The rise of fake news has made the need for effective detection methods, including in languages other than English, increasingly important. The study aims to address the challenges of Bangla which is considered a less important language. To this end, a complete dataset containing about 50,000 news items is proposed. Several deep learning models have been tested on this dataset, including the bidirectional gated recurrent unit (GRU), the long short-term memory (LSTM), the 1D convolutional neural network (CNN), and hybrid architectures. For this research, we assessed the efficacy of the model utilizing a range of useful measures, including recall, precision, F1 score, and accuracy. This was done by employing a big application. We carry out comprehensive trials to show the effectiveness of these models in identifying bogus news in Bangla, with the Bidirectional GRU model having a stunning accuracy of 99.16%. Our analysis highlights the importance of dataset balance and the need for continual improvement efforts to a substantial degree. This study makes a major contribution to the creation of Bangla fake news detecting systems with limited resources, thereby setting the stage for future improvements in the detection process.

Nowadays, credit card transactions are exponentially increasing as a form of online payment. That creates technical pressure on the financial institution to incur losses due to credit card fraud. It is no wonder that credit card fraud is making people feel insecure and unsafe about using the services provided by banks. Data mining reasons on offline and online transactions can cause fraud detection. Number one is the changes in the behaviour of the frauds that are changing, and number two is the fraudulent dataset, which is asymmetric. In addition, the variables and techniques used by the researcher can impact fraud detection activities for credit cards. Therefore, the paper intends to investigate the suitability of the k-nearest-neighbour, decision tree, support vector machine, logistic regression, and Catboost. The data set has a total of 284807 transactions coming from the European credit card holder. Evaluation of the performance can be measured through the specificity, accuracy, sensitivity, precision, and lastly, the recall rate. After completing the comprehensive cross-validation, it was discovered that the catboost’s accuracy was extraordinary, which amounted to 93.39%, leaving the other classification matrix far behind.

Blood cell classification and counting are vital for the diagnosis of various blood-related diseases, such as anemia, leukemia, lymphoma, and thrombocytopenia. The manual process of blood cell classification and counting is time-consuming, prone to errors, and labor-intensive. Therefore, we have proposed a deep learning (DL)-based automated system for blood cell classification and counting from microscopic blood smear images. We classify a total of nine types of blood cells, including Erythrocyte, Erythroblast, Neutrophil, Basophil, Eosinophil, Lymphocyte, Monocyte, Immature Granulocytes, and Platelet. Several preprocessing steps like image resizing, rescaling, contrast enhancement and augmentation are utilized. To segment the blood cells from the entire microscopic images, we employed the U-Net model. This segmentation technique aids in extracting the region of interest (ROI) by removing complex and noisy background elements. Both pixel-level metrics such as accuracy, precision, and sensitivity, and object-level evaluation metrics like Intersection over Union (IOU) and Dice coefficient are considered to comprehensively evaluate the performance of the U-Net model. The segmentation model achieved impressive performance metrics, including 98.23% accuracy, 98.40% precision, 98.26% sensitivity, 95.97% Intersection over Union (IOU), and 97.92% Dice coefficient. Subsequently, a watershed algorithm is applied to the segmented images to separate overlapped blood cells and extract individual cells. We have proposed a BloodCell-Net approach incorporated with custom light weight convolutional neural network (LWCNN) for classifying individual blood cells into nine types. Comprehensive evaluation of the classifier’s performance is conducted using metrics including accuracy, precision, recall, and F1 score. The classifier achieved an average accuracy of 97.10%, precision of 97.19%, recall of 97.01%, and F1 score of 97.10%. A 5-fold cross-validation technique is applied to split the data, which not only aids in reducing overfitting but also helps in generalizing the model.

Heart failure is a life-threatening condition that affects millions of people worldwide. The ability to accurately predict patient survival can aid in early intervention and improve patient outcomes. In this study, we explore the potential of utilizing data pre-processing techniques and the Extra-Tree (ET) feature selection method in conjunction with the Random Forest (RF) classifier to improve survival prediction in heart failure patients. By leveraging the strengths of ET feature selection, we aim to identify the most significant predictors associated with heart failure survival. Using the public UCL Heart failure (HF) survival dataset, we employ the ET feature selection algorithm to identify the most informative features. These features are then used as input for grid search of RF. Finally, the tuned RF Model was trained and evaluated using different matrices. The approach was achieved 98.33% accuracy that is the highest over the exiting work.

Securing Health: The Convergence of AI and Cybersecurity in Healthcare explores how emerging technologies are revolutionizing modern medicine, ensuring not just innovation but safety and trust in the digital age. As artificial intelligence and cybersecurity rapidly transform industries, this timely book offers an in-depth look at their powerful convergence within the healthcare sector. Organized into four comprehensive sections, the book unveils cutting-edge strategies and practical applications shaping the future of medicine. From blockchain integration and smart wearables to adversarial machine learning and federated learning, each chapter offers critical insights into securing sensitive data, enhancing diagnostic accuracy, and preserving patient privacy. Discover how blockchain is being used to protect medical records and power real-time health monitoring for athletes, with a special focus on deployments in regions like Kurdistan. Learn how AI-driven tools are predicting complex diseases like cerebral palsy and post-transplant diabetes, offering hope for earlier intervention and better outcomes. Dive into the world of deep learning as it redefines medical imaging, from detecting COVID-19 via X-rays to advancing brain MRI segmentation. With contributions grounded in global case studies and research, Securing Health addresses the ethical, technical, and regulatory challenges of modern healthcare technology. Whether you're a medical professional, tech innovator, or policy maker, this book equips you with the knowledge to navigate and lead in an era where AI and cybersecurity aren't just enhancements—they're essentials. Empowering, forward-thinking, and solution-focused, Securing Health is your essential guide to the future of secure, intelligent healthcare. <!----> <!----> <!----> <!---->