Automatic Translation Of Languages Research Articles

Hand Gesture Recognition Based on Various Deep Learning YOLO Models

Some varieties of sign languages are used by deaf or hard-of-hearing people worldwide to interact with others more effectively, consequently sign language's automatic translation is expressive and important. Significant improvements in computer vision have been made recently, notably in tasks based on object detection using deep learning. By locating things in visual photos or videos, the genuine cutting-edge one-step object detection approach greatly provides exceptional detection accuracy. With the help of messaging or video calling, this study suggests a technique to get beyond these obstacles and enhance communication for such persons, regardless of their disability. To recognize motions and classes, we provide an enhanced model based on Yolo (You Look Only Once) V3, V4, V4-tiny, and V5. The dataset is clustered using the suggested algorithm, requiring only manual annotation of a reduced number of classes and analysis for patterns that aid in target prediction. The suggested method outperforms the current object detection approaches based on the YOLO model, according to experimental results.

Sistem Klarifikasi Bahasa Isyarat Indonesia (Bisindo) Dengan Menggunakan Algoritma Convolutional Neural Network

Communication is needed to interact and socialize in order to connect with the environment and other people. In general, communication uses spoken or written words. However, in some cases in the community there are some people who cannot communicate verbally due to physical limitations such as deaf and speech impaired. Usually, they use nonverbal communication such as body movements and this communication is commonly referred to as sign language. The sign language method is used to spell or pronounce words. However, not everyone can understand the sign language used by the deaf and mute, so a system or tool is needed to bridge communication between the deaf or mute and normal people. One solution that can be offered is the use of computer technology as a tool to identify sign language. The technology is in the form of an automatic language translator system design with processing input images in the form of letter classes A to E, I, You and I Love You using the Convolutional Neural Network (CNN) architecture which by using this method the accuracy value can reach 99.82%.

An Improved English-to-Mizo Neural Machine Translation

Machine Translation is an effort to bridge language barriers and misinterpretations, making communication more convenient through the automatic translation of languages. The quality of translations produced by corpus-based approaches predominantly depends on the availability of a large parallel corpus. Although machine translation of many Indian languages has progressively gained attention, there is very limited research on machine translation and the challenges of using various machine translation techniques for a low-resource language such as Mizo. In this article, we have implemented and compared statistical-based approaches with modern neural-based approaches for the English–Mizo language pair. We have experimented with different tokenization methods, architectures, and configurations. The performance of translations predicted by the trained models has been evaluated using automatic and human evaluation measures. Furthermore, we have analyzed the prediction errors of the models and the quality of predictions based on variations in sentence length and compared the model performance with the existing baselines.

CERC: an interactive content extraction, recognition, and construction tool for clinical and biomedical text

BackgroundAutomated summarization of scientific literature and patient records is essential for enhancing clinical decision-making and facilitating precision medicine. Most existing summarization methods are based on single indicators of relevance, offer limited capabilities for information visualization, and do not account for user specific interests. In this work, we develop an interactive content extraction, recognition, and construction system (CERC) that combines machine learning and visualization techniques with domain knowledge for highlighting and extracting salient information from clinical and biomedical text.MethodsA novel sentence-ranking framework multi indicator text summarization, MINTS, is developed for extractive summarization. MINTS uses random forests and multiple indicators of importance for relevance evaluation and ranking of sentences. Indicative summarization is performed using weighted term frequency-inverse document frequency scores of over-represented domain-specific terms. A controlled vocabulary dictionary generated using MeSH, SNOMED-CT, and PubTator is used for determining relevant terms. 35 full-text CRAFT articles were used as the training set. The performance of the MINTS algorithm is evaluated on a test set consisting of the remaining 32 full-text CRAFT articles and 30 clinical case reports using the ROUGE toolkit.ResultsThe random forests model classified sentences as “good” or “bad” with 87.5% accuracy on the test set. Summarization results from the MINTS algorithm achieved higher ROUGE-1, ROUGE-2, and ROUGE-SU4 scores when compared to methods based on single indicators such as term frequency distribution, position, eigenvector centrality (LexRank), and random selection, p < 0.01. The automatic language translator and the customizable information extraction and pre-processing pipeline for EHR demonstrate that CERC can readily be incorporated within clinical decision support systems to improve quality of care and assist in data-driven and evidence-based informed decision making for direct patient care.ConclusionsWe have developed a web-based summarization and visualization tool, CERC (https://newton.isye.gatech.edu/CERC1/), for extracting salient information from clinical and biomedical text. The system ranks sentences by relevance and includes features that can facilitate early detection of medical risks in a clinical setting. The interactive interface allows users to filter content and edit/save summaries. The evaluation results on two test corpuses show that the newly developed MINTS algorithm outperforms methods based on single characteristics of importance.

