Abstract:
Artificial intelligence (AI) solutions are used to help make choices that include a high
precision of choices they recommend and a deep understanding of choices so that the
chiefs can trust them. Verifiable, non-emblematic learning methods have greater
perceptual accuracy. Hybrid AI systems analyze the data and exploratory
characteristics of approach types. The fundamental purpose of this commitment is to
differentiate between a proper AI strategy for choice of assistance that produces
reliable and fair outcomes, depending on the various AI techniques, which provide an
analysis of different approaches. In this study, we present a hybrid framework for
classification and feature extraction. Such a hybrid framework is required for the
selection of dataset to Machine learning classifier that will have different results with
unlike datasets We have utilized five different types of datasets in this study. As datasets
are imbalanced so preprocessing of data is performed firstly. Input with maximum
accurate results will be reproduced from our hybrid approach because this approach
shows which type of classifiers should be used under what type of dataset, meanwhile
exception of the generous fact based on results should be different among different
classifiers when applied to a various dataset. After that, a comparative analysis of
generic Machine learning algorithms with various datasets has been made as well. The
accuracy of the hybrid approaches is compared with the generic approaches, a clear
improvement in results in the form of accuracy, precision, recall, and F1 score is found.
We used the initial layers of CNN for feature extraction and passess them to ML
algorithms for classification.Each information image will go through a progression of
convolution layers with channels (Kernels), pooling, fully linked layers (FC) and
applying Softmax capabilities to define an object with probabilistic qualities anywhere
in the range of 0 and 1. It's one of the simple classes for acknowledging images,
arrangements for photos. At the end of this study, we infer which Machine learning
algorithm improves the classification accuracy for which type of dataset.
