Yi-Chinese translation system based on Android platform and deep learning

2021 ◽  
Author(s):  
Yuhang Ma ◽  
Wei Xiang
Keyword(s):  
Deep Learning ◽  
Translation System ◽  
Chinese Translation ◽  
Android Platform

scholarly journals Application of Feature Extraction Algorithm in the Construction of Interactive English Chinese Translation Mode

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Guangjun Dong ◽  
Youchao Yang ◽  
Qiankun Zhang
Keyword(s):  
Feature Extraction ◽  
English Translation ◽  
Selection Process ◽  
Translation System ◽  
Chinese Translation ◽  
Feature Extraction Algorithm ◽  
Extraction Algorithm ◽  
Optimal Feature Selection ◽  
Translation Accuracy ◽  
Optimal Feature

In the process of English translation, traditional interactive English translation system is not obvious in English semantic context. The optimal feature selection process does not achieve the optimal translation solution, and the translation accuracy is low. Based on this, this paper designs an interactive English Chinese translation system based on a feature extraction algorithm. By introducing the feature extraction algorithm, the optimal translation solution is selected, and the semantic mapping model is constructed to translate the best translation into English Chinese translation. The real experiment results show that the interactive English Chinese translation system based on feature extraction algorithm can get the best solution.

scholarly journals Machine Translation System Using Deep Learning for English to Urdu

2022 ◽  
Vol 2022 ◽  
pp. 1-11
Author(s):  
Syed Abdul Basit Andrabi ◽  
Abdul Wahid
Keyword(s):  
Deep Learning ◽  
Machine Translation ◽  
Communication Technology ◽  
Target Language ◽  
Translation System ◽  
Neural Machine Translation ◽  
Parallel Corpus ◽  
Proposed Model ◽  
Learning Technique ◽  
Machine Translation System

Machine translation is an ongoing field of research from the last decades. The main aim of machine translation is to remove the language barrier. Earlier research in this field started with the direct word-to-word replacement of source language by the target language. Later on, with the advancement in computer and communication technology, there was a paradigm shift to data-driven models like statistical and neural machine translation approaches. In this paper, we have used a neural network-based deep learning technique for English to Urdu languages. Parallel corpus sizes of around 30923 sentences are used. The corpus contains sentences from English-Urdu parallel corpus, news, and sentences which are frequently used in day-to-day life. The corpus contains 542810 English tokens and 540924 Urdu tokens, and the proposed system is trained and tested using 70 : 30 criteria. In order to evaluate the efficiency of the proposed system, several automatic evaluation metrics are used, and the model output is also compared with the output from Google Translator. The proposed model has an average BLEU score of 45.83.

scholarly journals Networked Artificial Intelligence English Translation System Based on an Intelligent Knowledge Base and Translation Method Thereof

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Shuping Ren
Keyword(s):  
Artificial Intelligence ◽  
Knowledge Base ◽  
Structure Analysis ◽  
English Translation ◽  
Language Translation ◽  
Translation Method ◽  
Translation System ◽  
Chinese Translation ◽  
Lexical Structure ◽  
Traditional Language

Language translation is often conducted in work and study. Traditional language translation is based on lexical structure analysis. However, natural language is not so standardized, which causes this translation method to have fundamental defects, no matter how much the algorithm is improved. The translation results and human translation will be very different. This paper mainly studies the networked artificial intelligence. The English translation system and translation methods are based on a smart knowledge base. Bringing an example of English-Chinese translation to suggest translations according to the intelligent knowledge base explains in detail the principle of intelligent knowledge-based translation and the advantage of this translation method compared with the traditional translation method based on lexical structure analysis. In the experiment of this paper, when the variance is 2/N, 30/N, 100/N, and 2N, it is the experimental data for an in-depth study. When the variance is 2/N, 30/N, and 100/N, the result is the same as that when the variance is 0.5; the result when the variance is 2N also conforms to the trend in the tables, which is close to the effect of the smoothing algorithm, which verifies the effectiveness of the system in this paper.

scholarly journals Machine Translation System Based on Deep Learning

2021 ◽  
Vol 2030 (1) ◽  
pp. 012098
Author(s):  
Ting Yang ◽  
Shinan Zhao ◽  
He Chen ◽  
Bo Chen
Keyword(s):  
Deep Learning ◽  
Machine Translation ◽  
Translation System ◽  
Machine Translation System

scholarly journals Real-time BISINDO Hand Gesture Detection and Recognition with Deep Learning CNN

2021 ◽  
Vol 1 (1) ◽  
pp. 71-80
Author(s):  
Febri Damatraseta ◽  
Rani Novariany ◽  
Muhammad Adlan Ridhani
Keyword(s):  
Deep Learning ◽  
Sign Language ◽  
Gesture Recognition ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Translation System ◽  
Deaf People ◽  
Model Data ◽  
Skin Segmentation ◽  
Hand Gesture Detection

BISINDO is one of Indonesian sign language, which do not have many facilities to implement. Because it can cause deaf people have difficulty to live their daily life. Therefore, this research tries to offer an recognition or translation system of the BISINDO alphabet into a text. The system is expected to help deaf people to communicate in two directions. In this study the problems encountered is small datasets. Therefore this research will do the testing of hand gesture recognition, by comparing two model CNN algorithms, that is LeNet-5 and Alexnet. This test will look for which classification technique is better if the dataset conditions in an amount that does not reach 1000 images in each class. After testing, the results found that the CNN technique on the Alexnet architectural model is better to used, this is because when doing the testing process by using still-image and Alexnet model data which has been released in training process, Alexnet model data gives greater prediction results that is equal to 76%. While the LeNet model is only able to predict with the percentage of 19%. When that Alexnet data model used on the system offered, only able to predict correcly by 60%.   Keywords: Sign language, BISINDO, Computer Vision, Hand Gesture Recognition, Skin Segmentation, CIELab, Deep Learning, CNN.

Sign in / Sign up

Export Citation Format

Share Document