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6th IEEE International Conference on Artificial Intelligence and eXtended and Virtual Reality, AIxVR 2024

作者： Wu, Ssu-Hsuan Wang, Kai-Ting Chiang, Chia-Chen Chang, Hsin-Yu Yeh, Cheng-Yun Hu, Min-Chun National Tsing Hua University International Intercollegiate Ph.D.Program Hsinchu Taiwan National Tsing Hua University Master of Computer Science Program Hsinchu Taiwan National Tsing Hua University Interdisciplinary Program of Electrical Engineering and Computer Science Hsinchu Taiwan National Tsing Hua University Interdisciplinary Program of Technology and Art Hsinchu Taiwan National Tsing Hua University Hsinchu Taiwan

ISBN: (纸本)9798350372021

This research aims to create a virtual reality-based practice system that simulates real-stage performance environments, assisting amateur dancers in overcoming stage fright and practicing group choreography that is difficult to achieve during individual practice. In this study, users can engage in performance simulations within the created system using the HTC VIVE virtual reality device. Within the system, we have developed a virtual stage space system that only requires a 2m x 3.5m physical area to simulate a large virtual stage experience. Users can also simulate choreographic positioning with virtual dance partners within this system, providing amateur dancers with group dance rehearsal practice opportunities that are not feasible during solo practice. Additionally, by creating three different audience modes, we aim to help users simulate various audience scenarios they may encounter during a performance. The system includes a recording feature that captures users' simulated performances, allowing them to review their actions and receive feedback after practice. © 2024 IEEE.

关键词： Virtual reality

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Classification and Prediction of Knee Osteoarthritis by Deep Learning Approach 3rd

Classification and Prediction of Knee Osteoarthritis by Deep...

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3rd International Conference on Intelligent Computing Techniques for Smart Energy Systems, ICTSES 2023

作者： Saraswat, Amit Tooley, Tanya Shrivastav, Snigdha Srivastava, Devesh Kumar Department of Electrical Engineering Manipal University Rajasthan Jaipur303007 India Senior Analyst EY Haryana Gurgaon India Master of Science in Data Science program at the University of Rochester New York United States Department of Information Technology Manipal University Rajasthan Jaipur303007 India

ISBN: (纸本)9789819784288

The purpose of this research work on medical image analysis is to identify and diagnose knee osteoarthritis (OA) utilizing medical images by applying machine learning techniques. Knee OA is a degenerative joint disease that is becoming more prevalent in aged people worldwide. The study examines the effectiveness of several machine learning algorithms, including deep learning, to analyze medical images of knee OA, such as X-rays and magnetic resonance imaging (MRI). The research findings suggest that using machine learning algorithms might greatly improve the accuracy and efficacy of detecting knee OA, especially in its early stages. Moreover, machine learning algorithms can assist in the creation of specialized treatment plans by predicting the progression of the disease and recommending appropriate remedies. The findings of this study show how machine learning-based medical image analysis has the potential to enhance patient outcomes and quality of life for those with knee OA. Cancer detection diagnosis, neurological condition identification, and cardiovascular function analysis are a few instances of medical picture analysis employing machine learning. Moreover, image-guided treatments have been made more accurate and surgical planning has been made easier with the use of machine learning algorithms. Various methods are applied to achieve a test accuracy of 76.10% which is just 5% less than the accuracy provided by the Kellen-Lawrence Grading Scale. Overall, machine learning-based medical image analysis has the potential to revolutionize medical practice by facilitating earlier and more precise diagnosis, individualized treatment planning, and improved patient outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Diseases

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Implementation Graph Database Framework for Credit Card Fraud Detection 8

Implementation Graph Database Framework for Credit Card Frau...

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8th International Conference on Computer science and Computational Intelligence, ICCSCI 2023

作者： Mauliddiah, Nurrahmi Suharjito Computer Science Department BINUS Online Learning Bina Nusantara University Jakarta11480 Indonesia Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta11480 Indonesia

Credit card use is becoming more and more commonplace every day. Financial organizations and credit card customers lose a lot of money because of complicated illegal transactions. Fraudsters constantly stay on top of new technology to quickly perpetrate fraud against customer transaction patterns. We analyze credit card transaction networks and identify suspicious patterns, such as transactions connected to multiple accounts or unusual transaction patterns, transactions made at unusual times, and to monitor credit card transactions in real-time and quickly identify suspicious transactions. TigerGraph is used to analyze data, display results on a dashboard, and send notifications via email. One meth'\ Vc 1``13-od commonly used in anomaly detection is to compare data values against the standard deviation. In this research, we explain the use of TigerGraph as a platform for anomaly detection above the standard deviation, as well as the use of the Louvain algorithm in finding merchant communities used by fraudsters. The data used in this study comes from Sparkov simulation data obtained from Kaggle. Our results show that by using TigerGraph, we managed to achieve a very high accuracy rate of 99.77%, precision 82.84%, recall 72.38%, and f1-score 77,26% in predicting transaction fraud on Sparkov simulation data. This is much better than the results reported in a paper that uses the supervised machine learning method with the AdaBoost algorithm which achieves the highest accuracy of 77%. © 2023 The Authors. Published by Elsevier B.V.

关键词： Alert Credit card fraud detection Dashboard TigerGraph

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Comparison of YOLOv8 and EfficientDet4 Algorithms in Detecting the Ripeness of Oil Palm Fruit Bunch 10

Comparison of YOLOv8 and EfficientDet4 Algorithms in Detecti...

