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Informatica (Slovenia) 2025年第19期49卷 183-196页

作者： Hakim, Dimara Kusuma Gernowo, Rahmat Nirwansyah, Anang Widhi Haryanto, Totok Department of Informatics Universitas Muhammadiyah Purwokerto 53182 Indonesia Department of Physic Faculty of Mathematics and Natural Sciences Diponegoro University Indonesia Department of Geography Education Universitas Muhammadiyah Purwokerto 53182 Indonesia Bachelor of Management Program Faculty of Economics and Business Universitas Muhammadiyah Purwokerto 53182 Indonesia

Conventional flood hazard maps depict a static perspective on flood risk and are helpful in flood forecasting. Then, time-series flood forecasting research was developed and widely conducted. Still, it has limitations because the univariate method used does not consider the influence of other variables, unlike regression. Also, the determination of flood hazard weights, which is usually conducted through empirical studies using AHP, has a good level of accuracy but has the drawback of expert subjectivity. A new approach is proposed for flood hazard forecasting, combining the Triple ES method with NN and determining weights in a data-driven manner using NN. A combination of time series and regression methods (significantly non-linear), namely Triple ES with NN, results in good accuracy with an MSE value of 3.03, MAE 1.20, RMSE 1.74, R2 0.54, and a MAPE of 32.93%. The evaluation results for flood hazard weight determination with an MSE of 0.0111 and a MAPE of 7.81% show promising results, and the weights can be used in Hazard Flood GIS. Visualization in the form of a GIS Hazard Map can be done after all related raster data have been combined. The computational outcomes, particularly the MSE and MAPE values, demonstrate the effectiveness of the proposed approach, providing a clear understanding of the model's performance. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Linear regression

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Performance Evaluation of Oversampling Methods on Deep Learning-Based Skin Cancer Classification

Performance Evaluation of Oversampling Methods on Deep Learn...

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2023 International Seminar on Application for Technology of Information and Communication, iSemantic 2023

作者： Supriyanto, Catur Salam, Abu Zeniarja, Junta Wijaya, Adi Universitas Dian Nuswantoro Bachelor Program in Informatics Engineering Faculty of Computer Science Semarang Indonesia Universitas Indonesia Maju Department of Health Information Management Jakarta Indonesia

ISBN: (纸本)9798350339215

The field of dermatology faces considerable challenges when it comes to early detection of skin cancer. Our study focused on using different datasets, including original data, augmented data, and SMOTE oversampled data, to identify skin cancer. Our dataset consisted of images of skin lesions from the MNIST Skin Cancer dataset (HAM 10000), including samples of both cancerous and benign cases in the dataset. We employed data augmentation to expand the dataset's size and increase the diversity of skin lesion features. Furthermore, to tackle class imbalance in the dataset, we applied the SMOTE oversampling technique to generate synthetic samples for the under-represented group. With the original, augmented, and SMOTE oversampled datasets, we trained a Convolutional Neural Network (CNN) model. The performance of the model was evaluated using accuracy, recall, precision, and F1-score. The comparison between the results obtained from the original data, augmented data, and SMOTE oversampling data clearly revealed distinctions in performance. Our findings clearly demonstrate that employing data augmentation and SMOTE oversampling can significantly enhance the efficacy of skin cancer detection. © 2023 IEEE.

关键词： Neural network models

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A VAE-Based Data Augmentation Method for Fall Detection System using Wearable Sensor 10

A VAE-Based Data Augmentation Method for Fall Detection Syst...

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10th International Conference on Applied System Innovation, ICASI 2024

作者： Tu, Yu-Chen Wang, Hsuan-Chih Liu, Chien-Pin Hsieh, Chia-Yeh Chan, Chia-Tai National Yang Ming Chiao Tung University Department of Biomedical Engineering Taipei City112 Taiwan Bachelor's Program in Medical Informatics and Innovative Applications Fu Jen Catholic University New Taipei City242062 Taiwan

ISBN: (纸本)9798350394924

Fall incidents among the elderly represent a significant global concern, often resulting in physical injuries and psychological distress. It is crucial to develop reliable fall detection systems which are capable of identifying fall events immediately and triggering alerts for assistance. However, real-world fall occurrences are infrequent, leading to a highly imbalanced class situation. Training a model with imbalanced datasets may result in biased models with poor performance in fall detection. To address this challenge, various techniques such as data transformation and Synthetic Minority Oversampling Technique (SMOTE) have been proposed. However, these methods are constrained by issues such as limitations in input data size or sensitivity to outliers. Compared to other methods, variational autoencoder (VAE) can generate data with a similar probability distribution to the original input data while constraining the latent representation in a Gaussian distribution. This study proposes a VAE-based data augmentation method for wearable-based fall detection system. The proposed method is validated on the FallAllD public dataset, achieving a F-score of 99.46%. The performance has been increased by 2.21%. The results demonstrate the effectiveness of VAE-based data augmentation technique in enhancing fall detection systems and its superior performance compared with other traditional data augmentation methods. © 2024 IEEE.

