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6th IEEE Eurasia Conference on Biomedical engineering, Healthcare and Sustainability, ECBIOS 2024

作者： Chang, Chin-Chih Wei, Chi-Hung Hsiao, Sean Yang, Chyuan-Huei Thomas Chung Hua University Department of Computer Science and Information Engineering Hsinchu Taiwan Chung Hua University Ph.D. Program in Engineering Science Hsinchu Taiwan Chung Hua University Program in Applied Foreign Language and Smart Commerce Hsinchu Taiwan School of Information Engineering Shandong Vocational and Technical University of International Studies Shandong China

ISBN: (纸本)9798350396133

People increasingly prioritize a balanced diet to enhance well-being, yet making informed dietary choices remains challenging amidst the abundance of options. To address this, we developed a meal image recognition and healthy meal combination recommender system, integrating generative artificial intelligence (AI). Convolutional neural networks were used for precise meal image recognition to identify diverse food items. The generative AI-augmented recommendation engine offers personalized meal suggestions aligned with nutritional goals and dietary preferences, utilizing a nutrition knowledge base to ensure overall well-being. The system's feasibility was validated, illustrating its excellence in meal recognition accuracy, recommendation diversity, and user engagement. By integrating generative AI, the system shows its potential to enhance dietary recommendations and public health. © 2024 IEEE.

关键词： Generative adversarial networks

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The Model Regularized CLAHE-CNN for the Traditional Script Character of Kaganga Bengkulu Recognition 9

The Model Regularized CLAHE-CNN for the Traditional Script C...

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9th International Conference on Informatics and Computing, ICIC 2024

作者： Putra, Erwin Dwika Ermatita, Ermatita Abdiansyah, Abdiansyah Universitas Sriwijaya Engineering Program Palembang Indonesia Universitas Sriwijaya Faculty of Computer Science Palembang Indonesia

ISBN: (纸本)9798331517601

Itis necessary to preserve the Kaganga script to be in demand again, especially by the younger generation today. Using and developing current technology, namely image processing techniques and deep learning, can be one way to reintroduce and preserve the culture of Kaganga script recognition. The purpose of this study is to implement the CNN method in recognizing the Kaganga script, observe the results of the implementation of the L2 regularization technique in reducing the overfitting process in CNN, and increase the accuracy of Kaganga script recognition using CLAHE and L2 regularization technique in CNN. The collected datasets are divided into test, valid, and training data. To acquire primary data for the Kaganga script, 50 respondents were involved. Each respondent was requested to write a Kaganga script on the provided paper. Organizations engaged in cultural heritage research are gathering data for this project: Yayasan Budaya Nusantara Digital (Jakarta), Yayasan Insani Mandiri Santani (Bengkulu), and Yayasan Sejahtera Bersama (Bengkulu). This study will evaluate using an accuracy measurement model, precision, and recall, which is included in the confusion matrix measurement model. In this research, we conducted four experiments, including CNN with L1, CNN with L2, CNN with dropout and the CLAHE-CNN with best regularization. From the experimental results, CLAHE-CNN with L2 will be found that accuracy during the training stage gets a value of 100%, accuracy during the validation stage gets a value of 85% and accuracy during the testing stage gets a value of 80.75% © 2024 IEEE.

关键词： CLAHE CNN kaganga regularization script

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Enhancing Security Information and Event Management (SIEM) by Incorporating Machine Learning for Cyber Attack Detection 9

Enhancing Security Information and Event Management (SIEM) b...

