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SUMMARY With the introduction of mobile‐wearable gadgets, the embedding of health‐tracking capabilities has advanced dramatically. The Covid‐19 years have accelerated research toward wearable sensor‐based health monitoring. One of the most important applications in health monitoring is human activity identification. Daily behaviors like walking, sitting and jogging, as well as crucial activities such as falling forward and backward, provide a barrier in HAR (human action recognition) since they are semantically comparable. Previously developed deep learning algorithms have addressed some of these issues. However, these algorithms are hindered by a lack of training data. To cope with nonuniform samples in the human activities data, which can lead to overfitting results, this work introduces the synthetic minority oversampling approach. This article proposes a unique configuration of stacked convolutional neural network (CNN)‐AR‐DenseNet. With Bayesian optimization, the parameters of AR‐DenseNet are optimally optimized. When compared to state‐of‐the‐art stacked CNN network methods, the classification accuracy improved by up to 3.22%, with a substantial improvement of 5.8% over the standard CNN algorithm.

In today's day and age, the need for private communication has been increased. The outcry over security agencies snooping on our personal data underlines the need of more secure means of transferring data. This is where steganography comes into picture. Here we will focus on a particular technique of steganography called F5 Algorithm which is one of the newer techniques. Steganography is the act of hiding important information, a message or file, inside another file so that it is undetected by any eavesdroppers. Earlier methods of Steganography were weak against visual and statistical attacks, F5 shields against all such attacks while offering large steganographic capacity.