Multi-layer perceptron (MLP) neural network technique for offline handwritten Gurmukhi character recognition

The international tourism growth forces governments to make a big effort to improve the security of national borders. The compulsory passport stamping is used in guaranteeing the safekeeping of the entry point of the border. For each passenger, the border police must check the existence of exit stamps and/or the entry stamps of the country that the passenger visits, in all the pages of his passport. However, the systematic control considerably slows the operations of the border police. Protecting the borders from illegal immigrants and simplifying border checkpoints for law-abiding citizens and visitors is a delicate compromise. The purpose of this paper is to perform a flexible and scalable system that ensures faster, safer and more efficient stamp controlling. An automatic system of stamp extraction for travel documents is proposed. We incorporate several methods from the field of artificial intelligence, image processing and pattern recognition. At first, texture feature extraction is performed in order to find potential stamps. Next, image segmentation aimed at detecting objects of specific textures are employed. Then, isolated objects are extracted and classified using multi-layer perceptron artificial network. Promising results are obtained in terms of accuracy, with a maximum average of 0.945 among all the images, improving the performance of MLP neural network in all cases.

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SPATIAL RELATIONSHIP BETWEEN CITY CENTER AND ECONOMIC ACTIVITY CENTER ON URBAN GROWTH IN TSUNAMI-PRONE CITY: THE CASE OF BANDA ACEH, INDONESIA

Jurnal Teknologi ◽

10.11113/jt.v75.2653 ◽

2015 ◽

Vol 75 (1) ◽

Cited By ~ 3

Author(s):

Ashfa Achmad ◽

Sirojuzilam Hasyim ◽

Badaruddin Rangkuti ◽

Dwira N. Aulia

Keyword(s):

Neural Network ◽

Land Use ◽

Urban Growth ◽

Economic Activity ◽

Spatial Relationship ◽

Multi Layer Perceptron ◽

City Center ◽

Mlp Neural Network ◽

Activity Center ◽

Banda Aceh

The main purpose of this study is to examine the impacts of the distance to city center (CIC) and distance to economic activity center (EAC) on urban growth. Land use/cover (LUC) map of 2005 and 2009 are used to analyze the variables. The variables were tested using Multi-Layer Perceptron (MLP) Neural Network in IDRISI®Selva. The result of MLP process shows that the distance to CIC and the distance to EAC contributed to the urban growth in Banda Aceh between 2005 to 2009. The distance to CIC more influential than the distance to EAC on urban growth.

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Handwritten Character Recognition using Different Kernel based SVM Classifier and MLP Neural Network (A COMPARISON)

International Journal of Computer Applications ◽

10.5120/8466-2387 ◽

2012 ◽

Vol 53 (11) ◽

pp. 25-31 ◽

Cited By ~ 6

Author(s):

Parveen Kumar ◽

Nitin Sharma ◽

Arun Rana

Keyword(s):

Neural Network ◽

Character Recognition ◽

Svm Classifier ◽

Handwritten Character Recognition ◽

Mlp Neural Network ◽

Handwritten Character

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Monthly rainfall forecast study in southeastern Brazil using multi-layer perceptron (MLP) neural networks

Ciência e Natura ◽

10.5902/2179460x45220 ◽

2020 ◽

Vol 42 ◽

pp. e4

Author(s):

Cleber Souza Corrêa ◽

Diogo Machado Custodio ◽

Haroldo De Campos Velho

Keyword(s):

Neural Network ◽

Neural Networks ◽

Time Series ◽

Prediction Error ◽

Monthly Rainfall ◽

Southeastern Brazil ◽

Multi Layer Perceptron ◽

Mlp Neural Network ◽

Rainfall Forecast ◽

Mlp Neural Networks

This work uses the MLP neural network technique to make monthly rainfall forecast estimates for Guarulhos airport in southeastern Brazil using a time series of approximately 70 years. Neural network structures with two or more hidden layers showed a better result, minimizing the prediction error.

