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The detection of tomato leaf disease is one of the many objectives of this research, and one of those objectives is to increase the accuracy of the determination. In order to achieve this objective, the Novel Visual Geometry Group 19 (VGG19) technique will be utilised, and the performance of this approach will be examined in contrast to that of the K-Nearest Neighbor Algorithm (KNN). The Constituents and the Methods involved: For the purpose of identifying occurrences of tomato leaf disease, the Visual Geometry Group 19 and K-Nearest Neighbor algorithms were utilised. The dataset that was used consisted of 19,167 photographs. There were 171 rows and 171 columns, and every single picture was the same way. In order to determine the proper sample size for each iteration of the algorithms, ClinCalc was utilised to compute a sample size of ten, with a pretest power of 0.8 and an alpha of 0.05. This determination was made in order to identify the suitable sample size. With the purpose of measuring how effectively the algorithms worked in recognizing tomato leaf disease, the accuracy of the algorithms was taken into consideration. When compared to the K-Nearest Neighbor Algorithm, the Novel Visual Geometry Group 19 algorithm has a mean accuracy of 92.12 percent, which is much greater than the K-Nearest Neighbor Algorithm (67.69 percent). In terms of accuracy and loss, the level of significance is 0.006 (p < 0.05), indicating a significant difference. Statistically speaking, this indicates that there is a difference between the two approaches that is statistically significant on a level of statistical significance. The K-Nearest Neighbor algorithm is not as effective as the Novel Visual Geometry Group 19 approach when it comes to the identification of Tomato leaf Disease. The Novel Visual Geometry Group 19 method obtains a higher mean accuracy than the K-Nearest Neighbor algorithm. [ABSTRACT FROM AUTHOR]

This project will use machine learning technologies, namely the XgBoost algorithm and Novel Random Forest, to find out which restaurant cuisines are the most delectable. The Parts and Methods: This inquiry is built around two algorithms: the XgBoost approach and the Novel Random Forest method. It goes through a total of 10 rounds. These computations were carried out using ClinCalc, with a significance level of α equal to 0.05 and a Gpower value of 0.8. The dataset used in this inquiry was obtained from Kaggle and contains 8,67,000 analyses in total. This database is used to find the best restaurants in terms of food quality; it comprises 51,000 rows and 17 columns. If you want to get the most accurate prediction of the best meals a restaurant has to offer, you should use the Novel Random Forest technique with 10 observations and the XgBoost algorithm with 10 observations. Consequently, compared to the XgBoost algorithm's mean accuracy of 76.08%, the Novel Random Forest approach achieves a far higher mark (63.35 percent). The numerical value of p = 0.004 (Independent Sample T-Test p < 0.05) indicates that there is a statistically significant difference between the two algorithms with respect to accuracy. But when looking at loss, the two groups are significantly different from one another. In studies attempting to forecast which restaurant cuisines will be most popular, the Novel Random Forest algorithm outperforms the XgBoost algorithm with respect to its mean accuracy. [ABSTRACT FROM AUTHOR]

The main goal of this research is to enhance the accuracy of identifying and classifying fresh and decayed fruits using the Novel Visual Geometry Group-16 network and the Naive Bayes algorithm. Improving the detection and classification procedures will achieve this goal. Using machine learning approaches to increase fruit recognition accuracy while decreasing detection loss is the primary goal. What We Need and How It Works: The Novel Visual Geometry Group-16 and Naive Bayes algorithms are computed using ClinCalc. The parameters include a subsample size of 10 and a total sample size of 20, with Gpower maintained at 0.8, a pretest power of 0.8, and an alpha value of 0.05. The dataset is subsequently processed using these algorithms to identify fruit quality, which achieves an average accuracy of 94.60%. The dataset was comprised of 577,086 images of fruit quality detection that were collected from Kaggle. Each and every one of these images includes 224 columns and rows all at once. One way to tell if fruit is of high quality is by measuring its accuracy and loss. With a significance level of 0.014, the statistical analysis showed that VGG-16 had a more substantial impact. It was determined that this result was acceptable. The results of the independent sample T-test showed that the two algorithms were significantly different from one another. The p-value was below than the significance level of 0.05, coming in at 0.014. In conclusion, this study aims to identify the shortcomings of the existing methods and provide an enhanced approach to distinguishing fresh from spoiled fruit samples. The p-value of 0.014 (p<0.05) indicates that the Novel Visual Geometry Group-16 and the Naive Bayes technique have significantly different mean accuracy. [ABSTRACT FROM AUTHOR]

With the use of Decision Tree and Neural Network Algorithms, anti-money-laundering systems may have their false positive rate reduced. Methods and Instruments for Research: Classification accuracy for anti-money-laundering analysis is 87.75% for a recurrent neural network technique (N=10) and 69.60% for a decision tree (N=10). With a pre-test power of 0.8 and an alpha of 0.05, we calculate the sample size using GPower. Finally, when comparing mean accuracy (87.75%), Neural Networks perform better than Decision Trees. Both the accuracy and loss results are statistically significant (p>0.05) at the 0.557 level. Finally, the Mean Accuracy of Neural Networks Anti-Money Laundering Systems Is Higher Than That of Decision Trees. [ABSTRACT FROM AUTHOR]

The goal is to use a Novel Adaboost Ensemble Classifier on top of a FeedForward Neural Network to categorize medications according to patients' medical records. A feedforward neural network (N=10) is used to implement classification with atop it. Given a pretest the is computed using GPower. Compared to FeedForward Neural Network's 92.54% average accuracy, content-based AdaBoost's 98.47% is much higher. Both the accuracy and loss measures have Using patient data, the medication classification system AdaBoost often outperforms FeedForward Neural Network. [ABSTRACT FROM AUTHOR]

This paper's objective is to improve accuracy in plant leaf disease detection using Machine learning techniques such as K-Nearest Neighbors classifier (KNN) with a sample size of 10 and Novel Convolution Neural Network (CNN). This article consists of 2 groupsi.e Convolutional Neural Network (CNN) and K-Nearest Neighbors Classifier with a sample size of 10 for each group. G Power software is used to determine sample size with a pretest power value is 0.8 and alpha 0.05. The accuracy evaluated using both algorithms is compared and CNN appears to be the most significant with an accuracy of 92.48% and KNN with an accuracy of 74.14% with p=0.041. An Independent sample t-test has been used and analyzed with a confidence level of 95 percent. Results show that the CNN algorithm is significantly better than KNN in detecting plant leaf disease. [ABSTRACT FROM AUTHOR]

The project's goal is to identify vehicles, measure count and provide results as the vehicle count. Materials and methods: The performance measure for highest accuracy rate in novel vehicle detection using back propagation neural network (N=10)over Oriented fast and rotated brief which identifies and counts distance. identification can be done using an image set to distinguish vehicles. The Gpower test used in about 85% (g power setting parameters: α=0.05 and power=0.85) Result: Back propagation neural network (94.32%) identifies vehicle and measures the count accurately over Oriented fast and rotated brief (91.16%) with a level of significance as 0.506 (Two tailed,p>0.05). Conclusion: The accuracy rate of Back propagation neural network is higher compared with that of oriented fast and rotated brief. [ABSTRACT FROM AUTHOR]