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All Journal International Journal of Advances in Intelligent Informatics Buletin Ilmiah Sarjana Teknik Elektro
Uda Hashim
Universiti Malaysia Sabah
Computer Science & IT Electrical & Electronics Engineering

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MobileNet-driven detection of bacterial and viral pneumonia with Grad-CAM heatmap insights Yuri Pamungkas; Adrian Jaleco Forca; Muhammad Nur Afnan Uda; Uda Hashim
International Journal of Advances in Intelligent Informatics Vol 12, No 2 (2026): May 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Pneumonia is a major cause of childhood illness and death, and chest X-rays remain the most accessible diagnostic tool. Differentiating bacterial from viral pneumonia, however, is difficult because of overlapping radiographic patterns. This study explores MobileNet architectures combined with Grad-CAM visualization to provide efficient and interpretable pneumonia classification. The main contribution of this research is to demonstrate that MobileNet combined with Grad-CAM not only produces accurate predictions but also highlights radiologically meaningful regions of the lungs, thereby improving transparency and trust in automated diagnosis. A dataset of 5,842 pediatric chest X-rays from Guangzhou Women and Children’s Medical Center was used, including bacterial, viral, and normal cases. MobileNet and MobileNetV2 were trained with stochastic gradient descent, categorical cross-entropy, 20 epochs, and batch size of 32, and validated through 10-fold cross-validation. Grad-CAM was applied to generate heatmaps for model interpretability. Results indicated that MobileNet outperformed MobileNetV2. MobileNet achieved 79.32% accuracy, 81.02% precision, 78.15% recall, 77.82% F1-score, and 89.49% specificity. Its AUC-ROC reached 94.64% (macro) and 90.52% (micro). MobileNetV2 obtained 76.44% accuracy, 74.45% F1-score, and 93.61% macro AUC-ROC. Grad-CAM confirmed that both models attended to pneumonia-related lung regions, with MobileNet producing sharper localized activations and MobileNetV2 showing broader patterns. In conclusion, MobileNet with Grad-CAM provides an accurate, efficient, and interpretable framework for pneumonia detection, making it suitable for deployment in resource-limited clinical settings.
Advances in Brain-Computer Interfaces for Taste Perception: Current Insights and Future Directions Yuri Pamungkas; Abdul Karim; Gao Yulan; Muhammad Nur Afnan Uda; Uda Hashim
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 1 (2026): February
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i1.14718

Abstract

Human taste perception is a complex multisensory process that integrates chemical, emotional, and cognitive responses within the brain. Traditional methods for evaluating taste rely on subjective reporting, which limits reproducibility and accuracy. Brain-Computer Interface (BCI) technology provides an objective solution by decoding neural activity associated with taste perception using non-invasive techniques such as EEG and fNIRS. The research contribution aims to deliver an extensive overview of the latest advancements in BCI-oriented taste research, emphasizing various applications, methodological frameworks, and potential future pathways that connect the domains of neuroscience and sensory technology. This review examines the use of EEG and fNIRS modalities for signal acquisition, preprocessing, feature extraction, and classification across 36 studies conducted between 2020 and 2025. These works employ both traditional algorithms and deep learning models, including SVM, CNNs, and Transformer-based frameworks, to decode neural signatures of basic tastes and multisensory interactions. Results show that BCIs have successfully identified distinct brain responses for sweet, sour, salty, bitter, and umami stimuli. They have also been applied in multisensory integration, hedonic evaluation, consumer behavior analysis, clinical diagnosis of taste disorders, and affective monitoring. However, challenges remain in signal noise, dataset standardization, and model interpretability. In conclusion, BCIs represent a promising and interdisciplinary approach for objectively studying and enhancing human taste perception through the integration of neuroscience, engineering, and artificial intelligence.
Trends and Gaps in Transformer-Based EEG Modeling: A Review of Recent Developments Yuri Pamungkas; Abdul Karim; Myo Min Aung; Muhammad Nur Afnan Uda; Uda Hashim
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 2 (2026): April
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i2.14933

Abstract

In recent years, Transformer-based deep learning architectures have emerged as a powerful paradigm for modeling EEG signals, offering superior capability in capturing spatial–temporal dependencies compared to traditional convolutional or recurrent networks. However, the diversity of model designs, limited dataset generalization, and lack of standardization have created challenges in evaluating their true potential for real-world applications. This review addresses these issues by systematically examining the evolution, performance, and methodological trends of Transformer-based EEG models published between 2022 and 2024, highlighting both achievements and research gaps. The main contribution of this study is to provide a comprehensive mapping and critical analysis of Transformer architectures applied to EEG classification, feature extraction, and signal decoding tasks. Using the Scopus database, a structured search was conducted following specific inclusion criteria (English, peer-reviewed, open-access journal papers from 2022–2024) and a well-defined query combining EEG and Transformer-related keywords. Data from 63 eligible studies were extracted and categorized according to authorship, dataset, architecture type, EEG application, and evaluation metrics. Results show that hybrid Transformer models dominate recent research, achieving accuracies above 90% in tasks such as motor imagery, emotion recognition, seizure detection, and sleep staging. Pure Transformers like ViT and BERT-like models also demonstrate competitive performance but face scalability and interpretability challenges. In conclusion, Transformer-based EEG modeling is advancing rapidly, yet future efforts must focus on model efficiency, explainability, and benchmark standardization to enable broader clinical and real-world adoption.
Co-Authors Abdul Karim Adrian Jaleco Forca Gao Yulan Muhammad Nur Afnan Uda Myo Min Aung Yuri Pamungkas