Silhouette validity score has been used to validate the quality of clusters. Three diseases have been linked with their drugs to be the research data set (diabetes, leukemia,and allergy). In the third layer, the final clustering is organized by reforming clusters depends on the calculation of cluster centers and merging of the nearest clusters according to a carefully selected threshold. The main contributions are concerned in the forming primal clusters according to neighbors' proximity and distance. The normalization and standardization are satisfied in the second layer. In the first layer,the features have been adapted to the network weights. Anew model is proposed for the clustering process, specifically the Disease_Drugs_Clustering_Deep_Nural_network (DDC_DNN) as a type of deep neural network. The aim of this paper is to obtain optimal clustersof the drugs according to utilization. It needs to be accurate because it is involved in the provision, manufacture, and marketing of medicines. The process of analyzing quantities consumed based on the drug name and brand is complex. Therefore, it is suggested that this method can be applied to send secret messages through applications of special importance across the IoT.ĭrug consumption data needs to be linked to the disease. The results appear promising and acceptable. The peak signal-to-noise (PNSR) metric was used to measure the quality of the resulting image after the steganography process. The compression of the stego image using GZIP is the last level of security. 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 first level is represented by applying the Conformal Mapping on the secret image. The proposed method provides four levels of security for the confidential message (in this case, an image). The present article proposes a new encryption method for important messages that are sent via IoT applications. 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.

This is an open access article under the CC BY-SA license. 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. The second stage is building a prediction model and the third stage was descripting analysis of the prediction results.

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. Then a model is built to predict the future numbers of vaccinators and a model that predicts the number of negative opinions or tweets. This article provides an analysis of vaccinators and analysis of people's opinions of the vaccine's efficacy and whether negative or positive. When announcing the availability of a vaccine, the world was divided over the effectiveness and harms of this vaccine. 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.

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