Industry 5.0 has transformed manufacturing with smart factories as a key component. This report reviews the latest research on smart factories and industrial data management. Focusing on automation, AI, machine learni...
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The proposed method, HybridNet-NDM, integrates three vital algorithms-Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Graph Convolutional Networks (GCNs)-in a synergistic manner for a...
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With outdated technology, the majority of sports analytics systems just can't meet the needs of real-time analyzing data. Worse still, these systems tend to have data latency due to manual input and rudimentary st...
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Urban traffic management is a major challenge for cities worldwide, but with the help of technology, such as big dataanalytics and intelligent transport systems, cities are working to improve their situations. An int...
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Automotive customer complaints forecast as a difficult-to-solve and data-driven machine learning classification problem. Key findings from background research include the problems associated with data imbalance, short...
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The fourth Industrial Revolution (4th IR) i.e., Industry 4.0 features the deployment of extensive automation in the form of smart factories and smart machines facilitating improved information and data flows across th...
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In the field of financial technology, the creation of an automated decision system for the decentralized execution of stock market operations is a noteworthy development. The challenges of the generic approach, such a...
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In this paper, a dynamic planning and intelligent decision-making model of power grid project reserve based on multi-source data and deep learning is proposed to meet the new challenges of power grid project reserve a...
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Artificial intelligent systems have been developed by many industries and academic institutes, and there are also many recent studies about emotion or sentiment. The previous studies about the emotion commonly treated...
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ISBN:
(纸本)9798350370027;9798350370034
Artificial intelligent systems have been developed by many industries and academic institutes, and there are also many recent studies about emotion or sentiment. The previous studies about the emotion commonly treated the emotion as knowledge trainable from emotion-annotated data, and focused on emotion perception or expression but not emotion generation. In this paper, we design a graph attention network (GAT) for emotion generation based on the assumption that emotion is intrinsic and propagates as more knowledge is acquired. We represent the knowledge as a graph, and formulate that the knowledge acquisition as an expansion of the knowledge graph. The graph gets larger as time-step goes by, and the GAT learns from the time-series graph data. We simulate this knowledge acquisition based on the assumption that emotion propagates to the newly acquired knowledge. Interestingly, the simulation results exhibited behaviors that are consistent with previous findings of psychiatry. We believe our study will contribute to development of human-like emotional agents that have its own unique emotion about what it experienced or learned.
Integrating Artificial Intelligence (AI) with Business Intelligence (BI) systems is revolutionizing the ways of governance in smart cities through data management, real-time analytics, and forecasting. These systems i...
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