Hierarchical text classification is a challenging task, in particular when complex taxonomies, characterized by multi-level labeling structures, need to be handled. A critical aspect of the task lies in the scarcity o...
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Recent advances in applying Large Language Models (LLMs) to natural language processing raise the challenge of integrating them with ontological models, to harness the features of Knowledge Graphs (KG) alongside the e...
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This article is devoted to studying the solutions having the form of traveling wave for a nonlocal dispersal equations in connection with Belousov-Zhabotinsky term, the main aim of which is to formulate the formation ...
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A novel idea, physics informed neural networks introduced a few years back to solve forward and inverse problems for differential equations using the physics information that lies inside them. The central pillar of th...
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A novel idea, physics informed neural networks introduced a few years back to solve forward and inverse problems for differential equations using the physics information that lies inside them. The central pillar of the physics informed neural networks is automatic differentiation, which is based on the chain rule of differentiation. Automatic differentiation is not applicable to non-local operators because the standard chain rule of differentiation is not valid for non-local operators. Therefore, this work presents non-local physics informed neural networks, which use standard approximation methods for non-local operators and automatic differentiation for local operators to solve differential equations containing non-local operators (forward problems) as well as learn differential equations involving non-local operators (inverse problems). In this work, we consider the Caputo fractional derivative, Volterra integral, and Itô integral as non-local operators. Moreover, we demonstrate the efficiency of the non-local physics informed neural networks with different test examples like the time-fractional diffusion equation in one and two dimensions, the time-fractional Burgers’ equation (both equations involving Caputo fractional derivative as non-local operators), the fractional integro-differential equation (Caputo fractional derivative and Volterra integral as non-local operators), and the stochastic fractional integro-differential equation (Caputo fractional derivative, Itô integral, and Volterra integral as non-local operators). Furthermore, for the non-smooth solution, we use the approximation method on non-uniform mesh for non-local operators and compare the results with the approximation method on uniform mesh. We also discuss the error analysis and convergence of the proposed non-local physics informed neural networks. Finally, we take real-world data, which is described by the differential equation containing non-local operators, and show the effectiveness of no
In crowdsourcing systems, where substantial amounts of data from various contributors are aggregated to discern reliable information, privacy concerns are often managed through differential privacy techniques. However...
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An emergency rescue team assignment model is proposed to solve the problem of earthquake disaster emergency rescue team assignment. Considering the rescue teams to participate, the establishment of a rescue teams to r...
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This paper is going to investigate a time-delay Belousov-Zhabotinsky model with reaction terms, the purpose of which is used to describe the generation process of bromic acid. In light of the nonlinear functional anal...
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In an indoor fire, the robot inspection process is impossible. The development of the robot and the Internet of Things (IoT) technology provides a new solution for indoor fire identification by a robot. Using IoT tech...
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This paper presents a hybrid rectifier mode control for broad-range output power regulation in wireless power transfer (WPT) systems. The proposed control method employs a secondary-side active rectifier for output tu...
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Northern Thailand faces a recurring ecological threat: forest fires. These fires, prevalent during the dry season, spread rapidly and consume vital forest fuels. Human activities exacerbate the problem, with agricultu...
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