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All Journal Journal of Embedded Systems, Security and Intelligent Systems
Ayutri Wahyuni
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Computer Science & IT

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AI-Powered Botox Dosage Classification: A Comparative Study of CNN Architectures on Facial Wrinkle Analysis Ayutri Wahyuni; Resky Ayu Dewi Talasari
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 3 (2025): September 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i3.10050

Abstract

Botulinum Toxin (BOTOX) injections are widely used as a non-invasive cosmetic treatment to enhance facial appearance. However, determining the optimal dosage still relies on subjective visual assessment by medical professionals, which can lead to inconsistency. This study proposes a model deep learning–based classification framework using convolutional neural networks (CNNs) to automate BOTOX dosage prediction from forehead wrinkle images. Four CNN architectures Inception-V3, ResNet-34, ResNet101-V2, and EfficientNetB2 were evaluated on an augmented dataset of 168 cropped images, equally divided between 2-unit and 4-unit dosages. The dataset was generated through flipping and rotation augmentation to address class imbalance and enhance model generalization. Among the models, EfficientNetB2 achieved the highest accuracy of 92.8%, surpassing Inception-V3 85.7%, ResNet-34 82.1%, and ResNet101-V2 80.3%. The superior performance of EfficientNetB2 reflects its capability to extract fine-grained wrinkle patterns efficiently while maintaining computational efficiency. The novelty of this work lies in integrating CNN-based visual feature extraction with expert-labeled clinical image data for objective BOTOX dosage determination. Although limited by dataset size, this study highlights the potential clinical application of deep learning in supporting accurate, consistent, and data-driven facial aesthetic treatments.
Analyzing the Impact of Data Filtering on Anomaly Detection under Distribution Shift Conditions Talasari, Resky Ayu Dewi; Ayutri Wahyuni
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 3 (2025): September 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i3.10051

Abstract

One of the main challenges in anomaly detection for Windows Event Logs and Sysmon is distribution shift, where changes in data distribution invalidate the model's learned normality reference. This study evaluates how data filtering setting value boundaries classified as normal affects the model's ability to handle distribution shifts across three experimental scenarios. This research is among the first to systematically quantify the trade-off between filtering efficiency and model adaptability across varying magnitudes of distribution shifts in anomaly detection systems. The experimental design employs three scenarios: Scenario 1 evaluates filtering under complete cross-environment shift using Dataset A for training and Dataset B for testing, Scenario 2 examines filtering with partial Dataset B training data, and Scenario 3 validates model adaptability without filtering constraints. The goal is to determine whether filtering improves performance under small, adaptable shifts and to measure its impact under large shifts that push the distribution far from the initial training data. Shift magnitude is measured using Jensen Shannon Divergence and Hellinger Distance, followed by evaluation of model performance through precision, recall and F1-score. Results show that filtering can help for minor shifts but substantially impairs adaptation under substantial distributional changes: filtered models remain constrained by prior baseline behavior and fail to learn new patterns, while unfiltered models adapt successfully and maintain accurate detection. These findings suggest critical implications for designing adaptive anomaly detection systems in dynamic operational environments where changes frequently alter normal behavior patterns. Future approaches should incorporate adaptive filtering mechanisms that dynamically adjust baseline boundaries rather than relying solely on static training data distributions.
Co-Authors Resky Ayu Dewi Talasari Talasari, Resky Ayu Dewi