Smart home packages have become more and more standard in nowadays society, giving customers the potential to without problems connect and control various devices within their domestic. Self-sustaining and adaptive co...
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A fully optically integrated Mixture-of-Experts (MoE) system is introduced to address the explosive growth in computational power demands in the development of artificial intelligence technologies. Here, we highlights...
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A new broadband high-gain microstrip quasi-yagi antenna is proposed. With the aim of designing an end-firing antenna for X-band by utilizing the good directivity of yagi antennas. The main radiating dipole patches of ...
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The embedded fog computing is motivating a major prototype transferal in healthcare systems. Branded by decentralized data processing and storage, moving these functions closer to the source of data generation, fog co...
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This study introduces an interdisciplinary prediction framework as part of a novel approach that integrates the Inventive Design Method (IDM), Topic Modeling, and Generative AI to foster innovation across academic fie...
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This study introduces an interdisciplinary prediction framework as part of a novel approach that integrates the Inventive Design Method (IDM), Topic Modeling, and Generative AI to foster innovation across academic fields. Identifying interdisciplinary connections is essential for solving complex, multi-domain problems. Our research uses a supervised machine learning classifier to identify interdisciplinary documents within the Semantic Scholar corpus, extracting latent insights. The Text Convolutional Neural Network model performed best, achieving an F1 score of 0.80. We find that approximately 25% of human knowledge is interdisciplinary. This framework helps create comprehensive knowledge maps across multiple domains, promoting innovation through effective cross-domain knowledge transfer.
The advancement of big data has accelerated over the years to change how companies utilize data to drive decisions. And so, this paper aims to look at big data and data mining in different perspectives while highlight...
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We present a new concept of Bipolar Fermatean Fuzzy matrices and explore several of its operations in this paper. Score and accuracy functions are introduced for comparing Bipolar Fermatean Fuzzy Matrices, along with ...
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This research paper addresses the pressing issue of air quality monitoring and prediction using machine learning algorithms. With a dataset comprising pollutant levels from various states, we employed a comprehensive ...
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In this paper, We introduce a new concept of Bipolar Pythagorean Fuzzy matrices and explore some of their operations. Additionally, we present score and accuracy functions for comparing Bipolar Pythagorean Fuzzy matri...
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Despite significant advances in educational technology and design methodologies, current educational games demonstrate a fundamental limitation: educators are unable to modify content after the games are deployed, lim...
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