Characteristics recognition and soft multimedia system for Japanese machine translation and edge-driven hardware implementations

Abstract

With the development of economic globalization, international exchanges and cooperation are increasingly frequent and in-depth. In this process, there is a serious obstacle, that is, language differences. Therefore, the development of high quality and practical machine translation system is of great signi cance. Japan has a close relationship with China, so it is necessary to acquire and process Japanese information. Japanese translation is the basis of Japanese information processing, which plays an important role in cross language information retrieval, machine translation, information extraction and other practical applications. In recent years, machine translation has made remarkable progress, but there is still much room for improvement in the quality of translation. Multimedia assisted instruction is an important application of computer technology in teaching eld. In recent years, the wide application of multimedia courseware in all kinds of teaching has received good teaching results. With the development and deepening of Japanese teaching reform, contextualized teaching has been paid more and more attention. In this context, Japanese intensive teaching is also increasingly showing the trend of network, multimedia assisted. Therefore, this paper realizes the feature recognition and hardware structure of Japanese machine translation.

Special issue on soft computing for edge-driven applications

“…The third section discusses about the real-time applications of soft computing-driven models by: developing an adaptive neuro-fuzzy inference system to monitor and manage the soil quality in order to build a sustainable farming culture (Remya 2022); introducing a supervised learning-based techniques for predicting the seed germination ability in a precision farming environment (Yasam et al 2022); incorporating an improved sine cosine optimization algorithm for achieving an efficient color image segmentation (Mookiah et al 2022); enhancing singlechannel speech quality and intelligibility under multiple noise conditions by using Wiener filter and deep CNN (Hepsiba and Justin 2021); introducing modified bee algorithm to efficiently classify and predict heart diseases (Velswamy 2021); utilizing the characteristics recognition and soft multimedia system for performing Japanese machine translation and edge-driven hardware implementations (Song 2021); utilizing soft computing-driven social network analysis approach to trace the fake news propagation path (Sivasankari and Vadivu 2021); designing and developing a novel deep learning approach for assisting a gait-based fall prediction model (Murthy et al 2021); and designing and developing a novel generative adversarial networks [GAN] model for generating a high-quality sign language (Natarajan and Elakkiya 2021).…”