Alzheimer’s disease (AD) is mainly a neurodegenerative sickness. The primary characteristics are neuronal atrophy, amyloid deposition, and cognitive, behavioral, and psychiatric disorders. Numerous machine learning (ML) algorithms have been investigated and applied to AD identification over the past decades, emphasizing the subtle prodromal stage of mild cognitive impairment (MCI) to assess critical features that distinguish the disease’s early manifestation and instruction for early detection and treatment. Identifying early MCI (EMCI) remains challenging due to the difficulty in distinguishing patients with cognitive normality from those with MCI. As a result, most classification algorithms for these two groups perform poorly. This paper proposes a hybrid Deep Learning Approach for the early detection of Alzheimer’s disease. A method for early AD detection using multimodal imaging and Convolutional Neural Network with the Long Short-term memory algorithm combines magnetic resonance imaging (MRI), positron emission tomography (PET), and standard neuropsychological test scores. The proposed methodology updates the learning weights, and Adam’s optimization is used to increase accuracy. The system has an unparalleled accuracy of 98.5% in classifying cognitively normal controls from EMCI. These results imply that deep neural networks may be trained to automatically discover imaging biomarkers indicative of AD and use them to identify the illness accurately.

Inline validation in web forms is essential when the complexity of fields in a form increases the likelihood that users would enter invalid or incorrect data, or to confirm appropriate answers. However, little is known about how users examine inline validation and how effective it is in supporting interaction in web transactional tasks. This paper describes an eye tracking study of web form interaction in which immediate inline validation is examined by analyzing user behavior as well as eye gaze patterns on Arabic web form fields for password entry. Findings suggest that web users exhibit sufficient visual attention on inline feedback, however users tend to respond more when the feedback is related error handling or warnings than when the feedback is a confirmation message. We conclude with design implications for the design of web forms.

No abstract

No abstract

No abstract