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6 pages, 530 KB

Open AccessProceeding Paper

Classification of Guava Leaf Disease Using Support Vector Machine and You Only Look Once Version 8

by Paul Jess C. Rosero, Frances Mae P. Domingo and Analyn N. Yumang

Eng. Proc. 2026, 134(1), 1; https://doi.org/10.3390/engproc2026134001 - 26 Mar 2026

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Abstract

Guava is a popular fruit in the Philippines, as it offers various health benefits. Its leaves are used in traditional medicine to aid in wound healing, stomach disorders, pain relief, and more. In this study, we classified guava leaf diseases using Support Vector Machine (SVM) and You Only Look Once version 8 (YOLOv8). Raspberry Pi 4 is used to control the image preprocessing and the program that utilizes the proposed trained model. The SVM model conducts image classification, while YOLOv8 handles feature extraction and object detection. Grayscale and color thresholding segmentation feature extraction is also implemented in the proposed model. The developed model combines both YOLOv8 and SVM algorithms to evaluate their accuracy using a confusion matrix, achieving a 92.5% accuracy. With its very low error rate, the system can accurately classify guava leaf diseases. Full article

(This article belongs to the Proceedings of The 7th Eurasia Conference on IoT, Communication and Engineering 2025 (ECICE 2025))

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10 pages, 873 KB

Open AccessProceeding Paper

Utilizing Residual Network 50 Convolutional Neural Network Architecture for Enhanced Philippine Regional Language Classification on Jetson Orin Nano

by John Paul T. Cruz, Aaron B. Abadiano, FP O. Sangilan, Emmy Grace T. Requillo and Roben C. Juanatas

Eng. Proc. 2026, 134(1), 2; https://doi.org/10.3390/engproc2026134002 - 26 Mar 2026

Abstract

Visual speech recognition systems encounter significant challenges in multilingual nations such as the Philippines, where numerous regional languages, including Cebuano and Ilocano, feature distinct phonetic-visual characteristics. Deep learning models such as the Lip Reading Network and the Lightweight Crowd Segmentation Network have demonstrated strong performance with 3D Convolutional Neural Networks (CNNs). However, their substantial computational requirements restrict deployment on portable edge devices. We introduce a more efficient alternative that integrates a 2D Residual Network 50 architecture with a Long Short-Term Memory network and Connectionist Temporal Classification for lip-reading classification of Philippine regional languages. The proposed model is deployed on the Jetson Orin Nano, a high-performance edge device optimized for real-time inference through Compute Unified Device Architecture acceleration. Using a dataset of 2000 annotated videos encompassing 10 lexicons each for Cebuano and Ilocano, the model’s effectiveness was evaluated. Results achieved a regional language classification accuracy of 90%, with lexicon-level accuracies of 74% for Cebuano and 66% for Ilocano. This work represents a step toward developing accessible and scalable communication aids for deaf communities in linguistically diverse environments, leveraging transfer learning on pretrained models. Full article

(This article belongs to the Proceedings of The 7th Eurasia Conference on IoT, Communication and Engineering 2025 (ECICE 2025))

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866 KB

Open AccessProceeding Paper

Inspection for Solder Joint Defects in Voltage Regulator ICs of Automotive Charging Applications

by Yi-Hsuan Chiu and Kuang-Chyi Lee

Eng. Proc. 2026, 134(1), 6; https://doi.org/10.3390/engproc2026134006 (registering DOI) - 27 Mar 2026

Abstract

In automated production lines for automotive chargers, solder joint inspection is critical due to the widespread adoption of automotive electronics and electric vehicles. This study establishes a You Only Look Once Version 8 (YOLOv8)-based single-pin solder joint classification model for an 8-pin automotive voltage regulator IC. Solder joints were categorized into four types: normal, misalignment, insufficient fillet, and cold joint. The model achieved a single-pin training accuracy of 0.987 (4000 samples) and a test accuracy of 0.973 (4800 samples), while overall IC-level evaluation exceeded 0.90. Normal and cold joint categories were detected with the highest reliability, whereas occasional misclassifications occurred in the insufficient fillet and misalignment categories. These results demonstrate that the proposed method is feasible for efficient and accurate detection of solder joint defects, providing a practical approach to support automated inspection and ensure consistent production quality. Full article

(This article belongs to the Proceedings of The 7th Eurasia Conference on IoT, Communication and Engineering 2025 (ECICE 2025))

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