This study addresses the pressing need for improved lung cancer diagnosis and treatment by leveraging computational methods and omics data analysis. Lung cancer remains a leading cause of cancer‐related deaths globally, highlighting the urgency for more effective diagnostic and therapeutic approaches. Current diagnostic methods, such as imaging and biopsies, suffer from limitations in sensitivity, specificity, and accessibility, often due to factors such as poor data quality, small sample sizes, and variability in data sources. These limitations highlight the necessity for the development of advanced noninvasive techniques. Computational methods utilizing omics data have shown promise in overcoming these challenges by comprehensively understanding the molecular pathways involved in lung cancer. We propose a novel approach that utilizes RNA‐Seq data and employs LASSO regression with attention mechanisms to identify lung cancer biomarkers. Our results demonstrate the effectiveness of this approach in identifying potential biomarkers for lung cancer, including well‐known genes such as TP53, EGFR, KRAS, ALK, and PIK3CA, validating the model’s ability to uncover key genes associated with lung cancer development and progression. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses revealed significant associations of the identified genes with critical biological processes and pathways, including protein synthesis, folding, cell adhesion, gene regulation, and immune responses. The PPI network analysis, constructed using the STRING database and Cytoscape application, highlighted a highly interconnected interaction landscape, with central hub genes playing pivotal roles in lung cancer progression. RPSA emerged as a crucial hub gene, consistently identified across different centrality measures. This study sheds light on the potential of computational methods and omics data analysis in improving lung cancer diagnosis and treatment, offering new insights for future research directions and personalized medicine strategies.

Identity management (IDM) systems in cloud computing struggle to securely manage user identities and access privileges in distributed environments. However, centralized IDM solutions come with high trust costs, single points of failure, and a need for appropriate security response. This paper proposes a novel decentralized IDM framework utilizing blockchain technology and automatic provisioning (AP) techniques to improve cloud computing’s security, scalability, and operational efficiency. The framework employs Ethereum smart contracts and role‐based access control (RBAC) to ensure secure, transparent, and automated management of user identities. Key features include support for single sign‐on (SSO), multifactor authentication (MFA), and delegated proof‐of‐stake (DPoS) consensus for secure transaction validation. Our proposed scheme utilizes the Ethereum blockchain and smart contracts for managing user access, ensuring transparent and immutable record‐keeping. The scheme introduces RBAC mechanisms to ensure precise privilege allocation and dynamic updates. The scheme also supports key IDM processes, including SSO, MFA, and lifecycle management of identities. The framework incorporates DPoS consensus to enhance security for efficient transaction validation and the prevention of fraud. To address fraudulent activities, the scheme uses machine learning to detect blockchain fraud with 99.1% accuracy, demonstrating robustness and efficiency for large‐scale cloud infrastructures.

Breast cancer is ranked as the second most common cancer among women globally, highlighting the critical need for precise and early detection methods. Our research introduces a novel approach for classifying benign and malignant breast ultrasound images. We leverage advanced deep learning methodologies, mainly focusing on the vision transformer (ViT) model. Our method distinctively features progressive fine‐tuning, a tailored process that incrementally adapts the model to the nuances of breast tissue classification. Ultrasound imaging was chosen for its distinct benefits in medical diagnostics. This modality is noninvasive and cost‐effective and demonstrates enhanced specificity, especially in dense breast tissues where traditional methods may struggle. Such characteristics make it an ideal choice for the sensitive task of breast cancer detection. Our extensive experiments utilized the breast ultrasound images dataset, comprising 780 images of both benign and malignant breast tissues. The dataset underwent a comprehensive analysis using several pretrained deep learning models, including VGG16, VGG19, DenseNet121, Inception, ResNet152V2, DenseNet169, DenseNet201, and the ViT. The results presented were achieved without employing data augmentation techniques. The ViT model demonstrated robust accuracy and generalization capabilities with the original dataset size, which consisted of 637 images. Each model’s performance was meticulously evaluated through a robust 10‐fold cross‐validation technique, ensuring a thorough and unbiased comparison. Our findings are significant, demonstrating that the progressive fine‐tuning substantially enhances the ViT model’s capability. This resulted in a remarkable accuracy of 94.49% and an AUC score of 0.921, significantly higher than models without fine‐tuning. These results affirm the efficacy of the ViT model and highlight the transformative potential of integrating progressive fine‐tuning with transformer models in medical image classification tasks. The study solidifies the role of such advanced methodologies in improving early breast cancer detection and diagnosis, especially when coupled with the unique advantages of ultrasound imaging.