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A class of neural networks system with neutral delays is investigated. The existence and uniqueness of almost periodic solution for the system are obtained by using fixed point theorem; we extend some results in the references.

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<p style='text-indent:20px;'>In this paper, we consider a two-species competitive and diffusive system with nonlocal delays. We investigate the existence of traveling wave fronts of the system by employing linear chain techniques and geometric singular perturbation theory. The existence of the traveling wave fronts analogous to a bistable wavefront for a single species is proved by transforming the system with nonlocal delays to a six-dimensional system without delay.</p>

With the development of the Internet of Things, the requirement of a wide range of human-centered services may now make use of as many computing resources for media technologies and holographic images. The IoT system can monitor the status of equipment in real-time with a robust infrared image recognition algorithm. However, few researchers discuss data mining on images with valuable information. In this study, we present a generic approach that is based on the mining decision tree and holographic image improvement data analysis. We employed advanced data mining techniques to achieve image stability and use light to form a three-dimensional image with real space. The suggested model improves digital image signal transmission and noise through the grey neural network technique and, furthermore, utilization decision tree induction to create attributes-to-target label relations from image pixels. The experimental results show that the suggested approach may be highly efficient and effective for interactive image systems and image mining. Our approach may also be widely utilized and includes extremely efficient convergence systems for essential framework elements.