Objectives:To present an extraction technique for the classification of the hyperspectral crop using the spatial-spectral feature. Methods: This paper presents a spatial-spectral feature extraction method employing the Image fusion technique and intrinsic feature extraction and a model for Improved Decision Boundary (IDB) using Support Vector Machine (SVM). Findings:The experiments have been conducted by using the Indian pines dataset which was extracted using the AVIRIS sensor. The dataset comprises of 16 distinctive classes such as corn, wheat, oats etc, which have used for evaluation of our model. Before the evaluation of the dataset the model has been trained using different training datasets in order to increase the accuracy and reduce misclassification. Moreover, the Spatial-Spectral Feature (SSF) model aided in distinguishing between crop intrinsic features and shadow element under dynamic environment condition. Our model attained 99.54%, 99.4%, 99.25% and 9.8 sec for OA accuracy, AA accuracy, Kappa and Time respectively. Furthermore, the overall accuracy of the model for the Support Vector Machine-3-dimensional discrete wavelet transform (SVM-3DDWT), Convolutional Neural Network and Spatial-Spectral Feature Extraction Technique showed result of 94.28%, 96.12% and 99.4% respectively. Other existing models showed a low accuracy for the same dataset. Further, for addressing class imbalance issues this work modelled an improved decision boundary model for SVM by considering soft-margin rather than hard margin. The SSF-IDBSVM model achieves much better accuracies with less misclassification in comparison with recent deep learning-based HSI classification model. Novelty: Recently, several feature extraction and deep learning-based crop classification model have been modelled. However, existing crop classification fails to distinguish crop intrinsic feature concerning shadow feature; further, consider hard decision boundary; as a result, high misclassification is induced for smaller class size. Hence, this study provides an extraction feature which provides the classification of the crop in less time with higher classification and less misclassification.
Abstract-As the electricity plays very important role now a days, the reliability analysis of power distribution also has same important role. The daily load data of lakya feeder collected by the log book chikkamagaluru muss and various indices are calculated. The outages are classified in to types, frequency and duration. The reliability indices are calculated on monthly basis for an year from January to December from the year 2013 to 2016.The average availability of lakya feeder is 0.68.The suggestions were made to minimize the outages and hence to improve reliability.
<p>Accessing and Utilization of data and information from remote location is one of the major requirements of present world. Due to the increase in the requirement of the data access from remote locations, challenges in the enhancement of technology based systems also have increased proportionately. Technology based solution for accessibility of remote data with available infrastructure is the need of the hour. Implementation of technology based solutions for the challenges may be expensive due to the current technical limitations. In this paper, we have attempted to design a secured cloud based framework for Online Voting System and analyzed its performance based on the three cryptographic algorithms namely Blowfish, AES and RSA.</p>
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