Product image classification using Eigen Colour feature with ensemble machine learning

Oyewole, S.A.; Olugbara, Oludayo O.

doi:10.1016/j.eij.2017.10.002

Cited by 23 publications

(17 citation statements)

References 76 publications

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“…The training data utilize the calculation of the average value to get the target value. The training process with a separate target value used for artificial neural networks can recognize each personality disorder separately [9]. The results of the training will be used to predict the tendency for personality disorders.…”

Section: Research Methodology a Bpnn Proceduresmentioning

confidence: 99%

Architecture of Back Propagation Neural Network Model for Early Detection of Tendency to Type B Personality Disorders

Hayat

Limong

Sagala

2019

khif

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Personal disorder is a type of mental illness. People with personal disorder cannot respond changes and demands of life in normal ways. Women with type B personal disorder tend to have high risk of violence. It is important to make early detection of this personal disorder, so that it can be anticipated properly. This paper reports an architecture model of back propagation neural network (BPPN) for early detection of type B personal disorder. The back propagation process divided into two phases, training and testing. The training process used 43 data and the testing process used 34 data. The output classified into 4 diagnosis categories of type B personal disorder, namely: anti-social, borderline, histrionic, and narcissistic. The optimal parameters of BPPN model consist of maximum epoch of 1000, maximum mu of 10000000000, increase mu of 25, decrease mu of 0.1, and neuron hidden layer of 25. The MSE of training is 3.07E-14 and MSE of testing is 1.00E-03. The accuracy of training is 90.7%, while the accuracy of testing is 97.2%.

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Section: Research Methodology a Bpnn Proceduresmentioning

confidence: 99%

Architecture of Back Propagation Neural Network Model for Early Detection of Tendency to Type B Personality Disorders

Hayat

Limong

Sagala

2019

khif

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“…There are two common forms of data utilised by previous researchers, which are text and image data sets. Out of these two data formats, text data is widely used for product classification rather than image data [4]. The former data format needs less storage and time to be extracted compared to the latter data format.…”

Section: Data Extractionmentioning

confidence: 99%

“…Researchers had conducted studies to overcome these problems and provide a good product classification model using different methodologies. The product classification is based on supervised learning models that are commonly used to classify products using either text or image data [4]. Most of the studies implement several important steps, such as data pre-processing, feature extraction and selection, and model selection.…”

Section: Introductionmentioning

confidence: 99%

A Mini Review on Supervised Learning Models for E-Commerce Product Classification

2020

IJATCSE

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Nowadays, consumers use e-commerce websites to find their desired products. Some researchers and related services collect data from these websites for research purposes. The misclassification of the product may distract them from finding the right products. Thus, product classification is essential in e-commerce websites. Machine learning models are widely used to classify the products according to their categories. This review presents the supervised learning model and its applications in this field. Various articles were analysed on the use of different supervised learning models for product classification. The review provides different types of supervised learning models and its enhancement in dealing with e-commerce products data. The findings are crucial for other researchers and practitioners that can be used to improve their current supervised learning model for e-commerce product classification.

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“…Other studies such as the work of [7] use the Eigen-color feature with ensemble machine learning combining artificial neural networks and support vector machines for the classification of product images. The little identified on the application of product classification does not address discontinuation and the vast majority focus on supervised models.…”

Section: Introductionmentioning

confidence: 99%

An Analytical Intelligence Model to Discontinue Products in a Transnational Company

Loy-García

Rodríguez-Aguilar

Marmolejo-Saucedo

2021

Advances in Intelligent Systems and Computing

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This work is a proposal of an analytical intelligence model for the discontinuation of products in a transnational soft drink company. The objective is to identify products that due to their volume and sales value should leave the company's catalog. For this, the integration of an analytical intelligence model that considers unsupervised classification algorithms integrating key information about the products to be evaluated is proposed. The results generated show that the product classification makes it possible to identify a set of products that are candidates for discontinuation due to their volumes and sales value, likewise, the detailed information of these products allows evaluating the characteristics of the cluster to be discontinued and thus planning production and distribution in the medium and long term. The planned model allows timely monitoring of the discontinuation process automatically as well as the monitoring of executive reports through the cloud.

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Product image classification using Eigen Colour feature with ensemble machine learning

Cited by 23 publications

References 76 publications

Architecture of Back Propagation Neural Network Model for Early Detection of Tendency to Type B Personality Disorders

Architecture of Back Propagation Neural Network Model for Early Detection of Tendency to Type B Personality Disorders

A Mini Review on Supervised Learning Models for E-Commerce Product Classification

An Analytical Intelligence Model to Discontinue Products in a Transnational Company

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