140,960 research outputs found
Study and Observation of the Variations of Accuracies for Handwritten Digits Recognition with Various Hidden Layers and Epochs using Neural Network Algorithm
In recent days, Artificial Neural Network (ANN) can be applied to a vast
majority of fields including business, medicine, engineering, etc. The most
popular areas where ANN is employed nowadays are pattern and sequence
recognition, novelty detection, character recognition, regression analysis,
speech recognition, image compression, stock market prediction, Electronic
nose, security, loan applications, data processing, robotics, and control. The
benefits associated with its broad applications leads to increasing popularity
of ANN in the era of 21st Century. ANN confers many benefits such as organic
learning, nonlinear data processing, fault tolerance, and self-repairing
compared to other conventional approaches. The primary objective of this paper
is to analyze the influence of the hidden layers of a neural network over the
overall performance of the network. To demonstrate this influence, we applied
neural network with different layers on the MNIST dataset. Also, another goal
is to observe the variations of accuracies of ANN for different numbers of
hidden layers and epochs and to compare and contrast among them.Comment: To be published in the 4th IEEE International Conference on
Electrical Engineering and Information & Communication Technology (iCEEiCT
2018
Computational Intelligence Application in Electrical Engineering
The Special Issue "Computational Intelligence Application in Electrical Engineering" deals with the application of computational intelligence techniques in various areas of electrical engineering. The topics of computational intelligence applications in smart power grid optimization, power distribution system protection, and electrical machine design and control optimization are presented in the Special Issue. The co-simulation approach to metaheuristic optimization methods and simulation tools for a power system analysis are also presented. The main computational intelligence techniques, evolutionary optimization, fuzzy inference system, and an artificial neural network are used in the research presented in the Special Issue. The articles published in this issue present the recent trends in computational intelligence applications in the areas of electrical engineering
Artificial Neural Network Prediction of Aluminium Metal Matrix Composite with Silicon Carbide Particles Developed Using Stir Casting Method
Aluminium matrix composites (AMCs) are range of advanced engineering materials used for a wide range of applications. AMCs consist of a non-metallic reinforcement incorporated into Aluminium matrix providing advantageous properties over base metal alloys.
In this paper, artificial neural network (ANN) is used to predict the micro-hardness, yield strength, tensile extension, modulus, ultimate tensile strength and stress, time to fracture, load at maximum extension, tenacity, electrical resistivity and conductivity. Information obtained from ANN model predictions can be used as guidelines during the conceptual design and optimisation of manufacturing processes; thus, reducing time and costs
An Efficient Automatic Mass Classification Method In Digitized Mammograms Using Artificial Neural Network
In this paper we present an efficient computer aided mass classification
method in digitized mammograms using Artificial Neural Network (ANN), which
performs benign-malignant classification on region of interest (ROI) that
contains mass. One of the major mammographic characteristics for mass
classification is texture. ANN exploits this important factor to classify the
mass into benign or malignant. The statistical textural features used in
characterizing the masses are mean, standard deviation, entropy, skewness,
kurtosis and uniformity. The main aim of the method is to increase the
effectiveness and efficiency of the classification process in an objective
manner to reduce the numbers of false-positive of malignancies. Three layers
artificial neural network (ANN) with seven features was proposed for
classifying the marked regions into benign and malignant and 90.91% sensitivity
and 83.87% specificity is achieved that is very much promising compare to the
radiologist's sensitivity 75%.Comment: 13 pages, 10 figure
Analog CMOS implementation of cellular neural networks
Ankara : Department of Electrical and Electronics Engineering and Institute of Engineering and Sciences, Bilkent Univ., 1991.Thesis (Master's) -- Bilkent University, 1991.Includes bibliographical references leaves 38-39An analog CMOS circuit realization of cellular neural networks with transconductance
elements is presented in this thesis. This realization can be easily
adapted to various types of applications in image processing by just choosing
the appropriate transconductance parameters according to the predetermined
coefficients. The noise-reduction and edge detection examples have shown
the effectiveness of the designed networks in real time image processing applications.
For “fix function” cellular neural network circuits the number of
transistors are reduced further by a new multi-input voltage-controlled current
source.Baktır, İzzet AdilM.S
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