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9th IEEE International Conference on Computing, engineering and Design, ICCED 2023

作者： Ayu, Media Anugerah Erlangga, Dimas Mantoro, Teddy Handayani, Dini Sampoerna University Faculty of Engineering and Technology Department of Computer Science Jakarta Indonesia Nusa Putra University - Sukabumi Postgraduate Program - School of Computer Science Indonesia

ISBN: (纸本)9798350370126

Network security is a crucial component of Information Technology, yet organizations continue to grapple with meeting established security benchmarks. Given the rise in cyber-attacks and the continuous emergence of new attack types, it's practically infeasible to persistently update attack patterns or signatures within security parameters. Key tools such as Intrusion Detection Systems (IDS) and Security Information and Event Management (SIEM) are instrumental in monitoring network traffic and identifying potential threats. However, these tools face limitations, such as the high volume of alerts produced by IDS and the use of rule-based method, also the inability of SIEM tools to analyze logs comprehensively to identify inappropriate activities. This research has conducted anomaly detection using machine learning process to classify cyber-attacks network flow collected from IDS that installed incident network infrastructure. The analysis of IDS using machine learning, integrated with SIEM. The algorithm used in this research was Random Forest Classifier using CSE-CID-IDS2018 dataset pre-processed with Principal Component Analysis (PCA). Results of the experiments show that Random Forest Classifier Model, when combined with Principal Component Analysis (PCA), yields the most commendable results when applied to a 70/30 training/testing data ratio with accuracy of 0.99953. © 2023 IEEE.

关键词： Network security

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Transfer Learning Implementation for Batik Besurek Motifs Classification: A Comparative Study 9

Transfer Learning Implementation for Batik Besurek Motifs Cl...

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9th International Conference on Informatics and Computing, ICIC 2024

作者： Utami, Marissa Ermatita, Ermatita Abdiansyah, Abdiansyah Universitas Sriwijaya Engineering Program Palembang Indonesia Universitas Sriwijaya Faculty of Computer Science Palembang Indonesia

ISBN: (纸本)9798331517601

This research discussion will be a comparative model proposal for introducing Besurek batik fabric by implementing MobileNet, ResNet50, InceptionV3 and VGG16. This research also analyzed the influence of augmentation data methods, namely rotation, zoom, width shift and height shift, on the performance of the Besurek batik fabric recognition algorithm based on accuracy, precision and recall results. The size of the batik besurek fabric motif image as an input image is 224 × 224 with three channels consisting of red, green, and blue, totalling five classes, namely kaligrafi, bunga rafflesia, burung kuau, lung paku and rambutan. The ratio of sharing training data and test data is 70:30. Accuracy evaluations are carried out on several epochs and data that have been shared. Based on the experiment, the accuracy value of MobileNetV2 can be obtained by training accuracy of 98.14%, validation accuracy of 94.60%, and testing accuracy of 96.46%. Then, the accuracy value of ResNet50 can be obtained by training accuracy at 99.60%, validation accuracy at 97.44, and testing accuracy at 98.12%. The accuracy value of InceptionV3 can be obtained by training accuracy at 96.35%, validation accuracy at 96.59%, and testing accuracy at 92.50%. Finally, the accuracy value of VGG16 can be obtained by training accuracy at 99.67%, validation accuracy at 96.02%, and testing accuracy at 95.00%. The accuracy of VGG 16 can be concluded as the best model for Batik recognition. © 2024 IEEE.

关键词： batik besurek classification comparative transfer learning VGG16

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Sentiment Analysis with N-Gram Preprocessing for Online-Shopping Reviews in Indonesian Language 9

Sentiment Analysis with N-Gram Preprocessing for Online-Shop...

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9th IEEE International Conference on Computing, engineering and Design, ICCED 2023

作者： Asian, Jelita Putra, Oktavianus Tegar N.K. Ayu, Media Anugerah Mantoro, Teddy Nusa Putra University Postgraduate Program - School of Computer Science Sukabumi Indonesia Sampoerna University Faculty of Engineering and Technology Department of Computer Science Jakarta Indonesia

