There has been an increase in credit card fraud as e-commerce has become more widespread. Financial transactions are essential to our economy, so detecting bank fraud is essential. Experiments on automated and real-time fraud detection are needed here. There are numerous machine learning techniques for identifying credit card fraud, and the most prevalent are support vector machine (SVM), logic regression, and random forest. When models penalise all errors equally during training, the quality of these detection approaches becomes crucial. This paper uses an innovative sensing method to judge the classification algorithm by considering the misclassification cost and at the same time by employing SVM hyperparameter optimization using grid search cross-validation and separating the hyperplane using the theory of reproducing kernels like linear, Gaussian, and polynomial, and the robustness is maintained. Because of this, credit card fraud has been identified significantly more successful than in the past.
Summary
An efficient technique called channel Assignment (CA) is used to exploit the multiple non‐overlapping channels that improves capacity and decreases intervention in the wireless mesh network. Although it can reduce total network interference, there may be certain design issues by which network performance can be unfair. For maximizing the usage of wireless LAN network spectrum in these environments, many studies have discovered how to use the complete spectrum while transferring the data via mesh network. The proposed paper elaborates about the Enhanced Traffic‐Aware Channel Assignment Protocol. This protocol helps the mesh nodes present in the large‐scale mesh networks to assign the channels.
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