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.

The adoption of Digital and Communication Technologies (DCTs) is a critical success factor in port industry. Several empirical evidence are demonstrating that advanced knowledge infrastructures support port efficiency and competitiveness, for example through Intelligent Transport Systems, such as sensors, actuators, and platforms. In this perspective, the paper evaluates the impact of investments in DCTs—i.e., interactive websites and social media marketing solutions—on port efficiency. To this end, we perform the non-parametric method of Data Envelopment Analysis, both in its crisp and fuzzy approaches, in order to account for the vague and imprecise nature of some data. Findings pinpoint that port efficiency is generally supported by DCT solutions, and for some ports the effect is particularly relevant. The outcomes provide managerial suggestions for port authorities, policy makers, and industrial practitioners to identify critical investments for improving port competitiveness.