关键词： Wearable sensors

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A Zero-Watermarking Image Scheme in Normalized Cross-Correlation with Robust Copyright Protections 2nd

A Zero-Watermarking Image Scheme in Normalized Cross-Correla...

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2nd International Conference on Security and Information Technologies with AI, Internet Computing and Big-data Applications, SITAIBA 2023

作者： Ko, Hung-Jui Huang, Cheng-Ta WANG, Shiuh-Jeng Information Management Division Changhua County Police Department Changhua Taiwan International Bachelor Program in Informatics Yuan Ze University Taoyuan Taiwan Department of Information Management Yuan Ze University Taoyuan Taiwan Department of Information Management Central Police University Taoyuan Taiwan

ISBN: (纸本)9789819777853

For sensitive images, e.g., military and medical images, traditional digital image watermarking methods are insufficient to realize effective protection. The original image is distorted due to traditional digital image watermarking methods embedding a watermark into it. We suggest a method for generating a zero-watermark image that is robust and uses discrete wavelet transform and singular value decomposition techniques. The Arnold transform is used in the zero-watermark generation and verification phases to enhance the security of the proposed method. The feature image of the original image is extracted after normalization and then frequency transformed. Various attacks were evaluated to determine the robustness of the proposed method, and the experimental results indicate that it exhibits good robustness against most attacks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Digital watermarking Image normalization Image protection Zero-watermarking

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High Capacity Information Hiding Method Based on Interpolation and Ones' complement

High Capacity Information Hiding Method Based on Interpolati...

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2022 IET International Conference on Engineering Technologies and Applications, IET-ICETA 2022

作者： Lin, Cheng-Yi Shongwe, Njabulo Huang, Cheng-Ta Yuan Ze University International Bachelor Program in Informatics Taoyuan City Taiwan Yuan Ze University Department of Information Management Taoyuan City Taiwan

ISBN: (数字)9781665491389

ISBN: (纸本)9781665491389

Data hiding is an important technology for privacy protection. The proposed information hiding method is based on neighborhood mean interpolation (NMI) and 1' s complement. Different from other previous schemes, the proposed method can embed at least one bit of secret data in each pixel. Also, in some cases, secret data is converted to its one's complement. This makes the hiding ability better than other schemes. Experimental results comparing the proposed interpolation method with other methods show that the proposed method achieves more than 20% improvement in hiding ability and 8% improvement in PSNR. © 2022 IEEE.

关键词： Interpolation

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Authentication of Image Fidelity in Watermarking-QR-Code Approving Copyright Systems 2nd

Authentication of Image Fidelity in Watermarking-QR-Code App...

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2nd International Conference on Security and Information Technologies with AI, Internet Computing and Big-data Applications, SITAIBA 2023

ISBN: (纸本)9789819777853

In this study, we propose a digital watermarking scheme with quick response (QR) codes for authenticating image fidelity. We hide an image hash value in a QR code as a watermark and then embed the watermark using our robust digital watermarking scheme. The proposed scheme takes advantage of QR codes for error correction. Using a QR code as a watermark can decrease the error rate after watermark extraction from an attacked image. To protect the content of images, we perform the perceptual hash algorithm for evaluation. The values of the perceptual hash algorithm can be stored in QR codes. Therefore, perceptual hash values are secured, even when the watermarked image has been damaged. Using perceptual hash values, we can determine the difference between the attacked and original images. The proposed scheme provides an effective method for protecting public and copyrighted digital images. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Error correction

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Prefallkd: Pre-Impact Fall Detection Via CNN-ViT Knowledge Distillation 48

Prefallkd: Pre-Impact Fall Detection Via CNN-ViT Knowledge D...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Chi, Tin-Han Liu, Kai-Chun Hsieh, Chia-Yeh Tsao, Yu Chan, Chia-Tai National Yang Ming Chiao Tung University Department of Biomedical Engineering Taiwan Academia Sinica Research Center for Information Technology Innovation Taiwan Fu Jen Catholic University Bachelor's Program in Medical Informatics and Innovative Applications Taiwan

ISBN: (纸本)9781728163277

Fall accidents are critical issues in an aging and aged society. Recently, many researchers developed "pre-impact fall detection systems"using deep learning to support wearable-based fall protection systems for preventing severe injuries. However, most works only employed simple neural network models instead of complex models considering the usability in resource-constrained mobile devices and strict latency requirements. In this work, we propose a novel pre-impact fall detection via CNN-ViT knowledge distillation, namely PreFallKD, to strike a balance between detection performance and computational complexity. The proposed PreFallKD transfers the detection knowledge from the pre-trained teacher model (vision transformer) to the student model (lightweight convolutional neural networks). Additionally, we apply data augmentation techniques to tackle issues of data imbalance. We conduct the experiment on the KFall public dataset and compare PreFallKD with other state-of-the-art models. The experiment results show that PreFallKD could boost the student model during the testing phase and achieves reliable F1-score (92.66%) and lead time (551.3 ms). © 2023 IEEE.

