Deterministic mathematical models (called Compartmental models) of disease propagation such as the SIR model and its variants (MSIR, Carrier state, SEIR, SEIS, MSEIR, MSEIRS models) are used to study the propagation of COVID19 in a large population with specific reference to India.

Histopathology image analysis is widely accepted as a gold standard for cancer diagnosis. The Cancer Genome Atlas (TCGA) contains large repositories of histopathology whole slide images spanning several organs and subtypes. However, not much work has gone into analyzing all the organs and subtypes and their similarities. Our work attempts to bridge this gap by training deep learning models to classify cancer vs. normal patches for 11 subtypes spanning seven organs (9,792 tissue slides) to achieve high classification performance. We used these models to investigate their performances in the test set of other organs (cross-organ inference). We found that every model had a good cross-organ inference accuracy when tested on breast, colorectal, and liver cancers. Further, high accuracy is observed between models trained on the cancer subtypes originating from the same organ (kidney and lung). We also validated these performances by showing the separability of cancer and normal samples in a high-dimensional feature space. We further hypothesized that the high cross-organ inferences are due to shared tumor morphologies among organs. We validated the hypothesis by showing the overlap in the Gradient-weighted Class Activation Mapping (GradCAM) visualizations and similarities in the distributions of nuclei features present within the high-attention regions.

The objective of this paper is to study a treatment to social network analysis using the principles of statistical mechanics. After revisiting the popular models and random graph frameworks of complex networks, a formalism to statistical mechanism based on the conventional concepts like phase space, interactions and ensembles is devised. Specific machine learning techniques are employed for the purpose of figuring out the relevant phase-space equations. Thereafter, specific applications of the formalism is explored in the context of business partnership optimization and disease transmission. Several analogues with the statistical mechanics treatment of thermodynamics have also been made.

<div>This paper, discusses the implementation of RSA (Rivest–Shamir–Adleman) algorithm of encrypting and decrypting data. This is an assymetric algorithmic approach, which implies that two different keys, one public and another private have been used to accomplish the purpose. This paper also extends the application of RSA algorithm by choosing random prime numbers for every byte of transferred data. The programs involved are entirely based on the microprocessor INTEL 8085.</div><div><br></div>

<div>This paper, discusses the implementation of RSA (Rivest–Shamir–Adleman) algorithm of encrypting and decrypting data. This is an assymetric algorithmic approach, which implies that two different keys, one public and another private have been used to accomplish the purpose. This paper also extends the application of RSA algorithm by choosing random prime numbers for every byte of transferred data. The programs involved are entirely based on the microprocessor INTEL 8085.</div><div><br></div>