E-Learning is a sought-after option for learners during pandemic situations. In e-Learning platforms, there are many courses available, and the user needs to select the best option for them. Thus, recommender systems play an important role to provide better automation services to users in making course choices. It makes recommendations for users in selecting the desired option based on their preferences. This system can use machine intelligence (MI)-based techniques to carry out the recommendation mechanism. Based on the preferences and history, this system is able to know what the users like most. In this work, a recommender system is proposed using the collaborative filtering mechanism for e-Learning course recommendation. This work is focused on MI-based models such as K-nearest neighbor (KNN), Singular Value Decomposition (SVD) and neural network–based collaborative filtering (NCF) models. Here, one lakh of Coursera’s course review dataset is taken from Kaggle for analysis. The proposed work can help learners to select the e-Learning courses as per their preferences. This work is implemented using Python language. The performance of these models is evaluated using performance metrics such as hit rate (HR), average reciprocal hit ranking (ARHR) and mean absolute error (MAE). From the results, it is observed that KNN is able to perform better in terms of higher HR and ARHR and lower MAE values as compared to other models.

The Fourth Industrial Revolution (Industry 4.0) represents a significant change in how we live and interact. A new era of human development has begun due to massive technological developments similar to the Industrial Revolution. These advances are integrating the physical and digital worlds in a way that also carries significant risks. Revolutions are driving us to rethink how nations evolve. Industry 4.0 offers opportunities for all citizens from all socioeconomic levels and countries. There is real hope in going outside of technology and creating a method to enable it. It takes a large number of people to have a positive influence on an individual, organization, and community. Achieving resource-efficient and user-friendly production solutions require intelligent and precise equipment. Industry 4.0 is expected to benefit from new technologies and applications by increasing production and providing customized products on demand. The term “Industry 4.0” first appeared a few years ago in response to the concept of Industry 4.0. This has become a concern for policymakers and organizations due to the impact of the pandemic, a focus on topics such as sustainability and resilience, and a renewed focus on human needs. Major industries around the world are looking for headlines about cutting-edge technologies that can help them streamline operations and reduce expenses. Executives at logistics companies do the same to better prepare their companies for the future. After all, the logistics business has traditionally relied on manual operations and data storage, and new technological solutions help it a lot. Cutting-edge technology refers to technical equipment and processes. We will discuss cutting-edge technology and how it fits into Industry 4.0. We present challenges and solutions to enabling cutting-edge technologies that support Industry 4.0. We focus on an open problem and many research questions that have been developed to enable cutting-edge technologies that support Industrial Revolution 4.0.

The purpose of this study is to overcome coordination flaws and enhance end-to-end security in the drug distribution market (DDM). One of the major issues in drug market coordination management is the absence of a centralized monitoring system to provide adequate market control and offer real-time prices, availability, and authentication data. Further, tampering is another serious issue affecting the DDM, and as a consequence, there is a significant global market for counterfeit drugs. This vast counterfeit drug business presents a security risk to the distribution system. This study presents a blockchain-based solution to challenges such as coordination failure, secure drug delivery, and pharmaceutical authenticity. To optimize the drug distribution process (DDP), a framework for drug distribution is presented. The proposed framework is evaluated using mathematical modeling and a real-life case study. According to our results, the proposed technique helps to maintain market equilibrium by guaranteeing that there is adequate demand while maintaining supply. Using the suggested framework, massive data created by the medication supply chain would be appropriately handled, allowing market forces to be better regulated and no manufactured shortages to inflate medicine prices. The proposed framework calls for the Drug Regulatory Authority (DRA) to authenticate users on blockchain and to monitor end-to-end DDP. Using the proposed framework, big data generated through drug supply chain will be properly managed; thus, market forces will be better controlled, and no artificial shortages will be generated to raise drug costs.

Air pollution has detrimental impacts on our physical health and the quality of our living environment, particularly in smart cities. Monitoring and predicting air pollution is crucial to empower individuals to make informed decisions that protect their health. Predicting air quality accurately plays an important effective action plan to mitigate air pollution and create healthier and more sustainable environments. This can be achieved by relying on the Air Quality Index, one of the most reliable indicators for air pollutant concentration levels in certain cities. This study provides a comparative analysis study for air quality prediction using three regression techniques: Random Forest regression, Linear regression, and Decision Tree regression, employing the AQI values. This comparison aims to identify the most efficient model based on various evaluation criteria, such as Mean Absolute Error and R ² measures. Additionally, it considers both error rate minimization and processing time efficiency of each regression model evaluated within two distinct frameworks as other important measures to determine the best-fitted model. The findings of this study showcase the superiority of the Decision Tree regression technique over the other models, demonstrating its exceptional accuracy with a high R ² score and low error rate. Moreover, integrating cloud computing technology has yielded significant improvements in model execution time, substantially enhancing the overall efficiency of the prediction process. By leveraging distributed computing resources, real-time air quality forecasting becomes feasible, enabling timely decision-making and proactive measures to address air pollution episodes.

In today’s world, the use of computer networks is everywhere, and to access the home network we use the Internet. IoT networks are the new range of these networks in which we try to connect different home appliances and try to give commands from a remote place. Access to any device over an insecure network invites various types of attacks. User authentication can be performed using some password or biometric technique. However, when it comes to authenticating a device, it becomes challenging to maintain data security over a secure network such as the Internet. Many encryptions and decryption algorithms assert confidentiality, and hash code or message authentication code MAC is used for authentication. Traditional cryptographic security methods are expensive in terms of computational resources such as memory, processing capacity, and power consumption. They are incompatible with the Internet of Things devices that have limited resources. Although automatic Device-to-Device communication enables new potential applications, the limited resources of the networks’ machines and devices impose various constraints. This paper proposes a home device authentication scheme when these are accessed from a remote place. An authentication device is used for the home network and controller device to control home appliances. Our scheme can prevent various attacks such as replay attacks, server spoofing, and man-in-the-middle attack. The proposed scheme maintains the confidentiality and authenticity of the user and devices in the network. At the same time, we check the system in a simulated environment, and the results show that the network’s performance does not degrade much in terms of delay, throughput, and energy consumed.