关键词： Inertial measurement units Knowledge distillation Pre-impact fall detection Vision transformer Wearable sensors

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Reversible data hiding based on pixel segmentation and prediction for improving hiding capacity

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Signal, Image and Video Processing 2024年第6-7期18卷 5043-5059页

作者： Huang, Cheng-Ta Weng, Chi-Yao Zheng, Guo-Ting Weng, Hao-Yu International Bachelor Program in Informatics Yuan Ze University Taoyuan320315 Taiwan Department of Information Management Yuan Ze University Taoyuan320315 Taiwan Department of Computer Science and Information Engineering National Chiayi University Chiayi City600355 Taiwan Department of Computer Science and Artificial Intelligence National Pingtung University Pingtung900391 Taiwan Department of Computer Science and Information Engineering National Central University Taoyuan City320317 Taiwan

The idea of the traditional histogram shifting technique is to hide a message within the cover-image pixel distribution. However, the embedding capacity is limited by the peak point occurrences. To solve this problem, many reversible data hiding (RDH) techniques are developed to combine pixel prediction and histogram shifting to increase the embedding payload. In this paper, a RDH scheme based on chessboard prediction and pixel partitioning is proposed for enhancing the hiding payload. Pixel partitioning is used to separate a pixel into two parts: most significant bit and least significant bit. Then, the chessboard predictor is adopted to generate prediction errors and separate the pixels. Thereafter, histogram shifting is applied to embed the secret messages into prediction errors. Due to the high similarity of adjacent pixels in an image, the generated prediction errors are highly close to zero, resulting in a large embedding space. The experimental results indicate that the proposed method has superior performance in embedding capacity with acceptable image visual quality compared to existing schemes since it adopts the adaptive adjustment strategy to achieve this improved balance. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Pixels

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Cluster Dominance Analysis of Strength Training Motion Characteristics

Cluster Dominance Analysis of Strength Training Motion Chara...

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Hapnes Toba Elizabeth Wianto Maya Malinda Agung Wijaya Al Halim Chien-Hsu Chen Master Program in Computer Science Universitas Kristen Maranatha Bandung Indonesia Bachelor Program in Visual Communication Design Universitas Kristen Maranatha Bandung Indonesia Bachelor Program in Management Universitas Kristen Maranatha Bandung Indonesia Bachelor Program in Informatics Universitas Kristen Maranatha Bandung Indonesia Industrial Design Department National Cheng Kung University Tainan Taiwan

This paper presents an approach to analyze clusters as a means to determine the characteristics of strength training motion patterns. The proposed method emphasizes the observation of dominance sequences within clusters and is reinforced by the formation of specific characteristics within each cluster. Data collection is performed using video-guided strength training exercises equipped with 1 kg dumbbells and recorded by a sensor embedded in smartwatches. The analysis method involves applying the concept of density affinity, which calculates the density ratio of clusters to the recognized motions. Subsequently, the dominance sequence is observed to identify which clusters exhibit distinct characteristics, ultimately determining the intended motions. The research findings demonstrate the potential for further investigation into a more comprehensive understanding of motion patterns, leading to the development of models that can be integrated into mobile devices or smartwatches.

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Bloom-epistemic and sentiment analysis hierarchical classification in course discussion forums

arXiv

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arXiv 2024年

作者： Toba, Hapnes Hernita, Yolanda Trixie Ayub, Mewati Wijanto, Maresha Caroline Master of Computer Science Study Program Faculty of Information Technology Maranatha Christian University Bandung Indonesia Bachelor of Informatics Study Program Faculty of Information Technology Maranatha Christian University Bandung Indonesia

Online discussion forums are widely used for active textual interaction between lecturers and students, and to see how the students have progressed in a learning process. The objective of this study is to compare appropriate machine-learning models to assess sentiments and Bloom's epistemic taxonomy based on textual comments in educational discussion forums. The proposed method is called the hierarchical approach of Bloom-Epistemic and Sentiment Analysis (BE-Sent). The research methodology consists of three main steps. The first step is the data collection from the internal discussion forum and YouTube comments of a Web programming channel. The next step is text preprocessing to annotate the text and clear unimportant words. Furthermore, with the text dataset that has been successfully cleaned, sentiment analysis and epistemic categorization will be done in each sentence of the text. Sentiment analysis is divided into three categories: positive, negative, and neutral. Bloom's epistemic is divided into six categories: remembering, understanding, applying, analyzing, evaluating, and creating. This research has succeeded in producing a course learning subsystem that assesses opinions based on text reviews of discussion forums according to the category of sentiment and epistemic analysis. © 2024, CC BY-SA.

关键词： Sentiment analysis

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