Heart Diseases Prediction Using Block-chain and Machine Learning

Most people around the globe are dying due to heart disease. The main reason behind the rapid increase in the death rate due to heart disease is that there is no infrastructure developed for the healthcare department that can provide a secure way of data storage and transmission. Due to redundancy in the patient data, it is difficult for cardiac Professionals to predict the disease early on. This rapid increase in the death rate due to heart disease can be controlled by monitoring and eliminating some of the key attributes in the early stages such as blood pressure, cholesterol level, body weight, and addiction to smoking. Patient data can be monitored by cardiac Professionals (Cp) by using the advanced framework in the healthcare departments. Blockchain is the world's most reliable provider. The use of advanced systems in the healthcare departments providing new ways of dealing with diseases has been developed as well. In this article Machine Learning (ML) algorithm known as a sine-cosine weighted k-nearest neighbor (SCA-WKNN) is used for predicting the Hearth disease with the maximum accuracy among the existing approaches. Blockchain technology has been used in the research to secure the data throughout the session and can give more accurate results using this technology. The performance of the system can be improved by using this algorithm and the dataset proposed has been improved by using different resources as well.Comment: page 23, figurse 1

Numerical Recipes in Python

Numerical Recipes in Python is to serve as Laboratory Manual of Simplified Numerical Analysis (Python Version): A companion book of the principal book: Simplified Numerical Analysis (Fourth Edition) by Dr. Amjad Ali

Data-Driven Reduced-Order Modeling of Unsteady Nonlinear Shock Wave using Physics-Informed Neural Network (PINN) Based Solution

This article presents a preliminary study on data-driven reduced-order modeling (ROM) of unsteady nonlinear shock wave. A basic form of such problem can be modeled using the Burgers’ equation. The physics-informed neural networks (PINN) approach is used to obtain numerical solutions to the problem at certain time steps. PINN is a cutting-edge computational framework that seamlessly integrates deep neural networks with the governing physics of the problem and is turning out to be promising for enhancing the accuracy and efficiency of numerical solutions in a wide array of scientific and engineering applications. Next, extraction of the Proper Orthogonal Decomposition (POD) modes from the solution field is carried out, providing a compact representation of the system’s dominant spatial patterns. Subsequently, temporal coefficients are computed at specific time intervals, allowing for a reduced-order representation of the temporal evolution of the system. These temporal coefficients are then employed as input data to train a deep neural network (DNN) model designed to predict the temporal coefficient at various time steps. The predicted coefficient can be used to form the solution. The synergy between the POD-based spatial decomposition and the data-driven capabilities of DNN results in an efficient and accurate model for approximating the solution. The trained ANN subsequently takes the value of the Reynolds number and historical POD coefficients as inputs, generating predictions for future temporal coefficients. The study demonstrates the potential of combining model reduction techniques with machine learning approaches for solving complex partial differential equations. It showcases the use of physics-informed deep learning for obtaining numerical solutions. The idea presented can be extended to solve more complicated problems involving Navier-Stokes equations

A Process Model for Developing Semantic Web Systems

Abstract: Before the Web era various software development methodologies have been proposed for the development of software applications for different domains. The main objectives of those methodologies were to meet user's requirements, find out means to suggest a systematic software development and reduce the maintenance cost of the developed software. On the emergence of the Web and to develop the web-based software systems, some existing methodologies have been extended. Also, new approaches (or informal methodologies) are introduced for the development of web-based systems because the development process for these systems is not considered as an extension of the classical software engineering, although both development processes for web-based systems and non web-based systems have the same basic objective which is software development. Of course, the development of the web-based systems needs a new kind of development methodologies which should meet and capture their unique and different requirements. Currently available software development methodologies are inappropriate and unsuitable to use for the development of web-based software systems, especially for the third generation web, called Semantic Web. In this paper, we present a brief review of the existing software development methodologies for the development of web-based systems. Some informal software development methodologies (or approaches) for the semantic web are also reviewed. Then, based on this analytical review, we propose a model for the development of semantic web systems. This model can be used as a benchmark to propose formal methodologies for the development of the semantic web systems