Prediction of Data Loss in Wireless Communicating Using SVM Based Machine Learning

Authors: Amit Kumar, Yeswant Kumar

Country: India

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Abstract: Wireless communication systems are prone to data loss due to various factors such as signal interference, channel fading, and noise. Predicting and mitigating data loss in such systems is crucial for ensuring reliable and efficient communication. In this study, we propose a Support Vector Machine (SVM) based machine learning approach for predicting data loss in wireless communication networks. The SVM algorithm is a powerful tool for classification and regression tasks, known for its ability to handle high-dimensional data and nonlinear relationships. In our approach, we utilize SVM to build a predictive model based on features extracted from the wireless communication environment. These features include signal strength, channel conditions, noise levels, and other relevant parameters. To train the SVM model, we use labelled datasets consisting of historical data on data loss occurrences and corresponding environmental conditions. The SVM model learns to classify the input feature vectors into two classes: data loss and no data loss. Once trained, the model can predict the likelihood of data loss for new input data, enabling proactive measures to be taken to mitigate potential losses. We evaluate the performance of our SVM-based approach using cross-validation techniques and compare it with other machine learning algorithms. Our results demonstrate the effectiveness of the proposed method in accurately predicting data loss in wireless communication systems, thereby providing valuable insights for improving system reliability and performance.

Keywords: Support Vector Machine, WSNs, Data Loss, Machine Learning

Paper Id: 230651

Published On: 2024-05-16

Published In: Volume 12, Issue 3, May-June 2024

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