The COVID-19 virus had easily affected people worldwide through direct contact. Individuals diagnosed with positive COVID-19 virus may be affected with many symptoms, such as fever, tiredness, dry cough, difficulty in...
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Road cavities, often called potholes, pose a major problem for our transportation infrastructure, causing accidents, costly repairs, and traffic jamming. This study shows using a computer vision technique called You O...
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The active regression problem of the single-index model is to solve minxf(Ax) − bp, where A is fully accessible and b can only be accessed via entry queries, with the goal of minimizing the number of queries to the en...
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The extractive automatic summarization method is capable of quickly and efficiently generating summaries through the steps of scoring, extracting and eliminating redundant sentences. Currently, most extractive methods...
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Metaverse has become a buzzword recently. Mobile augmented reality (MAR) is a promising approach to providing users with an immersive experience in the Metaverse. However, due to limitations of bandwidth, latency, and...
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Environmental, Social, and Governance (ESG) factors have become critical for assessing corporate sustainability and ethical responsibility. However, the vast volume of unstructured data available across corporate repo...
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ISBN:
(数字)9798331530631
ISBN:
(纸本)9798331530648
Environmental, Social, and Governance (ESG) factors have become critical for assessing corporate sustainability and ethical responsibility. However, the vast volume of unstructured data available across corporate reports, social media, and news sources poses a challenge for systematic ESG analysis. This paper explores the application of neurosymbolic AI, which combines neural networks’ pattern recognition capabilities with the structured reasoning of symbolic AI, to mine key aspects of ESG from large-scale, diverse data sources. By leveraging SenticNet for concept parsing and deep learning for sentiment analysis, we extract relevant ESG metrics, classify corporate practices, and identify trends. This hybrid approach enhances both the interpretability and scalability of ESG analysis, providing more accurate insights into corporate behaviors and their impact on sustainability goals. Results demonstrate that neurosymbolic AI not only improves the extraction of meaningful ESG aspects but also enables real-time monitoring, supporting data-driven decision-making for investors, regulators, and stakeholders.
In this work, we show how blockchain can be used to manage the supply chain for non-perishable agricultural food (NPAF). Keeping product quality high across the supply chain is a major challenge for the food business....
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Emotion classification and sentiment analysis represent crucial research areas within the field of Natural Language Processing. Previous studies have primarily focused on conducting sentiment classification and emotio...
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Graph convolutional networks (GCNs) are popular for a variety of graph learning tasks. ReRAM-based processing-in-memory (PIM) accelerators are promising to expedite GCN training owing to their in-situ computing capabi...
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Intelligent defect detection methods are important for the surface of the containment of nuclear power plants and face many challenges in the field of computer vision. Due to the irregular shapes and large variation o...
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