This proposed study investigates the potential of machine learning methodologies in facilitating early cancer risk assessment by analyzing complex medical datasets. The primary objective is to assess whether machine learning models can reliably identify patients at heightened risk before the disease becomes clinically evident. Through this approach, the study aims to contribute to the development of predictive systems that can trigger early interventions and encourage proactive health monitoring.
The research seeks to answer the core question: “Can machine learning models effectively assess the risk of early-stage cancer using molecular-level data, such as gene expression profiles, prior to the onset of clinical symptoms?”
Sub-questions to be explored include the accuracy of early-stage cancer detection using machine learning, the types of data that most influence prediction performance, and the feasibility of using such models to prompt timely medical evaluations in the absence of traditional diagnostic markers.
The findings are expected to support advancements in personalized medicine by laying the groundwork for tools that assist in identifying high-risk individuals, potentially transforming the current approach to cancer screening and prevention.
]]> within IoT systems based on YANG]]> network management protocol, the Network Configuration Protocol (NETCONF), within IoT
systems based on YANG is presented. The practical realization of this idea requires the
implementation of the NETCONF protocol together with REpresentational State Transfer web
services (RESTful). Such an interesting and innovative practical realization like this opens new
additional possibilities in domotics systems and these possibilities will be discussed in this paper.]]> ]]> The aim of this project was to introduce a new solution to these issues: Lightfin, a fiscalization, loan and subsidy management system built with a microservice architecture, designed to offer enhanced performance, low delays, easy scalability and high stability.
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