Synthetic tabular data is crucial for sharing and augmenting data across silos, especially for enterprises with proprietary data. However, existing synthesizers are designed for centrally stored data. Hence, they stru...
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This paper proposes a novel framework that significantly enhances the performance of semantic segmentation models in recognizing specific objects. Leveraging the capabilities of Generative Adversarial Networks (GANs),...
This paper proposes a novel framework that significantly enhances the performance of semantic segmentation models in recognizing specific objects. Leveraging the capabilities of Generative Adversarial Networks (GANs), particularly Cycle-GAN, this framework focuses on augmenting high-quality data to improve object recognition in autonomous driving and other applications. The study utilizes a dataset of 5,005 road images, enriched with polygon labels for precise object recognition. Key advancements in this research include the implementation of feature matching and fact forcing techniques to stabilize and integrate GAN performance, thereby overcoming common challenges like mode collapse, slow training, and overfitting. In the performance-enhanced GAN model, we improved the Discriminator Loss from the original 1.0517 to 0.0001, achieving convergence to zero 66.67% faster.
Heritage sites are central to the cultural identity and historical narrative of a community. The Al Qattara Oasis, located in the United Arab Emirates (UAE), illustrates this function well. This study addresses the pr...
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The threat of credit card fraud is high and so there should be efficient ways to combat it. The research is being employed to focus on how to enhance credit card fraud detection, machine learning methodologies can be ...
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With the global demand for sustainable energy solutions rising, hybrid renewable energy sources (HRES) are gaining popularity due to their reliability and cost-effectiveness. Effective monitoring systems using sensors...
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ISBN:
(数字)9798350352863
ISBN:
(纸本)9798350352870
With the global demand for sustainable energy solutions rising, hybrid renewable energy sources (HRES) are gaining popularity due to their reliability and cost-effectiveness. Effective monitoring systems using sensors and data analytics are essential for optimizing the performance of these systems, ensuring energy efficiency, and predicting maintenance needs. Various monitoring technologies, including SCADA, IoT-based platforms, and cloud storage systems, have been analyzed for their suitability in real-time data acquisition and control of energy systems. The paper examines the critical role of sensor, information, and communication technologies in managing hybrid renewable energy systems (RES), and also reviews several case studies and implementations of energy monitoring systems, emphasizing the importance of integrating these technologies to improve the operational efficiency of HRES.
This paper reports a new optically-transparent focused P(VDF-TrFE) (poly(vinylidene fluoride-co-trifluoroethylene)) transducer for photoacoustic microscopy (PAM), which is fabricated by a new process based on pre-cutt...
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An information retrieval system stores and indexes documents such that when users submit a query, the system gets relevant documents and assigns a score to each one. The higher the score, the more important the docume...
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In this paper, the problem of fault estimation and localization in the connecting dynamic elements of distributed heating and cooling systems are treated. The fault represents the physical parameter change related to ...
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As medical science is evolving;more diseases are being detected and have forayed into our world with high mortality rates. Accurate and immediate diagnosis and treatment has become extremely important. Some diseases t...
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In this paper, we introduce a novel holographic foveated near-eye display, leveraging the capabilities of two reflectivetype phase-modulating spatial light modulators (SLMs). Reconstructed holographic three-dimensiona...
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