Deep learning(DL) is a branch of artificial intelligence that emulates human brain functions through computational processes. It has demonstrated its effectiveness across various domains; healthcare is no exception. Encouraging outcomes have been achieved in multiple healthcare applications, which include the classification of cancer, its prognosis, diagnosis, and classifying different molecular subtypes of cancer. Molecular subtyping using gene expression data may provide biological insights into cancer heterogeneity, which is instrumental in developing personalized medicine. The samples' scarcity relative to the high dimensional feature space remains a challenge in implementing deep learning models. This research investigates the effectiveness of clustering for reducing the dimensionality of the transcriptomic data and its subsequent influence on classification accuracy. The proposed method clusters the features and leverages the cluster centroids to train the classification model to predict the cancer molecular subtypes of colorectal cancer. The result comparison of the model with and without clustering reveals improved performance, in our proposed framework, while achieving parity with accuracy levels in others.

Static system configuration provides a significant advantage for the adversaries to discover the assets and launch attacks. Configuration-based moving target defense (MTD) reverses the cyber warfare asymmetry by mutating certain configuration parameters to disrupt the attack planning or increase the attack cost significantly. In this research, we present a methodology for the formal verification of MTD techniques. We formally modeled MTD techniques and verified them against constraints. We use Random Host Mutation (RHM) as a case study for MTD formal verification. The RHM transparently mutates the IP addresses of end-hosts and turns into untraceable moving targets. We apply the formal methodology to verify the correctness, safety, mutation, mutation quality, and deadlock-freeness of RHM using the model checking tool. An adversary is also modeled to validate the effectiveness of the MTD technique. Our experimentation validates the scalability and feasibility of the formal verification methodology.

SysML is a graphical modeling language that is mostly used for the graphical representation of real-time systems, complex systems, safely critical systems, and embedded systems. In this paper, we present a methodology based on model checking tool for the correction and verification of SysML internal block diagram with discrete time constraint. We describe the mapping of SysML internal block diagram to PRISM input language and use Probabilistic Computational Tree Logic (PCTL) for the verification of properties. The methodology provides more reliable and quick results for the development of real time systems as PRISM supports parallel composition of components. Finally, we present the effectiveness of our approach with the help of a case study of real-time system. The discrete time factor is included in the case study to evaluate the performance characteristics of system functionality.