Medical notes contain valuable information about patient conditions, treatments, and progress. Extracting symptoms from these unstructured notes is crucial for clinical research, population health analysis, and decisi...
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Heart disease has emerged as one of the most dangerous illnesses, significantly affecting people's quality of life. To save patients from suffering an accurate and on-time diagnosis is extremely crucial. With the ...
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machinelearning has emerged as a powerful tool for predicting signal range in wireless communication networks, offering significant improvements over conventional models. Traditional methods, such as empirical path l...
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Load testing is essential for ensuring the performance and stability of modern large-scale systems, which must handle vast numbers of concurrent requests. Traditional load tests, often requiring extensive execution ti...
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ISBN:
(纸本)9798400712487
Load testing is essential for ensuring the performance and stability of modern large-scale systems, which must handle vast numbers of concurrent requests. Traditional load tests, often requiring extensive execution times, are costly and impractical within the short release cycles typical of contemporary software development. In this paper, we present our experience deploying MLOLET, a machinelearning optimized load testing framework, at Ericsson. MLOLET addresses key challenges in load testing by determining early stop points for tests and forecasting throughput and response time trends in production environments. By training a time-series model on key performance indicators (KPIs) collected from load tests, MLOLET enables early detection of abnormal system behavior and provides accurate performance forecasting. This capability allows load test engineers to make informed decisions on resource allocation, enhancing both testing efficiency and system reliability. We document the design of MLOLET, its application in industrial settings, and the feedback received from its implementation, highlighting its impact on improving load testing processes and operational performance.
The automotive industry is a major contributor to carbon emissions, posing a threat to the environment and human health. This research explores the potential of machinelearning for predicting vehicle CO2 emissions. I...
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In software development life cycle requirement engineering plays an vital role. Gathering the requirement and doing the proper classification is an important task in software engineering. The software requirement is t...
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Oral diseases are highly prevalent globally. Oral health also affects the general health of people. Therefore, the prevention and early treatment of oral diseases is important and beneficial. Diagnosis of oral health ...
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Nowadays, with the rapid development of mobile Internet and smartphones, credit card fraud has become more and more serious, causing significant credit and financial damage to cardholders and economics. Many previous ...
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In India, agriculture is a significant industry that is both essential for presence and a critical financial support point. There is an extraordinary opportunity to settle farming creation issues with valuable and pro...
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This study aims to develop a reliable, non-invasive method for predicting early health issues through iridology by leveraging computer vision and deep learning techniques. Iridology, which involves the analysis of iri...
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