ABSTRACT Alzheimer's disease (AD) diagnosis using MRI scans must be very accurate since the subtle differences throughout the course of the disease are difficult to identify. Traditional approaches are not effective, and new computational techniques are required that can provide fast and accurate diagnosis. In this paper, a novel deep learning methodology that greatly enhances the sensitivity and specificity of AD stage identification by analyzing in‐depth MRI scans is proposed. The model applies a novel Sequential Convolutional Neural Network (CNN) architecture, which has been deeply trained on the “Augmented Alzheimer MRI Dataset” made available by Kaggle, to integrate various layers of depth and complexity to identify and scan in‐depth features on MRI images. Major enhancements include the use of learning rate schedulers and dropout regularization to fine‐tune training as well as avoid overfitting, with a diagnosis accuracy of 94.2%. This level of accuracy not only makes diagnostic processes easier but also allows for early detection of Alzheimer's phases, which is crucial for timely interventions and effective management of the condition. The model is rigorously trained on a large set of augmented data with varying levels of AD to guarantee robustness and generalizability in various demographic and clinical settings. Batch normalization and higher‐order activation functions allow faster and stable convergence of training, and thus the model is more efficient and scalable. Application of this model to the clinic has the potential to sharply reduce time to diagnosis, lessen dependence on radiological expertise, and offer a high‐accuracy, scalable imaging device enabling early and accurate treatment in Alzheimer's care. This innovation represents a significant next phase in medical imaging with artificial intelligence, and it offers a highly effective tool for fine detection and staging of Alzheimer's disease.

Abstract Online shopping has become an essential part of modern life, with food delivery being one of the most popular services. In this study, we investigate the factors influencing customers' preferences in online food ordering, such as easy payment, customization, and fast delivery. Analysing customer ratings and reviews is crucial in ensuring the quality of food services and promoting a healthy lifestyle. However, the trustworthiness of online reviews remains a challenge. Based on their rating, new customers can get help from online shoppers for their preferences or demands. Data are collected from customer reviews through online platforms to improve food service quality in online shopping. This work aims to predict the preferences of customers in online shopping based on optimized feature extraction using Principal Component Analysis with a Social Spider Optimization (PCA‐SSO) algorithm. Despite existing algorithms for analysing customer preferences, most may have inaccurate predictions or take time to analyse data. To overcome these challenges, this model of PCA‐SSO is proposed to predict customers' more accurate online shopping preferences. This model provides valuable implications for online shopping owners regarding customer satisfaction. Compared to the accuracy rates of KNN (78.22%), LDA (83.16%), and PCA (88.56%) algorithms, our proposed algorithm of PCA‐SSO achieved a higher accuracy of 93.54%.

Unmanned aerial vehicles (UAVs) have enabled numerous inventive solutions to multiple problems, considerably facilitating our daily lives; however, UAVs frequently rely on an open wireless channel for communication, making them susceptible to cyber-physical threats. Also, UAVs cannot execute complicated cryptographic algorithms due to their limited onboard computing capabilities. Balancing high-security levels and minimum computation costs is imperative when developing a security solution for UAVs. Consequently, several proxy signature schemes have been proposed in the literature to fulfill these requirements. Nevertheless, many of these solutions face the issue of high computation costs, and some exhibit security vulnerabilities that could not be more feasible options for UAV communication. Considering these constraints in mind, in this article, we introduce an improvised certificate-based proxy signature scheme (ICPS), which leverages the concept of hyperelliptic curve cryptography (HECC) to meet the security and efficiency requirements of UAV networks. The proposed ICPS scheme offers a range of notable features, including its ability to address key escrow and secret key distribution issues. The proposed ICPS scheme’s security hardness has been evaluated using the widely known security tool, the random oracle model (ROM), proving its resilience against known and unknown cybersecurity threats. Finally, this study conducts a performance comparison of the proposed scheme against existing schemes, emphasizing its outstanding cost-efficiency. Notably, the computation cost is measured at 5.3536 ms and the communication cost at 1120 bits, substantially lower than relevant existing schemes.

End user license agreements, terms of service agreements and privacy policies all suffer from many of the same problems: people rarely read them and yet still agree to whatever is contained within them. There are many usability challenges with these policies: they are often lengthy, with jargon filled language that is difficult to quickly comprehend. However, these notices are the primary tool for users to understand the privacy implications of their digital activities and make informed decisions on which websites and software they use. Prior research has explored alternative designs for such notices, using more visual and structured interfaces for conveying information. We expand upon these results by exploring a comic-based interface, examining whether it can engage users to pay more attention to a terms of service agreement. Our results indicate that the comic version did hold user attention for longer than text-based alternatives, encouraging deeper investigation into comic-based interfaces.