A novel approach for the effective prediction of cardiovascular disease using applied artificial intelligence techniques.

AIMS: The objective of this research is to develop an effective cardiovascular disease prediction framework using machine learning techniques and to achieve high accuracy for the prediction of cardiovascular disease. METHODS: In this paper, we have utilized machine learning algorithms to predict cardiovascular disease on the basis of symptoms such as chest pain, age and blood pressure. This study incorporated five distinct datasets: Heart UCI, Stroke, Heart Statlog, Framingham and Coronary Heart dataset obtained from online sources. For the implementation of the framework, RapidMiner tool was used. The three-step approach includes pre-processing of the dataset, applying feature selection method on pre-processed dataset and then applying classification methods for prediction of results. We addressed missing values by replacing them with mean, and class imbalance was handled using sample bootstrapping. Various machine learning classifiers were applied out of which random forest with AdaBoost dataset using 10-fold cross-validation provided the high accuracy. RESULTS: The proposed model provides the highest accuracy of 99.48% on Heart Statlog, 93.90% on Heart UCI, 96.25% on Stroke dataset, 86% on Framingham dataset and 78.36% on Coronary heart disease dataset, respectively. CONCLUSIONS: In conclusion, the results of the study have shown remarkable potential of the proposed framework. By handling imbalance and missing values, a significantly accurate framework has been established that could effectively contribute to the prediction of cardiovascular disease at early stages.

REVIEW- Covid-19: Diagnosis, summary of essays and evolving approaches.

COVID-19 spread worldwide after its outbreak in December 2019. This review paper aims to educate the readers regarding SARS-CoV-2 diagnostic and detection tools and the issues experienced by researchers. We identify on-the-horizon point-of-care diagnostic tests and inspire scholars to develop their innovations past conception. It will also effectively avoid potential pandemics to establish plug-and-play diagnostic information to handle the SARS infection. The authors agree that arbitrary-access, interconnected systems with flexible functionality accessible at the point-of-care, would enable fast and precise diagnosis and tracking.

Secure Route-Obfuscation Mechanism with Information-Theoretic Security for Internet of Things.

As accessibility of networked devices becomes more and more ubiquitous, groundbreaking applications of the Internet of Things (IoT) find their place in many aspects of our society. The exploitation of these devices is the main reason for the cyberattacks in IoT networks. Security design is still an open problem and a crucial step in making IoT applications successful. In dicey environments, such as e-health, smart grid, and smart cities, real-time commands must reach the end devices in the scale of milliseconds. Traditional public-key cryptosystem, albeit necessary in the context of general Internet security, falls short in establishing new session keys in the scale of milliseconds for critical messages. In this paper, a systematic perspective for securing IoT communication, specifically satisfying the real-time constraint against certain adversaries in realistic settings. First, at the network layer, we propose a secret random route computation scheme using the software-defined network (SDN) based on a capability scheme using the network actions. The computed routes are random in the eyes of the eavesdropper. Second, at the application layer, the source breaks command messages into secret shares and sends them through the network to the destination. Only the legitimate destination device can reconstruct the command. The secret sharing scheme is efficient compared to PKI and comes with information-theoretic security against adversaries. Our proof formalizes the notion of security of the proposed scheme, and our simulations validate our design.