Cardiovascular Disease Prediction using Enhanced Support Vector Machine Algorithm

Manjunathan, N.; Girirajan, S.; Jaganathan, D.

doi:10.1109/iccmc53470.2022.9753916

Cited by 4 publications

(2 citation statements)

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“…In addition to the statistical methods mentioned earlier, researchers have also explored the application of machine learning models for crop yield prediction. Some of the commonly studied machine learning models in this domain include Support Vector Machines (SVM) [11][37] [38][3] [39], Random Forests (RF) [40] [41], and k-Nearest Neighbors (KNN) [42][43][44] [45]. However, it is important to note that machine learning models do not inherently capture temporal dependencies in time series data, which are essential for accurate crop yield forecasting.…”

Section: Related Workmentioning

confidence: 99%

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Time series forecasting of wheat crop productivity in Egypt using deep learning techniques

Mahmoud,

Mohammed,

abdel wahab

et al. 2024

Int J Data Sci Anal

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Egypt's agricultural sector plays a critical role in the country's economy, with wheat cultivation being vital for ensuring food security. However, the challenges faced by wheat farming in Egypt, such as climate change, water scarcity, and pest infestations, contribute to yield fluctuations, highlighting the need for accurate and timely predictions of wheat productivity. To address this need, time series analysis is employed, which involves analyzing data collected at regular intervals over a specific time frame. Time series data can encompass multiple variables recorded simultaneously at each interval, known as multivariate time series data. Traditional statistical methods have been commonly used for time series analysis. However, these methods have limitations when dealing with nonlinear and complex data patterns, especially in agricultural data exhibiting spatiotemporal characteristics. Deep learning techniques have emerged as a promising solution to address these limitations. In this paper, we have developed the Multivariate Multi-Head Attention Temporal Convolutional Networks (MATCN) model specifically to handle the spatiotemporal nature of multivariate time series data in the context of wheat crop productivity prediction in Egypt. The experimental results revealed that our proposed MATCN model exhibited the most effective prediction performance when compared to other state-of-the-art models.

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Section: Related Workmentioning

confidence: 99%

“…Support Vector Regression(SVR) [38]: SVR is a regression method derived from SVM, suitable for both linear and non-linear relationships between predictors and the target variable. It aims to find an optimal hyperplane that maximizes the margin between predicted values and a predefined range around the target values.…”

Section: Baseline Modelsmentioning

confidence: 99%