In the field of education, every institution stores a significant amount of data in digital form on the academic performance of students. If this data is correctly analysed to discover any pattern related to student learning, it can assist the institution in achieving a favorable outcome in the future. Because of this, the use of data mining techniques makes it much simpler to unearth previously concealed information or detect patterns in student data. We use a variety of data mining methods, such as Naive Bayes, Random Forest, Decision Tree, Multilayer Perceptron, and Decision Table, to predict the academic performance of individual students. In the real world, a dataset may contain many features, yet the mining process may only place significance on some of those aspects. The correlation attribute evaluator, the information gain attribute evaluator, and the gain ratio attribute evaluator are some of the feature selection methods that are used in data mining to remove features that are not important for the mining process. Other feature selection methods include the gain ratio attribute evaluator and the gain ratio attribute evaluator. In conclusion, each classification algorithm that is designed using some feature selection methods enhances the overall predictive performance of the algorithms, which in turn improves the performance of the algorithms overall.

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Cell phones, smart cards, and health monitoring gadgets are just a few examples of the numerous battery-powered embedded systems utilized to access, alter, and store sensitive and complicated data today. Users are concerned about the protection of their identity credentials, their software packages, and their information. These systems make considerable use of cryptographic algorithms to implement security measures. Many cryptographic algorithms do calculations that are hard to compute and waste a huge amount of energy as a result. In this study, the energy consumption of serial and parallel cryptography algorithms is analyzed. Using an eight-core parallel system and Joule metre (Microsoft's Research Tool), we were able to reduce energy consumption in comparison to sequential algorithms with promising results. The study says that low-frequency symmetric multiprocessors have shown promising results and can make a big difference in green computing, which would be good for society as a whole.