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Dengue fever modelling in endemic locations is critical to reducing outbreaks and improving vector-borne illness control. Early projections of dengue are a crucial tool for disease control because of the unavailability of treatments and universal vaccination. Neural networks have made significant contributions to public health in a variety of ways. In this paper, we develop a deep learning modelling using random forest (RF) that helps extract the features of the dengue fever from the text datasets. The proposed modelling involves the data collection, preprocessing of the input texts, and feature extraction. The extracted features are studied to test how well the feature extraction using RF is effective on dengue datasets. The simulation result shows that the proposed method achieves higher degree of accuracy that offers an improvement of more than 12% than the existing methods in extracting the features from the input datasets than the other feature extraction methods. Further, the study reduces the errors associated with feature extraction that is 10% lesser than the other existing methods, and this shows the efficacy of the model.

Ventricular tachycardia (VT) and ventricular fibrillation (VF) are known ventricular cardiac arrhythmias (VCA) that promote fast defibrillation treatment for the survival of patients and are defined as shock-oriented signals, perhaps the most common source of sudden cardiac arrest (SCA). The majority of existing VCA classifiers confront a difficult challenge of accuracy rate, which has generated the issue of continuous detection and classification approaches. In light of this, the authors present a feature learning strategy that uses the improved variational mode decomposition technique to detect VCA on ECG signals. The following SCA consists of a deep convolutional neural network (deep CNN) as a feature extractor and bat-rider optimization algorithm (BROA) as an optimized classifier. The MIT-BIH arrhythmia database is used to examine the approaches, and the analysis depends on performance indicators such as accuracy, specificity, sensitivity, recall, and F1-score.

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