The fifth generation (5G) mobile communication technology though, is being deployed in various parts of the world to improve the wireless systems in terms of infrastructure and quality of services, in view of mobile communication history, the 5G may be unable to handle the deluge mobile network traffic of the future digital society. In general, new wireless technologies are introduced every 10 years, and services mature every 20 years, and therefore sixth generation (6G) mobile communication technologies, which have a great potential to impact on the market, are expected to be commercialized around 2030. In order to secure technological leadership in mobile communication, which is an essential infrastructure for everyday life and industrial sites, advanced countries around the world have begun to develop 6G technology. The expected main features of the 6G technology include: providing 10 to 100 times more performance than 5G, maximizing performance by utilizing the terahertz band which was not utilized by existing communication networks before, use artificial intelligence techniques for smart networking, and combining the previously isolated technologies. This paper provides an overview on the profile, advancements, implications, architecture, expected applications, and challenges of the futuristic 6G wireless communication systems described by innovative researchers and major global organizations.

In this paper, the efficacy of machine learning (ML) techniques for predicting the academic success of students is investigated. In issues pertaining to higher education, as well as machine learning, deep learning, and its linkages to educational data, predicting student achievement is essential. The choice of courses and the development of effective future study plans for students can be easier with the help of the capacity to forecast a student's success. In addition to predicting student achievement, it makes it easier for instructors and administrators to keep an eye on children so that they can offer support and integrate trainings for the greatest outcomes. In this study, we define the idea of predicting the student performance in education and its several iterations. We discuss a number of ML approaches, such as the Fuzzy C-Means, the Multi-Layer Perceptron (MPL), the Logistic Regression (LR), and the Random Forest (RF) algorithms, for predicting student achievement in the classroom. The models for forecasting student performance that are now in use and those that have been proposed in this paper are carefully investigated. The paper examines different combinations of the algorithms including FCM – MLP, FCM – LR, and FCM – RF, and provides the detailed results of each combination. These strategies are assessed using quantitative standards including accuracy, detection rate, and false alarm rate.

This paper studies the performance analysis of machine learning (ML) and data mining techniques for anomaly detection in credit cards. As the usage of digital money or plastic money grows in developing nations, so does the risk of fraud. To counter these scams, we need a sophisticated fraud detection method that not only identifies the fraud but also detects it before it occurs efficiently. We have introduced the notion of credit card fraud and its many variants in this research. Numerous ML fraud detection approaches are studied in this paper including Principal Component Analysis (PCA) data mining and the Fuzzy C-Means methodologies, as well as the Logistic Regression (LR), Decision Tree (DT), and Naive Bayes (NB) algorithms. The existing and proposed models for credit card fraud detection have been thoroughly reviewed, and these strategies have been compared using quantitative metrics including accuracy rate and characteristics curves. This paper discusses the shortcomings of existing models and proposes an efficient technique to analyze the fraud detection.