Today, graph theory has become major instrument that is used in an array of fields. Some of these include electrical engineering, mathematical research, sociology, economics, computer programming/networking, business administration and marketing. Indeed, many problems can be modeled with paths formed by traveling along the edges of a certain graph. Frequently referenced problems are efficiently planning routes for mail delivery, garbage pickup and snow removal, which can be solved using models that involve paths in graphs. Given these kinds of problems, graphs can become extremely complex, and a more efficient way of representing them is needed in practice. This is where the concept of the adjacency matrix & adjacency list comes into play.
A phase-sharing scheme using the Mach-Zehnder interferometric setup is demonstrated. Two coherent light fields of the same wavelength which have orthogonal polarizations are used as sources at the two ends of a Mach-Zehnder interferometer. They are made to interfere independently at the opposing ends of the interferometer so that the phase estimated by two observers at the two opposing ends of the interferometer is nearly identical. The scheme could in principle be used by two observers to simultaneously monitor and study a phase object inserted in one of the arms of the interferometer. A pseudorandom phase plate which mimics atmospheric turbulence is inserted in one of the arms of the interferometer to demonstrate that such a phase-sharing scheme could be converted to a secret-key sharing scheme. Shared secret-key generation is demonstrated through evaluation of the phase correlates of the shared phase samples available at their respective ends. The shared random phases could also be used in a more direct manner by the respective observers for random phase encryption of images.
Cardiovascular or heart diseases consist a global major health concern. Cardiovascular diseases have the highest mortality rate worldwide, and the death rate increases with age, but an accurate prognosis at an early stage may increase the chances of surviving. In this paper, a combined approach, based on Machine Learning (ML) with an optimization method for the prediction of heart diseases is proposed. For this, the Improved Auto Categorical Particle Swarm Optimization (IACPSO) method was utilized to pick an optimum set of features, while ML methods were used for data categorization. Three heart disease datasets were taken from the UCI ML library for testing: Cleveland, Statlog, and Hungarian. The proposed model was assessed for different performance parameters. The results indicated that, with 98% accuracy, Logistic Regression (LR) and Support Vector Machine by Grid Search (SVMGS) performed better for the Statlog, SVMGS outperformed on the Cleveland, while the LR, Random Forest (RF), Support Vector Machine (SVM), and SVMGS performed better with 97% accuracy on the Hungarian dataset. The outcomes were improved by 3 to 33% in terms of performance parameters when ML was applied with IACPSO.
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