Industry 4.0 has created a whole new world for us to explore, and its effects can be seen in every facet of our lives, especially in the workplace where it calls for technology-driven employment. There is a growing need to teach individuals and assist them in transitioning to longer-term employment prospects to execute Industry 4.0 effectively. Although MOOCs revolutionized the way learners study, it is critical to investigate teaching techniques using Education 4.0 at this time. This article explores how the technologies of Industry 4.0 can be incorporated into MOOCs. This paper proposes MOOCs 5.0, whose features include better universal access, better learner engagement, adaptive learning, greater collaboration, security, and curiosity, which is being developed using Industry 4.0 technologies of the Internet of Things, Cloud Computing, Big Data, Artificial Intelligence/Machine Learning, Blockchain, Gamification Technologies, and the Metaverse and would incorporate the zones of ethics and humanism, while at the same time providing learners with a richer and more individualized experience.

Stress is a concerning issue in today’s world. Stress in pregnancy harms both the development of children and the health of pregnant women. As a result, assessing the stress levels of working pregnant women is crucial to aid them in developing and growing professionally and personally. In the past, many machine-learning (ML) and deep-learning (DL) algorithms have been made to predict the stress of women. It does, however, have some problems, such as a more complicated design, a high chance of misclassification, a high chance of making mistakes, and less efficiency. With these considerations in mind, our article will use a deep-learning model known as the deep recurrent neural network (DRNN) to predict the stress levels of working pregnant women. Dataset preparation, feature extraction, optimal feature selection, and classification with DRNNs are all included in this framework. Duplicate attributes are removed, and missing values are filled in during the preprocessing of the dataset.

Skin illness affects a large percentage of the world's population. The proposed study proposed a deep learning-based model for skin disease predication, In the traditional system it was time taking to predict the result and the accuracy is not accurate, Different machine learning methods can be used to classify skin disorders. In this study, we used machine learning algorithms to categories skin disease classes using ensemble approaches, and then used a feature selection method to compare the findings produced. Specialist can detect the disease type with the help of a web-based framework which is developed in Python Django frame- work. In the proposed study, we present a novel approach to detect the skin disease. Here we have used Support Vector Machine (SVM) Artificial Neural Network (ANN) and Convolutional Neural Network (CNN) classifiers to identify the disease. Specialist need to upload the image and Deep learning algorithms will predict the disease and display the accuracy. The proposed model is easy to use, but it also provides a higher level of accuracy than previous methods. As a result of this model, we were able to achieve a 95% accuracy rate in the diagnosis of various skin conditions. The proposed system provides a state of art accuracy for early skin disease detection

Predicting the stress levels of working professionals is one of the most time-consuming and difficult research topics of current day. As a result, estimating working professionals’ stress levels is critical in order to assist them in growing and developing professionally. Numerous machine learning and deep learning algorithms have been developed for this purpose in previous papers. They do, however, have some disadvantages, including increased design complexity, a high rate of misclassification, a high rate of errors, and decreased efficiency. To address these concerns, the purpose of this research is to forecast the stress levels of working professionals using a sophisticated deep learning model called the Deep Recurrent Neural Network (DRNN). The model proposed here comprises dataset preparation, feature extraction, optimal feature selection, and classification using DRNNs. Preprocessing the original dataset removes duplicate attributes and fills in missing values.

Immediate Security systems (WSN) can be an growing engineering and still have beneficial achievable for use inside important conditions including battlefields plus expert packages this kind of for instance producing, visitors checking, surroundings following plus clever domiciles and further scenarios. Among one of the keys problems instant indicator programs knowledge currently is definitely security. Whilst the actual execution regarding indicator nodes inside an untreated establishing helps make the programs at risk of many achievable problems, natural energy plus hard drive limitations regarding indicator nodes makes main-stream protection solutions unfeasible. The actual discovering engineering coupled with command energy plus instant relationship reasons it to be rewarding for use inside beneficial size inside future. Whenever, relationship engineering as well gains many forms regarding protection threats. That record investigates a wide selection of problems inside WSN along with their classification.