Chicken meat freshness identification using the histogram color feature

Asmara, Rosa Andrie; Rahutomo, Faisal; Hasanah, Qonitatul; Rahmad, Cahya

doi:10.1109/siet.2017.8304109

Cited by 18 publications

(4 citation statements)

References 13 publications

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“…Several color spaces such as RGB, HSV, CIE Lab, YCbCr, and XYZ are used in this study. RGB is a color space often used in many digital devices such as smartphones, cameras, television, and computers [11]. RGB consists of three layers, where each layer represents the color of red, green, and blue.…”

Section: Methodsmentioning

confidence: 99%

Melanoma Classification based on Simulated Annealing Optimization in Neural Network

Kusuma

Pantiawati

Handayani

2022

Knowl. Eng. Data Sci.

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Technology development in image processing and artificial intelligence field leads to the high demand for smart systems, especially in the health sector. Cancer is one of the diseases that has the highest mortality cases around the world. Melanoma is one of the cancer types that appear caused by high exposure to UV light. The earliest the melanoma was identified, the higher the chance the patient can be recovered. Therefore, this study carries the melanoma detection based on BPNN optimized by a simulated annealing algorithm. This research utilizes PH2 dermoscopic image data which contains 200 color digital images in BMP format. The data is processed using color feature extraction techniques to identify the characteristics of each image according to the target data. The color space extraction used includes mean RGB, HSV, CIE LAB, YCbCr, and XYZ. The evaluation result showed that the BPNN-SA method was able to increase the accuracy performance in classifying skin cancer when compared to the original BPNN method with an overall average accuracy of 84.03%.

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Section: Methodsmentioning

confidence: 99%

Melanoma Classification based on Simulated Annealing Optimization in Neural Network

Kusuma

Pantiawati

Handayani

2022

Knowl. Eng. Data Sci.

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“…where q is the unknown input data that need to be classified. The SVM method has been employed in a considerable range of realworld problems associated with different fields of automation, forensics, biotechnology, agriculture statistics, and is now being used in food sciences for the classification of bakery products, fresh produce, and meat product classifications (Liu et al, 2013;Chen and Zhang, 2014;Asmara et al, 2017;Chen et al, 2017;Arsalane et al, 2018). It has been proven that SVMs are persistently most appropriate for diverse supervised learning methods.…”

Section: Support Vector Machinesmentioning

confidence: 99%

Acceptability of Artificial Intelligence in Poultry Processing and Classification Efficiencies of Different Classification Models in the Categorisation of Breast Fillet Myopathies

et al. 2021

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Breast meat from modern fast-growing big birds is affected with myopathies such as woody breast (WB), white striping, and spaghetti meat (SM). The detection and separation of the myopathy-affected meat can be carried out at processing plants using technologies such as bioelectrical impedance analysis (BIA). However, BIA raw data from myopathy-affected breast meat are extremely complicated, especially because of the overlap of these myopathies in individual breast fillets and the human error associated with the assignment of fillet categories. Previous research has shown that traditional statistical techniques such as ANOVA and regression, among others, are insufficient in categorising fillets affected with myopathies by BIA. Therefore, more complex data analysis tools can be used, such as support vector machines (SVMs) and backpropagation neural networks (BPNNs), to classify raw poultry breast myopathies using their BIA patterns, such that the technology can be beneficial for the poultry industry in detecting myopathies. Freshly deboned (3–3.5 h post slaughter) breast fillets (n = 100 × 3 flocks) were analysed by hand palpation for WB (0-normal; 1-mild; 2-moderate; 3-Severe) and SM (presence and absence) categorisation. BIA data (resistance and reactance) were collected on each breast fillet; the algorithm of the equipment calculated protein and fat index. The data were analysed by linear discriminant analysis (LDA), and with SVM and BPNN with 70::30: training::test data set. Compared with the LDA analysis, SVM separated WB with a higher accuracy of 71.04% for normal (data for normal and mild merged), 59.99% for moderate, and 81.48% for severe WB. Compared with SVM, the BPNN training model accurately (100%) separated normal WB fillets with and without SM, demonstrating the ability of BIA to detect SM. Supervised learning algorithms, such as SVM and BPNN, can be combined with BIA and successfully implemented in poultry processing to detect breast fillet myopathies.

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“…For face detection besides Neural Network (NN) based, there was an old but still robust and became a trend in non-deep learning method, called Viola-Jones (VJ) [9][10][11]. Preprocessing data by rescalling and using normalized RGB space color [12] and [13] claimed can make the feature dataset become better. One of the methods for optimization is NBC which has successfully demonstrated its performance to perform the classification process in several fields, including pattern recognition, text classification, data mining, and bioinformatics [14][15][16][17].…”

Section: Literature Review 21 Related Workmentioning

confidence: 99%

“…This approach evaluates all classifiers that may be paired and thus induces binary classification. Each classifier is applied to the test data which will provide one value for the winning class [13,18,19,23,24].…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Comparison of Geometric Features and Color Features for Face Recognition

Rahmad¹,

Arai²,

Asmara³

et al. 2021

IJIES

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Face recognition plays an important role in the identity recognition system, the color and geometry feature has been claimed able to be used as parameter for face recognition. This study aims to analize the performance of geometric features, color features, and both of them on the human face using Gaussian Naïve Bayes (GNB) and the other Machine Learning method. This study using various geometric features: the distance between the eyes, nose, mouth by using Euclidean distance, and classified using GNB, K-Nearest Neighbour (KNN), and Support Vector Machine (SVM). The result compared with color feature: normalized RGB values, mean of normalized RGB, and RGB Variant as color features. The feature values obtained are assembled and processed using GNB and the other ML method to classified and recognized the faces. The dataset obtained from Aberdeen faces the dataset, which has 687 color faces from Ian Craw at Aberdeen. Between 1 and 18 images of 90 individuals. Some variations in lighting, varied viewpoints, and the resolution have varied between 336x480 to 624x544. The experimental results show that the system successfully recognized the face based on the determined algorithm and based on three models, SVM reached nearly 74.83%, GNB reached nearly 74.67%, and KNN with K = 5 reached nearly 72.17%.

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Chicken meat freshness identification using the histogram color feature

Cited by 18 publications

References 13 publications

Melanoma Classification based on Simulated Annealing Optimization in Neural Network

Melanoma Classification based on Simulated Annealing Optimization in Neural Network

Acceptability of Artificial Intelligence in Poultry Processing and Classification Efficiencies of Different Classification Models in the Categorisation of Breast Fillet Myopathies

Comparison of Geometric Features and Color Features for Face Recognition

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