ISBN: (纸本)9798350370126

Currently, online Shopping platforms have grown significantly, especially during the COVID-19 pandemic. This condition motivates the need for analyzing how the users/customers' opinions on using such platform. Sentiment analysis, as a process of detecting, extracting, and classifying users' opinions and attitudes toward specific topics, is a good tool for the required analysis. This study aims to evaluate the performance of machine learning approach which combined with N-Gram technique in doing sentiment analysis. The dataset used in this study comes from scraping reviews in Bahasa Indonesia regarding the Shopee Apps. In this study, N=2 for the N-Gram was employed in the preprocessing process. Our main goal is to investigate whether the performance of machine learning in doing sentiment analysis can be improved by adding the N-Gram technique in its preprocessing. This work applied the Naive Bayes Classifier and k-Nearest Neighbor with K=11 as the machine learning algorithms. The best accuracy in this study was achieved by Naive Bayes Classifier after applying N-Gram Terms (N=2) with Split Validation (8:2), which is 97.26%. © 2023 IEEE.

关键词： Sentiment analysis

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Features Reduction with PCA Technique for Malware Detection Using Machine Learning 9

Features Reduction with PCA Technique for Malware Detection ...

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9th IEEE International Conference on Computing, engineering and Design, ICCED 2023

作者： Mantoro, Teddy Ginanjar, Agung Ayu, Media Anugerah Aditiawarman, Umar Sampoerna University Faculty of Engineering and Technology Department of Computer Science Jakarta Indonesia Nusa Putra University Postgraduate Program - School of Computer Science Sukabumi Indonesia

ISBN: (纸本)9798350370126

Malware had been a problem for quite some times since it spreads easily and can cause various problems. Currently, malware is also one of the big threats for internet users. With a huge number of internet users today, techniques that can automatically detect malware before it infects the system is required. This study aims to develop malware detection using machine learning approach with Principal Component Analysis (PCA) as feature reduction. PCA (Principal Component Analysis) is expected to be able to reduce the number of features which then could also reduce the learning time but do not reduce its accuracy significantly. There were four machine learning classifiers used in this study, i.e. K-Nearest Neighbor, Decision Tree, Naïve bayes, and Random Forest. The n-components used in this study were 20 and 34 and the ratio of test and train in the dataset was 35% for test and 65% for training. The results have shown that the best performance come from the detection using random forest with 34 n-component and 100 n-estimator with the average accuracy was 0.991688. © 2023 IEEE.

关键词： Principal component analysis

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LOCOS: A cosine based local gene expression pattern finding algorithm on time-series data

LOCOS: A cosine based local gene expression pattern finding ...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Suk, Youjeong Jeon, Jaemin Jung, Inuk Kyungpook National University School of Computer Science and Engineering Daegu Korea Republic of Seoul National University Interdisciplinary Program in Bioinformatics Seoul Korea Republic of

ISBN: (纸本)9798350362480

In gene expression analysis, understanding a biological event that is observed at some time instance often requires capturing genes whose expression levels modulate before and after the event. Such genes are expected to be the responders to the event and will exhibit similar expression patterns during the event. However, after the effect of the event fades, their expression levels may loose their correlation. Hence, it is a non-trivial task to identify genes that share highly similar local expression patterns nearby the event and also allowing some level of divergent expression patterns further away from the event's time point. Here, we propose LOCOS (LOcal COSine), a novel time-course clustering algorithm tailored to cluster genes exhibiting similar expression patterns within a specified time interval. LOCOS is an extension of the traditional Non-negative Matrix Factorization (NMF), which incorporates the cosine similarity and Euclidean distances in its update procedure. LOCOS maximizes the cosine similarity within an interval of interest, while allowing a relaxed minimization of Euclidean distance outside it. Using synthetic and non-biological time-course data, we showed that LOCOS was able to correctly detect the known local patterns within a specified interval. Furthermore, we used longitudinal single-cell RNA-seq samples from four patients showing deteriorating and recovering health conditions to identify genes related to each of the phenotype. As a result, LOCOS was able to capture gene clusters with distinct expression patterns that aligned with the intervals embedding the clinical deterioration and recovery events. © 2024 IEEE.

关键词： Non-negative matrix factorization

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Supporting the Automated Generation of Acceptance Tests of Process-Aware Information Systems 19

Supporting the Automated Generation of Acceptance Tests of P...

