Simulating graph models for real world networks is made easy using software tools like Cytoscape. In this paper, we present the open-source CySpanningTree app for Cytoscape that creates a minimal/maximal spanning tree network for a given Cytoscape network. CySpanningTree provides two historical ways for calculating a spanning tree: Prim's and Kruskal's algorithms. Minimal spanning tree discovery in a given graph is a fundamental problem with diverse applications like spanning tree network optimization protocol, cost effective design of various kinds of networks, approximation algorithm for some NP-hard problems, cluster analysis, reducing data storage in sequencing amino acids in a protein, etc. This article demonstrates the procedure for extraction of a spanning tree from complex data sets like gene expression data and world network. The article also provides an approximate solution to the traveling salesman problem with minimum spanning tree heuristic. CySpanningTree for Cytoscape 3 is available from the Cytoscape app store.

In this paper, we describe how combinatorial testing can be applied to re-architecture Contiki and Cooja regression test suites. Contiki is the popular and widely accepted internet of things operating system. Combinatorial testing was pioneered by National Institute of Standards and Technology. National Institute of Standards and Technology offers a set of tools to public. One such tool is Automated Combinatorial Testing for Software. We describe how Automated Combinatorial Testing for Software can be used to generate a complete test suite for Contiki and Cooja. Coverage of base test suite is gathered using CodeCover, a code coverage tool for Java. The low percentage of coverage in Cooja indicated the need for a redesign of test suite. Once the base regression test suite is modified using Combinatorial Testing approach, it can be the new base regression test suite.

Magnetic Resonance Imaging (MRI) plays a crucial role in detecting structural and functional abnormalities in the brains of epilepsy patients. Automating this process using advanced AI/ML techniques can reduce the burden on experts, be a training resource, and make diagnosis accessible and affordable to a larger population. However, models developed using imaging data have not achieved acceptable accuracy levels. One reason for this is the limited number of data points available and artifacts in the data capture mechanism of the available datasets. There is a need to develop a generalized model that can take as input diverse images with multiple conditions, which predicts epilepsy and identifies the impacted brain regions. In this work, we present the design of deep-learning models capable of predicting epilepsy using 3D brain Magnetic Resonance Imaging (MRI) scans. Five deep learning models were developed to classify 3D MRI scans to identify specific brain regions involved in Hippocampal Sclerosis, Dysembryoplastic Neuroepithelial Tumors, Cavernomas, Focal Cortical Dysplasia, and other epileptic-related conditions. We find that our trained models predict epilepsy with over 91% accuracy on FLAIR images and nearly 100% accuracy on T1-weighted (T1-w) images. Our results show that deep learning architectures in 3D are highly effective in detecting epilepsy and identifying relevant pathological regions. These findings highlight not only the diagnostic accuracy of the models but also their potential utility in aiding clinical decision-making.