SIBI (Sistem Isyarat Bahasa Indonesia) translation using Convolutional Neural Network (CNN)

Deaf people are one of those with disabilities who cannot speak and hear. Deaf people using sign language for communication who use sign language with hand gestures, body and convey facial expressions using sign language. One important component in sign language is the alphabetical finger or the manual alphabet needed to complete communication. The alphabet of the finger is done by spelling words on spoken language, by spelling letter by letter using a finger. This method is used to spell the name or mention the word. However, not everyone understands sign language, so tools needed to bridge communication between people who are deaf with normal people. One solution that will be offered is to use computer technology as a tool to recognize sign language. The technology is an automatic language translator system that process input images using by using the Convolutional Neural Network (CNN). On this research consist of 3 class such us A, assalamualaikum and hallo which get the accuracy respectively 100% for each class.

Applying frame layout to hardware design in FPGA for seamless support of cross calls in CPU-FPGA coupling architecture

A cross call between a host processor and FPGA is one of the main challenges for supporting automatic translation of high-level languages into hardware description languages (HDL). In this paper, we present a novel communication framework between the processor and FPGA, which supports unlimited cross calls and hardware recursive calls by following the software’s frame layout in HDL code generation and sharing a stack space between software and hardware codes. Also, we introduce two implementation methods for our cross call, a direct and an indirect interfaces by an instruction-level and an interrupt communication, respectively. Our experiment shows that the proposed approach achieves our goal with small additional complexity in implementation and insignificant overhead in execution time.

Traduction automatique des langues et modélisations des interactions langagières

This paper aims to tackle the question of automatic translation of languages, from a psycholinguistic viewpoint. We shall consider the issue in relation to the following problematic areas: intra- and extra-linguistic interactions between dialogues and, more particularly, interactions between cultural substratum's. An ALT (Automatic Language Translation) system should be able to pick out the connections between what is said, by whom and to whom, and incorporate the cultural and developmental variables. The works presented here illustrate the complexity involved in constructing a method for the exchange of information between source language and target language, especially when the formatting can differ in many ways, for example in spatial and contextual dimensions. In fact, one cannot effectively use computers to study dialogues without first analysing the cognitive complexity of the representations generated by individual cultural and linguistic substratum's.

自動翻訳(その2) : 自動翻訳システムの動向と適用事例ATLAS IIでテクノサーチ・データベースを翻訳( ハイテク時代の情報技術(8))

自動翻訳(その2) : 自動翻訳システムの動向と適用事例ATLAS IIでテクノサーチ・データベースを翻訳( ハイテク時代の情報技術(8))

Formal Languages: Origins and Directions

Origins of the theory of formal languages and automata are surveyed starting from 1936 with the work of Turing and Post. Special attention is given to the machine translation projects of the 1950s and early 1960s and associated work in mathematical linguistics. The development of the Chomsky hierarchy of grammars, machines, and languages from 1956 to 1964 is traced. It is observed that the same important ideas emerged independently for the automatic analysis and translation of both natural and artificial languages. Since 1964, formal language theory is part of theoretical computer science. A few of the directions since 1964 are considered: restrictions and extensions of context-free grammars and pushdown store automata, unifying frameworks, and complexity questions.

Automatic translation of languages°

It is only necessary to mechanize an ordinary two-language dictionary and to expand its list of input words to include all inflected forms. When the input text has been understood, that is, when the correlational net corresponding to the text has been constructed, it is not always the case that everything is ready for representation in the output language. For it is true that everyone thinks by means of opening and closing correlations, but if the contents of thought are, for two peoples in two different civilizations, partly held in common, one of the two will still be richer or poorer in certain ways than the other, or will arrive at a different solution when faced with a given situation. Italian, for example, offers an example of richness that cannot be paralleled in other languages; a situation in which the two elements of an observational situation can be connected in eight different ways. For the purposes of mechanical translation, it was necessary to provide an explicit criterion for what remained the same in translation and what was transformed; it was, moreover, also necessary to lay down the general conditions for the input and the output language for cases where transformation was carried out.

Automatic translation of languages

This chapter focuses on automatic translation of languages. Most machine translation research projects show an obvious division into a linguistic section and a programming section. This reflects not only a certain natural division of skills between linguists and programmers but also the division between information about languages that ought to be valid without reference to any machine and techniques of giving the information to a particular machine for a particular purpose. Assuming the output language to be English, the synthesis involves the rearrangement of the items of the sentence into an English order, the inflection of English stems that were originally brought from the dictionary, the insertion and suppression of articles, the choice of English equivalents for polysemie words, especially prepositions, and the choice of English expressions for what the input language expresses by inflectional elements.