This study addresses the challenges of real-time data synchronization and big data processing in the construction of digital twin workshops under the background of intelligent manufacturing. A solution that integrates...
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As the size of modern datasets exceeds the disk and memory capacities of a single computer, machine learning practitioners have resorted to parallel and distributed computing. Given that optimization is one of the pil...
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Currently most all the information sharing takes place in the form of Text-based input. Diverse information sources, encompassing speech, text, and visual elements, offer opportunities for analyzing emotions. Presentl...
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Unsupervised vision clustering, a cornerstone in computer vision, has been studied for decades, yielding significant outcomes across numerous vision tasks. However, these algorithms involve substantial computational d...
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ISBN:
(纸本)9798331541378
Unsupervised vision clustering, a cornerstone in computer vision, has been studied for decades, yielding significant outcomes across numerous vision tasks. However, these algorithms involve substantial computational demands when confronted with vast amounts of unlabeled data. Conversely, quantum computing holds promise in expediting unsupervised algorithms when handling large-scale databases. In this study, we introduce QClusformer, a pioneering Transformer-based framework leveraging quantum machines to tackle unsupervised vision clustering challenges. Specifically, we design the Transformer architecture, including the self-attention module and transformer blocks, from a quantum perspective to enable execution on quantum hardware. In addition, we present QClusformer, a variant based on the Transformer architecture, tailored for unsupervised vision clustering tasks. By integrating these elements into an end-to-end framework, QClusformer consistently outperforms previous methods running on classical computers. Empirical evaluations across diverse benchmarks, including MS-Celeb-1M and DeepFashion, underscore the superior performance of QClusformer compared to state-of-the-art methods.
In the contemporary era of cloud computing, efficient and precise prediction of CPU utilization ensures optimal performance and energy efficiency in data centers. Traditional predictive models often need to be improve...
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Any intrusion detection system's efficacy is determined by how quickly it can identify fresh threats and stop subsequent assaults. This requires employing highly efficient machine learning techniques, including cl...
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This study performs a comparative analysis of Support Vector Machine (SVM) and Multi-Class Support Vector Machine (MSVM) for plant disease detection using a dataset consisting of images of healthy and diseased plant l...
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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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Facial expression recognition (FER) is vital in pattern recognition, artificial intelligence, and computer vision. It has diverse applications, including operator fatigue detection, automated tutoring systems, music f...
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ISBN:
(数字)9783031281839
ISBN:
(纸本)9783031281822;9783031281839
Facial expression recognition (FER) is vital in pattern recognition, artificial intelligence, and computer vision. It has diverse applications, including operator fatigue detection, automated tutoring systems, music for mood, mental state identification, and security. Image data collection, feature engineering, and classification are vital stages of FER. A comprehensive critical review of benchmarking datasets and feature engineering techniques used for FER is presented in this paper. Further, this paper critically analyzes the various conventional learning and deep learning methods for FER. It provides a baseline to other researchers about future aspects with the pros and cons of techniques developed so far.
The conversion process of one specific language to another either in a completely automatic manner or with considerable amount of human intervention by preserving the original meaning of the input source text is known...
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