A User Priority Determination Method and A Load-control Algorithm Based on Web Access History During Network Load

Yong Min Kil — 2026-08-10

Today, web services are rapidly growing and are being used by many users. This leads to network overload. To avoid overload, techniques for efficient load balancing and server clustering have been studied, and they are being widely used. While a common web service is running, it can occasionally be seen that network overload occurs due to access from many users. To avoid this occasional network overload, expanding computer resources to use normal network services results in the waste of computer resources and operational costs. User priority is determined according to the importance of web service, and there is a problem of limiting the access of low-priority users and providing stable web service to high-priority users at the time of the occasional network overload. Hence, a method was proposed to determine user priority based on web history during network overload under limited computer resources and to stabilize network services for high-priority users by blocking the access of low-priority users.

Generative AI for Automated Medical Report Generation Using Multimodal Clinical Data

Bipin Sule — 2026-09-09

The fast rate of Electronic Health Records (EHRs), medical imaging, and clinical documentation have greatly led to the administrative burden on healthcare professionals, which leaves little time to attend to patients directly. Automated creation of medical reports based on Generative Artificial Intelligence (GenAI) has become one of the potential solutions that can enhance the efficiency and consistency of documentation. Nevertheless, the current methods have the shortcomings of only single-modality processing, the absence of domain-specific knowledge, and the possibility of producing clinically inaccurate or hallucinated information. The paper proposes a new multimodal Generative AI architecture that combines structured clinical data, medical images, and physician notes to produce accurate, context-specific medical reports. The suggested system uses a modality-specific encoder, such as a Vision Transformer, to analyze medical images, a ClinicalBERT-based model to interpret text, and an attention-based fusion mechanism to capture a global picture of the patient. A domain-adapted large language model is additionally boosted with clinical knowledge grounded in standardized medical ontologies. To enhance stability, a reinforcement-oriented medical validation layer is proposed that can be used to assess the factual consistency and punish clinically incorrect predictions, which would decrease hallucinations and enhance diagnostic consistency. The proposed approach has proven superior to the currently available baseline methods in linguistic quality, clinical accuracy, and report completeness, while requiring much less time during the documentation phase. Explainability is also used in the system, which connects the generated content with supporting clinical evidence. The suggested framework is a scalable, trustworthy, and privacy-conscious approach to smart clinical documentation that can benefit radiology reporting, discharge summary creation, and real-time clinical decision support. This study is relevant to the creation of reliable and human-friendly Generative AI systems to be used in the next-generation healthcare app.

International Journal of Digital Technology and Network Security System (e-ISSN: 3108-3307)

A User Priority Determination Method and A Load-control Algorithm Based on Web Access History During Network Load

Generative AI for Automated Medical Report Generation Using Multimodal Clinical Data