This paper discusses detection of change in land usage in Davangere (Karnataka State, India) between the years 2016 and 2021. After the place has been declared as one of the smart cities identified by the Govt. of India in 2014 and subsequent to the international price crash for sugar, there were noticeable changes in land utilization in terms of urbanization and shift in traditional cropping pattern. The objective of this research work is to capture this change using remote sensing, the images from MSI Sentinel-2 were collected at two points of time and processed for LULC with the help of supervised machine learning classifiers such as Minimum Distance, Mahalanobis Distance and Maximum Likelihood to ascertain the accurate one. It was found that Maximum Likelihood classifier ensures highest accuracy of 95.2%. It was also found that during the study period, there was a significant change in the land use with respect to Built-up area and Area under cultivation of Paddy.

This paper presents a Fuzzy Inference based Ultrasound Image Analysis System for differential diagnosis of Thyroid Nodules (TNs). Thyroid Ultrasound (TUS) images containing TNs are preprocessed to remove speckle noise and are enhanced using histogram equalization method. Nodule boundaries are identified using the canny edge detection technique and required Region of Interest is obtained using Adaptive Regularized Kernel Fuzzy C-means (ARKFCM) segmentation algorithm. Nineteen texture features are extracted from the segmented images. Best First (BF), Genetic Search (GS) and Greedy Step Wise (GSW) search methods are applied to select best subset of features. Selected features are fuzzified. A novel, fuzzy system is built to discriminate benign from malignant TNs by employing Mamdani model to draw inferences and centroid scheme for defuzzification. Class Based Association (CBA) concept is used to generate fuzzy inference rules. The developed multiple input, single output FIUIAS resulted in an accuracy of 98%.