Traditional cryptographic methods often need complex designs that require substantial memory and battery power, rendering them unsuitable for small handheld devices. As the prevalence of these devices continues to rise, there is a pressing need to develop smart, memory-efficient cryptographic protocols that provide both high speed and robust security. Current solutions, primarily dependent on dynamic permutations, fall short in terms of encryption and decryption speeds, the cryptographic strength, and the memory efficiency. Consequently, the evolution of lightweight cryptographic algorithms incorporating randomised substitution properties is imperative to meet the stringent security demands of handheld devices effectively. In this paper, we present an advanced design of lightweight block ciphers that enhances traditional dynamic permutations with innovative randomised substitutions. This design utilises straightforward randomized encryption methods such as XOR, nibble swap, count ones, and left shift. The cryptographic robustness of our proposed block cipher has been rigorously tested through several standardised statistical tests, as recommended by the National Institute of Standards and Technology (NIST). These evaluations confirm that our algorithm maintains strong cryptographic properties with randomised substitutions and outperforms existing models in several key aspects. Moreover, comparative assessments reveal that our algorithm achieves a throughput of 853.31 Kbps while consuming only 1510 bytes of memory and demonstrating over 60% avalanche properties, significantly outperforming other solutions in terms of CPU utilisation and memory consumption. These results underscore the efficacy of our approach in fulfilling the advanced security requirements of modern handheld devices.

Lung diseases such as Honeycombing (HC) and Ground Glass Opacity (GGO) present significant challenges in medical image analysis due to their overlapping visual characteristics and complex structural patterns in Computed Tomography (CT) images. These conditions often exhibit similar appearances to normal lung tissue, making accurate differentiation difficult and highly dependent on imaging quality and expert interpretation. This paper presents a systematic literature review of studies published between 2011 and 2026 on lung disease detection and classification using medical imaging. A total of 98 selected studies is analyzed to examine recent developments in Machine Learning (ML) and Deep Learning (DL) techniques for automated lung disease analysis. The review covers key aspects including imaging modalities such as CT and chest X-ray, publicly available and private datasets, preprocessing techniques, feature extraction methods, classification models, and evaluation metrics. The findings indicate that traditional machine learning approaches were widely used in earlier studies, while recent research has increasingly shifted towards deep learning methods due to their ability to automatically learn discriminative features from medical images. Among these approaches, Convolutional Neural Networks (CNNs), ResNet-based architectures, DenseNet, and EfficientNet are the most commonly adopted models for lung disease classification. Despite significant advancements, several challenges remain, including limited availability of publicly accessible datasets, class imbalance, variability in imaging protocols, and lack of standardized evaluation frameworks. In addition, misclassification between visually similar conditions such as Honeycombing and GGO remains a persistent issue that affects diagnostic reliability. The review identifies key research gaps and highlights the need for standardized datasets, improved preprocessing techniques, and consistent evaluation protocols. Future research should focus on enhancing model generalisation and robustness across diverse datasets to support reliable automated lung disease diagnosis in clinical practice.

Early identification of COVID-19 patients requiring intensive care is critical for improving treatment prioritization, supporting clinical decision-making, and managing limited hospital resources. While structured electronic health record (EHR) data provide important physiological information, chest computed tomography (CT) imaging contains additional indicators related to disease severity. This study presents a multimodal clinical decision support framework for a multimodal risk prediction framework for severe COVID-19 outcomes using structured emergency clinical features and patient-level CT imaging from the COVID Data for Shared Learning (CDSL) dataset. After multimodal cohort construction, 784 patients with both structured clinical records and CT imaging were included in the analysis. Three predictive settings were evaluated: EHR-only prediction using Gradient Boosting, CT-only prediction using ResNet50-based feature extraction with Logistic Regression, and multimodal prediction using weighted late fusion. The experimental results indicate that the CT-based model surpassed the clinical baseline, yielding an F1-score of 0.42 and an ROC-AUC of 0.772, whereas the EHR-only model achieved scores of 0.30 and 0.715, respectively. Overall, the multimodal fusion framework achieved the strongest results among the approaches tested, reaching an F1-score of 0.47 and an ROC-AUC of 0.782. Taken together, these findings indicate that, although CT imaging alone carries meaningful predictive power for evaluating ICU risk, combining it with clinical data leads to predictions that are more reliable and robust. The proposed framework offers a practical and interpretable foundation for multimodal clinical decision support and demonstrates the potential of combining structured clinical data with medical imaging for intelligent critical care applications.