Development of vocabulary learning application by using machine learning technique

Brahin, Noor Mohd Ariff; Nasir, Haslinah Mohd; Jidin, Auzani; Zulkifli, Mohd Faizal; Sutikno, Tole

doi:10.11591/eei.v9i1.1616

Cited by 5 publications

(4 citation statements)

References 22 publications

(22 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…are used. In addition to the GitHub interface for Kinect offers a set of routines and access to Python functions for data acquisition from the Kinect sensor [39,40]. In the first stage of the design, the algorithm initializes the interface, and the Kinect sensors are enabled for motion capture and position data collection.…”

Section: Development Of the Fall Detection Modelmentioning

confidence: 99%

Model for the Detection of Falls with the Use of Artificial Intelligence as an Assistant for the Care of the Elderly

et al. 2022

View full text Add to dashboard Cite

Currently, telemedicine has gained more strength and its use allows establishing areas that acceptably guarantee patient care, either at the level of control or event monitors. One of the systems that adapt to the objectives of telemedicine are fall detection systems, for which artificial vision or artificial intelligence algorithms are used. This work proposes the design and development of a fall detection model with the use of artificial intelligence, the model can classify various positions of people and identify when there is a fall. A Kinect 2.0 camera is used for monitoring, this device can sense an area and guarantees the quality of the images. The measurement of position values allows to generate the skeletonization of the person and the classification of the different types of movements and the activation of alarms allow us to consider this model as an ideal and reliable assistant for the integrity of the elderly. This approach analyzes images in real time and the results showed that our proposed position-based approach detects human falls reaching 80% accuracy with a simple architecture compared to other state-of-the-art methods.

show abstract

Section: Development Of the Fall Detection Modelmentioning

confidence: 99%

Model for the Detection of Falls with the Use of Artificial Intelligence as an Assistant for the Care of the Elderly

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The BP method lowers the energy function by continuously sending signals from the current node to nearby nodes in the MRF network [31], [32]. Since [33]- [35]'s introduction of convolutional neural network (CNN) trained on tiny image patch pairs with known actual disparity, attention to a deep learning-based stereo vision system has grown significantly. CNN outperforms traditional approaches in terms of error rate and processing time, but it remains challenging to identify optimal corresponding spots in fundamentally ill-posed areas, such as areas with repetitive shapes, areas that are obscured, reflective planes, and texture-less areas.…”

Section: Introductionmentioning

confidence: 99%

Stereo matching algorithm using deep learning and edge-preserving filter for machine vision

Abd Gani,

Miskon,

Hamzah

et al. 2024

Bulletin EEI

View full text Add to dashboard Cite

Machine vision research began with a single-camera system, but these systems had various limitations from having just one point-of-view of the environment and no depth information, therefore stereo cameras were invented. This paper proposes a hybrid method of a stereo matching algorithm with the goal of generating an accurate disparity map critical for applications such as 3D surface reconstruction and robot navigation to name a few. Convolutional neural network (CNN) is utilised to generate the matching cost, which is then input into cost aggregation to increase accuracy with the help of a bilateral filter (BF). Winner-take-all (WTA) is used to generate the preliminary disparity map. An edge-preserving filter (EPF) is applied to that output based on a transform that defines an isometry between curves on the 2D image manifold in 5D and the real line to eliminate these artefacts. The transform warps the input signal adaptively to allow linear 1D filtering. Due to the filter's resistance to high contrast and brightness, it is effective in refining and removing noise from the output image. Based on experimental research employing a Middlebury standard validation benchmark, this approach gives high accuracy with an average non-occluded error of 6.71% comparable to other published methods.

show abstract

“…TensorFlow software embeds the Artificial intelligence (AI) capability into the mobile app. The app shows the high accuracy of the machine learning image classification approach with interactive learning provided [16]. Learn2Write is a mobile app implementing AR and Machine Learning techniques.…”

Section: Introductionmentioning

confidence: 99%

AI Educational Mobile App using Deep Learning Approach

Mohd Nasir,

Brahin,

Mohd Sani @ Ariffin

et al. 2023

JOIV : Int. J. Inform. Visualization

View full text Add to dashboard Cite

Moving to Industrial Revolution (IR 4.0), the early education sector is not left behind. More of the teaching method is being digitized into a mobile application to assist and enhance the children’s understanding. On the other hand, most of the applications offer passive learning, in which the children complete the activity without interacting with the environment. This study presents an educational mobile application that uses a deep learning approach for interactive learning to enhance English and Arabic vocabulary. Android Studio software and Tensorflow tool were used for this application development. The convolution neural network (CNN) approach was used to classify the item of each category of vocab through image recognition. More than thousands of images each time were pre-trained for image classification. The application will pronounce the requested item. Then, the children will need to move around looking for the item. Once the item’s found, the children must capture the image through the camera’s phone for image detection. This approach can be integrated with teaching and learning techniques for fun learning through interactive smartphone applications. This study attained high accuracy of more than 90% for image classification. In addition, it helps to attract the children's interest during the teaching using the current technology but with the concept of ‘Play’ and ‘Learn’. In the future, this paper recommended the involvement of IoT platforms to provide widen applications.

show abstract

Development of vocabulary learning application by using machine learning technique

Cited by 5 publications

References 22 publications

Model for the Detection of Falls with the Use of Artificial Intelligence as an Assistant for the Care of the Elderly

Model for the Detection of Falls with the Use of Artificial Intelligence as an Assistant for the Care of the Elderly

Stereo matching algorithm using deep learning and edge-preserving filter for machine vision

AI Educational Mobile App using Deep Learning Approach

Contact Info

Product

Resources

About