As we are aware that verbal communication can be hampered by speech impairment, and sign language is one of the best systems for resolving this problem. The goal of our paper is to create a system or application that ...
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作者：
Sriram, K.P.Anbalagan, E.Sasikumar, S.Kumar, M. Guru VimalParamesh, J.Sujatha, P. KolaAssistant Professor
Information technology St.Joseph's Institute of Technology OMR Chennai – 600119 Professor
Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Professor
Department of Computer Science and Engineering Saveetha Engineering College Chennai Associate Professor
Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Professor
Department of Computer Science and Engineering Mohamed Sathak A.J. College of Engineering Ekattur OMR Siruseri Chennai Associate Professor Department of Information Technology
MIT Campus Anna University Chennai Tamil Nadu India – 600044
This research paper is about the effectiveness of a fusion of machinelearning and artificial intelligence into smart irrigation systems in terms of both safety and benefit of the environment and the economy in farmin...
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Nearly three-quarters of the total power used in the world is consumed by the built environment sector. Therefore, looking for viable methods to reduce building energy needs and decrease negative environmental repercu...
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作者：
Zhu, YunyiGlasgow College
University of Electronic Science and Technology of China Chengdu China
Multimodal emotion analysis, blending machinelearning and deep learning, is transforming computer-based human emotion recognition. This review examines the complexity of human emotions, generally classified into six ...
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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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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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There has been a rapid implementation of artificial intelligence technologies in health care systems. Neural networks, machinelearning, deep learning, and other types of learning are the obvious next step in the auto...
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This study provides a comprehensive analysis of active machinelearning algorithms designed to improve the classification precision of image and text models. This study introduces the ALiPy (Active learning in Python)...
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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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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.
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