No abstract

Cancer is considered one of the most aggressive and destructive diseases that shortens the average lives of patients. Misdiagnosed brain tumours lead to false medical intervention, which reduces patients’ chance of survival. Accurate early medical diagnoses of brain tumour are an essential point for starting treatment plans that improve the survival of patients with brain tumours. Computer-aided diagnostic systems have provided consecutive successes for helping medical doctors make accurate diagnoses and have conducted positive strides in the field of deep and machine learning. Deep convolutional layers extract strong distinguishing features from the regions of interest compared with those extracted using traditional methods. In this study, different experiments are performed for brain tumour diagnosis by combining deep learning and traditional machine learning techniques. AlexNet and ResNet-18 are used with the support vector machine (SVM) algorithm for brain tumour classification and diagnosis. Brain tumour magnetic resonance imaging (MRI) images are enhanced using the average filter technique. Then, deep learning techniques are applied to extract robust and important deep features via deep convolutional layers. The process of combining deep and machine learning techniques starts, where features are extracted using deep learning techniques, namely, AlexNet and ResNet-18. These features are then classified using SoftMax and SVM. The MRI dataset contains 3,060 images divided into four classes, which are three tumours and one normal. All systems have achieved superior results. Specifically, the AlexNet+SVM hybrid technique exhibits the best performance, with 95.10% accuracy, 95.25% sensitivity, and 98.50% specificity.

Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.

No abstract

Higher education systems (HES) have become increasingly absorbed in applying big data analytics due to competition as well as economic pressures. Many studies have been conducted that applied big data analytics in HES; however, a systematic review (SR) of the research is scarce. In this paper, the authors conducted a systematic mapping study to address this deficiency. The qualitative and quantitative analysis of the mapping study resulted in highlighting the research progression over the last decade, and identification of three major themes, 12 subthemes, 10 motivation factors, 10 major challenges, three categories of tools and support techniques, and 16 models for applying big data analytics in higher education. This result contributes to the ongoing research on applying big data analytics in HES. It provides a better understanding of the level of contribution to research as well as identifies gaps for future research direction.