3D human pose estimation (HPE) has improved significantly through Graph Convolutional Networks (GCNs), which effectively model body part ***, GCNs have limitations, including uniform feature transformations across nod...
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Cloud computing has emerged as a transformative technology that offers numerous benefits to various industries, including the music industry. Cloud computing has revolutionized the way businesses operate and has had a...
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It can be argued that the Convolutional Neural Network (CNN) used in this research is an efficient algorithm for classifying images based on the end prediction of the path taken. In every plot that is made, there is a...
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The atmospheric turbulence as the natural media of optical propagation for the free-space optical (FSO) communications is the great advantage to reach flexible connection and low-cost deployment but also is the major ...
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Cloud computing has emerged as a transformative technology that offers numerous benefits to various industries, including the music industry. Cloud computing has revolutionized the way businesses operate and has had a...
Cloud computing has emerged as a transformative technology that offers numerous benefits to various industries, including the music industry. Cloud computing has revolutionized the way businesses operate and has had a significant impact on various industries, including the music industry Cloud computing, with its scalability, flexibility, and cost-effectiveness, presents an opportunity for the music industry to leverage technology to enhance its operations and meet the evolving demands of the digital era. This paper presents a systematic literature review on cloud computing migration strategies specifically tailored for the music industry. The review aims to identify existing research, frameworks, and best practices related to cloud migration in the music industry, as well as highlight the challenges and opportunities associated with such migrations. Through an analysis of relevant literature, this study provides valuable insights to assist music industry stakeholders in developing effective cloud migration strategies.
It can be argued that the Convolutional Neural Network (CNN) used in this research is an efficient algorithm for classifying images based on the end prediction of the path taken. In every plot that is made, there is a...
It can be argued that the Convolutional Neural Network (CNN) used in this research is an efficient algorithm for classifying images based on the end prediction of the path taken. In every plot that is made, there is a similar process for achieving a prediction of the final result. The implementation for each of these procedures follows the steps that are summarized into a flow of analysis stages that can help to develop the application of an algorithm. The initial stage is to take the handwriting from the user which is then pre-processed the image, to eliminate existing noise, and sharpen the contrast, so that the image can be seen clearly. Images will be processed and analyzed using the Convolutional Neural Network model, training will be carried out, with an average training of a dataset of 100 epochs or about 7 to 10 minutes, and labeling on the trained dataset. The accuracy of the training reached 98.89%, as a proportion of the different characteristics of the handwriting sample.
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