In the coming years, technology will impact the learning experience in many ways. Internet of Things (IoT) continues to confirm its important position in the context of Information and Communication Technologies and the development of society. With the support of IoT, institutions can enhance learning outcomes by providing more affluent learning experiences, improved operational efficiency, and by gaining real-time, actionable insight into student performance. The purpose of this study is to find out the potential of IoT in higher education and how to maximize its benefits and reducing the risks involved with it. Further efforts are necessary for releasing the full potential of IoT systems and technologies. Therefore, this paper presents a study about the impact of IoT on higher education especially universities. IoT stands to change dramatically the way universities work, and enhance student learning in many disciplines and at any level. It has huge potential for universities or any other educational institutions; if well prepared to ensure widespread and successful implementation by leadership, staff, and students. IoT needs development where universities can lead. Academics, researchers, and students are in a unique place to lead the discovery and development of IoT systems, devices, applications, and services. Moreover, this paper provides an evidences about the future of IoT in the higher education during the next few years, which have offered by a number of research organizations and enterprises. On the other hand, IoT also brings tremendous challenges to higher education. Hence, this paper also presents the perspective on the challenges of IoT in higher education.

Alzheimer’s disease (AD) is a progressive, incurable condition leading to decline of nerve cells and cognitive functions over time. Early detection is essential for improving quality of life, as treatment strategies aim to decelerate its progression. AD also impacts fine motor control, including handwriting. Utilizing machine learning (ML) with efficient data analysis methods for early detection of Alzheimer’s disease (AD) through handwriting analysis holds considerable promise for clinical diagnosis, albeit a challenging undertaking. In this study, we address this complexity by employing ensemble machine learning, which amalgamates diverse ML algorithms to enhance predictive performance. Our approach involves developing an ensemble model for analysis of handwriting kinetics, utilizing the stacking technique to integrate multiple base-level classifiers. The study encompasses 174 individuals, including 89 diagnosed with Alzheimer’s disease and 85 healthy participants, drawn from the DARWIN dataset (Diagnosis AlzheimeR WIth haNdwriting). To discern the most effective base classifiers, we employ both Repeated-k-fold and Monte-Carlo Cross Validation techniques. Subsequently, top k features are selected for each best-performing base classifier using analysis of variance (ANOVA) and recursive feature elimination (RFE). The final step involves consolidating predictions from the base classifiers through a stacking ensemble, resulting in an impressive performance. The ensemble model achieves 97.14% accuracy, 95% sensitivity, 100% specificity, 100% precision, 97.44% F1-score, 94.37% Matthews Correlation Coefficient (MCC), 94.21% Cohen Kappa, and 97.5% Area Under the Receiver Operating Characteristic Curve (AUC-ROC). Comparative performance analysis demonstrates that our proposed model surpasses all state-of-the-art models based on the DARWIN dataset for Alzheimer’s disease prediction. These findings underscore the potential of machine learning to offer highly accurate predictions for Alzheimer’s disease in an affordable and non-invasive manner, emphasizing its significant clinical utility, particularly through handwriting analysis.

In the context of Alzheimer's disease (AD), timely identification is paramount for effective management, acknowledging its chronic and irreversible nature, where medications can only impede its progression. Our study introduces a holistic solution, leveraging the hippocampus and the VGG16 model with transfer learning for early AD detection. The hippocampus, a pivotal early affected region linked to memory, plays a central role in classifying patients into three categories: cognitively normal (CN), representing individuals without cognitive impairment; mild cognitive impairment (MCI), indicative of a subtle decline in cognitive abilities; and AD, denoting Alzheimer's disease. Employing the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, our model undergoes training enriched by advanced image preprocessing techniques, achieving outstanding accuracy (testing 98.17 %, validation 97.52 %, training 99.62 %). The strategic use of transfer learning fortifies our competitive edge, incorporating the hippocampus approach and, notably, a progressive data augmentation technique. This innovative augmentation strategy gradually introduces augmentation factors during training, significantly elevating accuracy and enhancing the model's generalization ability. The study emphasizes practical application with a user-friendly website, empowering radiologists to predict class probabilities, track disease progression, and visualize patient images in both 2D and 3D formats, contributing significantly to the advancement of early AD detection.