In this paper, Supervised Maximum Likelihood Classification (MLC) has been nsed for analysis of remotely sensed image.The Landsat ETM+ image has used for classification. MLC is based on Bayes' classification and in this classification a pixelis assigned to a class according to its probability of belonging to a particnlar class.Mean vector and covariance metrics are the key component of MLC that can be retrieved from training data. Classification results have shown that MLC is the robnst techniqne and there is very less chances of misclassification. The classification accuracy has been achieved overall accuracy of 93.75%, producer accuracy 94%, user accuracy 96.09% and overall kappa accuracy 90.52%.

Quantum-enhanced machine learning plays a vital role in healthcare because of its robust application concerning current research scenarios, the growth of novel medical trials, patient information and record management, procurement of chronic disease detection, and many more. Due to this reason, the healthcare industry is applying quantum computing to sustain patient-oriented attention to healthcare patrons. The present work summarized the recent research progress in quantum-enhanced machine learning and its significance in heart failure detection on a dataset of 14 attributes. In this paper, the number of qubits in terms of the features of heart failure data is normalized by using min-max, PCA, and standard scalar, and further, has been optimized using the pipelining technique. The current work verifies that quantum-enhanced machine learning algorithms such as quantum random forest (QRF), quantum K nearest neighbour (QKNN), quantum decision tree (QDT), and quantum Gaussian Naïve Bayes (QGNB) are better than traditional machine learning algorithms in heart failure detection. The best accuracy rate is (0.89), which the quantum random forest classifier attained. In addition to this, the quantum random forest classifier also incurred the best results in F 1 score, recall and, precision by (0.88), (0.93), and (0.89), respectively. The computation time taken by traditional and quantum-enhanced machine learning algorithms has also been compared where the quantum random forest has the least execution time by 150 microseconds. Hence, the work provides a way to quantify the differences between standard and quantum-enhanced machine learning algorithms to select the optimal method for detecting heart failure.

In this paper, we have compared the accuracy of four supervised classification as Mahalanobis, Maximum Likelihood Classification (MLC), Minimum distance and Parallelepiped classification with remote sensing Landsat images of different time period and sensors. We have used Landsat Multispectral Scanner (MSS), Thematic Mapper (TM) and Enhanced Thematic Mapper+ (ETM+) images of 1972, 1998 and 2013 respectively of Jaipur district, Rajasthan, India. Accuracy has been calculated using Producer accuracy, User accuracy, Overall accuracy and Kappa statistics. We have found that remote sensing images with a different time period and sensors, when classified with supervised algorithms produced different results. Minimum distance classification produced better accuracy with Landsat MSS image than other three classifications while Maximum Likelihood Classification produced better accuracy with Landsat TM and ETM+ images than other three classifications.

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