This paper presents an application of Artificial Neural Networks (ANN) in predicting patient coronary heart disease status. Multilayer perceptron (MLP) which is a type of ANN architecture was used to develop the proposed model. Several experiments were carried out to determine the network optimal parameters. Overall, the optimised ANN system achieved a very high diagnosing accuracy of 92.2%, proving its usefulness in support of diagnosis process of coronary heart disease.

In recent years, users of Mobile Instant Messaging (MIM) apps like WhatsApp and Telegram are being targeted by phishing attacks. While user susceptibility to phishing in other media is well studied, the literature currently lacks studies on phishing susceptibility in MIM apps. This paper presents a study that offers the first insights into the susceptibility of users of MIM apps to phishing by investigating their behaviour towards shared links. Using an online survey, we collected data from 111 users of MIM apps and found that participants frequently click and forward links during instant messaging, while factors such as the user's relationship with the sender and the group context of the communication influence these behaviours. The results show that behaviours of most users towards shared links try to reduce their risk to phishing by trusting their friends, family and colleagues to protect them. This raises some interesting questions for further research on the effectiveness and reliability of their strategy.

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