The aim of this research is to enhance the accuracy of biometric palm print identification by using the Novel ResNet50 Algorithm as compared to the X Gradient Boosting. Materials and Methods: In this study, the ResNet50 and X Gradient Boosting algorithms were compared using a sample size of 10 for each algorithm, resulting in a total sample size of 20. The comparison was carried out with a G Power of 0.8 and a confidence interval (CI) of 95% to ensure statistical significance. For this study the Birjand University Mobile Palmprint Database (BMPD) dataset was collected from the Kaggle repository, which includes a total of 1640 images containing both left and right-hand palmprints. Result: According to the results, the ResNet50 algorithm achieved a higher accuracy rate (94.7%) compared to the X Gradient Boosting algorithm (92.4%) in identifying and measuring the images. The statistical analysis indicated a significant difference between the Novel ResNet50 algorithm and X Gradient Boosting, with a pvalue of 0.003 (Independent sample T-test p<0.05). This suggests that the ResNet50 algorithm outperformed the X Gradient Boosting algorithm in this experiment. According to the study’s findings, ResNet50 is more effective in accurately identifying biometric palm prints compared to X Gradient Boosting.
The aim is to create an artificial conversation entity(chatbot) using python to predict disease and medicine for healthcare treatments. Two algorithms fuzzy support vector machine algorithms are compared with Decision tree algorithm sample size taken 28. G power of 81% and sample size is calculated using the G power tool. Performances of the score model validated test set accuracy with 95% confidence interval for fuzzy support vector machine algorithm with different sub-samples has 91.60% accuracy comparing with Decision tree which has 87.90% accuracy.Independent Sample T-test a significance difference in accuracy and loss is observed p<0.005.From the results it is concluded that proposed algorithm Fuzzy support vector machine will produce better results than the existing algorithm.
The aim is to improve and develop a novel scheme to detect loyalties of customers using pattern growth method.Novel Pattern growth method compared with upper bound taxonomy sequence algorithm are used to detect online sales customer loyalties. Sample size is determined using the G Power calculator and found to be 10 per group. Totally 20 samples are used. Pretest power is 80% with CI of 95%. Based on the analysis Novel Pattern growth method has an accuracy of 80.8% and upper bound taxonomy sequence algorithm has 67.25%. Significance value is 0.0001 (p<0.05, two-tailed). Proposed algorithm Novel Pattern-Growth method has higher accuracy than Upper Bound Taxonomy for selected datasets for more reviews.
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