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Titlebook: Data Engineering and Applications; Proceedings of the I Jitendra Agrawal,Rajesh K. Shukla,Chin-Shiuh Shieh Conference proceedings 2024 The

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means the model can predict new observations accurately. Also, the adjusted . has been recorded to be 0.9943, which indicates the model behaves well when adjustments are made to variables or new variables are added or eliminated. Performance comparison has been performed with other existing models to establish the supremacy of the proposed one.
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scaling parameters. The suggested methodology utilized supervised learning to classify forgery patch pixels. The experiment obtains an accuracy of 94.37, a recall of 97.03, and an F1 value of 94.03. The performance of the suggested algorithms is compared to the state-of-the-art approach and found to be superior.
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ument online mode itself. For this, support vector machine (SVM) algorithm and optical character recognition (OCR) are used. For invalid documents, the comment is displayed on the screen to upload the correct document, before submitting data.
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Feature Extraction Using Levy Distribution-Based Salp Swarm Algorithm repository to legitimize the supremacy of proposed algorithm with other existing methodologies. The comparative outcome shows the distribution improves the performance of SSA method and surpasses over other optimization algorithm.
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