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All Journal Register: Jurnal Ilmiah Teknologi Sistem Informasi Indonesian Journal of Electrical Engineering and Computer Science
Asnor Juraiza Ishak
Universiti Putra Malaysia
Computer Science & IT

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Development of fall detection and activity recognition using threshold based method and neural network Sai Siong Jun; Hafiz Rashidi Ramli; Azura Che Soh; Noor Ain Kamsani; Raja Kamil Raja Ahmad; Siti Anom Ahmad; Asnor Juraiza Ishak
Indonesian Journal of Electrical Engineering and Computer Science Vol 17, No 3: March 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v17.i3.pp1338-1347

Abstract

Falls are dangerous and contribute to over 80% of injury-related hospitalization especially amongst the elderly. Hence, fall detection is important for preventing severe injuries and accidental deaths. Meanwhile, recognizing human activity is important for monitoring health status and quality of life as it can be applied in geriatric care and healthcare in general. This research presents the development of a fall detection and human activity recognition system using Threshold Based Method (TBM) and Neural Network (NN). Intentional forward fall and six other activities of daily living (ADLs), which include running, jumping, walking, sitting, lying, and standing are performed by 15 healthy volunteers in a series of experiments. There are four important stages involved in fall detection and ADL recognition, which are signal filtering, segmentation, features extraction and classification. For classification, TBM achieved an accuracy of 98.41% and 95.40% for fall detection and activity recognition respectively whereas NN achieved an accuracy of 97.78% and 96.77% for fall detection and activity recognition respectively.
Optimization of the VGG Deep Learning Model Performance for Covid-19 Detection Using CT-Scan Images Slamet Riyadi; Cahya Damarjati; Siti Khotimah; Asnor Juraiza Ishak
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 10 No 1 (2024): January
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v10i1.3598

Abstract

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-Cov-2) causes a pneumonia-like disease known as Coronavirus Disease 2019 (COVID-19). The Reverse Transcription Polymerase Chain Reaction (RT-PCR) test is the current standard for detecting COVID-19. However, CT scans can be applied for radiological inspection to detect infections in their earliest lung stages. Machine learning, specifically deep learning, can potentially speed up the evaluation of CT scan diagnoses of COVID-19. To date, no studies have been discovered that employ SGD, Adamax, or AdaGrad optimization methods with deep learning VGG model variants for COVID-19 detection in CT scan images with datasets comprising 2,038 images. This study aims to assess and compare the performance of various optimization methods for detecting COVID-19 utilizing variations of the VGG-16 and VGG-19 models based on CT scan images. Results from performance optimization comparison tests employing two VGG deep learning models were obtained, demonstrating the influence of optimization methods on model performance. The Adamax optimization method applied to the VGG-16 model performance achieved an average accuracy of 94.11% in COVID-19 detection using CT scan images, while the Adamax optimization method applied to the VGG-19 model performance achieved an average accuracy of 93.77%.
Co-Authors Azura Che Soh Cahya Damarjati Hafiz Rashidi Ramli Noor Ain Kamsani Raja Kamil Raja Ahmad Sai Siong Jun Siti Anom Ahmad Siti Khotimah Slamet Riyadi