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Background: Neuroimaging is an important tool in early detection of Alzheimer’s disease (AD) which is a serious neurodegenerative brain disease among the elderly subjects. Independent component analysis (ICA) is arguably one of the most widely used algorithm for the analysis of brain imaging data, which can be used to extract intrinsic networks of brain from functional magnetic resonance imaging (fMRI). Method: Witnessed by recent studies, a more flexible model known as restricted Boltzmann machine (RBM) can also be used to extract spatial maps and time courses of intrinsic networks from resting state fMRI, moreover, RBM shows superior temporal features than ICA. Here, we seek to employ RBM to improve performance of classifying individuals. Experiments are performed on healthy controls and subjects at early stage of AD, i.e., cognitive normal (CN) and early mild cognitive impairment participants (EMCI), and two types of data, i.e., structural magnetic resonance imaging (sMRI) and fMRI data. Results: (1) By separately employing ICA for sMRI and fMRI, the features extracted from fMRI improve classification accuracy by 7.5% for CN and EMCI; (2) instead of applying ICA to fMRI, using RBM further improves classification accuracy by 7.75% for CN and EMCI; (3) the lesions at early stage of AD are more likely to occur in the regions around slices 4, 6, 10, 14, 19, 51 and 59 of the whole brain in the longitudinal direction. Conclusion: By using fMRI instead of sMRI and RBM instead of ICA, we can classify CN and EMCI more efficient.

Integrating deep learning with learning management systems can result in intelligent course material and high accuracy without any manual intervention. This paper reviews factors that influence deep learning in education, and hence this article aims to achieve deep learning on a large scale in the smart education system with a deep learning model to predict. The proposed model can reduce development and maintenance costs, reduce risks, and facilitate communication between stakeholders.

In the fast-changing global environment, educational apps are widely used with hybrid and multi-cloud environments and intelligent devices. They offer various society services such as quality data for monitoring and prediction skills with protection and reliability. Smart learning systems pose a unique security risk because many people access and operate different techniques simultaneously over multiple networks. As a result, cybercrime has been brought about by the internet and the availability of intelligent devices. The security of online learning systems has received significant consideration. Because today's creative learning is open, distributed, and networked, ensuring that authorized users only have access to the appropriate data at the right time is a considerable challenge. The current security practices are outdated; hence, this chapter examines cyber-attacks on smart learning systems from their standard architecture and security requirements and proposes a cyber security model for educational operations from a multi/hybrid cloud-based learning environment.