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Total 62 records

    Translation-invariant optical neural network for image classification

    , Article Scientific Reports ; Volume 12, Issue 1 , 2022 ; 20452322 (ISSN) Sadeghzadeh, H ; Koohi, S ; Sharif University of Technology
    Nature Research  2022
    Abstract
    The classification performance of all-optical Convolutional Neural Networks (CNNs) is greatly influenced by components’ misalignment and translation of input images in the practical applications. In this paper, we propose a free-space all-optical CNN (named Trans-ONN) which accurately classifies translated images in the horizontal, vertical, or diagonal directions. Trans-ONN takes advantages of an optical motion pooling layer which provides the translation invariance property by implementing different optical masks in the Fourier plane for classifying translated test images. Moreover, to enhance the translation invariance property, global average pooling (GAP) is utilized in the Trans-ONN... 

    Use of artificial neural networks in a QSAR study of Anti-HIV activity for a large group of HEPT derivatives

    , Article Journal of Chemical Information and Computer Sciences ; Volume 40, Issue 1 , 2000 , Pages 147-154 ; 00952338 (ISSN) Jalali Heravi, M ; Parastar, F ; Sharif University of Technology
    American Chemical Society  2000
    Abstract
    Anti-HIV activity for a set of 107 inhibitors of the HIV-1 reverse transcriptase, derivatives of 1-[2-hydroxyethoxy)methyl]-6-(phenylthio)thymine (HEPT), was modeled with the aid of chemometric techniques. The activity of these compounds was estimated by means of multiple linear regression (MLR) and artificial neural network (ANN) techniques and compared with the previous works. The results obtained using the MLR method indicate that the anti-HIV activity of the HEPT derivatives depends on the reverse of standard shadow area on the YZ plane and the ratio of the partial charges of the most positive atom to the most negative atom of the molecule. The best computational neural network model was... 

    Transformer-based deep neural network language models for Alzheimer’s disease risk assessment from targeted speech

    , Article BMC Medical Informatics and Decision Making ; Volume 21, Issue 1 , 2021 ; 14726947 (ISSN) Roshanzamir, A ; Aghajan, H ; Soleymani Baghshah, M ; Sharif University of Technology
    BioMed Central Ltd  2021
    Abstract
    Background: We developed transformer-based deep learning models based on natural language processing for early risk assessment of Alzheimer’s disease from the picture description test. Methods: The lack of large datasets poses the most important limitation for using complex models that do not require feature engineering. Transformer-based pre-trained deep language models have recently made a large leap in NLP research and application. These models are pre-trained on available large datasets to understand natural language texts appropriately, and are shown to subsequently perform well on classification tasks with small training sets. The overall classification model is a simple classifier on... 

    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
    2007
    Abstract
    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... 

    WalkIm: Compact image-based encoding for high-performance classification of biological sequences using simple tuning-free CNNs

    , Article PLoS ONE ; Volume 17, Issue 4 April , 2022 ; 19326203 (ISSN) Akbari Rokn Abadi, S ; Mohammadi, A ; Koohi, S ; Sharif University of Technology
    Public Library of Science  2022
    Abstract
    The classification of biological sequences is an open issue for a variety of data sets, such as viral and metagenomics sequences. Therefore, many studies utilize neural network tools, as the well-known methods in this field, and focus on designing customized network structures. However, a few works focus on more effective factors, such as input encoding method or implementation technology, to address accuracy and efficiency issues in this area. Therefore, in this work, we propose an image-based encoding method, called as WalkIm, whose adoption, even in a simple neural network, provides competitive accuracy and superior efficiency, compared to the existing classification methods (e.g. VGDC,... 

    Average consensus in networks of dynamic multi-agents with switching topology: Infinite matrix products

    , Article ISA Transactions ; Volume 51, Issue 4 , 2012 , Pages 522-530 ; 00190578 (ISSN) Atrianfar, H ; Haeri, M ; Sharif University of Technology
    2012
    Abstract
    This paper deals with the average consensus problem in a multi-agent system with switching interaction topology modeled as a weighted digraph. The convergence analysis is performed in both discrete-time and continuous-time dynamics based on the theory of infinite matrix products. Conditions for system convergence to average consensus are derived in the form of constraints on direct and reverse graphs and the structure of adjacency elements among the agents. Furthermore, a sufficient condition is provided for convergence to average consensus in systems in which the interaction topology is balanced over infinite contiguous non-overlapping time intervals instead of being balanced continuously.... 