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SPEECH EMOTION CLASSIFICATION USING SVM AND MLP ON PROSODIC AND VOICE QUALITY FEATURES

Jurnal Teknologi ◽

10.11113/jt.v78.6925 ◽

2015 ◽

Vol 78 (2-2) ◽

Cited By ~ 1

Author(s):

Inshirah Idris ◽

Md Sah Hj Salam ◽

Mohd Shahrizal Sunar

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Voice Quality ◽

Classification Performance ◽

Support Vector ◽

Multi Layer Perceptron ◽

Emotion Classification ◽

Mlp Neural Network ◽

Set Up ◽

Quality Features

In this paper, a comparison of emotion classification undertaken by the Support Vector Machine (SVM) and the Multi-Layer Perceptron (MLP) Neural Network, using prosodic and voice quality features extracted from the Berlin Emotional Database, is reported. The features were extracted using PRAAT tools, while the WEKA tool was used for classification. Different parameters were set up for both SVM and MLP, which are used to obtain an optimized emotion classification. The results show that MLP overcomes SVM in overall emotion classification performance. Nevertheless, the training for SVM was much faster when compared to MLP. The overall accuracy was 76.82% for SVM and 78.69% for MLP. Sadness was the emotion most recognized by MLP, with accuracy of 89.0%, while anger was the emotion most recognized by SVM, with accuracy of 87.4%. The most confusing emotions using MLP classification were happiness and fear, while for SVM, the most confusing emotions were disgust and fear.

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Multi-layer perceptron (MLP) neural network for predicting the modified compaction parameters of coarse-grained and fine-grained soils

Innovative Infrastructure Solutions ◽

10.1007/s41062-021-00679-7 ◽

2021 ◽

Vol 7 (1) ◽

Author(s):

Gaurav Verma ◽

Brind Kumar

Keyword(s):

Neural Network ◽

Coarse Grained ◽

Multi Layer Perceptron ◽

Mlp Neural Network ◽

Fine Grained

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Multi-layer perceptron based neural network model predicting maximum severity of Spodoptera litura (Fabricius) on groundnut in relation to climate for Dharwad region of Karnataka (India)

MAUSAM ◽

10.54302/mausam.v68i3.708 ◽

2021 ◽

Vol 68 (3) ◽

pp. 537-542

Author(s):

GIRISH K. JHA ◽

GAJAB SINGH ◽

S. VENNILA ◽

M. HEGDE ◽

M. S. RAO ◽

...

Keyword(s):

Neural Network ◽

Network Model ◽

Neural Network Model ◽

Spodoptera Litura ◽

Weather Data ◽

Maximum Temperature ◽

Percentage Error ◽

Multi Layer Perceptron ◽

Moth Population ◽

Mlp Neural Network

A multi-layer perceptron (MLP) neural network model for predicting adult moth population of tobacco caterpillar (Spodoptera litura (Fabricius) in groundnut cropping system of Dharwad (Karnataka) was developed and tested using the long term (24 years : 1990-2013) trap catches of the pest and weather data of Kharif season [26 to 44 standard meteorological weeks (SMW)]. The weekly male moth catches of S. litura during maximum severity observed at 34 SMW was modelled using the weather parameters viz., maximum temperature (C), minimum temperature (°C), rainfall (mm) and morning and afternoon relative humidity (%) lagged by two weeks. The principle component analysis was performed using meteorological data of preceding two weeks (32 and 33 SMW) in order to create fewer linearly independent factors. Five principal component scores which together accounted for 90 per cent of variations in data were used as input variables for neural network model. A MLP neural network with five input nodes and one hidden layer consisting of eleven hidden nodes was found to be suitable in terms of adequacy measures for modelling the population dynamics of S. litura. While data sets of 1990-2009 were used for developing the model, data of four seasons (2010-2013) were used for testing and validation. The performance of the model was assessed in terms of root mean square error (RMSE) and mean absolute percentage error (MAPE). The validation results clearly showed that the neural network based model is effective in dealing with the apparently random behaviour of the S. litura dynamics on groundnut.

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