Cloud multi-factor authentication is a critical security measure that helps strengthen cloud security from unauthorized access and data breaches. Multi-factor authentication verifies that authentic cloud users are only authorized to access cloud apps, data, services, and resources, making it more secure for enterprises and less inconvenient for users. The number of authentication factors varies based on the security framework’s architecture and the required security level. Therefore, implementing a secured multi-factor authentication framework in a cloud platform is a challenging process. In this paper, we developed an adaptive multi-factor multi-layer authentication framework that embeds an access control and intrusion detection mechanisms with an automated selection of authentication methods. The core objective is to enhance a secured cloud platform with low false positive alarms that makes it more difficult for intruders to access the cloud system. To enhance the authentication mechanism and reduce false alarms, multiple authentication factors that include the length, validity, and value of the user factor is implemented with a user’s geolocation and user’s browser confirmation method that increase the identity verification of cloud users. An additional AES-based encryption component is applied to data, which are protected from being disclosed. The AES encryption mechanism is implemented to conceal the login information on the directory provider of the cloud. The proposed framework demonstrated excellent performance in identifying potentially malicious users and intruders, thereby effectively preventing any intentional attacks on the cloud services and data.

Gene expressions are considered among the most used features in cancer classification. The available gene expression data has a small number of samples and a relatively big number of dimensions, and that makes it not suitable for deep Convolutional Neural Networks (CNN) architectures, which exhibit state-of-the-art performance in many fields. In this paper, we propose a lightweight CNN architecture for breast cancer classification using gene expression data downloaded from Pan-Cancer Atlas using “Illumina HiSeq” platform. The downloaded gene expression data is preprocessed and then transformed into 2D-images. We started the preprocessing by removing the outlier samples, which are determined based on the Array-Array Intensity Correlation (AAIC), which defines a symmetric square matrix of Spearman correlation. Then we applied a normalization process on the gene expression data to ensure that we can infer the expression level from it correctly and avoid biases in the expression measures. Finally, filtering is applied on the data. Model selection or a parameters search strategy is conducted to choose the values of the CNN hyper-parameters that give optimal performance. Our experiments show that our proposed method achieves an accuracy of 98.76%, which is the highest compared to other competing methods.

Recent studies on integrating multiple omics data highlighted the potential to advance our understanding of the cancer disease process. Computational models based on graph neural networks and attention-based architectures have demonstrated promising results for cancer classification due to their ability to model complex relationships among biological entities. However, challenges related to addressing the high dimensionality and complexity in integrating multi-omics data, as well as in constructing graph structures that effectively capture the interactions between nodes, remain active areas of research. This study evaluates graph neural network architectures for multi-omics (MO) data integration based on graph-convolutional networks (GCN), graph-attention networks (GAT), and graph-transformer networks (GTN). Differential gene expression and LASSO (Least Absolute Shrinkage and Selection Operator) regression are employed for reducing the omics data dimensionality and feature selection; hence, the developed models are referred to as LASSO-MOGCN, LASSO-MOGAT, and LASSO-MOGTN. Graph structures constructed using sample correlation matrices and protein-protein interaction networks are investigated. Experimental validation is performed with a dataset of 8,464 samples from 31 cancer types and normal tissue, comprising messenger-RNA, micro-RNA, and DNA methylation data. The results show that the models integrating multi-omics data outperformed the models trained on single omics data, where LASSO-MOGAT achieved the best overall performance, with an accuracy of 95.9%. The findings also suggest that correlation-based graph structures enhance the models’ ability to identify shared cancer-specific signatures across patients in comparison to protein-protein interaction networks-based graph structures. The code and data used in this study are available in the link (https://github.com/FadiAlharbi2024/Graph_Based_Architecture.git).

Microarray technology has produced a massive amount of gene expression data. This data can be used efficiently for classification that facilitates disease diagnosis and prognosis. There are many computational methods that are utilized for cancer classification using these gene expression data. Artificial neural networks (ANN), support vector machines (SVM), and random forests (RF) are among the most successful methods for classifying tumors. Recent research shows that combining many classifiers can yield better results than using one classifier. In this paper, we used stacking ensemble to combine different classifiers, namely, ANN, SVM, RF, naive Bayes (NB), and knearest neighbors (KNN) for microarray-based cancer classification. Results show that stacking ensemble performed better in terms of accuracy, kappa coefficient, sensitivity, specificity, area under the curve (AUC), and receiver operating characteristic (ROC) curve, when applied to publicly available microarray data.

Breast cancer (BC) is the most incident cancer type among women. BC is also ranked as the second leading cause of death among all cancer types. Therefore, early detection and prediction of BC are significant for prognosis and in determining the suitable targeted therapy. Early detection using morphological features poses a significant challenge for physicians. It is therefore important to develop computational techniques to help determine informative genes, and hence help diagnose cancer in its early stages. Eight common hub genes were identified using three methods: the maximal clique centrality (MCC), the maximum neighborhood component (MCN), and the node degree. The hub genes obtained were CDK1, KIF11, CCNA2, TOP2A, ASPM, AURKB, CCNB2, and CENPE. Enrichment analysis revealed that the differentially expressed genes (DEGs) influenced multiple pathways. The most significant identified pathways were focal adhesion, ECM-receptor interaction, melanoma, and prostate cancer pathways. Additionally, survival analysis using Kaplan–Meier was conducted, and the results showed that the obtained eight hub genes are promising candidate genes to serve as prognostic and diagnostic biomarkers for BC. Furthermore, a correlation study between the clinicopathological factors in BC and the eight hub genes was performed. The results showed that all eight hub genes are associated with the clinicopathological variables of BC. Using an integrated analysis of RNASeq and microarray data, a protein-protein interaction (PPI) network was developed. Eight hub genes were identified in this study, and they were validated using previous studies. Additionally, Kaplan-Meier was used to verify the prognostic value of the obtained hub genes.

The practice of flowing effort is participating in various industries especially in nutrition productions all around the world. These fluids practices are utilized extensively in nutrition handling productions by making use of sticky liquids to produce valuable food manufactured goods in bulk. Nevertheless, such productions ought to guarantee that involved equipment such as pipelines are maintained clean as well as are cleared out for the efficient movement of fluids. The nonsimilar characteristics of involuntary convection from circular cylinder stretching in the axial direction subjected to an external flow of Sisko fluid characterized by the freely growing boundary layers (BL) are presented in this research. A circular cylinder is submerged in a stationary fluid. The axial stretching of the cylinder causes external fluid flow. The magnetic force of strength is enforced in the transverse direction. Because of the fluid's high viscosity, frictional heating due to viscous dissipation is quite significant. The flow is three dimensional but with no circumferential variations. The governing equations for axisymmetric flow that include the mass balance, -momentum, and heat equation are modeled through conservation laws. The dimensionless system is developed by employing appropriate nonsimilar transformations. The numerical analyses are presented by adapting local nonsimilarity via finite-difference (FDM)-based MATLAB algorithm bvp4c. The characteristics of dimensionless numbers are determined by graphs that are plotted on momentum and heat equations. The nonsimilar simulations have been compared with the existing local similar solutions. Fluid velocity is increased as the material and curvature parameters are increased, resulting in improved heat transfer. The deviation in skin friction and local Nusselt number against the various dimensionless numbers is also analyzed.