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In recent years, academics from many related study fields worldwide have begun to focus on educational data mining (EDM). To help academic planners in higher education institutions make better decisions, suggestions can be made using the information gained from the EDM. Various prediction models have been put out in the literature to forecast student performance. This paper suggests a distributed cluster-based architecture (CDA) for predicting student performance. The proposed CDA indicates clustering via water wave optimization based on K-means cluster and deep neural network (WWO-KMC-DNN), feature extraction using Multi-Linear Discriminant Analysis (M-LDA), and feature fusion using a Bayesian network. In the suggested design, the WWO algorithm is used to determine the DNN ideal weights. Accuracy, prediction rate, mean square error, and root mean square error is monitored in a real-time database to evaluate the proposed task. Using the MSE and RMSE values from the data, the proposed WWO-KMC-DNN model outperforms other models.

The main topic of study is mapping human cognition into automated analysis since it has exciting applications in practically every aspect of creating artificially intelligent devices. Studying electroencephalogram (EEG) patterns is the greatest approach to comprehending how the brain functions; hence a lot of research has been focused on this topic. It is challenging to create a generic affect classification system that can effectively deliver robust affect labeling to the EEG patterns since EEG recordings are subject-dependent and show variances according to external influences or kinds of recording devices. A unique generic framework for affect-based cognitive analysis is presented in the proposed study as a solution to this problem. The proposed system includes the following steps: EEG pattern, dataset, pre-processing, feature selection, an Enhanced Squirrel search optimized Naive Bayes (ESSOp-NB) step to reduce inter-class and intra-class variance, and finally, the processed pattern is sent to a trained classifier for classification into the proper effect categories. The effectiveness of well-known classifiers is evaluated using EEG data from single and multiple people from two separate datasets: Database for Emotion Analysis using Physiological Signals (DEAP) and SJTU Emotion EEG Dataset (SEED). The findings of the experiment indicate that the ESSOp-NB classifier is the most effective at categorizing data from both single and mixed participants. &nbsp