Comparison of American Sign Language Use Identification using Multi-Class SVM Classification, Backpropagation Neural Network, K - Nearest Neighbor and Naive Bayes

Communication is essential in conveying information from one individual to another. However, not all individuals in the world can communicate verbally. According to WHO, deafness is a hearing loss that affects 466 million people globally, and 34 million are children. So it is necessary to have a non-verbal language learning method for someone who has hearing problems. The purpose of this study is to build a system that can identify non-verbal language so that it can be easily understood in real-time. A high success rate in the system needs a proper method to be applied in the system, such as machine learning supported by wavelet feature extraction and different classification methods in image processing. Machine learning was applied in the system because of its ability to recognize and compare the classification results in four different methods. The four classifications used to compare the hand gesture recognition from American Sign Language are the Multi-Class SVM classification, Backpropagation Neural Network Backpropagation, K - Nearest Neighbor (K-NN), and Naïve Bayes. The simulation test of the four classification methods that have been carried out obtained success rates of 99.3%, 98.28%, 97.7%, and 95.98%, respectively. So it can be concluded that the classification method using the Multi-Class SVM has the highest success rate in the introduction of American Sign Language, which reaches 99.3%. The whole system is designed and tested using MATLAB as supporting software and data processing.

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Pengenalan Alfabet American Sign Language Menggunakan K-Nearest Neighbors Dengan Ekstraksi Fitur Histogram Of Oriented Gradients

Jurnal Teknik Informatika dan Sistem Informasi ◽

10.28932/jutisi.v5i3.1936 ◽

2020 ◽

Vol 5 (3) ◽

Author(s):

Muhammad Ezar Al Rivan ◽

Hafiz Irsyad ◽

Kevin Kevin ◽

Arta Tri Narta

Keyword(s):

American Sign Language ◽

Sign Language ◽

Euclidean Distance ◽

Nearest Neighbor ◽

Manhattan Distance ◽

American Sign ◽

K Nearest Neighbor ◽

K Nearest Neighbors ◽

Histogram Of Oriented Gradient ◽

Chebychev Distance

Sign Language use to communicate to people with dissabilities. American Sign Language (ASL) one of popular sign language. Histogram of Oriented Gradient (HOG) can be use as feature extraction. Then feature stored in database. K-Nearest Neighbor use to measure distance between feature train and feature test. There are three distance use in this paper consist of Euclidean Distance, Manhattan Distance and Chebychev Distance. The best result are 0,99 when using Euclidean Distance and Manhattan Distance with k=3 dan k=5

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Classification of American Sign Language by Applying a Transfer Learned Deep Convolutional Neural Network

2020 23rd International Conference on Computer and Information Technology (ICCIT) ◽

10.1109/iccit51783.2020.9392703 ◽

2020 ◽

Author(s):

Md. Mehedi Hasan ◽

Azmain Yakin Srizon ◽

Abu Sayeed ◽

Md. Al Mehedi Hasan

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

American Sign Language ◽

Sign Language ◽

Deep Convolutional Neural Network ◽

American Sign

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A convolutional neural network to classify American Sign Language fingerspelling from depth and colour images

Expert Systems ◽

10.1111/exsy.12197 ◽

2017 ◽

Vol 34 (3) ◽

pp. e12197 ◽

Cited By ~ 17

Author(s):

Salem Ameen ◽

Sunil Vadera

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

American Sign Language ◽

Sign Language ◽

American Sign

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Convolutional Neural Network Based American Sign Language Static Hand Gesture Recognition

International Journal of Ambient Computing and Intelligence ◽

10.4018/ijaci.2019070104 ◽

2019 ◽

Vol 10 (3) ◽

pp. 60-73 ◽

Cited By ~ 2

Author(s):

Ravinder Ahuja ◽

Daksh Jain ◽

Deepanshu Sachdeva ◽

Archit Garg ◽

Chirag Rajput

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

American Sign Language ◽

Sign Language ◽

Automated System ◽

World Health ◽

American Sign ◽

Hand Gestures ◽

The World ◽

Health Organization

Communicating through hand gestures with each other is simply called the language of signs. It is an acceptable language for communication among deaf and dumb people in this society. The society of the deaf and dumb admits a lot of obstacles in day to day life in communicating with their acquaintances. The most recent study done by the World Health Organization reports that very large section (around 360 million folks) present in the world have hearing loss, i.e. 5.3% of the earth's total population. This gives us a need for the invention of an automated system which converts hand gestures into meaningful words and sentences. The Convolutional Neural Network (CNN) is used on 24 hand signals of American Sign Language in order to enhance the ease of communication. OpenCV was used in order to follow up on further execution techniques like image preprocessing. The results demonstrated that CNN has an accuracy of 99.7% utilizing the database found on kaggle.com.

