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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Suksukont, Aekkarat
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Computer Science & IT Engineering

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Combining convolutional operators in unsupervised networks for kidney abnormalities Suksukont, Aekkarat; Prommakhot, Anuruk; Srinonchat, Jakkree
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4541-4551

Abstract

Deep learning plays a pivotal role in advancing the diagnosis of renal dysfunction, achieving performance levels comparable to those of medical experts. However, disease domain variations and model differences can impact learning quality. To address renal dysfunction, we propose dual stream convolutional (DSC) and dual-input convolutional (DIC) for unsupervised learning. The proposed network is designed to process multi scale data and employs parallel data aggregation to enhance learning capabilities, improving the reliability of the experimental results. DSC achieved training losses of 0.0069, 0.0056, 0.0042, and 0.0048 for normal, cyst, stone, and tumor datasets, respectively, while DIC achieved losses of 0.0066, 0.0063, 0.0044, and 0.0058 for the same categories. The experimental results demonstrate that our proposed models outperform state of-the-art approaches, making them well-suited for broad application in clinical research studies.
Designing a squeeze-and-excitation-capsule BiLSTM transformer for plant leaf disease recognition Suksukont, Aekkarat; Naowanich, Ekachai
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp5069-5080

Abstract

Deep learning (DL) is critical in plant disease recognition and classification with precision like those of expert human evaluators. However, development of effective systems is often disrupted due to the complexity and variability of disease pathogenesis. To address these challenges, this research applies to a hybrid DL architecture that integrates spatial encoding, sequential modelling, and attention for visual recognition. This proposed model can incorporate squeeze-and-excitation (SE) with residual blocks, capsule network (CapsNet), bidirectional long short-term memory (BiLSTM), and transformer network (TransNet)-based attention to realize spatial relationships and long-range dependencies for improving recognition accuracy. The proposed model is assessed on the corn leaf disease dataset (CLDD) and rice leaf diseases dataset (RLDD), and its performance is compared to leading-edge models. CLDD and RLDD achieved 99.88 and 99.10% training accuracy respectively. The area under the curve (AUC) reached almost ceiling recognition on CLDD, with 99.73, 99.96, 99.96, and 99.98% for blight (BL), common rust (CR), gray leaf spot (GL), and healthy (HE) result. RLDD results were also high, with 94.98, 93.70, 97.66, 84.57, 99.58, and 98.85% for bacterial leaf blight (BLB), brown spot (BS), HE, leaf blast (LB), leaf scald (LS), and narrow brown spot (NBS), respectively. The results of these tests show the remarkable promise and performance of the proposed model in plant disease recognition applications.
Co-Authors Naowanich, Ekachai Prommakhot, Anuruk Srinonchat, Jakkree