Thyroid disease represents a significant contributor to challenges in both medical diagnosis and the prediction of its onset, making it a complex area of study within medical research. This research thoroughly analyse...
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Identifying human actions in video sequences or static images presents significant challenges stemming from factors like background noise, obscured perspectives, variations in scale, angles, and lighting conditions. H...
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Making supportive decisions for crop production, such as crop name recommendations and forecasts for crop production, requires machinelearning implementation. Numerous machinelearning classifiers and algorithms are ...
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The escalating reliance of computer networks on security systems exposes them to increasing threats. In response, an advanced security system is proposed, utilizing machinelearning and deep learning techniques and le...
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Traditional farming based on conventional methods has been the pillar of agricultural practices for a long time. But, due to the escalating global population, unstable climatic patterns and limited resources, the effi...
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Crop classification is essential for local and national governments to make informed agricultural decisions. Remote sensing technology has made it possible to employ high-resolution hyperspectral images (HSIs) for lan...
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Recent advances in incorporating physical knowledge into deep neural networks can estimate previously unknown governing partial differential equations (PDEs) in a data-driven way. They have shown promising results in ...
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ISBN:
(纸本)9798400701689
Recent advances in incorporating physical knowledge into deep neural networks can estimate previously unknown governing partial differential equations (PDEs) in a data-driven way. They have shown promising results in spatiotemporal predictive learning. However, these methods typically assume universal governing PDEs across space, which is impractical for modeling complex spatiotemporal phenomena with high spatial variability (e.g., climate). Also, they cannot effectively model the evolution of potential errors in estimating the physical dynamics over time. This paper introduces a physics-guided neural network, SVPNET, which learns effective physical representations by estimating the error evolution in physics states for correction and modeling spatially varying physical dynamics to predict the next state. Experiments carried out in four scenarios, including benchmarks and real-world datasets, show that SVPNET outperforms state-of-the-art methods in spatiotemporal prediction tasks for natural processes and significantly improves prediction when training data are limited. Ablation studies also highlight that SVPNET is powerful in capturing physical dynamics in complex physical systems.
The insurance industry is becoming increasingly concerned about the possibility of fraudulent insurance claims as it uses Internet of Things (IoT) technologies to improve customer service and expedite procedures. In t...
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The insurance industry is becoming increasingly concerned about the possibility of fraudulent insurance claims as it uses Internet of Things (IoT) technologies to improve customer service and expedite procedures. In this context, a viable method to improve fraud detection capabilities in IoT-enabled insurance systems is the incorporation of machinelearning (ML) algorithms. This study suggests a fraud-detecting approach based on machine literacy that is tailored for insurance claims in an Internet of Things environment. The suggested solution makes use of real-time data from IoT detectors and actual claim records, applying machinelearning techniques like anomaly finding, bracketing, and clustering to spot suspicious trends and flag possibly fraudulent claims. The efficacy and efficiency of the suggested method are proven through a thorough examination utilizing deconstructed and real-world datasets, underscoring its possibility to reduce fraud hazards and improve the integrity of insurance operations in IoT environments.
Organizations often rely on large applications that are classified as legacy systems due to their dependence on outdated programming languages or platforms. To modernize these systems, it is necessary to understand th...
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ISBN:
(纸本)9798400705021
Organizations often rely on large applications that are classified as legacy systems due to their dependence on outdated programming languages or platforms. To modernize these systems, it is necessary to understand their architecture, functionality, and business rules. Our research aims to define a novel model-driven reverse engineering (MDRE) approach to extract Unified Modeling Language (UML) and Object Constraint Language (OCL) representations from source code using Large Language Models (LLMs).
With the rapid development of wireless communication technology and high-tech, the signal transmission frequency of high-speed electronic lines and wireless communication systems continues to increase, and intelligent...
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ISBN:
(数字)9798350377033
ISBN:
(纸本)9798350377040;9798350377033
With the rapid development of wireless communication technology and high-tech, the signal transmission frequency of high-speed electronic lines and wireless communication systems continues to increase, and intelligent electronic equipment and base stations are developing toward miniaturization and densification. Signal is more susceptible to interference in the transmission process, which leads to signal integrity problems, so signal integrity analysis is very important in the design of high-speed electronic lines and wireless communication systems. However, the traditional signal integrity simulation is slow and costly, which prolongs the design cycle of the system. Although the existing signal integrity analysis algorithms based on machinelearning can greatly improve the simulation speed, they still face the problems of high demand for labeled data and poor applicability. To solve the problems of high cost and long time of traditional signal integrity simulation, existing signal integrity simulation algorithms based on machinelearning require a large amount of labeled data and poor applicability. In this paper, a signal integrity simulation algorithm based on active learning and transfer learning and a circuit automatic parameter optimization algorithm based on deep reinforcement learning are proposed, aiming at a signal integrity simulation algorithm with low tagging cost, strong applicability and high simulation accuracy and a high-performance automatic parameter optimization algorithm for electronic circuits. In this paper, a signal integrity simulation algorithm based on active learning is proposed. Compared with other active learning algorithms applied to regression tasks, this algorithm models data annotation problems in active learning based on Markov decision process, and then learns data annotation strategies with the help of deep reinforcement learning algorithms. On this basis, in order to further reduce the cost of data annotation and improve the sim
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