Published Paper Details:

The rapid advancements in machine learning, big data analytics, and cyber-physical systems have significantly influenced diverse fields, including healthcare, manufacturing, and artificial intelligence. In healthcare, machine learning models have demonstrated promising capabilities in identifying gene biomarkers for breast cancer prognosis and treatment decisions, leveraging genomic and transcriptomic data. Circulating microRNAs and gene expression signatures have emerged as crucial diagnostic and prognostic biomarkers, facilitating personalized medicine approaches. In manufacturing, the integration of big data analytics and Industry 4.0 technologies has revolutionized defect detection, predictive maintenance, and flexible manufacturing systems. Cyber-physical systems and artificial intelligence have enabled intelligent decision-making, optimizing production processes and resource management. Virtualization and the Internet of Things (IoT) further enhance the interconnectedness of manufacturing ecosystems, promoting sustainability and efficiency. The evolution of artificial intelligence, particularly deep learning and support vector machines, has propelled advancements in data-driven decision-making. Techniques such as random forests, synthetic minority over-sampling (SMOTE), and Bayesian classification have enhanced predictive analytics across multiple domains. The application of big data in healthcare and manufacturing underscores the transformative potential of data-driven methodologies, ensuring improved outcomes, cost efficiency, and innovation. This study synthesizes research on machine learning, big data analytics, and cyber-physical systems, highlighting their implications in healthcare, manufacturing, and artificial intelligence. The findings provide insights into emerging trends, challenges, and future opportunities in these domains, emphasizing the role of advanced technologies in shaping modern industries.