This study focuses on developing a machine learning model through the process of analyzing. comparing, and evaluating the performance of five models: AdaBoost, Decision Tree, RandomForest, ExtraTree, and BernoulliNB. All models are implemented using the "Predict Student Dropout Dataset." Based on the results obtained after processing the data, the study will conduct an analysis based on two main criteria: evaluation by average percentage, standard deviation, and final outcomes, as well as evaluation using a time-series model of age (Balanced Accuracy Progression).

This paper proposes an output-capacitorless low-dropout regulator (OCL-LDO) with simple structure and fast load transient response for system-on-chip applications. The proposed OCL-LDO does not require output capacitance and remains stable at no-load condition.

Factors associated with dropout from treatment for eating disorders: a comprehensive literature review