This paper proposes an approach for constructing a system for career prediction by applying the eXtreme Gradient Boosting (XGBoost) Decision Tree model to the academic results of Ho Chi Minh International University’s School of Computer Science and Engineering graduates in the past 5 years. Initially, the dataset is cleaned up and normalized to be usable for the prediction algorithm with the help of Python 3 programming language. It is then split into 2 subsets: one for training (80 percent) and the other for testing (20 percent). After that, the algorithm uses the training subset to build the classification model. Finally, the testing subset is loaded into the model to predict each student’s career path based on the respective inputs and hyper-parameters tuning is employed to boost the model’s accuracy. By utilizing this solution, the problem related to predicting students’ future career paths based on their performance throughout their years studying at the university can be adequately addressed and handled.

Environmental pollution has had substantial impacts on human life, and trash is one of the main sources of such pollution in most countries. Trash classification from a collection of trash images can limit the overloading of garbage disposal systems and efficiently promote recycling activities; thus, development of such a classification system is topical and urgent. This paper proposed an effective trash classification system that relies on a classification module embedded in a hard-ware setup to classify trash in real time. An image dataset is first augmented to enhance the images before classifying them as either inorganic or organic trash. The deep learning–based ResNet-50 model, an improved version of the ResNet model, is used to classify trash from the dataset of trash images. The experimental results, which are tested both on the dataset and in real time, show that ResNet-50 had an average accuracy of 96%, higher than that of related models. Moreover, integrating the classification module into a Raspberry Pi computer, which controlled the trash bin slide so that garbage fell into the appropriate bin for inorganic or organic waste, created a complete trash classification system. This proves the efficiency and high applicability of the proposed system.