A variational inequality approach with SVM optimization algorithm for identifying mineral lithology

Mou, Dan; Wang, Zhuwen; Tan, Xili; Shi, Shaoyun

doi:10.1016/j.jappgeo.2022.104747

Cited by 7 publications

(3 citation statements)

References 24 publications

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“…Support Vector Machine (SVM) is a supervised learning method that can be used for regression and classification. SVM has several kernels, including linear kernels, polynomial kernels, and radial basis function (RBF) kernels (Mou et al 2022). SVM is a method used to find the distance between classes.…”

Section: Methodsmentioning

confidence: 99%

Shrimp and Fish Underwater Image Clasification Using Features Extraction and Machine Learning

Setiawan,

Hadiyanto,

Widodo

2024

J. emerg. sci. eng.

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Shrimp cultivation is one type of cultivation that has a significant impact on the social status of coastal communities. Shrimp farming traditionally faces several challenges, including water pollution, imbalances in temperature, feed, media, and costs. Monitoring the condition of shrimp in the cultivation environment is very necessary to determine the condition of shrimp in the water. Classification of shrimp and fish is the first step in monitoring the condition of shrimp underwater. This research proposes the development of a method for classifying shrimp and fish underwater using feature extraction and machine learning. The flow of this research is: (1) preparing data from ROI detection results, (2) extraction process of morphometric characteristics P and T, (3) calculating the value of morphometric characteristics P and T, (4) data breakdown for training data and testing data, (5) Model creation process, data training and data testing using SVM, RF, DT, and KNN, (6) Evaluation of classification results using a confusion matrix. From this research, it was found that the Random Forest method obtained the highest accuracy, namely 0.93. From this matrix, the values obtained are True Positive = 349, False Positive = 28, True Negative = 223, False Negative = 0.

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Section: Methodsmentioning

confidence: 99%

Shrimp and Fish Underwater Image Clasification Using Features Extraction and Machine Learning

Setiawan,

Hadiyanto,

Widodo

2024

J. emerg. sci. eng.

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“…KNN is an algorithm that classifies data based on similarity to other data. For the case of KNN regression, it provides the introduction of the nearest K in the neighbor regression [54]. BPNN is a supervised learning method, which uses an output error to change its weight value in backwards.…”

Section: 𝑐𝑓 =mentioning

confidence: 99%

Shrimp Body Weight Estimation in Aquaculture Ponds Using Morphometric Features Based on Underwater Image Analysis and Machine Learning Approach

Setiawan¹,

Hadiyanto²,

Widodo³

2022

RIA

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Shrimp is a marine culture found globally due to the ability of its yields to boost a country's economy. It is imperative to monitor its size to determine the condition of the shrimp underwater with complex noise using a non-invasive method. Therefore, this study aims to develop a new method for measuring the body weight of shrimp using morphometric features based on underwater image analysis and a machine learning approach. The method used consists of several steps, data collection using an underwater camera, image analysis using image grayscale, image binary, edge detection, region of interest detection, shrimp image morphometric features extraction, camera calibration using Triangle Similarity (TS), and Correction Factor (CF), calculation of morphometric features value, create machine learning model, training data, and testing data for estimation of underwater shrimp body weight. After testing the model, get the best accuracy value is RMSE = 0.05, MAE = 0.04, and R2 = 0.96 from the MLR method. In conclusion, the results showed that the hybrid method TS-CF-MLR is the best method for measuring underwater shrimp body weight estimation with the lowest error rate and highest coefficient of determination.

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“…Machine learning technology has been widely applied in lithology classification, with its excellent feature mining and data fitting ability. Examples include extreme learning machines [44,45], logistic regression [46,47], back-propagation neural networks [48,49], support vector machines [50][51][52], and multi-layer perceptrons [53,54]. The commonly used lithologic classification models can be divided into three categories, the space vector type, neural network type, and linear type.…”

Section: Introductionmentioning

confidence: 99%

Lithological Classification by Hyperspectral Images Based on a Two-Layer XGBoost Model, Combined with a Greedy Algorithm

Lin

Jiang

et al. 2023

Remote Sensing

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Lithology classification is important in mineral resource exploration, engineering geological exploration, and disaster monitoring. Traditional laboratory methods for the qualitative analysis of rocks are limited by sampling conditions and analytical techniques, resulting in high costs, low efficiency, and the inability to quickly obtain large-scale geological information. Hyperspectral remote sensing technology can classify and identify lithology using the spectral characteristics of rock, and is characterized by fast detection, large coverage area, and environmental friendliness, which provide the application potential for lithological mapping at a large regional scale. In this study, ZY1-02D hyperspectral images were used as data sources to construct a new two-layer extreme gradient boosting (XGBoost) lithology classification model based on the XGBoost decision tree and an improved greedy search algorithm. A total of 153 spectral bands of the preprocessed hyperspectral images were input into the first layer of the XGBoost model. Based on the tree traversal structural characteristics of the leaf nodes in the XGBoost model, three built-in XGBoost importance indexes were split and combined. The improved greedy search algorithm was used to extract the spectral band variables, which were imported into the second layer of the XGBoost model, and the bat algorithm was used to optimize the modeling parameters of XGBoost. The extraction model of rock classification information was constructed, and the classification map of regional surface rock types was drawn. Field verification was performed for the two-layer XGBoost rock classification model, and its accuracy and reliability were evaluated based on four indexes, namely, accuracy, precision, recall, and F1 score. The results showed that the two-layer XGBoost model had a good lithological classification effect, robustness, and adaptability to small sample datasets. Compared with the traditional machine learning model, the two-layer XGBoost model shows superior performance. The accuracy, precision, recall, and F1 score of the verification set were 0.8343, 0.8406, 0.8350, and 0.8157, respectively. The variable extraction ability of the constructed two-layer XGBoost model was significantly improved. Compared with traditional feature selection methods, the GREED-GFC method, when applied to the two-layer XGBoost model, contributes to more stable rock classification performance and higher lithology prediction accuracy, and the smallest number of extracted features. The lithological distribution information identified by the model was in good agreement with the lithology information verified in the field.

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A variational inequality approach with SVM optimization algorithm for identifying mineral lithology

Cited by 7 publications

References 24 publications

Shrimp and Fish Underwater Image Clasification Using Features Extraction and Machine Learning

Shrimp and Fish Underwater Image Clasification Using Features Extraction and Machine Learning

Shrimp Body Weight Estimation in Aquaculture Ponds Using Morphometric Features Based on Underwater Image Analysis and Machine Learning Approach

Lithological Classification by Hyperspectral Images Based on a Two-Layer XGBoost Model, Combined with a Greedy Algorithm

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