Machine intelligence can convert raw clinical data into an informational source that helps make decisions and predictions. As a result, cardiovascular diseases are more likely to be addressed as early as possible before affecting the lifespan. Artificial intelligence has taken research on disease diagnosis and identification to another level. Despite several methods and models coming into existence, there is a possibility of improving the classification or forecast accuracy. By selecting the connected combination of models and features, we can improve accuracy. To achieve a better solution, we have proposed a reliable ensemble model in this paper. The proposed model produced results of 96.75% on the cardiovascular disease dataset obtained from the Mendeley Data Center, 93.39% on the comprehensive dataset collected from IEEE DataPort, and 88.24% on data collected from the Cleveland dataset. With this proposed model, we can achieve the safety and health security of an individual.

Machine unlearning (MU) is increasingly studied across artificial intelligence systems in which target information may remain in model weights, adapters, retrieval indexes, caches, federated states, and temporally derived representations. However, existing reviews often examine methods, benchmarks, or deployment settings separately. Therefore, this scoping review collected and synthesized 159 sources to compare unlearning evidence across these connected system components. The evidence map separates 42 core MU sources—26 primary empirical or theoretical studies, 12 benchmark or evaluation frameworks, and 4 secondary evidence syntheses—from 117 contextual sources. First, a three-dimensional taxonomy distinguishes guarantee or reference type, update mechanism, and deployment or memory context. This structure prevents partitioning from being treated as a guarantee and influence estimation from being treated as an outcome. Next, the quantitative map identifies 13 centralized or general sources, 16 LLM- or benchmark-focused sources, 3 graph sources, 3 federated sources, 2 temporal sources, 2 quantized-network sources, and 3 cross-domain reviews. These results indicate a stronger reference-based foundation for centralized MU and a benchmark-rich but transformation-sensitive evidence base for large language models. In contrast, graph, federated, and temporal unlearning remain less mature because they are supported by smaller core evidence sets. Moreover, recent studies show that apparent forgetting may fail after 4-bit quantization, probabilistic decoding, alternative reference selection, recovery testing, benign query changes, or overlap between forget and retain knowledge. Accordingly, we present an audit-oriented deletion lifecycle that connects forgetting evidence, retained-utility testing, threat-model-specific attacks, post-transformation verification, and redeployment decisions. Calibration, citation grounding, provenance, and human review are treated as supplementary decision-readiness checks rather than direct proof of unlearning. Finally, the lifecycle is presented as a structured synthesis and reporting framework that still requires prospective validation in real systems and independent practitioner assessment.

In order to logically prioritize the urgent risks in the heterogeneous mobile network operating environment, we derive environmental factors that can reflect the characteristics of the heterogeneous network operating environment and present them as a...

A Distributed reflection denial of service (DRDoS) is a variant of DDoS attacks that threatens the availability of services to legitimate users. In response to this evolving threat landscape, the cybersecurity industry and service providers have intensified their efforts to develop effective countermeasures. Despite these efforts, attackers continue to innovate, developing new strategies and tools while becoming more sophisticated. Consequently, DRDoS attacks continue to be harmful. Therefore, ongoing research and development is essential to improve defense against DRDoS attacks. To advance our understanding and analysis of DRDoS attacks, this study examines the unique characteristics of DRDoS attacks and quantifies the risks involved. Additionally, it adopts a quantitative rather than traditional qualitative methods to derive and apply risk, particularly the probability of loss that can be caused by DRDoS attacks.