Designing Translation Tool:  Between Sign Language to Spoken Text on  Kinect Time Series Data Using Dynamic Time Warping

Putera, Zico Pratama; Anasanti, Mila Desi; Priambodo, Bagus

doi:10.22441/sinergi.2018.2.004

Cited by 5 publications

(3 citation statements)

References 13 publications

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“…The accuracy classifying the test set are being calculated based on the confusion matrix formula as described in (10) to evaluate the performance of both classification models. TP and TN are the numbers of correctly classified articles, while FP and FN are the numbers of falsely classified documents into another theme.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…The classification of Portable Document Format (PDF) articles is the key to processing the articles' information, which can help identify the theme of an article effectively and fast [10,11,12,13]. Text classification is defined as a process of automatically categorizing a document of texts into its desired class, and it is a method used for organizing a large number of textual electronic documents [14].…”

Section: Introductionmentioning

confidence: 99%

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Theme Identification using Machine Learning Techniques

Jayady

Antong

2021

JIAE

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With the abundance of online research platforms, much information presented in PDF files, such as articles and journals, can be obtained easily. In this case, students completing research projects would have many downloaded PDF articles on their laptops. However, identifying the target articles manually within the collection can be tiring as most articles consist of several pages that need to be analyzed. Reading each article to determine if the article relates theme and organizing the articles based on themes is time and energy-consuming. Referring to this problem, a PDF files organizer that implemented a theme identifier is necessary. Thus, work will focus on automatic text classification using the machine learning methods to build a theme identifier employed in the PDF files organizer to classify articles into augmented reality and machine learning. A total of 1000 text documents for both themes were used to build the classification model. Moreover, the pre-preprocessing step for data cleaning and TF-IDF feature extraction for text vectorization and to reduce sparse vectors were performed. 80% of the dataset were used for training, and the remaining were used to validate the trained models. The classification models proposed in this work are Linear SVM and Multinomial Naïve Bayes. The accuracy of the models was evaluated using a confusion matrix. For the Linear SVM model, grid-search optimization was performed to determine the optimal value of the Cost parameter.

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Section: Performance Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Theme Identification using Machine Learning Techniques

Jayady

Antong

2021

JIAE

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“…This approach is the novelty presented in this study. With the help of machine learning [1] and natural language processing (NLP) [2], this chatbot is capable of providing accurate responses. Through this, individuals without proper medical attention can access some guided therapy processes and acknowledge them.…”

Section: Introductionmentioning

confidence: 99%

Chatbot System for Mental Health in Bahasa Malaysia

Ismael

Hashim

Shah

et al. 2022

JIAE

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Chatbot has been the driving force of modern communication for business, customer service and even mental healthcare. At the same time, there are not many research and project regarding mental health chatbots in Bahasa Malaysia. This project focuses on developing a chatbot application for mental healthcare in Bahasa Malaysia. This chatbot system is integrated with artificial intelligence and natural language processing. This chatbot utilize the feedforward neural network model to train the datasets. Apart from the backend of the application, Kivy and KivyMD are used to create the app's graphical user interface.

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Machine translation from signed to spoken languages: state of the art and challenges

Coster

Shterionov

Herreweghe

et al. 2023

Univ Access Inf Soc

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Automatic translation from signed to spoken languages is an interdisciplinary research domain on the intersection of computer vision, machine translation (MT), and linguistics. While the domain is growing in terms of popularity—the majority of scientific papers on sign language (SL) translation have been published in the past five years—research in this domain is performed mostly by computer scientists in isolation. This article presents an extensive and cross-domain overview of the work on SL translation. We first give a high level introduction to SL linguistics and MT to illustrate the requirements of automatic SL translation. Then, we present a systematic literature review of the state of the art in the domain. Finally, we outline important challenges for future research. We find that significant advances have been made on the shoulders of spoken language MT research. However, current approaches often lack linguistic motivation or are not adapted to the different characteristics of SLs. We explore challenges related to the representation of SL data, the collection of datasets and the evaluation of SL translation models. We advocate for interdisciplinary research and for grounding future research in linguistic analysis of SLs. Furthermore, the inclusion of deaf and hearing end users of SL translation applications in use case identification, data collection, and evaluation, is of utmost importance in the creation of useful SL translation models.

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Designing Translation Tool: Between Sign Language to Spoken Text on Kinect Time Series Data Using Dynamic Time Warping

Abstract: --

Cited by 5 publications

References 13 publications

Theme Identification using Machine Learning Techniques

Theme Identification using Machine Learning Techniques

Chatbot System for Mental Health in Bahasa Malaysia

Machine translation from signed to spoken languages: state of the art and challenges

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