The rapid developments observed in the field of Internet of Things (IoT), along with the recently increasing dependence on this technology in home and financial applications, have made it necessary to pay attention to the security of information sent through these IoT applications. The present article proposes a new encryption method for important messages that are sent via IoT applications. The proposed method provides four levels of security for the confidential message (in this case, an image). The first level is represented by applying the Conformal Mapping on the secret image. The second level is represented by encoding the resulting image from the first level using the encryption and decryption (RSA) method, while the third level is the use of Less Significant Bit (LSB) as the hiding method to hide the message inside the cover image. The compression of the stego image using GZIP is the last level of security. The peak signal-to-noise (PNSR) metric was used to measure the quality of the resulting image after the steganography process. The results appear promising and acceptable. Therefore, it is suggested that this method can be applied to send secret messages through applications of special importance across the IoT.

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In the past two years, the world witnessed the spread of the coronavirus (COVID-19) pandemic that disrupted the entire world, the only solution to this epidemic was health isolation, and with it everything stopped. When announcing the availability of a vaccine, the world was divided over the effectiveness and harms of this vaccine. This article provides an analysis of vaccinators and analysis of people's opinions of the vaccine's efficacy and whether negative or positive. Then a model is built to predict the future numbers of vaccinators and a model that predicts the number of negative opinions or tweets. The model consists of three stages: first, converting data sets into a synchronized time series, that is, the same place and time for vaccination and tweets. The second stage is building a prediction model and the third stage was descripting analysis of the prediction results. The autoregressive integrated moving averages (ARIMA) method was used after decomposing the components of ARIMA and choosing the optimal model, the best results obtained from seasonal ARIMA (SARIMA) for both predictions, the last stage is the descriptive analysis of the results and linking them together to obtain an analysis describing the change in the number of vaccinators and the number of negative tweets.

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