Classification of Iris Plant Using Perceptron Neural Network

Datsi, Toufik; Oirrak, Ahmed El; Aznag, Khalid

doi:10.1007/978-3-030-36674-2_19

Cited by 2 publications

(2 citation statements)

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“…For the cleaning process and in order to extract the families of objects, in this section we use a classification performed by a perceptron neural network. The perceptron is the simplest structure for neural networks in pattern classification problems [15] .…”

Section: Gpr Image Cleaning Process -Features Extractionmentioning

confidence: 99%

GPR image interpretation advancement for smarter technical management of water leakage in urban water infrastructures

Ayala-Cabrera

Izquierdo

2021

Earth Resources and Environmental Remote Sensing/Gis Applications XII

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This paper combines such powerful non-destructive technique as ground penetrating radar (GPR) with intelligent data analysis in order to acquire new knowledge on mapping/monitoring/verification systems aimed at initially facilitating the evaluation of the health of the buried assets of water distribution system (WDS) infrastructures in urban areas. Preprocessing techniques based on a multi-agent approach coupled with a suitable analysis of the properties of the obtained groups of objects found, as well as a classification supported by machine learning techniques, are presented in this work. The work is based on GPR image studies conducted under controlled laboratory conditions using a commercial antenna. The buried objects correspond to pipes of various materials commonly used in WDSs. Furthermore, GPR images of various pipes either empty or full of water, as well as leaking water, are also included in the dataset of the study. The dataset is divided into two subsets that pursue: 1) the development of the methodology; i.e. capturing objects in GPR images to favour the feature extraction process, and 2) the evaluation of the feasibility of implementation of the proposed classification, as well as the response of the methodology to various environmental interactions recorded in the images. The results of this work are promising in the sense of promoting the inclusion of powerful tools such as GPR towards the provision of smarter tools that adequately support technical management in WDSs, and, eventually, that of other surrounding infrastructure(s).

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Section: Gpr Image Cleaning Process -Features Extractionmentioning

confidence: 99%

GPR image interpretation advancement for smarter technical management of water leakage in urban water infrastructures

Ayala-Cabrera

Izquierdo

2021

Earth Resources and Environmental Remote Sensing/Gis Applications XII

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“…It has become more competitive with other models used in regression and statistics in terms of its usefulness. An ANN contains a set of different types of neural networks, such as single layer perceptrons, feedforward, networks, multilayer perceptrons, recurrent neural networks, and CNN's (Datsi et al, 2019;Ramzan et al, 2018). It stimulates the biological function of nervous systems and can optimize complex systems that are difficult to model using other techniques such as mathematical modeling (Abiodun et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Digit recognition using decimal coding and artificial neural network

Datsi

2021

KJS publishes peer-review articles in Mathematics, Computer Sci

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Current artificial neural network image recognition techniques use all the pixels of an image as input. In this paper, we present an efficient method for handwritten digit recognition that involves extracting the characteristics of a digit image by coding each row of the image as a decimal value, i.e., by transforming the binary representation into a decimal value. This method is called the decimal coding of rows. The set of decimal values calculated from the initial image is arranged as a vector and normalized; these values represent the inputs to the artificial neural network. The approach proposed in this work uses a multilayer perceptron neural network for the classification, recognition, and prediction of handwritten digits from 0 to 9. In this study, a dataset of 1797 samples were obtained from a digit database imported from the Scikit-learn library. Backpropagation was used as a learning algorithm to train the multilayer perceptron neural network. The results show that the proposed approach achieves better performance than two other schemes in terms of recognition accuracy and execution time.

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Classification of Iris Plant Using Perceptron Neural Network

Cited by 2 publications

References 4 publications

GPR image interpretation advancement for smarter technical management of water leakage in urban water infrastructures

GPR image interpretation advancement for smarter technical management of water leakage in urban water infrastructures

Digit recognition using decimal coding and artificial neural network

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