Migrating from monolithic applications to Microservices architecture brings about a number of challenges. Splitting and sharing databases among Microservices is a case in point. In this article, we present a framework for managing the splitting of databases among Micorservices. The proposed solution maintains data consistency and independent deployment of Microservices while enhancing the solution to latency problems. The evaluation results show enhancement in performance as a result of using the proposed framework.

Prediction of chronic kidney disease (CKD) has emerged as a useful technique for early detection of at-risk persons and the introduction of appropriate management strategies. Machine learning and data-driven methods have been used in predictive modeling to examine massive databases of patient demographics, medical histories, test findings, and genetic information. These cutting-edge methods allow for the profiling of high-risk patients and the tailoring of healthcare administration approaches. Patient outcomes, complication rates, and healthcare system efficiency may all benefit greatly from CKD screening and prediction. Responsible use of CKD prediction algorithms, however, requires resolving issues with data availability, integration, and ethics. The area of medicine has benefited greatly from the use of Machine Learning (ML) methods, which have played an increasingly central role in illness prediction. In this study, we use a strategy that makes use of ML methods to construct effective tools for predicting the development of CKD. Multiple ML models are trained, and their results are compared using a variety of criteria. We applied five ML methods such as logistic regression (LR), Decision tree (DT), random forest (RF), support vector machine (SVM), and k-nearest neighbor (KNN). The LR and KNN have the highest accuracy with 99%.

Microservices architecture emerges as a promising software design approach that provides large scale software systems with flexibility, scalability and fault tolerance. Moreover, it is considered a suitable design to be implemented using software containers provided with several cloud providers. However, microservices suffer from several security challenges that hinder its progress. The concept of microservices is to break down the system functionality to a number of small coherent services. Hence, using microservices as a design approach increases the security risks by expanding the risk surface. In contrast to microservices, monolithic applications are implemented as a bulk of codes using single programming language. Such environment has several drawbacks related to flexibility and maintainability, but limits security issues. On the other hand, microservices implementation uses several programming languages and frameworks to implement small units of system functionality. Such environment opens the door to new critical security issues. The proposed work introduces the problem of securing microservices and provides a novel approach to protect microservices applications from masquerade attacks. The proposed framework also provides high protection to users from malicious services. The framework was implemented using 150 software containers to define users' HTTP requests and a set of 20 microservices were tested to proof its applicability and benefits

The paper discusses an improved phishing detection system that uses hybrid machine learning for analyzing URL-based features. Further improvement of prior work using better feature selection as well as ensemble methods is discussed. The proposed model enhances classification accuracy, precision, and recall by utilizing an innovative hybrid ensemble approach that integrates Logistic Regression, Support Vector Machine, and Decision Tree, alongside newly incorporated evaluation techniques. The results manifest high improvement in performance metrics as compared to the previous methods. Various comparisons and benchmarks with other methods have further proved the robustness of our proposed system for detecting malicious URLs.