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In this fast-changing educational landscape of today, data science and predictive analytics are tools critical to creating student success and transforming educational systems. This chapter will further explore how predictive analytics can be utilized to anticipate and improve student outcomes. It also includes methodologies in collecting and analyzing student data, algorithms predicting their academic performance, and insights for early interventions and adapted support by educators and administrators. The predictive model, based on historical and real-time data, can predict the at-risk or chance of succeeding in student and develop learning paths for each one. The chapter also tackles data privacy issues, ethical implications, and the AI technology integration processes in schools. This chapter explains how predictive analytics the power can have to offer a better personalized, fair, and effective learning environment that would ensure improved student success and retention

Skin infections are a major concern for human health, as they can cause significant skin damage, leading to loss of confidence and emotional distress in patients. Advancements in deep learning offer promising solutions for diagnosing and treating such conditions effectively. AI-driven approaches enable automated skin disease detection without requiring expert intervention, making diagnosis more accessible. Enhancing the user interface of these systems can further improve user experience. Early identification of skin disorders is crucial in preventing misdiagnosis as minor allergies, which can otherwise lead to severe complications. This research explores the application of deep learning for improved skin infection detection and treatment. Leveraging the power of AI, the study introduces a novel classifier combining the VGG-19 convolutional neural network with Grad-CAM (Gradient-weighted Class Activation Mapping). This approach aims to enhance diagnostic accuracy and reduce the risk of misdiagnosis, ultimately minimizing patient complications. The model was trained and evaluated using a dataset sourced from Kaggle, a popular platform for machine learning datasets. Performance was compared against baseline machine learning models, including decision trees and Support Vector Machines (SVMs). Results indicate that the proposed dual-input model, incorporating VGG-19 and Grad-CAM, achieved a remarkable accuracy of approximately 96%. This significantly outperforms the baseline models, demonstrating the potential of deep learning techniques for accurate and efficient skin condition diagnosis. The improved performance suggests that this approach could be a valuable tool for dermatologists and other medical professionals in the future

Diabetic Retinopathy (DR) is a prevalent eye condition among diabetic patients that can lead to irreversible vision loss if not detected and treated early. Early detection and continuous monitoring are crucial for effective management of DR. Traditional diagnostic methods, including visual acuity tests and non-invasive imaging, are often time-consuming and less efficient. Previous approaches, such as Residual Contrast Limited Adaptive Histogram Equalization (RCLAHE), have shown limitations in detecting DR. This research presents an Optimized Back Propagation based Deep Residual Learning Network (Op-BPDRLN) algorithm to enhance the detection of DR. By comparing the performance of different ensemble models on various retinal image datasets, this study aims to develop a more efficient and accurate algorithm for DR diagnosis. The comparison is based on key metrics such as accuracy, error, precision, and recall to determine the most effective classification model.