Maryam Nabil Al‐Berry

Multiple Sclerosis (MS) is a chronic brain disease that affects the brain and spinal cord, where Magnetic Resonance Imaging (MRI) plays a key role in diagn

Background: Early cancer diagnosis is essential for improving prognosis and guiding treatment. However, the high dimensionality and complexity of omics data present major challenges. Computational approaches that extract stable biomarkers and enable reliable classification across cancer types and stages are needed. Methods: A novel feature selection method, sDCFE (synergistic Discriminative Cluster-based Feature Extraction), was developed by extending Fisher-like variance analysis with a median absolute deviation (MAD) regularization term and a cluster separation component to enhance robustness and interpretability. Features selected by sDCFE were compared with those obtained from XGBoost, and the intersected set of 82 genes was evaluated through functional enrichment (KEGG, Reactome, GO BP), survival analysis (Kaplan–Meier, Cox regression), and biomarker novelty assessment against six external resources. Hybrid classification models integrating XGBoost, sDCFE, and deep learning were applied to pancancer classification, and the framework was further extended to lung squamous cell carcinoma (LUSC) staging using RNA-seq and methylation data. Results: The overlap between sDCFE and XGBoost yielded 82 candidate biomarkers enriched in cancer-related pathways, including cell cycle regulation, immune signalling, and DNA repair. Novelty assessment stratified these genes into established, emerging, and novel categories. Six genes—HFE2, LOC339674, SERINC2, SFTA3, SOX2OT, and ACPP—emerged as the most promising candidates, supported by enrichment and survival associations across multiple cancers. The hybrid model achieved near-perfect pancancer classification on TCGA (accuracy = 99.3%, MCC = 0.992, AUC = 1.0) and demonstrated strong generalizability on PCAWG (accuracy = 94%, MCC = 0.929, AUC = 0.997). In the LUSC staging task, multiomics integration improved classification performance: the CNN-based model reached 84% accuracy, while logistic regression applied to sDCFE-ranked features achieved 88.5% accuracy with superior calibration, highlighting the robustness of the selected features. Conclusion: sDCFE provides a principled extension of Fisher-like methods, enabling stable and interpretable biomarker selection. When combined with XGBoost and deep learning, the framework achieves highly accurate and biologically grounded cancer classification across both cancer types and stages. The identification of novel and prognostic biomarkers, including HFE2, LOC339674, SERINC2, SFTA3, SOX2OT, and ACPP, underscores its translational potential. These results position the framework as a promising precision oncology tool to support early diagnosis, risk stratification, and treatment decision-making.

Multiple Sclerosis (MS) is a type of brain disease that affects both the brain and spinal cord. In order to diagnose MS, many modalities are used, including Magnetic Resonance Imaging (MRI). MRI modalities are noninvasive medical tests that provide physicians with detailed images containing essential details on the anatomy and physiology of the brain. Diagnosing with MRI of the brain is crucial since it is laborious, time-consuming, and, above all, prone to manual or human error. In the past ten years, Artificial Intelligence (AI)- based Computer Aided Diagnostic (CAD) tools have been increasingly popular due to their low manual error rate and ability to produce accurate and dependable findings for diagnosing Multiple Sclerosis (MS) using MRI neuroimaging modalities. Automated MS diagnosis in AI is performed using both traditional and contemporary AI. Machine Learning (ML) approaches, which rely on selecting and extracting features through trial and error, were utilized in classical AI. Meanwhile, modern AI uses Deep Learning (DL) techniques, which extract and select suitable features automatically, save time and are more efficient than standard classical ML approaches. In this work, we give a summary of recent automated MS diagnosis approaches that combine MRI neuroimaging modalities with ML and DL algorithms. Segmentation and classification are two of the main categories into which AI techniques for MS medical diagnosis can be roughly divided, where each one of them is divided based on whether a supervised learning, un-supervised learning or both are used together to get better analysis for MS diagnosis. We briefly discuss the related work in each category, and finally, we present the important drawbacks and challenges for each work as well as some proposed ideas that can solve the drawbacks and challenges faced.

Internet simplified digital data transferring. This data needs to be secured; so securing digital data becomes an important concern. Steganography provides security for data by inserting it into a cover and concealing it. In this paper, a steganography algorithm was introduced. This algorithm used the stationary wavelet transform (SWT) and اhybrid-matrix decomposition techniques, singular value decomposition (SVD) and QR factorization to conceal a video in another video. The algorithm performance was measured with reference to the peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM) for the cover and the secret videos. The algorithm successfully hid a video in another cover video and both are of the same size; the hiding capacity was 100%. The algorithm achieved a SSIM that reached 0.97 and a high PSNR value that reached 68.8 proving that the imperceptibility of the proposed algorithm is very high. The comparative analysis shows that the suggested algorithm achieved higher imperceptibility than the other state-of-the-art algorithms regarding the average PSNR. The enhanced version of the proposed method was more robust against different types of attacks.