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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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.
A comprehensive analysis was performed using the AIDS Clinical Trials Group 175 dataset to improve the accuracy of predicting AIDS disease progression. The primary objective was to integrate machinelearning technique...
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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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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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VANETs, or vehicle impromptu networks, offer an efficient facility for applications requiring transportation systems with intelligence (ITS). In Communication using VANET, roadside units (RSUs) are the primary element...
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Linear systems of equations can be found in various mathematical domains, as well as in the field of machinelearning. By employing noisy intermediate-scale quantum devices, variational solvers promise to accelerate f...
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ISBN:
(纸本)9798331541378
Linear systems of equations can be found in various mathematical domains, as well as in the field of machinelearning. By employing noisy intermediate-scale quantum devices, variational solvers promise to accelerate finding solutions for large systems. Although there is a wealth of theoretical research on these algorithms, only fragmentary implementations exist. To fill this gap, we have developed the variational-lse-solver framework, which realizes existing approaches in literature, and introduces several enhancements. The user-friendly interface is designed for researchers that work at the abstraction level of identifying and developing end-to-end applications.
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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The threat of credit card fraud is high and so there should be efficient ways to combat it. The research is being employed to focus on how to enhance credit card fraud detection, machinelearning methodologies can be ...
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