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Convolutional and recurrent neural network for human activity recognition: Application on American sign language

PLoS ONE ◽

10.1371/journal.pone.0228869 ◽

2020 ◽

Vol 15 (2) ◽

pp. e0228869 ◽

Cited By ~ 2

Author(s):

Vincent Hernandez ◽

Tomoya Suzuki ◽

Gentiane Venture

Keyword(s):

Neural Network ◽

American Sign Language ◽

Sign Language ◽

Activity Recognition ◽

Human Activity ◽

Recurrent Neural Network ◽

Human Activity Recognition ◽

American Sign

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COMPARING NAIVE BAYES, K-NEAREST NEIGHBOR, AND NEURAL NETWORK CLASSIFICATION METHODS OF SEAT LOAD FACTOR IN LOMBOK OUTBOUND FLIGHTS

Jurnal Matematika Statistika dan Komputasi ◽

10.20956/jmsk.v16i2.7864 ◽

2019 ◽

Vol 16 (2) ◽

pp. 187

Author(s):

Mega Luna Suliztia ◽

Achmad Fauzan

Keyword(s):

Neural Network ◽

Nearest Neighbor ◽

Naive Bayes ◽

Naïve Bayes ◽

Load Factor ◽

Classification Methods ◽

Affecting Factors ◽

K Nearest Neighbor ◽

Accuracy Rate ◽

Ticket Prices

Classification is the process of grouping data based on observed variables to predict new data whose class is unknown. There are some classification methods, such as Naïve Bayes, K-Nearest Neighbor and Neural Network. Naïve Bayes classifies based on the probability value of the existing properties. K-Nearest Neighbor classifies based on the character of its nearest neighbor, where the number of neighbors=k, while Neural Network classifies based on human neural networks. This study will compare three classification methods for Seat Load Factor, which is the percentage of aircraft load, and also a measure in determining the profit of airline.. Affecting factors are the number of passengers, ticket prices, flight routes, and flight times. Based on the analysis with 47 data, it is known that the system of Naïve Bayes method has misclassifies in 14 data, so the accuracy rate is 70%. The system of K-Nearest Neighbor method with k=5 has misclassifies in 5 data, so the accuracy rate is 89%, and the Neural Network system has misclassifies in 10 data with accuracy rate 78%. The method with highest accuracy rate is the best method that will be used, which in this case is K-Nearest Neighbor method with success of classification system is 42 data, including 14 low, 10 medium, and 18 high value. Based on the best method, predictions can be made using new data, for example the new data consists of Bali flight routes (2), flight times in afternoon (2), estimate of passenger numbers is 140 people, and ticket prices is Rp.700,000. By using the K-Nearest Neighbor method, Seat Load Factor prediction is high or at intervals of 80% -100%.

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Adjusting the Margins: Harnessing the Foundations of American Sign Language for VR

Frameless ◽

10.14448/frameless.01.009 ◽

2019 ◽

Vol 1 (1) ◽

pp. 29-36

Author(s):

Luane Davis Haggerty ◽

Keyword(s):

New York ◽

American Sign Language ◽

Sign Language ◽

Performance Support ◽

Movie Industry ◽

American Sign ◽

Verbal Language ◽

Performance Technique ◽

Movement Study ◽

Historical Fact

Del-Sign is a physical approach to acting that uses elements of Francois Delsarte mime techniques with the foundations of American Sign Language. This acting and presentational technique uses cross-cultural physical communication as a way to deepen an actors’ performance, support a presenter’s lecture, or can be used as a format from which to create animations that communicate with or without verbal language. It is a historical fact that Deaf actors using the foundations of Sign Language influenced the movie industry (Higgins). In silent movie infancy Deaf performers were brought in as consultants to ensure that the gestures, relational positions, facial expression, camera angles and body language of the actors could have the strongest impact and the clearest meaning (Albert Ballin). At that time the standard acting technique was a codified movement study begun and refined by Francious Deslarte (1870-1890s Paris, 1880-1915 Steele MacKaye New York). By blending these two structures we find that an outline is gained for creating movement, posture and gesture (MPG) that easily communicates meaning. The applications of this performance technique are many and varied. From the obvious acting for stage application to lawyers, teachers, priests or other presenters. Del-Sign can now bridge into adding technology to the mix allowing for this approach to be used when creating characters and movement for VR, AR or MR.

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