Classifying the remote sensing images requires a deeper understanding of remote sensing imagery, machine learning classification algorithms, and a profound insight into satellite images' know-how properties. In this paper, a convolutional neural network (CNN) is designed to classify the multispectral SAT-4 images into four classes: trees, grassland, barren land, and others. SAT-4 is an airborne dataset that captures the images in 4 bands (R, G, B, infrared). The proposed CNN classifier learns the image's spectral and spatial properties from the ground truth samples provided. The contribution of this paper is three-fold. (1) A classification framework for feature extraction and normalization is built. (2) Nine different architectures of CNN models are built, and multiple experiments are conducted to classify the images. (3) A deeper understanding of the image structure and resolution is captured by varying different optimizers in CNN. The correlation between images of varying classes is identified. The experimental study shows that vegetation health is predicted most accurately by the proposed CNN models. It significantly differentiates the grassland vegetation from tree vegetation, which is better than other classical methods. The tabulated results show that a state-of-the-art analysis is done to learn varying land cover classification models.
The internet makes it easier for people to connect to each other and has become a platform to express ideas and share information with the world. The growth of the internet has indirectly led to the development of social networking sites. The reviews posted by people on these sites implies their opinion, and analysis over reviews is required to understand their intent. In this paper, natural language processing technique and machine learning algorithms are applied to classify the text data. The contributions of the proposed approach are three-fold: 1) chi square selector is applied to select the k-best features, 2) support vector machines is executed to classify the reviews (hyperparameters of the SVM classifier are tuned using GridSearch approach), and 3) bagging algorithm is applied with the base classifier over the newly built SVM classifier. The number of base classifiers of the bagging algorithm is varied accordingly. The results of the proposed approach are compared to the similar existing work, and hence, it is found to achieve better results as compared to the existing systems.
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