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Designing stair climbing wheelchairs with surface prediction using theoretical analysis and machine learning Chawaphan, Pharan; Maneetham, Dechrit; Crisnapati, Padma Nyoman
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i1.pp120-132

Abstract

Urban settings present considerable obstacles for those use personal mobility wheelchairs, especially when it comes to manoeuvring stairs. The objective of this study is to improve the safety and ease of use of wheelchairs designed for ascending stairs. The study aims to tackle the significant issue of instability and limited ability to adjust to different types of terrain. This research employs a holistic methodology that combines theoretical dynamic analysis, hardware design and simulation, and field testing, in addition to advanced machine learning approaches for surface prediction. Theoretical models guarantee the stability of the wheelchair, while hardware simulations offer valuable insights into its structural integrity. The data obtained from inertial measurement unit (IMU) sensors during field tests is analysed and categorised using models like random forest and gradient boosting, which exhibit exceptional accuracy in forecasting movement circumstances. The results demonstrate that the implementation of these combined techniques greatly enhances the wheelchair’s capacity to safely manoeuvre over urban barriers. The study finds that the suggested solutions show great potential for creating intelligent mobility aids, which might be used to improve accessibility for those with mobility limitations.
Hybrid object detection and distance measurement for precision agriculture: integrating YOLOv8 with rice field sidewalk detection algorithm Tungkasthan, Anucha; Jongsawat, Nipat; Crisnapati, Padma Nyoman; Thwe, Yamin
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1507-1517

Abstract

This study aims to propose a new approach to semantic segmentation of sidewalk images in rice fields using the YOLOv8 algorithm, with the objective of enhancing agricultural monitoring and analysis. The experimental process involved preparing the development environment, extracting data from JSON, and training the model using YOLOv8. Evaluation reveals consistent and accurate sidewalk detection with a confidence score of 0.9-1.0 across various environmental conditions. Confusion matrix and precision-recall analysis confirmed the robustness and accuracy of the model. These findings validate the effectiveness of the approach in detecting and measuring sidewalks with high precision, potentially improving agricultural monitoring. The novelty of this study lies in the utilization of the RIFIS-D algorithm as an integral part of a hybrid approach with YOLOv8. This hybridization enriches the model with additional capability to detect the distance between the sidewalk and the tractor, addressing specific needs in agricultural applications. This contribution is significant in the advancement of automatic navigation and monitoring technology in agriculture, enabling the implementation of more sophisticated and efficient systems in field operations.
Evaluating the Effectiveness of Alzheimer’s Detection Using GANs and Deep Convolutional Neural Networks (DCNNs) Pamungkas, Yuri; Syaifudin, Achmad; Crisnapati, Padma Nyoman; Hashim, Uda
International Journal of Robotics and Control Systems Vol 5, No 2 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i2.1855

Abstract

Alzheimer’s is a gradually worsening condition that damages the brain, making timely and precise diagnosis essential for better patient care and outcomes. However, existing detection methods using DCNNs are often hampered by the problem of class imbalance in datasets, particularly OASIS and ADNI, where some classes are underrepresented. This study proposes a novel approach integrating GANs with DCNNs to tackle class imbalance by creating synthetic samples for underrepresented categories. The primary focus of this research is demonstrating that using GANs for data augmentation can significantly strengthen DCNNs performance in Alzheimer's detection by balancing the data distribution across all classes. The proposed method involves training DCNNs with both original and GAN-generated data, with data partitioning of 80:10:10 for training/ validation/ testing. GANs are applied to generate new samples for underrepresented classes within the OASIS and ADNI datasets, ensuring balanced datasets for model training. The experimental results show that using GANs improves classification performance significantly. In the case of the OASIS dataset, the mean accuracy and F1 Score rose from 99.64% and 95.07% (without GANs) to 99.98% and 99.96% (with GANs). For the ADNI dataset, the average accuracy and F1 Score improved from 96.21% and 93.01% to 99.51% and 99.03% after applying GANs. Compared to existing methods, the proposed GANs + DCNNs model achieves higher accuracy and robustness in detecting various stages of Alzheimer's disease, particularly for minority classes. These findings confirm the effectiveness of GANs in improving DCNNs' performance for Alzheimer's detection, providing a promising framework for future diagnostic implementations.
Co-Authors Achmad Syaifudin Ade Widiyantara, I Putu Adi Yoga Dewantara, I Made Agus Sutrisna, I Kadek Aprilia Yustika Dewi, Aprilia Ardipa, Gede Sukra Arief Hadi Prasetyo Arief Hadi Prasetyo, Arief Hadi Arisandi, Ni Made Desi Aryasih, Putu Putri Ayu Juli Astari, Ni Made Ayu Nirma Lestari, Gusti Bunga Anindya, Made Cahyani, Agung Ayu Hanna Chawaphan, Pharan Duika Adi Sucipta, I Kadek Dwi Suparyanta, Kadek Gusti Ngurah Putra Arimbawa, I Hanna Cahyani, Agung Ayu Haryantara, I Nyoman Hashim, Uda I Gede Mahendra Darmawiguna I Gusti Ayu Sri Melati, I Gusti Ayu I Ketut Resika Arthana I Komang Agus Ady Aryanto I Komang Ariesta Ananta, I Komang Ariesta I Komang Try Adi Stanaya, I Komang Try Adi I Made Gede Sunarya I Nyoman Haryantara I Nyoman Kusuma Wardana I Putu Dana Arista, I Putu Indriani, Ratri Dwi Jongsawat, Nipat Kadek Artawan Kadek Artawan, Kadek Lanang Nugraha, Made Lenny Natalia, Lenny M.Cs S.Kom I Made Agus Wirawan . Made Frans Aditya Bramantya Kusuma Made Frans Aditya Bramantya Kusuma, Made Frans Aditya Bramantya Made Windu Antara Kesiman Maemonah, Maemonah Maneetham, Dechrit Mariyantoni, I Kadek Yostab Merta, I Gede Muchammad Naseer Ni Kadek Sumiari, Ni Kadek Ni Komang Oktari Permata Sari Oky Sanjaya, Kadek Pandu Wibawa S, I Wayan Panji Anggara, Dicky Prawira, Putu Yoka Angga Putra Yasa, Gede Agus Putu Angga Sudyatmika Putu Devi Novayanti Putu Putri Sanjani, Dewa Ayu Ratna Kartika Wiyati, Ratna Kartika Reditya Ary Prasetya, Agus Nyoman Rendy Syahrial, Lalu Ricky Aurelius Nutanto Diaz, Ricky Aurelius Roman Apriyansyah, Roman Shofwan Hanief Sri Darmaningsih, Luh Suarningsih, Putu Susena, I Komang Sutrisna Oka, Ketut Tri Thwe, Yamin Tirta Murdika, I Made Tungkasthan, Anucha Udayana, Ketut Wira Widiantara, Eka Putra Wikan Paramasila, Kadek Yoga Antara, I Made Yoka Angga Prawira, Putu Yostab Mariyantoni, I Kadek Yuda Danuarta, I Putu Yudiantara, I Made Yuri Pamungkas