Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
P-Index
This Author published in this journals
All Journal IAES International Journal of Robotics and Automation (IJRA) International Journal of Informatics and Communication Technology (IJ-ICT) International Journal of Engineering, Science and Information Technology
Chin-Shiuh Shieh
National Kaohsiung University of Science and Technology
Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Artificial Intelligence Driven Skin Cancer Detection Using R-FCN Enhanced Deep Convolutional Neural Networks with SMOTE Balancing A Ronald Doni; Chin-Shiuh Shieh; Siva Shankar S; Prasun Chakrabarti; G Nagarajan; S Murugan
International Journal of Engineering, Science and Information Technology Vol 5, No 4 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i4.1196

Abstract

Skin cancer is a serious worldwide health issue, and earlier diagnosis is crucial for patient outcomes and efficient treatment.  However, due to the variety of skin cancer types and the complexity of medical imaging, making an accurate diagnosis can be challenging.  This study tackles this issue by introducing a new deep learning (DL) algorithm that is specifically designed for skin tumor diagnosis and employs the Convolutional Neural Network (CNN) technology. This study offers a novel approach that makes use of Region-based Fully Convolutional Networks (R-FCN) to address the crucial problem of skin cancer lesion categorization. The suggested system seeks to increase classification efficiency by using region-based detection which improves classification accuracy and localization. The HAM10000 and ISIC-2020 datasets, which are difficult and unbalanced, were used to thoroughly evaluate the created Deep CNN (DCNN) architecture. The Synthetic Minority Over-sampling Technique (SMOTE) was purposefully used as the method of random sampling in order to lessen the imbalanced datasets. This greatly enhanced the suggested models generalization and robustness. The results demonstrate the remarkable efficacy of the research contribution, yielding performance metrics consistently above 98% for F1-score, specificity, sensitivity, recall, accuracy, precision, and the area under the ROC curve (AUC). In terms of balancing speed and accuracy the suggested approach also performs better than traditional methods like R-CNN and YOLOv8. The study demonstrates that a strong framework for automatic skin cancer detection and classification is provided by combining R-FCN with SMOTE and CNN techniques. This framework facilitates early diagnosis and aids dermatologists in clinical decision-making.
Robotic mist bath wheelchair: innovations in automated body drying and sanitization for improved patient hygiene Vijay Mahadeo Mane; Harshal Ambadas Durge; Chin-Shiuh Shieh; Rajesh Dey; Rupali Atul Mahajan; Siddharth Bhorge
IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v14i3.pp301-310

Abstract

This paper presents the development and evaluation of the robotic mist bath wheelchair (MBWC), a multifunctional assistive device designed to enhance hygiene and comfort for individuals with limited mobility. The MBWC integrates mist-based bathing, automated sanitization, and warm air-drying into a compact, wheelchair-mounted system suitable for home and clinical settings. Experimental evaluations demonstrated effective temperature maintenance and a 30% reduction in bathing time compared to conventional methods. User trials with 20 participants indicated a 92% satisfaction rate, reflecting improvements in hygiene, comfort, and operational ease. MBWC provides a cost-effective, hygienic alternative to traditional bathing methods, addressing critical challenges in eldercare and rehabilitation environments.
Explainable AI-based hybrid GNN-MLP model for strawberry fruit disease detection using hyperspectral imaging Varsha Kiran Bhosale; Chin-Shiuh Shieh
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i3.pp1364-1375

Abstract

Strawberries are severely affected by the main fungal diseases such as anthracnose fruit rot, grey mould, and powdery mildew, directly reducing commercial yield and post-harvest quality. In this paper, we propose an enhanced hybrid deep-learning method by combining graph neural networks (GNNs) and multi-layer perceptrons (MLPs) for effective strawberry disease detection in real environments of fields. This dataset contains images acquired from both public domain repositories as well as farm operational settings, encompassing three classes of diseases with varying lighting conditions, background noise, and occlusion. A structured pre-processing workflow comprising contrast enhancement, denoising, and synthetic hyperspectral simulation is used to enhance subtle lesion features and stabilize the subsequent feature extraction. The hybrid GNN–MLP framework possesses the merits of spatially local lesion topology and two-point contextual information, which can improve disease classification compared with common CNN-based structures. 5-fold cross-validation shows that the model holds 93.59% accuracy and a macro F1-score of 90.39%, showing good generalization even with heterogeneous input regimes. Transparent models use local interpretable model-agnostic explanations (LIME) and gradient-weighted class activation mapping (Grad-CAM) in combination, highlighting disease-relevant areas that give an interpretable rationale for each prediction. To conclude, the developed system offers an accurate and explainable solution that has a computationally efficient commitment for real-time monitoring of disease in smart agriculture settings, particularly on low-cost hardware assets.
Co-Authors A Ronald Doni G Nagarajan Harshal Ambadas Durge Prasun Chakrabarti Rajesh Dey Rupali Atul Mahajan S Murugan Siddharth Bhorge Siva Shankar S Varsha Kiran Bhosale Vijay Mahadeo Mane