Comparison of Support Vector Machine (SVM), K-Nearest Neighbor (K-NN), and Stochastic Gradient Descent (SGD) for Classifying Corn Leaf Disease based on Histogram of Oriented Gradients (HOG) Feature Extraction

Solihin, Firdaus; Syarief, Muhammad; Rochman, Eka Mala Sari; Rachmad, Aeri

doi:10.21831/elinvo.v8i1.55759

Cited by 3 publications

(1 citation statement)

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“…These vectors refer to the nearest data points to the hyperplane. SVM excels in managing high-dimensional data and limited training samples due to its adherence to the Structural Risk Minimization (SRM) principle, which maximizes margin and minimizes expected risk in the face of uncertainty [25].…”

Section: Support Vector Machinementioning

confidence: 99%

Classification of Salt Quality Based on the Content of Several Elements in the Salt Using Machine Learning

Rochman,

Setiawan,

Hardi

et al. 2024

MMEP

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Salt is one of the commodities in Indonesia. Salt has a very strategic and sustainable role for human life. Apart from being used for daily consumption, salt is also used as a raw material for various industries Indonesia, as a country surrounded by coastlines, can be self-sufficient in salt production and meet domestic salt needs. However, not all the salt produced maintains sufficient quality for consumption. Therefore, monitoring of the produced salt's quality is necessary to categorize it. Even though the categorization of salt quality is still carried out manually, this research employs data mining techniques with three different algorithms: Naï ve Bayes, K-Nearest Neighbor (K-NN), and Support Vector Machine (SVM), to simplify and enhance the efficiency of the classification process. The dataset used was obtained from salt data in the Sumenep region of Madura that consists of 349 records with seven attributes: sulfate, magnesium, water content, calcium, not dissolved, NaCl(wb), and NaCl(db) with four data classes that represent grades of salt quality (K1, K2, K3, and K4), and the salt data is divided into training and testing sets using the k-fold cross-validation method. Test results indicate that the K-NN method provides better outcomes compared to other methods, with an AUC value reaching 99.0%, accuracy of 91.7%, F1 Score reaching 91.6%, precision of around 91.9%, and recall of around 91.7%.

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Section: Support Vector Machinementioning

confidence: 99%

Classification of Salt Quality Based on the Content of Several Elements in the Salt Using Machine Learning

Rochman,

Setiawan,

Hardi

et al. 2024

MMEP

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A Multimodal Biometric System for Iris and Face Traits Based on Hybrid Approaches and Score Level Fusion

Najah Kadhim,

Hasan Abdulameer,

Mahdi Hadi Al-Mayali

2024

BIO Web Conf.

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The increasing need for information security on a worldwide scale has led to the widespread adoption of appropriate rules. Multimodal biometric systems have become an effective way to increase recognition precision, strengthen security guarantees, and reduce the drawbacks of unimodal biometric systems. These systems combine several biometric characteristics and sources by using fusion methods. Through score-level fusion, this work integrates facial and iris recognition techniques to present a multimodal biometric recognition methodology. The Histogram of Oriented Gradients (HOG) descriptor is used in the facial recognition system to extract facial characteristics, while the deep Wavelet Scattering Transform Network (WSTN) is applied in the iris recognition system to extract iris features. Then, for customized recognition classification, the feature vectors from every facial and iris recognition system are fed into a multiclass logistic regression. These systems provide scores, which are then combined via score-level fusion to maximize the efficiency of the human recognition process. The realistic multimodal database known as (MULB) is used to assess the suggested system's performance. The suggested technique exhibits improved performance across several measures, such as precision, recall, accuracy, equal error rate, false acceptance rate, and false rejection rate, as demonstrated by the experimental findings. The face and iris biometric systems have individual accuracy rates of 96.45% and 95.31% respectively. The equal error rates for the face and iris are 1.79% and 2.36% respectively. Simultaneously, the proposed multimodal biometric system attains a markedly enhanced accuracy rate of 100% and an equal error rate as little as 0.26%.

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Harnessing Machine Learning for Accurate Corn Leaf Disease Classification

Pavithra,

Reddy,

Bhargavi

2024

2024 1st International Conference on Trends in Engineering Systems and Technologies (ICTEST)

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Comparison of Support Vector Machine (SVM), K-Nearest Neighbor (K-NN), and Stochastic Gradient Descent (SGD) for Classifying Corn Leaf Disease based on Histogram of Oriented Gradients (HOG) Feature Extraction

Cited by 3 publications

References 0 publications

Classification of Salt Quality Based on the Content of Several Elements in the Salt Using Machine Learning

Classification of Salt Quality Based on the Content of Several Elements in the Salt Using Machine Learning

A Multimodal Biometric System for Iris and Face Traits Based on Hybrid Approaches and Score Level Fusion

Harnessing Machine Learning for Accurate Corn Leaf Disease Classification

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