&lt;strong&gt;Application of KNN algorithm in determining the total antioxidant capacity of flavonoid-containing foods&lt;/strong&gt;

Yordi, Estela Guardado; Koeling, Raul; Mota, Yailé Caballero; Matos, María João; Santana, Lourdes; Uriarte, Eugenio; Molina, Enrique

doi:10.3390/ecsoc-19-e002

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…Many data analysis methods have been developed to deal with the large amount of data, for modeling such as partial least squares (PLS) (J.H. Cheng & Sun, 2017), artificial neural network (ANN) (Yiqun, Kangas, & Rasco, 2007), support vector machine (SVM) (Pouladzadeh, Villalobos, Almaghrabi, & Shirmohammadi, 2012), random forest (Bossard, Guillaumin, & Gool, 2014), k-nearest neighbor (KNN) (Yordi et al, 2015), and so on. For feature extraction, such as principal component analysis (PCA) (Granato, Santos, Escher, Ferreira, & Maggio, 2018), wavelet transform (WT) (Ma, 2017), independent component correlation algorithm (ICA) (Monakhova, Tsikin, Kuballa, Lachenmeier, & Mushtakova, 2014), scale-invariant feature transform (Giovany, Putra, Hariawan, & Wulandhari, 2017), speedup robust features (Bay, Ess, Tuytelaars, & Van Gool, 2008), histogram of oriented gradient (Ahmed & Ozeki, 2015), and so on.…”

Section: Introductionmentioning

confidence: 99%

Application of Deep Learning in Food: A Review

Zhou

Zhang

Liu

et al. 2019

Comp Rev Food Sci Food Safe

361

166

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Deep learning has been proved to be an advanced technology for big data analysis with a large number of successful cases in image processing, speech recognition, object detection, and so on. Recently, it has also been introduced in food science and engineering. To our knowledge, this review is the first in the food domain. In this paper, we provided a brief introduction of deep learning and detailedly described the structure of some popular architectures of deep neural networks and the approaches for training a model. We surveyed dozens of articles that used deep learning as the data analysis tool to solve the problems and challenges in food domain, including food recognition, calories estimation, quality detection of fruits, vegetables, meat and aquatic products, food supply chain, and food contamination. The specific problems, the datasets, the preprocessing methods, the networks and frameworks used, the performance achieved, and the comparison with other popular solutions of each research were investigated. We also analyzed the potential of deep learning to be used as an advanced data mining tool in food sensory and consume researches. The result of our survey indicates that deep learning outperforms other methods such as manual feature extractors, conventional machine learning algorithms, and deep learning as a promising tool in food quality and safety inspection. The encouraging results in classification and regression problems achieved by deep learning will attract more research efforts to apply deep learning into the field of food in the future.

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Section: Introductionmentioning

confidence: 99%

Application of Deep Learning in Food: A Review

Zhou

Zhang

Liu

et al. 2019

Comp Rev Food Sci Food Safe

361

166

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“…For modelling, severaltechniques of data analysis areformulated to manage a large amount of data, viz. Artificial Neural Network (ANN) [10], Partial Least Squares (PLS) [15], Random Forest [7], [10], [16], [11], [9], Support Vector Machine (SVM) [10], [17], [11], [12], [9], K-Nearest Neighbor (KNN) [7], [18], [9], and so on.…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of Indian Food Based on Machine learning Classification Models

Nayak

Beura²,

Siddique

et al. 2021

JSRR

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For human life, Food is highly necessary and essential for human to live the life. The objective of the current study is to characterise, classify and compare the food consumption patterns of many Indian food diets such as non-vegetarian and vegetarian. Given data about different Indian dishes, we try to predict here the dish is vegetarian or not. To get the best predictive model, this study is conducted with the comparison of Decision Tree, K-Nearest Neighbor (KNN), Support Vector Machine (SVM) and Random Forest algorithms. In this study, the concept and implementation of all these four models be made for prediction of Indian food. For training and testing the models, Indian food dataset is used that contains, in total 255 records to fit with all these four models. In short, the classification and prediction of Decision tree and KNN model provides less performance than the other models used here. However, the Random Forest model was generally more accurate than SVM, KNN and Decision Tree model, which have got from the simulation.

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