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The study presents an innovative diagnostic framework that synergises Convolutional Neural Networks (CNNs) with a Multi-feature Kernel Supervised within-class-similar Discriminative Dictionary Learning (MKSCDDL). This integrative methodology is designed to facilitate the precise classification of individuals into categories of Alzheimer's Disease, Mild Cognitive Impairment (MCI), and Cognitively Normal (CN) statuses while also discerning the nuanced phases within the MCI spectrum. Our approach is distinguished by its robustness and interpretability, offering clinicians an exceptionally transparent tool for diagnosis and therapeutic strategy formulation. We use scandent decision trees to deal with the unpredictability and complexity of neuroimaging data. Considering that different people's brain scans are different, this enables the model to make more detailed individualised assessments and explains how the algorithm illuminates the specific neuroanatomical regions that are indicative of cognitive impairment. This explanation is beneficial for clinicians because it gives them concrete ideas for early intervention and targeted care. The empirical review of our model shows that it makes diagnoses with a level of accuracy that is unmatched, with a classification efficacy of 98.27%. This shows that the model is good at finding important parts of the brain that may be damaged by cognitive diseases. [ABSTRACT FROM AUTHOR]

With the growing demand and commercial availability of cloud services, the need for comparison of their functionality against different prices and performance has arisen. A relevant and fair comparison is still challenging due to diverse deployment options and dissimilar features of different services. This paper addresses a hybrid multi-criteria decision-making model involving the selection of cloud services among the available alternatives. The proposed methodology assigns various ranks to cloud services based on the quantified quality-of-service parameters using a novel extended Grey Technique for Order of Preference by Similarity to Ideal Solution integrated with analytical hierarchical process. Further, we analyse the proposed cloud service selection method in terms of sensitivity analysis, adequacy under change in alternatives, adequacy to support group decision-making, and handling of uncertainty. This analysis helps both researchers and practitioners for analysing more fruitful approaches for cloud service selection. [ABSTRACT FROM AUTHOR]

Several cloud services with comparable functionality are now available to customers at different prices and performance levels. Often, there may be trade-offs among different functional and non-functional requirements fulfilled by different cloud providers. Hence, it is difficult to evaluate the relative performances of the cloud services and their ranking based on various quality of service attributes. In this paper, we propose a modified data envelopment analysis and a modified super-efficiency data envelopment analysis for evaluating the cloud services and their efficiencies considering user preferences. We compare these methods of cloud service selection based on sensitivity analysis, adequacy to changes in DMUs, adequacy to support decision making and modeling of uncertainty. The comparison helps customers to choose a cloud service that is most suitable to their requirements and also creates a healthy competition among the cloud service providers. [ABSTRACT FROM AUTHOR]