A Comparison Between SVM and K-NN for classification of Plant Diseases

Jasim, Sarah Saadoon; Al-Gheethi, Adel

doi:10.24237/djps.1402.383b

Cited by 9 publications

(3 citation statements)

References 3 publications

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“…Using the permissions dataset, we assessed the various classifiers-Random Forest, SVM, Naïve Bayes, and Rotation Forest. [27], [28] Although the field of research on using intelligent algorithms to detect ransomware is still in its early stages, it is expanding, suggesting that there may be opportunities for future advancements in this area. There are still untapped possibilities for future advancements in machine learning algorithms for ransomware detection.…”

Section: Dataset Names Behavioral Features Machine Learning Deep Lear...mentioning

confidence: 99%

Mobile based Malware Detection using Artificial Intelligence Techniques a review

Faez mahdi,

Saadoon Jasim

2024

J. Al-Qadisiyah Comp. Sci. Math.

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Malware attacks on mobile devices are becoming more common and more complicated every year. Malware writers see the open-source Android app as their main target because so many people use it. Artificial intelligence is used by most of the literature's mobile malware detection methods to find ransomware. Our research, on the other hand, makes it clear that most of the earlier studies used different metrics and models, as well as different datasets and classification features that came from static, dynamic, or hybrid analysis strategies. This makes comparing the different suggested detection methods more difficult and may also make the results less certain. The goal of this work is to solve the problem of AI-powered malware detection by sorting current methods and approaches into three groups: the type of dataset, the type of detection method used (machine learning models, deep learning models, and Behavioral Analysis model), and how well the method works. In this way, we suggest a convergent plan that can be used as a basis for future methods of finding malware on Android and as a solid standard for artificial intelligence work in this area.

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Section: Dataset Names Behavioral Features Machine Learning Deep Lear...mentioning

confidence: 99%

Mobile based Malware Detection using Artificial Intelligence Techniques a review

Faez mahdi,

Saadoon Jasim

2024

J. Al-Qadisiyah Comp. Sci. Math.

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“…x is the input vector while K is the kernel function to operates on two vectors. b is the scalar value [25].…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Comparison of color-based feature extraction methods in banana leaf diseases classification using SVM and K-NN

Said

Madzin

Ali

et al. 2021

IJEECS

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In Malaysia, banana is a top fruit production which contribute to the economy growth in agriculture field. Hence, it is significant to have a quality production of banana and important to detect the plant diseases at the early stage. There are many types of banana leaf diseases such as Banana Mosaic, Black Sigatoka and Yellow Sigatoka. These three diseases are related to color changes at banana. This research paper is an experiment based and need to identify the best color feature extraction method to classify banana leaf diseases. Total of 48 banana leaf images that are used in this research paper. Four types of color feature extraction methods which are color histogram, color moment, hue, saturation, and value (HSV) histogram and color auto correlogram are experimented to determine the best method for banana leaf diseases classification. While for the classifiers, support vector machine (SVM) and k-Nearest neighbors (k-NN) are used to evaluate the performance and accuracy of each color feature extraction methods. There are also preliminary experiments to identify accurate parameters to use during classification for both classifiers. Our experimental result express that HSV histogram is the best method to classify banana leaf diseases with 83.33% of accuracy and SVM classifier perform better compared to k-NN.

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“…There are no predefined statistical methods to find the most favorable value of K. However, some researchers e.g. [36,37], found that k = 1 is the optimal value when performing a categorization task, because when k = 1, the KNN algorithm classifies the object to its nearest neighbor. In this article, and after many tests, we found that k = 1 gives the best recognition rate, so we decided to use this value in all our experiments.…”

Section: Choice Of "K"mentioning

confidence: 99%

Color Based Object Categorization Using Histograms of Oriented Hue and Saturation

Hamdini¹,

Diffellah²,

Namane³

2021

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In the last few years, there has been a lot of interest in making smart components, e.g. robots, able to simulate human capacity of object recognition and categorization. In this paper, we propose a new revolutionary approach for object categorization based on combining the HOG (Histograms of Oriented Gradients) descriptors with our two new descriptors, HOH (Histograms of Oriented Hue) and HOS (Histograms of Oriented Saturation), designed it in the HSL (Hue, Saturation and Luminance) color space and inspired by this famous HOG descriptor. By using the chrominance components, we have succeeded in making the proposed descriptor invariant to all lighting conditions changes. Moreover, the use of this oriented gradient makes our descriptor invariant to geometric condition changes including geometric and photometric transformation. Finally, the combination of color and gradient information increase the recognition rate of this descriptor and give it an exceptional performance compared to existing methods in the recognition of colored handmade objects with uniform background (98.92% for Columbia Object Image Library and 99.16% for the Amsterdam Library of Object Images). For the classification task, we propose the use of two strong and very used classifiers, SVM (Support Vector Machine) and KNN (k-nearest neighbors) classifiers.

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A Comparison Between SVM and K-NN for classification of Plant Diseases

Cited by 9 publications

References 3 publications

Mobile based Malware Detection using Artificial Intelligence Techniques a review

Mobile based Malware Detection using Artificial Intelligence Techniques a review

Comparison of color-based feature extraction methods in banana leaf diseases classification using SVM and K-NN

Color Based Object Categorization Using Histograms of Oriented Hue and Saturation

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