Design and Development of a User Specific Dynamic E-Magazine

Internet and electronic media gaining more popularity due to ease and speed, the count of Internet users has increased tremendously. The world is moving faster each day with several events taking place at once and the Internet is flooded with information in every field. There are categories of information ranging from most relevant to user, to the information totally irrelevant or less relevant to specific users. In such a scenario getting the information which is most relevant to the user is indispensable to save time. The motivation of our solution is based on the idea of optimizing the search for information automatically. This information is delivered to user in the form of an interactive GUI. The optimization of the contents or information served to him is based on his social networking profiles and on his reading habits on the proposed solution. The aim is to get the user's profile information based on his social networking profile considering that almost every Internet user has one. This helps us personalize the contents delivered to the user in order to produce what is most relevant to him, in the form of a personalized e-magazine. Further the proposed solution learns user's reading habits for example the news he saves or clicks the most and makes a decision to provide him with the best contents.Comment: 19 pages, 6 figure

Higher order organizational features can distinguish protein interaction networks of disease classes: a case study of neoplasms and neurological diseases

Neoplasms (NPs) and neurological diseases and disorders (NDDs) are amongst the major classes of diseases underlying deaths of a disproportionate number of people worldwide. To determine if there exist some distinctive features in the local wiring patterns of protein interactions emerging at the onset of a disease belonging to either of these two classes, we examined 112 and 175 protein interaction networks belonging to NPs and NDDs, respectively. Orbit usage profiles (OUPs) for each of these networks were enumerated by investigating the networks' local topology. 56 non-redundant OUPs (nrOUPs) were derived and used as network features for classification between these two disease classes. Four machine learning classifiers, namely, k-nearest neighbour (KNN), support vector machine (SVM), deep neural network (DNN), random forest (RF) were trained on these data. DNN obtained the greatest average AUPRC (0.988) among these classifiers. DNNs developed on node2vec and the proposed nrOUPs embeddings were compared using 5-fold cross validation on the basis of average values of the six of performance measures, viz., AUPRC, Accuracy, Sensitivity, Specificity, Precision and MCC. It was found that nrOUPs based classifier performed better in all of these six performance measures.Comment: 14 pages, 3 figures, 3 table

Stochastic Coalitional Better-response Dynamics and Strong Nash Equilibrium

We consider coalition formation among players in an n-player finite strategic game over infinite horizon. At each time a randomly formed coalition makes a joint deviation from a current action profile such that at new action profile all players from the coalition are strictly benefited. Such deviations define a coalitional better-response (CBR) dynamics that is in general stochastic. The CBR dynamics either converges to a strong Nash equilibrium or stucks in a closed cycle. We also assume that at each time a selected coalition makes mistake in deviation with small probability that add mutations (perturbations) into CBR dynamics. We prove that all strong Nash equilibria and closed cycles are stochastically stable, i.e., they are selected by perturbed CBR dynamics as mutations vanish. Similar statement holds for strict strong Nash equilibrium. We apply CBR dynamics to the network formation games and we prove that all strongly stable networks and closed cycles are stochastically stable