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19th International Conference on Web Information Systems and Technologies, WEBIST 2023

作者： Paiva, Tales M. Oliveira, Toacy C. Pillat, Raquel M. Alencar, Paulo S.C. Computer and Systems Engineering Program Universidade Federal do Rio de Janeiro Rio de Janeiro Brazil Software Engineering Program Universidade Federal do Pampa Alegrete Brazil Cheriton School of Computer Science University of Waterloo Waterloo Canada

ISBN: (纸本)9789897586729

Software quality assurance is a crucial process that ensures software products meet specified requirements and quality standards. Achieving an exhaustive test coverage is essential for quality assurance, particularly in complex and dynamic Process-Aware Information Systems (PAIS) built upon the Business Process Model and Notation (BPMN). Manual testing in such systems is challenging due to many execution paths, dependencies, and external interfaces. This paper proposes a model-based testing strategy that uses BPMN models and build specifications as input to generate a Robotic Process Automation (RPA) script that automates a comprehensive User Acceptance Test procedure. Leveraging on Robotic Process Automation (RPA) to automate user interactions allows for reducing the need for testers to manually input PAIS-related information when handling user forms. We also present a Case Study to demonstrate the feasibility of our approach. Copyright © 2023 by SCITEPRESS - science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

关键词： Acceptance tests

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Development of a Phase-Locked Loop Circuit in Running Frequency Mode for Capacitance-to-Voltage Conversion 13

Development of a Phase-Locked Loop Circuit in Running Freque...

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13th International Electrical engineering Congress, iEECON 2025

作者： On-Khong, Tada Sisuk, Noppadon Wardkein, Paramote Katman, Ratchanoo Prompak, Kriangsak Naresuan University Faculty of Science Department of Physics Phitsanulok Thailand Phibunsongkhram Rajabhat University Program in Computer Engineering Faculty of Industrial Technology Phitsanulok Thailand King Mongkut's Institute of Technology Ladkrabang School of Engineering Department of Telecommunication Bangkok Thailand Phibunsongkhram Rajabhat University Program of Physics Faculty of Science and Technology Phitsanulok Thailand

ISBN: (纸本)9798331543952

Capacitance-to-voltage conversion is essential for capacitive sensors in various industries, including touch interfaces, medical devices, and moisture measurement. However, circuit design faces challenges like non-linearity, noise, and small capacitance variations affecting voltage signals. This article proposes a phase-locked loop in free-running oscillator mode with a frequency-to-voltage converter to enhance accuracy and stability. Test results show a 97.9% measurement accuracy, demonstrating reduced noise and improved stability. The proposed circuit is ideal for high-precision applications in diverse environments, overcoming limitations of traditional methods. © 2025 IEEE.

关键词： Phase locked loops

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A Physical-Based Artificial Neural Networks Compact Modeling Framework for Emerging FETs

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IEEE Transactions on Electron Devices 2024年第1期71卷 223-230页

作者： Yang, Ya-Shu Li, Yiming Kola, Sekhar Reddy Reddy National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory The Institute of Communications Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Institute of Communications Engineering Institute of Biomedical Engineering Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan

We report a compact modeling framework based on the Grove-Frohman (GF) model and artificial neural networks (ANNs) for emerging gate-all-around (GAA) MOSFETs. The framework consists of two ANNs;the first ANN constructed with the drain current model not only can capture the main trend of device I - V characteristics but also can predict its variation even when the amount of training data for the ANN is insufficient or outside the range of applied biases. The second one is then designed to improve the model accuracy by further minimizing the errors between the target and the model outputs. We implement the proposed framework to accurately model emerging GAA nanosheet (NS) MOSFETs and complementary FETs (CFETs) without suffering from divergent issues in circuit simulation. In addition, nonphysical behaviors, such as nonzero current at zero bias, do not occur in the modeling framework. Compared to recently reported machine-learning (ML) models, our approach can achieve a similar level of model accuracy with merely 20% amount of the training data. © 1963-2012 IEEE.

关键词： Neural networks

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