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The SARS-CoV-2 virus, which induces an acute respiratory illness commonly referred to as COVID-19, had been designated as a pandemic by the World Health Organization due to its highly infectious nature and the associated public health risks it poses globally. Identifying the critical factors for predicting mortality is essential for improving patient therapy. Unlike other data types, such as computed tomography scans, x-radiation, and ultrasounds, basic blood test results are widely accessible and can aid in predicting mortality. The present research advocates the utilization of machine learning (ML) methodologies for predicting the likelihood of infectious disease like COVID-19 mortality by leveraging blood test data. Age, LDH (lactate dehydrogenase), lymphocytes, neutrophils, and hs-CRP (high-sensitivity C-reactive protein) are five extremely potent characteristics that, when combined, can accurately predict mortality in 96% of cases. By combining XGBoost feature importance with neural network classification, the optimal approach can predict mortality with exceptional accuracy from infectious disease, along with achieving a precision rate of 90% up to 16 days before the event. The studies suggested model's excellent predictive performance and practicality were confirmed through testing with three instances that depended on the days to the outcome. By carefully analyzing and identifying patterns in these significant biomarkers insightful information has been obtained for simple application. This study offers potential remedies that could accelerate decision-making for targeted medical treatments within healthcare systems, utilizing a timely, accurate, and reliable method. [ABSTRACT FROM AUTHOR]

This paper focused on the novel design of receiver side of wavelength division multiplexing passive optical network (WDM-PON). It serves the need for a dramatic change in demand for high data rate beyond 10 gigabits per second (Gbps) at the last mile for a larger coverage area or distance. This increase in demand for higher bandwidth is due to the emerging future technologies like Internet of things (IoT), silicon on chip (SoC), cloud computing, machine learning (ML), and artificial intelligence (AI). Simulation results demonstrated for the different coverage areas which compare the bit error rate (BER), quality (Q)-Factor, and eye height as per the guidelines of ITU-T. It has been predicted that the proposed WDM-PON design supports up to a distance of 60 km. Results have been verified for 40 km, 50 km, 60 km, and 70 km, respectively, at 100 GHz frequency spacing with acceptable BER and better quality (Q)-factor as defined by ITU. [ABSTRACT FROM AUTHOR]

Increased adoption of artificial intelligence (AI) systems into scientific workflows will result in an increasing technical debt as the distance between the data scientists and engineers who develop AI system components and scientists, researchers and other users grows. This could quickly become problematic, particularly where guidance or regulations change and once‐acceptable best practice becomes outdated, or where data sources are later discredited as biased or inaccurate. This paper presents a novel method for deriving a quantifiable metric capable of ranking the overall transparency of the process pipelines used to generate AI systems, such that users, auditors and other stakeholders can gain confidence that they will be able to validate and trust the data sources and contributors in the AI systems that they rely on. The methodology for calculating the metric, and the type of criteria that could be used to make judgements on the visibility of contributions to systems are evaluated through models published at ModelHub and PyTorch Hub, popular archives for sharing science resources, and is found to be helpful in driving consideration of the contributions made to generating AI systems and approaches toward effective documentation and improving transparency in machine learning assets shared within scientific communities. [ABSTRACT FROM AUTHOR]