Artificial Intelligence (AI) is the subject of a challenge and attention in the field of oncology and raises many promises for preventive diagnosis, but also fears, some of which are based on highly speculative visions for the classification and detection of tumors. A brain tumor that is malignant is a life-threatening disorder. Glioblastoma is the most prevalent kind of adult brain cancer and the 1 with the poorest prognosis, with a median survival time of less than a year. The presence of O⁶ -methylguanine-DNA methyltransferase (MGMT) promoter methylation, a particular genetic sequence seen in tumors, has been proven to be a positive prognostic indicator and a significant predictor of recurrence. This strong revival of interest in AI is modeled in particular to major technological advances which have significantly increased the performance of the predicted model for medical decision support. Establishing reliable forecasts remains a significant challenge for electronic health records (EHRs). By enhancing clinical practice, precision medicine promises to improve healthcare delivery. The goal is to produce improved prognosis, diagnosis, and therapy through evidence-based sub stratification of patients, transforming established clinical pathways to optimize care for each patient’s individual requirements. The abundance of today’s healthcare data, dubbed “big data,” provides great resources for new knowledge discovery, potentially advancing precision treatment. The latter necessitates multidisciplinary initiatives that will use the knowledge, skills, and medical data of newly established organizations with diverse backgrounds and expertise. The aim of this paper is to use magnetic resonance imaging (MRI) images to train and evaluate your model to detect the presence of MGMT promoter methylation in this competition to predict the genetic subtype of glioblastoma based transfer learning. Our objective is to emphasize the basic problems in the developing disciplines of radiomics and radiogenomics, as well as to illustrate the computational challenges from the perspective of big data analytics.

Background Phishing attacks pose a significant threat to Industrial Internet of Things (IoT) networks, with a documented 350% increase in IoT-targeted phishing attempts since 2020. This alarming trend necessitates robust security measures to protect critical infrastructure. Methods We propose a novel methodology integrating a CNN-LSTM model for initial phishing detection and a sophisticated LLM for comprehensive analysis. Our approach combines spatial feature extraction via CNN layers with temporal pattern recognition through LSTM, enhanced by explainable AI techniques for transparency. Results Our CNN-LSTM model achieves 93.26% accuracy on the Web URL dataset and the DistilBERT model achieves an accuracy of 98.64% on text and email dataset. The addition of explainable AI improves transparency by 41% compared to black-box approaches, which is crucial for IoT security operations. Conclusion This approach not only improves phishing detection accuracy but also contributes to adaptive security measures tailored to evolving cybersecurity threats. By reducing false positives by 41% compared to traditional methods and providing human-interpretable security insights through LLM integration, our framework ensures the resilience of IoT networks while maintaining operational efficiency.

The process of medical diagnosis requires several steps to identify the types of cardiac pathologies. The segmentation step is used to determine the measurements for cardiac abnormalities on the short axis of the 4D acquired in MRI, but this phase remains limited on the blood flow sequences. The MRI modality allows the experts to quantify the stenosis and the regurgitation of aortic blood flow. The parameters extracted from the flow sequences, after segmentation, make it possible to identify the valvular pathologies, but they are not sufficient to complete the medical prognosis as well as the lack of precision of these measurements. In this paper, we propose to make a coupling between the 4D cardiac cuts with their study of blood flow through the technique of registration and reconstruction. The interest of the purpose of the blood flow as the fifth dimension is to improve the accuracy of the cardiac parameters and the extracting of measurements for valvulopathies in the process of assistance to the decision for experts. An example was introduced in this context through the development of cloud services for patient-specific simulations of blood flows through aortic valves as well as an OsiriX software for 5D visualization that combines a 4D sequence and the functional flow dimension. In this framework, we proposed a processing chain to lead towards a 5D solution. Another problem raised is the choice of the appropriate architecture to solve the problem of hybrid parallelization for processing these cardiac images. To test the constraints of time of the 5D concept, we need a GPU graphics processor acquired in MRI, as well as a CPU processor to perform the complexity of calculation and the operations applied to the algorithms of image processing.

A comparative study has been depicted between the contour and topographic watershed segmentation approach for short-axis 5D cardiac sequences with MRI for medical decision. The fifth dimension has been defined as the excitation of pixels based on the gray scale around the myocardium without consideration of the morphological structure of the heart in 3D and fourth dimension (time). Three patients were performed the first is healthy, the second has a genetic disease, and the third had a heart failure syndrome for a dimension ROI = 150mm, average age is 54 years old, and mean of weight = 86 kg. A contouring and watershed segmentation algorithm for a sample of 63 Cine Fiesta MRI sequences for short-axis cuts with Matlab and its in-box toolbox complements was implemented. For a healthy patient 13.4% tolerance rate for the estimation of the stroke fraction, 6.4% for a patient with genetic disease, 8.7% error rate for a patient with heart failure symptom. The results show that the regurgitation fraction by the contour approach for a patient case with symptom of the presence of a genetic disease is 0.0335% for an aortic valve, 0.248% for a mitral valve, an error rate 0.16% for estimating this parameter for the aortic orifice with the watershed segmentation approach. In return, for a patient with suspected heart failure (stenosis or regurgitation) the regurgitation fraction is estimated by 0% for aortic valve, 1.49 e ^{+ 03}% for a mitral valve, an error rate 11.76% compared to the watershed segmentation approach. The results are validated clinically. The Optimization of the topographic watershed approach with mutual information was simulated for the extraction of measurements (ejection fraction, regurgitation rate) within the left ventricle for three patient types (healthy, genetic pathology and heart failure). The results are considered interesting compared to the clinical assessment.