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Titlebook: Advances on Intelligent Computing and Data Science; Big Data Analytics, Faisal Saeed,Fathey Mohammed,Mohammed Al-Sarem Conference proceedi

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Przemysław Garsztka,Paweł Kliberases in tomato crops using leaf images. The process involves building a convolutional neural network using a pre-trained VGG16 model that pre-processes the images according to its requirements and performs segmentation on images before training and testing the data. The model obtained an accuracy of
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Katarzyna Kuziak,Krzysztof Pionteknd moth-flame optimization (MFO) are effective at optimising functions. This work introduces a novel hybrid sentiment-based SVM optimised by particle swarm and moth-flame algorithms (SVMPSOMFO) to improve predicting accuracy. SVMPSOMFO optimises the model’s parameter values by combining PSO and MFO,
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Krzysztof Piasecki,Joanna Siwekich are used to determine if transactions are good or bad. The findings of data analysis using Logistic Regression, Linear Discriminant Analysis, Gaussian Naive Bayes, K-Nearest Neighbors Classifier, Decision Tree Classifier, Support Vector Machines, and Random Forest are compared and contrasted in
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Contemporary Trends in Local Governanceactory, as it was capable of predicting evidence of having a heart condition in a specific patient utilizing DL and the ML Model (Random-Forest-Classifier) that had high accuracies when compared to other employed classifiers. The proposed DL methodology for predicting heart disease is going to impro
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Advances on Intelligent Computing and Data ScienceBig Data Analytics,
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