A Comparative Analysis of Euclidean, Jaccard and Cosine Similarity Measure and Arabic Wordnet for Automated Arabic Essay Scoring

Alobed, Mohammad; Altrad, Abdallah Mahmoud Mousa; Bakar, Zainab Binti Abu

doi:10.1109/camp51653.2021.9498119

Cited by 3 publications

(1 citation statement)

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“…The contribution to this research lies in the image segmentation process, which is carried out using the cosine similarity method on the k-means clustering algorithm in determining the initial centroid. Cosine similarity is a similarity method on data that produces a level of similarity with a smaller error value than the Euclidean and Jaccard similarity methods [22].…”

Section: Introductionmentioning

confidence: 99%

KNN Algorithm for Identification of Tomato Disease Based on Image Segmentation Using Enhanced K-Means Clustering

Nasution

Alvin²,

Siregar³

et al. 2022

KINETIK

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Image segmentation is an important process in identifying tomato diseases. The technique that is often used in this segmentation is k-means clustering. One of the main problems in this technique is the case of local minima, where the cluster that is formed is not suitable due to the incorrect selection of the initial centroid. In image data, this case will have an impact on poor segmentation results because it can erase parts that are actually important to be lost or there is still background in the recognition process, which has an impact on decreasing accuracy results. In this research, a method for image segmentation will be proposed using the k-means clustering algorithm, which has been added with the cosine similarity method as the proposed contribution. The use of the cosine method will determine the initial centroid by calculating the level of similarity of each image feature based on color and dividing them into several categories (low, medium, and high values). Based on the results obtained, the proposed algorithm is able to segment and distinguish between leaf and background images with good results, with the kNN reaching a value of 94.90% for accuracy, 99.50% for sensitivity, and 93.75% for specificity. The results obtained using the kNN method with k-means segmentation obtained a value of 92.46% for accuracy, 96.30% for sensitivity, and 91.50% for specificity. The results obtained using the kNN method without segmentation obtained a value of 90.22% for accuracy, 93.30% for sensitivity, and 89.45% for specificity.

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Section: Introductionmentioning

confidence: 99%