In this paper, we are presenting an epidemiological model for exploring the transmission of outbreaks caused by viral infections. Mathematics and statistics are still at the cutting edge of technology where scientific experts, health facilities, and government deal with infection and disease transmission issues. The model has implicitly applied to COVID-19, a transmittable disease by the SARS-CoV-2 virus. The SIR model (Susceptible-Infection-Recovered) used as a context for examining the nature of the pandemic. Though, some of the mathematical model assumptions have been improved evaluation of the contamination-free from excessive predictions. The objective of this study is to provide a simple but effective explanatory model for the prediction of the future development of infection and for checking the effectiveness of containment and lock-down. We proposed a SIR model with a flattening curve and herd immunity based on a susceptible population that grows over time and difference in mortality and birth rates. It illustrates how a disease behaves over time, taking variables such as the number of sensitive individuals in the community and the number of those who are immune. It accurately model the disease and their lessons on the importance of immunization and herd immunity. The outcomes obtained from the simulation of the COVID-19 outbreak in India make it possible to formulate projections and forecasts for the future epidemic progress circumstance in India.

E-learning controlling quality ready to demonstrate the achievement of the conveyance and plan of the framework by understanding the user satisfaction. Besides, the qualification between learning substance and site content should be parallel whereby both will impact one another and influence user satisfaction t and fulfillment. After the examination has closed, concern emerges on how the elements of e-learning in service, system and information quality influence the nature of e-learning framework in keeping fit. Besides, realizing that client is a mainstay of e-learning framework, this exploration expected to see if the user fulfillment has any impact on e-learning framework quality. Framework plan, framework conveyance and framework result been utilized as a stage for estimating the accomplishment of e-learning framework.

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The advent of Internet of Things (IoT) in agriculture has revolutionized the way farmers monitor and manage their crops. IoT-enabled sensors can provide real-time data on various environmental parameters such as temperature, humidity, soil moisture, and crop growth, which can be used to make informed decisions and optimize crop yield. However, the vast amount of data generated by these sensors poses a significant challenge in terms of data processing and communication. To address this challenge, clustering is often used to group the sensors into clusters and elect a Cluster Head (CH) to communicate with the gateway node. The selection of an appropriate CH and the optimal path for data transmission are critical factors that affect the performance of the IoT system. In this paper, we propose a novel approach to optimize the CH selection and path selection using modified Fuzzy Logic, Whale optimization algorithm (WOA) and Enhanced Crow Swarm Optimization (ECSO). Fuzzy Logic is used to evaluate the relevant parameters such as energy, distance, overhead, trust, and node degree to select the most suitable CH. ECSO is then employed to find the optimal path for data transmission based on the selected CH. We evaluate the proposed approach using simulation experiments in a smart agriculture scenario. The results show that our approach outperforms existing approaches in terms of throughput, packet delivery ratio, delay, and energy efficiency. Our proposed approach can significantly improve the performance of IoT-enabled smart agriculture systems, leading to better crop yield and higher profitability for farmers. The results of our simulation experiments demonstrate the superiority of our approach over existing one’s throughput, Packet Delivery Ratio (PDR), delay, energy consumption efficiency is found in the result section.