Журнал компьютерных наук и системной биологии

In the era of big data, the challenge of extracting meaningful insights and knowledge from various domains has never been more significant. Cross-domain data mining has emerged as a powerful approach to leverage knowledge from one domain and apply it to another. This research article explores the techniques and applications of CDDM, focusing on knowledge transfer and generalization across different domains. We delve into the methodologies and tools that enable the seamless flow of information between domains, fostering innovation, efficiency, and improved decision-making.

Cloud computing has become a ubiquitous technology in the digital age, offering scalability, flexibility, and cost-efficiency to a wide range of applications and services. To harness the full potential of cloud resources, dynamic resource allocation is essential. This research article explores the application of Fuzzy Network Control for dynamic resource allocation in cloud networks. Fuzzy logic, with its ability to handle imprecise and uncertain information, offers a promising approach to optimizing resource allocation in dynamic cloud environments. The article presents an indepth analysis of the concept, its methodology, benefits, challenges, and potential future developments.

The Industrial Internet of Things has revolutionized the way industries operate by providing real-time data from various sensors and devices. However, the vast amount of data generated in IIoT networks poses a significant challenge in identifying anomalies and potential security threats. In this research article, we explore the use of data mining and machine learning techniques for efficient anomaly detection in IIoT networks. We present a comprehensive analysis of various methodologies and tools that can be employed to enhance the security and reliability of industrial systems. Our findings suggest that a combination of feature engineering, supervised learning, and unsupervised learning techniques can lead to highly effective and efficient anomaly detection systems.

The explosive growth of data in the digital age has enabled organizations to derive valuable insights and knowledge through data mining. However, the extraction of knowledge from large datasets raises significant privacy concerns. This research article explores privacy-preserving data mining techniques, which balance the need for knowledge discovery with the imperative of safeguarding personal data. We discuss various methods to protect privacy during data mining, emphasizing their importance for secure and ethical knowledge discovery.