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All Journal Green Intelligent Systems and Applications Makara Journal of Technology
Angelina, Clara Lavita
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Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology Mechanical Engineering

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A Benchmark Study of DeepLabV3+, U-Net++, and Attention U-Net for Blood Cell Segmentation Angelina, Clara Lavita; Rospawan, Ali
Green Intelligent Systems and Applications Volume 5 - Issue 1 - 2025
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v5i1.607

Abstract

Cell segmentation is a critical process in biomedical image analysis. This study evaluated the performance of three state-of-the-art deep learning models—DeepLabV3+, U-Net++, and Attention U-Net—using the Blood Cell Count and Detection (BCCD) dataset, which contains annotated images of blood cells. The models were rigorously analyzed through qualitative and quantitative evaluations, employing accuracy, precision, recall, and F1 score metrics. The results demonstrated that all three models achieved high segmentation performance, with U-Net++ excelling in accuracy (0.9740), precision (0.9511), and F1 score (0.9576), Attention U-Net achieving the highest recall (0.9692), and DeepLabV3+ providing a balanced performance across all metrics. Qualitative analyses revealed that U-Net++ delivered superior segmentation of complex and overlapping cell structures, while Attention U-Net exhibited exceptional sensitivity to dense cell clusters. Training and validation curves of the models confirmed their stability and generalizability, indicating efficient convergence without overfitting. By highlighting the unique strengths of each model, this study emphasized the importance of selecting architectures tailored to specific tasks. Future research will expand the application of these models to diverse biomedical datasets to further advance automated image analysis and its impact on healthcare outcomes.
Direct Control Strategy using Polynomial Fuzzy-Based Adaptive Fractional Order PID Controller Rospawan, Ali; Angelina, Clara Lavita; Samsuri, Faisal; Baihaqi, Muhammad Yeza; Halawa, Edmun; Munajat, Muhammad; Vincent, Vincent; Setiyadi, Surawan; Purnama, Irwan; Simatupang, Joni Welman
Makara Journal of Technology Vol. 29, No. 2
Publisher : UI Scholars Hub

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Abstract

This paper presents a novel direct control strategy using a polynomial fuzzy neural network-based adaptive fractional order proportional integral derivative (PFNN-AFOPID) controller for nonlinear and time-varying systems. The proposed approach integrates the enhanced flexibility of fractional order calculus PID with the superior nonlinear approximation capabilities of polynomial fuzzy models, enabling dynamic adjustment of all control parameters without requiring precise mathematical modeling of system dynamics. By extending traditional PID control with fractional-order operations, the controller achieves improved frequency response and robustness against disturbances. Experimental validation on a DC motor position control system demonstrates significant performance improvements. Compared to traditional PID, the proposed PFNN-AFOPID achieved a performance improvement of 53.69% in RMSE, 78.56% in ISE, 69.92% in IAE, and 83.98% in ITAE. When compared to the existing fuzzy neural network-based adaptive PID (FNN-APID), our approach delivered improvements of 21.06% in RMSE, 28.79% in ISE, 5.69% in IAE, and 32.86% in ITAE. These results confirm the superior capability of the proposed approach in handling system nonlinearities while maintaining precise control under varying operational conditions, without requiring prior system dynamics knowledge or extensive offline training.
Co-Authors Baihaqi, Muhammad Yeza Halawa, Edmun Joni Welman Simatupang Munajat, Muhammad Purnama, Irwan Rospawan, Ali Samsuri, Faisal Setiyadi, Surawan Vincent Vincent, Vincent