    Wavelet packet decomposition of a new filter -based on underlying neural activity- for ERP classification

    , 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 1876-1879 ; 05891019 (ISSN) ; 1424407885 (ISBN); 9781424407880 (ISBN) Raiesdana, S ; Shamsollahi, M. B ; Hashemi, M. R ; Rezazadeh, I ; Sharif University of Technology
    2007
    Abstract
    This paper introduces a wavelet packet algorithm based on a new wavelet like filter created by a neural mass model in place of wavelet. The hypothesis is that the performance of an ERP based BCI system can be improved by choosing an optimal wavelet derived from underlying mechanism of ERPs. The wavelet packet transform has been chosen for its generalization in comparison to wavelet. We compared the performance of proposed algorithm with existing standard wavelets as Db4, Bior4.4 and Coif3 in wavelet packet platform. The results showed a lowest cross validation error for the new filter in classification of two different kinds of ERP datasets via a SVM classifier. © 2007 IEEE  

    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
    Abstract
    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  

    The simulation of microbial enhanced oil recovery by using a two-layer perceptron neural network

    , Article Petroleum Science and Technology ; Vol. 32, Issue. 22 , 2014 , pp. 2700-2707 ; ISSN: 10916466 Morshedi, S ; Torkaman, M ; Sedaghat, M. H ; Ghazanfari M.H ; Sharif University of Technology
    Abstract
    The authors simulated a reservoir by using two-layer perceptron. Indeed a model was developed to simulate the increase in oil recovery caused by bacteria injection into an oil reservoir. This model was affected by reservoir temperature and amount of water injected into the reservoir for enhancing oil recovery. Comparing experimental and simulation results and also the erratic trend of data show that the neural networks have modeled this system properly. Considering the effects of nonlinear factors and their erratic and unknown impacts on recovered oil, the perceptron neural network can develop a proper model for oil recovery factor in various conditions. The neural networks have not been... 

    Bottleneck of using a single memristive device as a synapse

    , Article Neurocomputing ; Volume 115 , September , 2013 , Pages 166-168 ; 09252312 (ISSN) Merrikh Bayat, F ; Bagheri Shouraki, S ; Esmaili Paeen Afrakoti, I ; Sharif University of Technology
    2013
    Abstract
    In this study we will show that the variation rate of the memristance of the memristive device depends completely on its current memristance which means that it can change significantly with time during the learning phase. This phenomenon can degrade the performance of learning methods like Spike Timing-Dependent Plasticity (STDP) and cause the corresponding neuromorphic systems to become unstable  

    QSAR modelling of integrin antagonists using enhanced bayesian regularised genetic neural networks

    , Article SAR and QSAR in Environmental Research ; Volume 22, Issue 3-4 , May , 2011 , Pages 293-314 ; 1062936X (ISSN) Jalali Heravi, M ; Mani Varnosfaderani, A ; Sharif University of Technology
    2011
    Abstract
    Bayesian regularised genetic neural network (BRGNN) has been used for modelling the inhibition activity of 141 biphenylalanine derivatives as integrin antagonists. Three local pattern search (PS) methods, simulated annealing and threshold acceptance were combined with BRGNN in the form of a hybrid genetic algorithm (HGA). The results obtained revealed that PS is a suitable method for improving the ability of BRGNN to break out from the local minima. The proposed HGA technique is able to retrieve important variables from complex systems and nonlinear search spaces for optimisation. Two models with 8-3-1 artificial neural network (ANN) architectures were developed for describingα 4β 7 and α 4β... 

