The aim of to minimize the time utilized for the developing the aircraft enables the collaborative foundation for the integrated design of aircraft. The work presented in this paper is a simulated work in Simulink whi...
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As the world faces growing challenges in ensuring food security, predicting crop yields accurately be- comes increasingly crucial. Traditionally, this prediction re- lied on methods limited by their inability to handl...
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Human-computer interaction systems rely mostly on hand gesture recognition since they give people a natural and simple way to communicate with digital devices. In this work, a Convolutional Neural Network (CNN) based ...
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machinelearning (ML) has been a main force behind important breakthroughs in patient tracking, personalized medicine, medical tests, and operating efficiency in the healthcare business in recent years. machine learni...
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Classification and detection of Partial discharge (PD) play a crucial role in high-voltage equipment condition monitoring. This study presents an approach using Convolutional Neural Networks (CNNs) for automated PD cl...
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With the extensive utilization of lithium ion batteries as renewable energy source in electronics devices, smart network and electric vehicles, supplementary enhancements in the performance of lithium-ion batteries an...
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With the extensive utilization of lithium ion batteries as renewable energy source in electronics devices, smart network and electric vehicles, supplementary enhancements in the performance of lithium-ion batteries and accurate prediction of state of charge (SOC) are still a great challenge to battery research and innovation community. machinelearning (ML), which is one of the essential tools of artificial intelligence, is promptly changing many areas with its capability to learn from provided data and solve multifaceted tasks, and it has emerged as a new method used to solve research issues in the area of lithium ion batteries. In this paper, we investigate the relationship between input factors including current, voltage and temperature, and predicted SOC of lithium ion battery. The effectiveness of three ML models - linear regression, Gaussian process regression (GPR) and support vector machine (SVM) were assessed and compared. It was found that the predictions made by these models accurately matched the data from experiments.
Recent advancements in skin disease identification leverage machinelearning for automated diagnosis. However, safeguarding the integrity of sensitive medical data remains a top priority. This paper explores the integ...
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This study investigates the usage of water in urban areas, with particular attention to location, age, water quality, and bathing habits. We examined the data using machinelearning, more especially a RandomForestClas...
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machinelearning techniques have emerged as potential tools in the field of extensive research led by the growing interest in predicting the future price of Ethereum. This paper fills a major knowledge gap in the area...
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