Accurately estimating the number of infections that actually occur in the earliest phases of an outbreak and predicting the number of new cases per day in various countries is crucial for real-time monitoring of COVID-19 transmission. Numerous studies have used mathematical models to predict the progression of infection rates in several countries following the appearance of epidemiological outbreaks. In this study, we analyze the data reported and then study several logistical-type phenomenological models and their application in practice for forecasting infection evolution. When several epidemic waves follow one another, it is important to stress that a traditional logistic model cannot necessarily be fully adapted to the data made available. New models are being introduced to simultaneously take account of human behavior, measures taken by the government, and epidemiological conditions. This research used a generalized logistic model based on parameters that vary over time to describe trends in COVID-19-infected cases in countries that have undergone several waves. In two-wave scenarios, the parameters of the model evolve dynamically over time following a logistic function, where the first and second waves are characterized by two extreme values for the early period and the late one, respectively.

In today’s rapidly evolving landscape of communication technologies, ensuring the secure delivery of sensitive data has become an essential priority. To overcome these difficulties, different steganography and data encryption methods have been proposed by researchers to secure communications. Most of the proposed steganography techniques achieve higher embedding capacities without compromising visual imperceptibility using LSB substitution. In this work, we have an approach that utilizes a combination of Most Significant Bit (MSB) matching and Least Significant Bit (LSB) substitution. The proposed algorithm divides confidential messages into pairs of bits and connects them with the MSBs of individual pixels using pair matching, enabling the storage of 6 bits in one pixel by modifying a maximum of three bits. The proposed technique is evaluated using embedding capacity and Peak Signal-to-Noise Ratio (PSNR) score, we compared our work with the Zakariya scheme the results showed a significant increase in data concealment capacity. The achieved results of our work show that our algorithm demonstrates an improvement in hiding capacity from 11% to 22% for different data samples while maintaining a minimum Peak Signal-to-Noise Ratio (PSNR) of 37 dB. These findings highlight the effectiveness and trustworthiness of the proposed algorithm in securing the communication process and maintaining visual integrity.

Mobile networks possess significant information and thus are considered a gold mine for the researcher’s community. The call detail records (CDR) of a mobile network are used to identify the network’s efficacy and the mobile user’s behavior. It is evident from the recent literature that cyber-physical systems (CPS) were used in the analytics and modeling of telecom data. In addition, CPS is used to provide valuable services in smart cities. In general, a typical telecom company has millions of subscribers and thus generates massive amounts of data. From this aspect, data storage, analysis, and processing are the key concerns. To solve these issues, herein we propose a multilevel cyber-physical social system (CPSS) for the analysis and modeling of large internet data. Our proposed multilevel system has three levels and each level has a specific functionality. Initially, raw Call Detail Data (CDR) was collected at the first level. Herein, the data preprocessing, cleaning, and error removal operations were performed. In the second level, data processing, cleaning, reduction, integration, processing, and storage were performed. Herein, suggested internet activity record measures were applied. Our proposed system initially constructs a graph and then performs network analysis. Thus proposed CPSS system accurately identifies different areas of internet peak usage in a city (Milan city). Our research is helpful for the network operators to plan effective network configuration, management, and optimization of resources.