Design of Sign Language Recognition Using E-CNN

Suardi, Citra; Handayani, Anik Nur; Asmara, Rosa Andrie; Wibawa, Aji Prasetya; Hayati, Lilis Nur; Azis, Huzain

doi:10.1109/eiconcit50028.2021.9431877

Cited by 18 publications

(7 citation statements)

References 20 publications

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“…Based on CSL-BS, two-way VTN achieves 87.9% accuracy while two-way I3D is 84.2%. Finally, Suardi et al [14] created a trial of combining CNN models using the Ensemble method has been successfully carried out with the results being able to increase the accuracy value to 99.4%. and proved that using Ensemble can increase the higher accuracy value.…”

Section: Related Workmentioning

confidence: 99%

Bisayan Dialect Short-time Fourier Transform Audio Recognition System using Convolutional and Recurrent Neural Network

Cerna¹,

Cascaro²,

Juan³

et al. 2023

IJACSA

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Speech is a form of oral communication that reinforces thoughts and ideas that have general purpose and meaning. In the Philippines, Filipinos can speak at least three languages. English, Filipino, native language. The Philippine government says the Philippines has more than 150 regional native languages, one of which he says is Cebuano. This research aims to implement automatic speech recognition (ASR) specifically for the Bisayan dialect, and researchers use machine learning techniques to create and operate the system. ASR has served its purpose in recent years not only in the official language of the Philippines, but also in various foreign languages. The required datasets were collected throughout the study to train and build the models selected for the speech recognition engine. Audio files are recorded in waveform file format and contain Visayan phrases and sentences. Audio was captured through hours of recorded audio and process using Tensorflow short time Fourier transform (STFT) algorithm to ensure the accurate representation. In order to analyze the audio data, the recordings were specially converted to digital format, specifically .wav and making it sure all records are uncorrupted with only one channel, and finally have a sample rate of 22050kHz. A data mining process was carried out by integrating CNN layers, dense layers, and RNNs to predict the transcription of speech input using multiple layers that determine the output of the speech data. The researchers used the JiWER Python library, which was used in parallel when evaluating WER. This is because the trained scripted data set contains at least 500 time recordings totaling 61.78 minutes. Overall, the WER output is at best 99.53% and the percentage of records used is acceptable.

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Section: Related Workmentioning

confidence: 99%

Bisayan Dialect Short-time Fourier Transform Audio Recognition System using Convolutional and Recurrent Neural Network

Cerna¹,

Cascaro²,

Juan³

et al. 2023

IJACSA

View full text Add to dashboard Cite

show abstract

“…The research paper [5] studies the performance of fingerprint scanning using the SGD optimizer for 64×64 and 48×48 inputs with 95.32 % accuracy. As well, [6] developed the model for sign language recognition using a CNN-based architecture such as 2 layers, 4 layers, 21 layers. The three models blended together showed a 98.6 % correct identification rate.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Identification of CNN hyper-parameters for tobacco leaf quality classification on Nvidia Jetson Nano

Setiawan,

Siradjuddin,

Sumari

et al. 2023

EEJET

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At the moment, there are some inaccuracies in manual classification for tobacco leaf quality selection, which are influenced by some factors, such as human fatigue or poor lighting. These cases lead to the need for another method that is more consistent, faster and reliable. This research is an implementation of CNN (Convolutional Neural Network) in the classification of fresh tobacco leaves in terms of maturity grades. The primary objective is to develop an efficient CNN model capable to automize the classification of tobacco leaves into three maturity criteria: immature, mature, and old. This methodology consists of some key factors, including color thresholding strategies to purge the noise from the background, Basic Image Manipulation approaches, the systemized screening of different input sizes, and CNN models to enhance the results. The result of this research proves that the developed CNN model has 97.9 % accuracy achieved following 200 training sessions. The model is trained on a dataset comprising 1,249 fresh leaf photos, with a balanced 80:10:10 for train, validation and test ratio. However, the study emphasizes that the CNN model has successfully supported the tobacco leaf discrimination on a Jetson Nano Single-Board Computer with a Graphic Processing Unit (GPU). The study extends beyond the mere theoretical contribution to practical applications in sorting “Gagang Rejeb Sidi” tobacco leaf, the highest quality tobacco variety in South Malang, East Java, Indonesia. Classification using a webcam as an input device shows the fastest processing time of 203.17 ms and the maximum is 1,363 ms. This CNN model algorithm will be applied to a tobacco leaf selector machine, which has a high-speed conveyor and a three-position selector arm. The machine will be operated close to the field in post-harvest time under uniform lighting conditions. Overall, the result of this research is highly relevant in terms of the short duration and accuracy for understanding the commodity classification. It provides a new angle toward speeding up the classification process and improving Indonesian tobacco quality

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“…To pass this test, the system would need to possess capabilities that are currently the subject of study in machine learning, such as natural language processing [ 41 ], knowledge representation [ 42 ], and automated reasoning [ 43 ]. Given the advances in AI models, several applications are being used to improve the quality of life of people with physical disabilities and improve applications for smart healthcare [ 44 ], such as using smart robots [ 45 , 46 , 47 ], or more specific applications, such as in sign language [ 48 , 49 , 50 , 51 , 52 , 53 ].…”

Section: Assisted Technology Aiot and Machine Learningmentioning

confidence: 99%

Artificial Intelligence of Things Applied to Assistive Technology: A Systematic Literature Review

Freitas

Piai

Farias

et al. 2022

Sensors

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According to the World Health Organization, about 15% of the world’s population has some form of disability. Assistive Technology, in this context, contributes directly to the overcoming of difficulties encountered by people with disabilities in their daily lives, allowing them to receive education and become part of the labor market and society in a worthy manner. Assistive Technology has made great advances in its integration with Artificial Intelligence of Things (AIoT) devices. AIoT processes and analyzes the large amount of data generated by Internet of Things (IoT) devices and applies Artificial Intelligence models, specifically, machine learning, to discover patterns for generating insights and assisting in decision making. Based on a systematic literature review, this article aims to identify the machine-learning models used across different research on Artificial Intelligence of Things applied to Assistive Technology. The survey of the topics approached in this article also highlights the context of such research, their application, the IoT devices used, and gaps and opportunities for further development. The survey results show that 50% of the analyzed research address visual impairment, and, for this reason, most of the topics cover issues related to computational vision. Portable devices, wearables, and smartphones constitute the majority of IoT devices. Deep neural networks represent 81% of the machine-learning models applied in the reviewed research.

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Design of Sign Language Recognition Using E-CNN

Cited by 18 publications

References 20 publications

Bisayan Dialect Short-time Fourier Transform Audio Recognition System using Convolutional and Recurrent Neural Network

Bisayan Dialect Short-time Fourier Transform Audio Recognition System using Convolutional and Recurrent Neural Network

Identification of CNN hyper-parameters for tobacco leaf quality classification on Nvidia Jetson Nano

Artificial Intelligence of Things Applied to Assistive Technology: A Systematic Literature Review

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