Case-Based Reasoning Diagnostic System for Antenatal Research Database

Abstract

E-health is an exciting area of research with immense potential for benefit of mankind. The research in e-health domain is of such a nature that it provides an opportunity for researchers from different fields to work together for collective research benefit. Antenatal care is a critical issue for the health of the mother and new born. There are multiple complications which can arise during the pregnancy. The availability of all the relevant data for effective diagnosis and treatment is very important for every case of antenatal care. In this paper, we have proposed an efficient Intelligent Electronic Medical Record system for antenatal care and used Case-Based Reasoning to predict diagnosis of the patient in noisy environment. The experimental results obtained have confirmed the soundness of our approach. These results have also shown that performance of e-health can be enhanced when system development is a collective effort of various stakeholders including medical professionals, software developers and researchers.

Federated-Learning Topic Modeling Based Text Classification Regarding Hate Speech During COVID-19 Pandemic

“…These ML and DL techniques are most widely used in the sentiment analysis of the textual content from social media platforms and have yielded excellent outcomes thus far. Moreover, they are several other applications of ML and DL techniques in various domains like mentioned in [12], [13] and [14]. Our goal is to use them for tackling the spread of hate speech such that results in Islamophobia.…”