardiovascular diseases (CVDs) have ranked first cause of deaths globally. In 2016, about 17.7 million people died from CVDs representing 31% of all world deaths. So, early intelligent detection of cardiovascular disease could help to save many lives in worldwide. There are several methods to analyze heart activity and to detect any abnormalities including Electrocardiogram, Stress test, Echocardiography, cardiac catheterization and coronary angiography.Among all methods, Electrocardiogram (ECG) is the most common and convenient type where it measures heart electrical activity and records it as a series of pulses. Analyzing these pulses would provide useful information about normal and... 

    , M.Sc. Thesis Sharif University of Technology Allah Yari, Mahdi (Author) ; Soltanieh, Mohammad (Supervisor) ; Moslehi Moslehabadi, Parivash (Supervisor)
    Air pollution caused by industrialization is the problem which adversely affects human life. Among air pollutants suspended particles, especially particles smaller than 10 microns (PM10), for their high concentration in air in large cities are the major index as air pollutant. Due to their small size, PM10 can penetrate into the aspiration organs causing harmful effects. The objective of this work is to develop an Artificial Neural Network (ANN) model for prediction of short-term concentration of PM10 in the city of Tehran. Complex mechanism of reactions, numerous types of pollutant materials produced from transportation and industrial activities, variety of sources, difficulties in data... 

    The Application of Signal Processing in Oil Well Logging

    , M.Sc. Thesis Sharif University of Technology Tahmasebi Moradi, Faezeh (Author) ; Hajsadeghi, Khosro (Supervisor)
    Determining the porosity, water saturation and permeability values in the reservoir rock, are major steps in the petroleum engineering and formation evaluation. Today in the oil industry, these parameters are obtained using Helium gas injection technique on core (Plug) samples. However, the coring operation is difficult and costly. In addition, there isn't the possibility of coring in some of wells, such as horizontal wells. In fact, the present research is the modelling of artificial neural networks to estimate permeability in one of the Iranian oil field reservoirs by using oil well logging data. In this research, MLP neural network has been used and for network training, the evolutionary... 

    Improving Artificial Neural Network Predictive Performance Using Panel Data

    , M.Sc. Thesis Sharif University of Technology Alirezaei, Hamid Reza (Author) ; Khedmati, Majid (Supervisor) ; Rafiee, Majid (Supervisor)
    The purpose of the present study is to develop neural network estimation method for hybrid or panel data which have a combination of two cross-sectional and time series structures and because of the features of both structures, their use in different sciences offers many advantages; and the analytical methods for this data structure are also different from other one-dimensional structures, so different and specific regression models are presented for this data structure. However, in the artificial neural network method, modeling the development for this data structure is neglected, so in the present study, using the concepts of panel regression methods and their application to the... 

    Investigation and Comparison of Data Mining Techniques Used for Pharmaceutical Drug Consumption Pattern Prediction

    , M.Sc. Thesis Sharif University of Technology Bastani Allahabadi, Shahrzad (Author) ; Haji, Alireza (Supervisor) ; Fatahi Valilai, Omid (Co-Supervisor)
    Data mining is the process of extracting information from large data sets using algorithms and methods derived from the field of statistics, machine learning and database management systems. Data mining, popularly known as knowledge discovery in big data, enables companies and organizations to make informed decisions by collecting, aggregating, analyzing and accessing company data. The pharmaceutical industry is one of the most important levels of the drug supply chain, which has a significant impact on the healthcare sector of any society. In this context data mining can be used in various procedure such as discovery of a new medicine, sequential registration of clinical trials, combining... 

    Accelerating Flash Calculations in Compositional Simulators Using Machine Learning Algorithms

    , M.Sc. Thesis Sharif University of Technology Asadian, Amir Hossein (Author) ; Pishvaie, Mahmoud Reza (Supervisor)
    Flash calculations using equations of state are the basis of compositional simulators and inaccuracy in these calculations leads to errors in some time steps or even complete failure of a simulation. One of the problems of this approach is the use of iterative loops, which leads to a high computational cost. The purpose of this research is to use deep machine learning methods, namely multi-layer perceptrons and convolutional neural networks to solve this problem. At first, we defined two 5-component and 10-component hydrocarbon fluid models and used one of the conventional methods of flash calculations (Modified Successive Substitution method) in order to generate the data required for... 

