This paper expounds the automatic recognition method of parts based on computer vision. The feature database of the processed parts is constructed by using machinelearning method. Image preprocessing, threshold segme...
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As the training sample data in deep learning is not always easy to obtain, and the labeling task of sample data is very labor-intensive, weakly supervised learning method as a branch of machinelearning has increasing...
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In the era of big data information, along with the continuous development and application of 5G, online teaching, machinelearning and other high-tech, the amount of information and data in many fields, such as educat...
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With the increasing complexity of business environment, the importance of data analysis in business decision-making has become increasingly prominent. As a powerful data analysis tool, machinelearning algorithm has b...
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Air traffic control flow prediction is one of the key issues in aviation transportation management, crucial for optimizing resource allocation, enhancing control efficiency, and ensuring flight operation safety. This ...
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This work focuses on musical instrument recognition in miscellaneous audio sources, a fundamental issue of the machine auditory system. We contrast traditional machinelearning techniques against deep learning strateg...
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Due to their diverse species and complex morphology, the automatic identification of marine plankton has always been a challenging task. In response to this problem, this study proposes an innovative deep learning-bas...
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Recently, Educational datamining (EDM) has increased remarkable consideration. The significant aim of educational institutions is to offer efficient education for students to improve academic performance. When data m...
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Automatic Speech recognition (ASR) is a prevalent approach for attaining human-machine interaction by enabling machines to transcribe speech data. We propose a Continuous Speech recognition model in the Kannada langua...
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ISBN:
(纸本)9783031640667;9783031640674
Automatic Speech recognition (ASR) is a prevalent approach for attaining human-machine interaction by enabling machines to transcribe speech data. We propose a Continuous Speech recognition model in the Kannada language using deep learning techniques such as Convolutional Neural Networks (CNN) and Bidirectional Gated Recurrent Units (Bi-GRU). The model was trained and validated using 100 and 20 h of data, respectively. The experiment has generated encouraging results with a Character Error Rate (CER) of 15.62% and a Word Error Rate of 34.47% (WER).
Voice assistant applications have become integral parts of modern technology ecosystems, offering users convenient and efficient ways to interact with their devices. This paper introduces Voice Ai, a versatile voice a...
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