Deep feedforward neural network learning using Local Binary Patterns histograms for outdoor object categorization

Bouhamed, Heni; Ruichek, Yassine

doi:10.18280/ama_b.610309

Cited by 6 publications

(14 citation statements)

References 17 publications

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“…Input face image is converted into gray image. (2). Take the part of region as a window of 3x3 pixel.…”

Section: Extraction Of Features Using Local Binary Patternmentioning

confidence: 99%

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Gender Classification Based on Face Images of Local Binary Pattern Using Support Vector Machine and Back Propagation Neural Networks

Sajja¹,

Kalluri²

2019

AMA_B

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In this paper, presented a Gender classification through Support Vector Machine (SVM) and Scaled Conjugate Gradient Back Propagation Neural Network (SCGBPNN) from face images using Local Binary Patterns. To achieve better classification performance, need to be applied pre-processing technique first and then extracted the features on facial images from Local Binary Pattern Histogram (LBPH) method. These extracted features were stored into a vector called feature vector. Later, the feature vector is inputted to Polynomial SVM and SCG Back Propagation Neural Network classification methods along with labelled target vector. The performance of the both classifiers is measured by the labelled AT&T face database and Nottingham Scan Database.

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“…Input face image is converted into gray image. (2). Take the part of region as a window of 3x3 pixel.…”

Section: Extraction Of Features Using Local Binary Patternmentioning

confidence: 99%

“…Feature extraction and classification are the two main steps in face recognition. LBPH [1][2] (Local Binary Pattern histogram) is used for extracting the features from facial images. SVM [3][4][5] (Support Vector Machine) and NN (Neural Networks) both are supervised learning.…”

Section: Introductionmentioning

confidence: 99%

Gender Classification Based on Face Images of Local Binary Pattern Using Support Vector Machine and Back Propagation Neural Networks

Sajja¹,

Kalluri²

2019

AMA_B

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“…Several studies invoke the prominence of deep neural networks (DNNs) which surpass the performance of the previous dominant paradigm in diverse machine learning applications [13][14][15][16][17]. Deep Learning is a set of machine learning methods allowing to model data with a high level of abstraction.…”

Section: Introductionmentioning

confidence: 99%

“…Deep Learning is a set of machine learning methods allowing to model data with a high level of abstraction. It is based on articulate architectures of various transformations in the nonlinear space [13,18]. It is also considered as part of the Big Data domain.…”

Section: Introductionmentioning

confidence: 99%

COVID-19, Bacille Calmette-Guérin (BCG) and Tuberculosis: Cases and Recovery Previsions with Deep Learning Sequence Prediction

Bouhamed¹

2020

ISI

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In this study, we use a Deep Learning sequence prediction models for the continuous monitoring of the infection and recovering processes while considering the potential impacts of Bacille Calmette-Gué rin (BCG) vaccination and tuberculosis (TB) infection rates in populations. This model was built based on the epidemic data evolution in several countries between the date of their first case and March 13, 2020. The data was based on 14 variables for cases prediction and 15 variables for recoveries prediction. Prevision results were very promising and the suspicions on the BCG vaccination and TB infections rates' implications turned out to be quite warranted. The model can evolve by continuously updating and enriching data, adding experiences of all affected countries.

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“…Several studies invoke the prominence of deep neural networks (DNNs) which surpass the performance of the previous dominant paradigm in diverse machine learning applications (Bouhamed & Ruichek, 2018;Hinton et al, 2012;Mohamed et al, 2012;Ciresan et al, 2010;Yu et al, 2011). Deep Learning is a set of machine learning methods allowing to model data with a high level of abstraction.…”

Section: Introductionmentioning

confidence: 99%

COVID-19 Deaths Previsions With Deep Learning Sequence Prediction

Bouhamed

2020

International Journal of Big Data and Analytics in Healthcare

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In this study, the authors use deep learning sequence prediction models for the continuous monitoring of the epidemic while considering the potential impacts of Bacille Calmette-Guérin (BCG) vaccination and tuberculosis (TB) infection rates in populations. Three models were built based on the epidemic data evolution in several countries between the date of their first case and April 1, 2020. The data was based on 14 variables for cases prediction, 15 variables for recoveries prediction, and 16 variables for deaths prediction. Prevision results were very promising, and the suspicions on the BCG vaccination and TB infections rates' implications turned out to be warranted. The model can evolve by continuously updating and enriching data, adding the experiences of all affected countries.

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Deep feedforward neural network learning using Local Binary Patterns histograms for outdoor object categorization

Cited by 6 publications

References 17 publications

Gender Classification Based on Face Images of Local Binary Pattern Using Support Vector Machine and Back Propagation Neural Networks

Gender Classification Based on Face Images of Local Binary Pattern Using Support Vector Machine and Back Propagation Neural Networks

COVID-19, Bacille Calmette-Guérin (BCG) and Tuberculosis: Cases and Recovery Previsions with Deep Learning Sequence Prediction

COVID-19 Deaths Previsions With Deep Learning Sequence Prediction

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