The effective SVM-based binary prediction of ground water table

Mallikarjuna, Basetty; Sathish, K.; Krishna, P. Venkata; Viswanathan, R.

doi:10.1007/s12065-020-00447-z

Cited by 15 publications

(5 citation statements)

References 18 publications

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“…Support Vector Machine (SVM) is a classification model that uses a hyperplane as an intermediary for each category as a differentiator for each category [15]. Usually, SVM is used for two categories, but it is possible to use it for more than two categories [16,17]. The hyperplane in SVM is determined from the midpoint of calculating the maximum margin for each category [18].…”

Section: Support Vector Machinementioning

confidence: 99%

SVM-CNN Hybrid Classification for Waste Image Using Morphology and HSV Color Model Image Processing

Sunardi,

Yudhana,

Fahmi

2023

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Waste is a significant problem that is around us. The problem occurs because waste volume speed could be faster. This problem can be solved by implementing machine learning in the waste sorting process based on two categories which are organic and inorganic. Knowing the most efficient image processing and classification machine learning model is necessary. This research uses the Support Vector Machine classification model hybridized with the Convolutional Neural Network, image processing morphology, and the HSV color model. The dataset is collected from the images available on the Kaggle website and executed using Python. The data used amounted to 25,077 with a training and test data ratio of 85:15. The data is processed using the proposed method, namely the morphology and HSV color model, to determine the performance between using the image process and those that do not. The data that has been processed is classified using the SVM-CNN Hybrid classification model. The performance results are an accuracy rate of 99.34% and a loss of 1.67% without overfitting.

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Section: Support Vector Machinementioning

confidence: 99%

SVM-CNN Hybrid Classification for Waste Image Using Morphology and HSV Color Model Image Processing

Sunardi,

Yudhana,

Fahmi

2023

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“…SVM, which originated from the new statistical theory [31][32][33] in the 1990s, is a commonly used model in machine learning. It can not only be used for the classification of nonlinear samples but also be applied to the field of Mathematical Problems in Engineering regression prediction.…”

Section: Stone Cultural Relics Crack Predictionmentioning

confidence: 99%

Crack Prediction Based on Wavelet Correlation Analysis Least Squares Support Vector Machine for Stone Cultural Relics

Liu

et al. 2021

Mathematical Problems in Engineering

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Preventive protection of cultural relics is to make use of all the science and technology beneficial to the research and protection of archaeological heritage to predict the disease of cultural relics. The existing preventive cultural relics protection system has made some achievements in environmental monitoring, but the analysis and utilization of large data of cultural relics are still insufficient. In this paper, under the idea of multisource information fusion, a least squares support vector machine regression method based on multivariate time series wavelet correlation analysis is proposed to achieve accurate crack prediction of stone cultural relics. Firstly, the correlation of multivariate time series of stone cultural relics are quantitatively analyzed and the validity of characteristic variables of the crack is discriminated by wavelet correlation analysis; then, a least squares support vector machine prediction model is constructed based on the correlation obtained from the analysis; finally, the good performance of the method is verified by using the environmental monitoring data of the rock mass fracture in the North Qianfo Cliff of Dafo Temple in Binzhou City of Shaanxi Province. The experimental results show that the proposed method is more effective than the traditional backpropagation neural network, support vector machine, and relevance vector machine regression methods. This method is universal and easy to implement for multisource data prediction of nonmovable cultural relics diseases. It provides a scientific theoretical reference for the preventive protection of cultural relics.

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“…The IT industry recruit engineers during lockdown and increases the number projects and looking for in-depth programming knowledge in computer science, not only from the computer Science and IT branch in engineering, however other branches like Electrical, Electronics, and Mechanical studies [30]. The growth of demand for computer science education in the USA and Canada has been rapid, boosted by the invention of Blockchain Technology, Cyber Security, Robotics, and Artificial Intelligence [31].…”

Section: The Demand Of Job Opportunitiesmentioning

confidence: 99%

OBE: Perception of Computer Science Learners and Teachers in online learning during COVID-19

Mallikarjuna¹,

Sabharwal²,

Kumar³

et al. 2021

EAI Endorsed Transactions on Creative Technologies

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The effective SVM-based binary prediction of ground water table

Cited by 15 publications

References 18 publications

SVM-CNN Hybrid Classification for Waste Image Using Morphology and HSV Color Model Image Processing

SVM-CNN Hybrid Classification for Waste Image Using Morphology and HSV Color Model Image Processing

Crack Prediction Based on Wavelet Correlation Analysis Least Squares Support Vector Machine for Stone Cultural Relics

OBE: Perception of Computer Science Learners and Teachers in online learning during COVID-19

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