Coin recognition using texture feature based on SPLM and SGLDM algorithm

Veena, H.N.; Muruganandam, M.; Kumaran, T. Senthil

doi:10.1063/1.5079006

Cited by 1 publication

(1 citation statement)

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“…The estimated values for these probability density functions are denoted as S(i,j|d,θ), where 0 takes the values of 0 • , 45 • , 90 • , 135 • , 225 • , 270 • , and 315 • . This estimation process forms the basis of the spatial gray level dependence method (SGLDM) [26][27][28][29]. Upon the creation of the SGLDM, a set of seven distinct features is derived following Equations ( 13)- (19).…”

Section: The Spatial Gray Level Dependence Methodsmentioning

confidence: 99%

Classifying Chewing and Rumination in Dairy Cows Using Sound Signals and Machine Learning

Abdanan Mehdizadeh,

Sari,

Orak

et al. 2023

Animals

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This research paper introduces a novel methodology for classifying jaw movements in dairy cattle into four distinct categories: bites, exclusive chews, chew-bite combinations, and exclusive sorting, under conditions of tall and short particle sizes in wheat straw and Alfalfa hay feeding. Sound signals were recorded and transformed into images using a short-time Fourier transform. A total of 31 texture features were extracted using the gray level co-occurrence matrix, spatial gray level dependence method, gray level run length method, and gray level difference method. Genetic Algorithm (GA) was applied to the data to select the most important features. Six distinct classifiers were employed to classify the jaw movements. The total precision found was 91.62%, 94.48%, 95.9%, 92.8%, 94.18%, and 89.62% for Naive Bayes, k-nearest neighbor, support vector machine, decision tree, multi-layer perceptron, and k-means clustering, respectively. The results of this study provide valuable insights into the nutritional behavior and dietary patterns of dairy cattle. The understanding of how cows consume different types of feed and the identification of any potential health issues or deficiencies in their diets are enhanced by the accurate classification of jaw movements. This information can be used to improve feeding practices, reduce waste, and ensure the well-being and productivity of the cows. The methodology introduced in this study can serve as a valuable tool for livestock managers to evaluate the nutrition of their dairy cattle and make informed decisions about their feeding practices.

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