Breast Cancer (BC) is one of the leading cause of deaths worldwide. Approximately 10 million people pass away internationally from breast cancer in the year 2020. Breast Cancer is a fatal disease and very popular among women globally. It is ranked fourth among the fatal diseases of different cancers, for example colorectal cancer, cervical cancer, and brain tumors. Furthermore, the number of new cases of breast cancer is anticipated to upsurge by 70% in the next twenty years. Consequently, early detection and precise diagnosis of breast cancer plays an essential part in enhancing the diagnosis and improving the breast cancer survival rate of patients from 30 to 50%. Through the advances of technology in healthcare, deep learning takes a significant role in handling and inspecting a great number of X-ray, Magnetic Resonance Imaging (MRI), computed tomography (CT) images. The aim of this study is to propose a deep learning model to detect and classify breast cancers. Breast cancers has eight classes of cancers: benign adenosis, benign fibroadenoma, benign phyllodes tumor, benign tubular adenoma, malignant ductal carcinoma, malignant lobular carcinoma, malignant mucinous carcinoma, and malignant papillary carcinoma. The dataset was collected from Kaggle depository for breast cancer detection and classification. The measurement that was used in the evaluation of the proposed model includes: F1-score, recall, precision, accuracy. The proposed model was trained, validated and tested using the preprocessed dataset. The results showed that Precision was (97.60%), Recall (97.60%) and F1-Score (97.58%). This indicates that deep learning models are suitable for detecting and classifying breast cancers precisely.

The quality of instructors' performance mainly influences the quality of educational services in higher educational institutions. One of the major challenges of higher educational institutions is the accumulated amount of data and how it can be utilized to boost the academic programs quality. The recent advancements in Artificial Intelligence techniques, including machine and deep learning models, have led to the expansion in practical prediction for various fields. In this paper, a dataset was collected from UCI Repository, University of California, for the prediction of instructor performance. In order to find how effective the instructor in the higher education systems is, a group of machine and deep learning algorithms were applied to predict instructor performance in higher education systems. The best machine-learning algorithm was Extra Trees Regressor with Accuracy (98.78%), Precision (98.78%), Recall (98.78%), F1-score (98.78%); however, the proposed deep learning algorithm achieved Accuracy (98.89%), Precision (98.91%), Recall (98.94%), and F1-score (98.92%).

Based on the advancement in the field of Artificial Intelligence, there is still a room for enhancement of student university retention. The main objective of this study is to assess the probability of using Artificial Intelligence techniques such as deep and machine learning procedures to predict university student retention. In this study a variable assessment is carried out on the dataset which was collected from Kaggle repository. The performance of twenty supervised algorithms of machine learning and one algorithm of deep learning is assessed. All algorithms were trained using 10 variables from 1100 records of former university student registrations that have been registered in the University. The top performing algorithm after hyperparameters tuning was NuSVC Classifier. Therefore, we were able to use the current dataset to create supervised Machine Learning (ML) and Deep Learning (DL) models for predicting student retention with F1-score (90.32 percent) for ML and the proposed DL algorithm with F1-score (93.05 percent).

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