Problem statement: Segmentation is a vital aspect of medical imaging. It aids in the visualization of medical data and diagnostics of various diseases. Ultrasound image segmentation, in particular echocardiographic image segmentation, is required to identify the regions of interest such as Left Ventricle (LV) and other cardiac cavities. Existing methods do not address the drawbacks of speed and quality of segmentation. A faster method is required for effective, accurate and scalable clinical analysis and diagnosis. Approach: In this research, a novel approach is used to segment the 2D echo images of various views. A modified K-Means clustering algorithm, called "Fast SQL K-Means" is proposed using the power of SQL in DBMS environment. In K-Means, Euclidean distance computation is the most time consuming process. However, here it computed with a single database table and no joins. This method takes less than 10 sec to cluster an image size of 400×250 (100K pixels), whereas the running time of direct K-Means is around 900 sec. Since the entire processing is done with database, additional overhead of import and export of data is not required. The 2D echo images are acquired from the local Cardiology Hospital for conducting the experiments. Results: The proposed algorithm was tested by considering a number of echo images in apical four chamber, long-axis and short axis views. We have compared the direct K-Means implementation with the proposed algorithm by varying the data size from 10-100K and found that the results outperformed compared to the results of other authors. The pattern of the data and the number of clusters had almost no impact on the clustering time. Conclusion: An efficient and nontraditional model for echo image segmentation is presented by using the SQL. Fast algorithms are required for immediate analysis of echo images within ICUs, remote places, telemedicine. The challenge is that ultrasound images are prone to speckle noise, segmented echo images carry gaps in the cardiac regions which in turn causes difficulties in boundary tracing and selection of seed values for the K-Means. Future research can enhance the speed by partitioning the database tables and use of parallel SQL statements.

In Common parlance, the traditional software reliability estimation methods often rely on assumptions like statistical distributions that are often dubious and unrealistic. The ability to predict the number of faults during development phase and a proper testing process helps in specifying timely release of software and efficient management of project resources. In the Present Study Enhancement and Comparison of Ant Colony Optimization Methods for Software Reliability Models are studied and the estimation accuracy was calculated. The Enhanced method shows significant advantages in finding the goodness of fit for software reliability model such as finite and infinite failure Poisson model and binomial models.

Smartphones are equipped with various types of sensors which make them a promising tool to assist diverse digital farming tasks because of their mobility, cost, accessibility, and computing power allow us to perform real-time practical applications. This paper presents utilisation of various non-destructive methods of nutrient and disease classification techniques using smartphone collected images, processed through various image segmentation algorithms. Both in vivo and in vitro estimations shows comparable results with both chlorophyll and nitrogen contents of a crop shoot. Moreover, the correlation between SPAD measured values and nitrogen of crop shoot showed a significant linear association (R 2 =0.7309), revealing the potency of in vivo observation for prediction of actual chlorophyll content in tea crop. SPAD values and yield have a strong linear relationship (R 2 =0.7103), in which SPAD-meter performed better detection at very low values. The study concluded that the proposed techniques could be used for automatic detection as well as classification of foliar diseases and nutrients in tea.