Mais Haj Qasem

Detecting brain tumors can be challenging as a clinical problem because of tumor heterogeneity and reliance on manual neuroimaging interpretation, which can be prone to human error. Artificial intelligence (AI) has shown strong potential as a clinical decision-support tool, assisting radiologists in improving diagnostic accuracy and supporting the interpretation of neuroimaging data. AI using machine learning (ML) and deep learning (DL) algorithms has performed credibly in tumor detection, segmentation, and classification tasks. Challenges such as dataset bias, limited generalization, lack of explainability, and high computational costs must be addressed before clinical application. This article provides a comprehensive review of AI methods applied to brain tumor imaging, with a primary focus on adult diffuse gliomas and secondary coverage of brain metastases, meningiomas, and pediatric tumors where relevant. The major contribution of this review is a new three-factor (diagnostic tasks, learning strategies, and data modalities) taxonomy. Beyond accuracy-based metrics, we provide a qualitative assessment of robustness, generalization, and the principal barriers to clinical adoption identified in the published literature, while acknowledging that comprehensive clinical utility evidence remains an open research direction.

A hybrid parallel storage system is a system which has a hierarchy of storage; each is a primary or secondary storage device, which has significantly different rates of reading the data. The more one goes up the storage hierarchy, greater will be the speed of data access. Moving critical application data to higher levels can greatly lessen application I/O wait time. In general, it assists in cutting down the total time consumption of the app to finish a process. This study presents a prototype of a two-level parallel hybrid storage system with SSD and HDD. The proposed system uses data mining methods to systematically evaluate and classify the application data. While the application runs and requests data, the solution at the same time works to reactively predict what data will be useful next. Data blocks that are classified as high priority are automatically elevated to SSD within the same hybrid cloud storage disk group. With the help of simulation, the data migration strategy backed up by data mining will improve user productivity. The application execution time decreases significantly if any of the application’s data access trace is particular and over 57.21% is used. New hybrid storage architectures paired with predictive data management help applications run faster than ever before.