    Fuzzy equations and Z-numbers for nonlinear systems control

    , Article 9th International Conference on Theory and Application of Soft Computing, Computing with Words and Perception, ICSCCW 2017, 22 August 2017 through 23 August 2017 ; Volume 120 , 2017 , Pages 923-930 ; 18770509 (ISSN) Razvarz, S ; Tahmasbi, M ; Sharif University of Technology
    Elsevier B.V  2017
    Abstract
    Various systems with nonlinearity can be modeled by utilizing uncertain linear-in-parameter models. In this paper, the uncertain parameters are in the form of Z-number coefficients. Fuzzy equations are utilized to represent the models of the uncertain nonlinear systems. The solutions associated with fuzzy equations are considered to be controllers while the desired references are outputs. The existence condition associated with the solution is laid down. Two various structure of neural networks are applied for approximating solutions of fuzzy equations with Z-number coefficients. The suggested techniques are validated by implementing an example. © 2018 The Authors. Published by Elsevier B.V  

    Evaluation of a new neutron energy spectrum unfolding code based on an adaptive neuro-fuzzy inference system (ANFIS)

    , Article Journal of Radiation Research ; Volume 59, Issue 4 , 2018 , Pages 436-441 ; 04493060 (ISSN) Hosseini, S. A ; Esmaili Paeen Afrakoti, I ; Sharif University of Technology
    Oxford University Press  2018
    Abstract
    The purpose of the present study was to reconstruct the energy spectrum of a poly-energetic neutron source using an algorithm developed based on an Adaptive Neuro-Fuzzy Inference System (ANFIS). ANFIS is a kind of artificial neural network based on the Takagi-Sugeno fuzzy inference system. The ANFIS algorithm uses the advantages of both fuzzy inference systems and artificial neural networks to improve the effectiveness of algorithms in various applications such as modeling, control and classification. The neutron pulse height distributions used as input data in the training procedure for the ANFIS algorithm were obtained from the simulations performed by MCNPX-ESUT computational code... 

    AQM controller design for networks supporting TCP vegas: A control theoretical approach

    , Article ISA Transactions ; Volume 47, Issue 1 , 2008 , Pages 143-155 ; 00190578 (ISSN) Bigdeli, N ; Haeri, M ; Sharif University of Technology
    ISA - Instrumentation, Systems, and Automation Society  2008
    Abstract
    In this paper, a mathematical model and control theoretical framework for designing AQM controllers in networks supporting TCP Vegas is introduced. We have emphasized on a modified TCP Vegas algorithm that can respond to congestion signals through explicit congestion notification (ECN). The overall nonlinear delayed differential equations of the dynamics model of closed loop system have been derived based on TCP Vegas model. The model is then linearized to derive a transfer function representation between the packet marking probability and the bottleneck router queue length as the input and output of the modified TCP Vegas/AQM system. The model properties have been then examined especially... 

    Adjustable primitive pattern generator: A novel cerebellar model for reaching movements

    , Article Neuroscience Letters ; Volume 406, Issue 3 , 2006 , Pages 232-234 ; 03043940 (ISSN) Vahdat, S ; Maghsoudi, A ; Haji Hasani, M ; Towhidkhah, F ; Gharibzadeh, S ; Jahed, M ; Sharif University of Technology
    2006
    Abstract
    Cerebellum has been assumed as an array of adjustable pattern generators (APGs). In recent years, electrophysiological researches have suggested the existence of modular structures in spinal cord called motor primitives. In our proposed model, each "adjustable primitive pattern generator" (APPG) module in the cerebellum is consisted of a large number of parallel APGs, the output of each module being the weighted sum of the outputs of these APGs. Each spinal field is tuned by a coefficient, representing a descending supraspinal command, which is modulated by ith APPG correspondingly. According to this model, motor control can be interpreted in terms of the modification of these coefficients.... 

    Dynamic simulation of natural gas transmission pipeline systems through autoregressive neural networks

    , Article Industrial and Engineering Chemistry Research ; Volume 60, Issue 27 , 2021 , Pages 9851-9859 ; 08885885 (ISSN) Fakhroleslam, M ; Bozorgmehry Boozarjomehry, R ; Sahlodin, A. M ; Sin, G ; Mansouri, S. S ; Sharif University of Technology
    American Chemical Society  2021
    Abstract
    Transmission of natural gas from its sources to end users in various geographical locations is carried out mostly by natural gas transmission pipeline networks (NGTNs). Effective design and operation of NGTNs requires insights into their steady-state and, particularly, dynamic behavior. This, in turn, calls for efficient computer-aided approaches furnished with accurate mathematical models. The conventional mathematical methods for the dynamic simulation of NGTNs are computationally intensive. In this paper, the use of autoregressive neural networks for cost-effective dynamic simulation of NGTNs is proposed. Considering the length, diameter, roughness, and elevation as the main... 