    Data-driven Investigations on Physics and Characteristics of Flow-blurring Spray Using Machine Learning

    , M.Sc. Thesis Sharif University of Technology Vaezi, Erfan (Author) ; Morad, Mohammad Reza (Supervisor)
    This research investigates the break-up physics and spray characteristics of flow-blurring spray by implementing machine learning on numerical and experimental datasets. To do so, five crucial parameters of atomization, including SMD, axial and radial velocity components, penetration length, break-up length, and spray angle, are selected to be studied. Firstly, size and velocity distribution datasets are gathered using available experimental papers. Prior to modeling by Multi-Layer Perceptron neural networks, the datasets were pre-processed in terms of the existence of multi-value and outlier instances. Secondly, the physics of mixing flow inside the injection system was numerically... 

    Introducing neural networks as a computational intelligent technique

    , Article Applied Mechanics and Materials ; Vol. 464 , 2014 , pp. 369-374 ; ISSN: 16609336 Azizi, A ; Entessari, F ; Osgouie, K. G ; Rashnoodi, A. R ; Sharif University of Technology
    Neural networks have been applied very successfully in the identification and control of dynamic systems. The universal approximation capabilities of the multilayer perceptron have made it a popular choice for modeling nonlinear systems and for implementing general-purpose nonlinear controllers. In this paper we try to model and control the mass-spring-damper mechanism as a 1 DOF system using neural networks. The control architecture used in this paper is Model reference controller (MRC) as one of the popular neural network control architectures  

    Nationwide prediction of drought conditions in Iran based on remote sensing data

    , Article IEEE Transactions on Computers ; Vol. 63, issue. 1 , Jan , 2014 , p. 90-101 ; 0018-9340 Jalili, M ; Gharibshah, J ; Ghavami, S. M ; Beheshtifar, M ; Farshi, R ; Sharif University of Technology
    Iran is a country in a dry part of the world and extensively suffers from drought. Drought is a natural, temporary, and iterative phenomenon that is caused by shortage in rainfall, which affects people's health and well-being adversely as well as impacting the society's economy and politics with far-reaching consequences. Information on intensity, duration, and spatial coverage of drought can help decision makers to reduce the vulnerability of the drought-affected areas, and therefore, lessen the risks associated with drought episodes. One of the major challenges of modeling drought (and short-term forecasting) in Iran is unavailability of long-term meteorological data for many parts of the... 

    Bi-level image compression technique using neural networks

    , Article IET Image Processing ; Volume 6, Issue 5 , July , 2012 , Pages 496-506 ; 17519659 (ISSN) Sahami, S ; Shayesteh, M. G ; Sharif University of Technology
    This study presents the utilisation of neural-network for bi-level image compression. In the proposed lossy compression method, the locations of pixels of image are applied to the inputs of a multilayer perceptron neural-network. The output of the network denotes the pixel intensity (0 or 1). The final weights of the trained neural-network are quantised, represented by a few bits, Huffman encoded and then stored as the compressed image. In the decompression phase, by applying the pixels locations to the trained network, the output determines the intensity. The results of experiments on more than 4000 different images indicate higher compression rate of the proposed structure compared with... 

    Modelling correlation between hot working parameters and flow stress of IN625 alloy using neural network

    , Article Materials Science and Technology ; Volume 26, Issue 5 , Jul , 2010 , Pages 621-625 ; 02670836 (ISSN) Montakhab, M ; Behjati, P ; Sharif University of Technology
    In this work, an optimum multilayer perceptron neural network is developed to model the correlation between hot working parameters (temperature, strain rate and strain) and flow stress of IN625 alloy. Three variations of standard back propagation algorithm (Broyden, Fletcher, Goldfarb and Shanno quasi-Newton, Levenberg-Marquardt and Bayesian) are applied to train the model. The results show that, in this case, the best performance, minimum error and shortest converging time are achieved by the Levenberg-Marquardt training algorithm. Comparing the predicted values and the experimental values reveals that a well trained network is capable of accurately calculating the flow stress of the alloy...