Competence Classification of Twitter Users Using Support Vector Machine (SVM) Method

Rifaldi, Muhammad Haqqi Ghufran; Setiawan, Erwin Budi

doi:10.1109/icoict.2019.8835191

Cited by 2 publications

(1 citation statement)

References 3 publications

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“…SVM model (Rifaldi and Setiawan, 2019) is a commonly used machine learning algorithm, mainly for classification and regression problems, which is uniquely suited for dealing with complex problems with finite samples, high dimensionality, and nonlinear data. It maps the independent variables to a high-dimensional feature space through a nonlinear mapping and finds an optimal classification surface in the high-dimensional feature space such that the error obtained from this optimal classification surface is minimized for all training samples (Zan et al, 2004).…”

Section: Support Vector Machinementioning

confidence: 99%

The use of spaceborne lidar to map Antarctic krill distributions and biomass in the Southern Ocean

Zhong,

Chen,

Zhang

et al. 2024

Front. Mar. Sci.

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The Antarctic krill is a pivotal species in the Southern Ocean ecosystem, primarily due to its extraordinary nutritional content and plentiful resources. Studying the distribution of these resources and their environmental impact factors is crucial for the successful development of Antarctic krill fisheries. Traditional methodologies such as acoustic measurements, however, often face limitations in their capacity to provide a comprehensive and uninterrupted assessment. Moreover, the six-month duration of polar nights in polar regions presents significant challenges for traditional satellite observations. In this context, LiDAR, an active remote sensing observation method, offers a promising alternative. Known for their high resolution, flexibility, and efficiency, LiDAR systems can obtain detailed information on diurnal ocean parameters in polar regions on a vast scale and in a systematic way. Our study utilizes the spaceborne LiDAR system, CALIPSO, to successfully attain continuous Antarctic krill CPUE over the past decade, using various models such as the generalized linear model (GLM), artificial neural network (ANN), and support vector machine (SVM). A comparative analysis of the prediction results reveals that while both ANN and SVM models outperform the GLM, the SVM’s prediction capabilities are somewhat unstable. Our findings reveal CALIPSO’s potential in overcoming challenges associated with traditional satellite observations during polar winters. In addition, we found no obvious pattern of interannual variation in krill CPUE, with high values predominantly occurring from February to May. This suggests that krill is mainly located around the South Shetland Islands during January-April, before moving offshore towards South Georgia in May-June. A substantial krill aggregation community is found in the South Atlantic waters, indicating high potential for krill fishing. The optimum mix layer depth range for high krill CPUE is 270-390 m, with a chlorophyll concentration of approximately 0.1 mg m-3. The optimum sea surface temperature range is between -1.4-5.5°C, and the sea ice coverage range is approximately 0-0.1×106 km2. The predicted Antarctic krill bioresource has risen from 2.4×108 tons in 2011 to 2.8×108 tons in 2020. This increase in krill biomass aligns with the biomass of krill assessed by CCAMLR.

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Section: Support Vector Machinementioning

confidence: 99%

The use of spaceborne lidar to map Antarctic krill distributions and biomass in the Southern Ocean

Zhong,

Chen,

Zhang

et al. 2024

Front. Mar. Sci.

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Automatic classification of the phenotype textures of three Thunnus species based on the machine learning SVM algorithm

Liu

Chen

et al. 2023

Can. J. Fish. Aquat. Sci.

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Tuna resources are an important part of China’s pelagic fishery production. However, for China’s tuna fishery, tuna species caught at sea are still manually classified, which is a time-consuming and inefficient process, so China’s tuna fishery needs to develop towards automation. This study uses GLCM (gray level co-occurrence matrix) and VGG16 to visualize phenotypic texture through local images of three Thunnus species. At the same time, texture feature index data (TFD), deep feature data (DFD) and their combined feature data (CFD) are obtained from texture images. SVM with different kernel functions is used to classify phenotypic texture of tuna automatically. The study shows that visualized texture images of different tuna using GLCM and VGG16 have biological characteristics. In the classification results without cross-validation, the average classification accuracy of TFD in polynomial was 83%, the average classification accuracy of DFD in RBF was 93%, and the average classification accuracy of CFD in RBF was 95%. It is concluded that tuna phenotype texture can be efficiently classified by using SVM with different kernel functions.

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Competence Classification of Twitter Users Using Support Vector Machine (SVM) Method

Cited by 2 publications

References 3 publications

The use of spaceborne lidar to map Antarctic krill distributions and biomass in the Southern Ocean

The use of spaceborne lidar to map Antarctic krill distributions and biomass in the Southern Ocean

Automatic classification of the phenotype textures of three Thunnus species based on the machine learning SVM algorithm

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