    COVID-19 diagnosis using capsule network and fuzzy c -means and mayfly optimization algorithm

    , Article BioMed Research International ; Volume 2021 , 2021 ; 23146133 (ISSN) Farki, A ; Salekshahrezaee, Z ; Mohammadi Tofigh, A ; Ghanavati, R ; Arandian, B ; Chapnevis, A ; Sharif University of Technology
    Hindawi Limited  2021
    Abstract
    The COVID-19 epidemic is spreading day by day. Early diagnosis of this disease is essential to provide effective preventive and therapeutic measures. This process can be used by a computer-aided methodology to improve accuracy. In this study, a new and optimal method has been utilized for the diagnosis of COVID-19. Here, a method based on fuzzy C-ordered means (FCOM) along with an improved version of the enhanced capsule network (ECN) has been proposed for this purpose. The proposed ECN method is improved based on mayfly optimization (MFO) algorithm. The suggested technique is then implemented on the chest X-ray COVID-19 images from publicly available datasets. Simulation results are... 

    Simulation of nuclear reactor core kinetics using multilayer 3-D cellular neural networks

    , Article IEEE Transactions on Nuclear Science ; Volume 52, Issue 3 II , 2005 , Pages 719-728 ; 00189499 (ISSN) Boroushaki, M ; Ghofrani, M. B ; Lucas, C ; Sharif University of Technology
    2005
    Abstract
    Different nonelectrical problems can be effectively modeled by their equivalent electrical circuit, using cellular neural network (CNN). Dynamics of such large scale systems with partial differential state equations can be simulated by this technique in real-time. In this paper, we described an originally derived method to model and solve nuclear reactor kinetic equations via multilayer CNN. We proposed an innovative method for online calculation of spatio-temporal distribution of the reactor core neutron flux. One of the main applications of the proposed approach can be development of a new hardware for online simulation and control of nuclear reactor core via very large scale integration... 

    Application of silver nanoparticles and principal component-artificial neural network models for simultaneous determination of levodopa and benserazide hydrochloride by a kinetic spectrophotometric method

    , Article Spectrochimica Acta - Part A: Molecular and Biomolecular Spectroscopy ; Volume 82, Issue 1 , November , 2011 , Pages 25-30 ; 13861425 (ISSN) Tashkhourian, J ; Hormozi Nezhad, M. R ; Khodaveisi, J ; Sharif University of Technology
    2011
    Abstract
    A multicomponent analysis method based on principal component analysis-artificial neural network model (PC-ANN) is proposed for the simultaneous determination of levodopa (LD) and benserazide hydrochloride (BH). The method is based on the reaction of levodopa and benserazide hydrochloride with silver nitrate as an oxidizing agent in the presence of PVP and formation of silver nanoparticles. The reaction monitored at analytical wavelength 440 nm related to surface plasmon resonance band of silver nanoparticles. Differences in the kinetic behavior of the levodopa and benserazide hydrochloride were exploited by using principal component analysis, an artificial neural network (PC-ANN) to resolve... 

    Classification of anti-HIV compounds using counterpropagation artificial neural networks and decision trees

    , Article SAR and QSAR in Environmental Research ; Volume 22, Issue 7-8 , Oct , 2011 , Pages 639-660 ; 1062936X (ISSN) Jalali Heravi, M ; Mani Varnosfaderani, A ; Eftekhar Jahromi, P ; Mohsen Mahmoodi, M ; Taherinia, D ; Sharif University of Technology
    2011
    Abstract
    The main aim of the present work was to collect and categorize anti-HIV molecules in order to identify general structure-activity relationships. In this respect, a total of 5580 drugs and drug-like molecules was collected from 256 different articles published between 1992 and 2010. An algorithm called genetic algorithm-pattern search counterpropagation artificial neural networks (GPS-CPANN) was proposed for the classification of compounds. In addition, the CART (classification and regression trees) method was used for construction of decision trees and finding the best molecular descriptors. The results revealed that the developed CPANN models and decision tree can correctly classify the...