Leveraging ML for Anomaly Detection in Healthcare Data Warehouses (Published)
The rapid emergence of digitalisation leads to unprecedented growth in the generation of the healthcare sector-particularly EHRs and medical equipment data. This extended the way for challenges for integrity in managing data and anomaly detection, including fraudulent transactions, medication errors, and many more system failures. Modern healthcare data poses a challenge to traditional methods of anomaly detection due to high and complex dimensionality. Machine learning provides a strong solution, using algorithms such as Gaussian Mixture Models, One-Class SVM and deep learning algorithms such as Autoencoders, and Recurrent Neural Networks in the detection of anomalies in healthcare data warehouse settings [1]. This study reports how ML can help advance care for patients, enable the validity of the data and reduce costs through real-time monitoring, fraud detection, and early detection of diseases. Applying anomaly detection through ML would most likely bring better operational performance, patient safety, and decision-making in health care for organizations as issues of poor data quality, lack of interpretability of models, and real-time detection would be addressed [2].
Keywords: Operational Efficiency, Patient Safety, anomaly detection, fraud detection healthcare data warehouses, machine learning
Development of Secure Cloud-Based Government Solutions (Published)
Government agencies face significant security and efficiency challenges when adopting cloud solutions. These challenges include data breaches, unauthorized access, and compliance with stringent regulatory standards. This paper explores the development of secure and efficient cloud-based solutions tailored specifically for government needs, aiming to address these critical issues. These solutions protect sensitive government data by focusing on robust security protocols, advanced encryption methods, multi-factor authentication, and continuous monitoring. Additionally, integrating technologies such as artificial intelligence and machine learning enhances the ability to predict and mitigate potential threats. Compliance with regulatory standards, such as those set by the National Institute of Standards and Technology (NIST) and ISO 27001, is emphasized to ensure global security adherence. Implementing “Security by Design” and Zero Trust Architecture further strengthens the security framework. This research highlights the importance of a multi-faceted approach, including collaboration with cloud service providers, regular security audits, and employee training programs. Developing secure cloud-based solutions enhances national security and improves public service delivery, making it a vital endeavor for government agencies. Future research should explore emerging technologies and international cooperation to stay ahead of evolving cyber threats.
Keywords: Data protection, National Security, Operational Efficiency, cloud security, government solutions