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p-Index From 2021 - 2026
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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Applied Power Engineering (IJAPE)
Ezreen Farina Shair
Universiti Teknikal Malaysia Melaka
Computer Science & IT Electrical & Electronics Engineering Engineering

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Parkinsonian gait classification in older adults using time–frequency spectrograms and 2D convolutional neural network Kazi Ashikur Rahman; Ezreen Farina Shair; Nur Zawani Saharuddin; Muhammad Hazwan Adlin Jumaris
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2698-2708

Abstract

Gait disorders in adults aged 50 years and above are a common concern and are often linked to reduced mobility, a higher risk of falls, and a lower quality of life. This study presents a deep learning-based approach to detect gait disorders using vertical ground reaction force (vGRF) signals. The data were collected from older adults, including individuals with Parkinson’s disease (PD) and healthy controls, using force-sensitive resistor sensors. The raw signals were first processed using band-pass filtering and wavelet denoising to remove noise and unwanted variations. After that, the signals were converted into time–frequency representations using the continuous wavelet transform (CWT). These representations were then used as input to a convolutional neural network (CNN) for classification. The model achieved a validation accuracy of 93.48%, with precision, recall, and F1-score all above 92% for both groups. The results show that combining CWT with CNN provides a reliable and efficient way to detect gait disorders. This approach can support clinical evaluation by offering a practical and scalable method for analyzing gait patterns in older adults.
Enhanced intentional controlled islanding with BESS integration Amar Saufi Nasir; Nur Zawani Saharuddin; Izham Zainal Abidin; Ezreen Farina Shair; Sharin Ab Ghani
International Journal of Applied Power Engineering (IJAPE) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v15.i1.pp80-89

Abstract

Severe power system outages can lead to uncontrolled failures and system instability. Intentional controlled islanding is a strategy that deliberately splits the power system into balanced, stand-alone islands to ensure continuous electricity supply until full restoration. However, the execution of islanding may result in certain islands being unbalanced in terms of generation and load. In such cases, load shedding is implemented to achieve balanced stand-alone islands. Nevertheless, load shedding is not the best option as it will result in more users experiencing power disruptions. Therefore, this study explores the integration of battery energy storage systems (BESS) to enhance intentional controlled islanding, with the aim to form balance islands without the need to execute load shedding. This study evaluates the effectiveness of BESS in forming balanced islands and optimizing islanding strategies. The IEEE 30-bus and IEEE 118-bus test systems were used to validate the effectiveness of BESS in enhancing the intentional controlled islanding implementation. The results demonstrated the role of BESS in facilitating intentional controlled islanding, forming stable and balanced island operations without the need for a load shedding scheme. These findings highlight the potential of BESS to enhance the reliability and effectiveness of intentional controlled islanding.
Co-Authors Amar Saufi Nasir Izham Zainal Abidin Kazi Ashikur Rahman Muhammad Hazwan Adlin Jumaris Nur Zawani Saharuddin Sharin Ab Ghani