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10th International Conference on ICT for Smart Society, ICISS 2023

作者： Priyadi, Muhammad Reza Azhar Suharjito Bina Nusantara University BINUS Online Learning Computer Science Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program-Master of Industrial Engineering Industrial Engineering Department Jakarta11480 Indonesia

ISBN: (纸本)9798350339543

Despite Indonesia's leading position in palm oil processing, the inaccurate assessment of oil palm fruit maturity poses challenges in determining the optimal harvest timing and maintaining product quality. With advancements in information technology, including image processing and object detection techniques like YOLO and EfficientDet, there is potential for automated and precise identification of palm fruit maturity. To explore this, the authors conducted research to identify the most suitable algorithm for detecting the ripeness of palm oil fruit. The study utilized a dataset consisting of 8299 images with six maturity levels, employing augmentation techniques to increase image variability. Results show that YOLO V8s outperformed EfficientDet Lite 4 in terms of object detection precision, achieving higher mean average precision (mAP) scores. YOLO V8s showed a 4% improvement in mAP@50 and a 16% improvement in mAP@50-95 compared to EfficientDet Lite 4. YOLO V8s achieved a mAP of 0.978 for mAP@50 and 0.765 for mAP@50-95, surpassing EfficientDet Lite 4. However, EfficientDet Lite 4 had an advantage in terms of class accuracy and bounding box placement, as indicated by smaller box loss and cIs- loss values. YOLO V8s also demonstrated better frames per second (FPS) performance, ranging from 10 to 15 FPS, compared to EfficientDet Lite 4. © 2023 IEEE.

关键词： Object detection

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Evaluated Material Attractiveness of Plutonium Composition from Economic Simplified Boiling Water Reactor (ESBWR) 10

Evaluated Material Attractiveness of Plutonium Composition f...

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10th Asian Physics Symposium 2023, APS 2023

作者： Nasyidiah, Fungky Iqlima Permana, Sidik Pramuditya, Syeilendra Master Program in Nuclear Science and Engineering Faculty of Mathematics and Natural Sciences Institute Technology of Bandung Ganesa 10th Street Bandung40132 Indonesia Department of Nuclear Science and Engineering Faculty of Mathematics and Natural Sciences Institute Technology of Bandung Ganesa 10th Street Bandung40132 Indonesia

The potential for misuse of nuclear fuel has prompted many to make efforts to prevent a country from acquiring nuclear explosive devices (weapons). Weapon utility equated with nuclear material attractiveness when applied to protection and security. Material attractiveness evaluated using an equation first developed by Masaki Saito, namely ATTR. ESBWR is one of the reactors of the BWR reactor type that is still operating. The purpose of this study is to evaluate Material Attractiveness based on the composition of plutonium in the ESBWR reactor, during reactor operation and after reactor operation. The ATTR concept utilizes the plutonium isotope composition to evaluate the material attractiveness aspect. Parameters such as BCM, Rossi-alpha, and neutron prompt life become additional aspects in evaluating material attractiveness. In the research results, the ATTR value decreased from the beginning of reactor operation until the reactor stopped operating. At the beginning of the reactor operation, it was categorizing as a weapon-grade with an ATTR value of 0.19 and when the reactor stopped operating it was categorizing as an unusable grade with an ATTR value of 0.012. At the time of cooling time up to 1x106 (one million) cooling time, the ATTR value increases by 0.019 which was categorizing as an unusable grade. After a cooling time of 1x106 (one million) years up to 1x107 (ten million) years, the ATTR value was 0.001 which categorized as an exempt level. This states that the increasing burnup value will further reduce the ATTR value. Likewise with the Rossi-Alpha and Bare Critical Mass values. Conversely, it will decrease the value of neutron lifetime. The ATTR value after the reactor operates up to one hundred years of cooling time increases and then decreases significantly after 1x106 (one million) years due to the half-life of plutonium isotopes. Likewise, the Rossi-Alpha and Bare critical mass values. However, it is different with the neutron fast lifetime. © Publis

关键词： Boiling water reactors

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Enhancing Efficiency in Yolov8n for Real-Time Detection of Oil Palm Fruits in Plantations Using Mobileone and Cross-Fusion

SSRN

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SSRN 2024年

作者： Hugo, Gregory Mushlik, Muhammad Suharjito Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia Industrial Engineering Department BINUS Graduate Program – Master of Industrial Engineering Bina Nusantara University Jakarta11480 Indonesia

Accurate detection of fresh fruit bunches (FFB) ripeness in complex plantation environments is challenging due to occlusion, varying lighting, intricate visual features, and limited computational resources of mobile devices. To address this, a resource-efficient, real-time detection system was developed using an optimized YOLOv8n model. The model integrates MobileOne modules and Cross-Fusion blocks to enhance efficiency without compromising accuracy. Specifically, Conv and C2f modules were replaced with MobileOne modules, reducing parameters by 85% and 81%, respectively. Cross-Fusion improved feature fusion, and its combination with MobileOne (CF-MobileOne) achieved a 51% reduction in parameters while maintaining performance. The dataset comprised images from videos captured in plantations, annotated into five maturity stages: flower, unripe, underripe, ripe, and abnormal, and underwent augmentation with class balancing through manual undersampling. The model outperformed baseline YOLOv8 variants and heavier models like YOLOv9c and Faster R-CNN, achieving an mAP50 of 0.795 and an inference speed of 140.57 FPS. Hyperparameter tuning with a batch size of 64, SGD optimizer, and a learning rate of 0.01 yielded optimal results. Despite challenges in detecting certain classes due to visual similarities and environmental variations, the system effectively enhances productivity and efficiency in precision agriculture for the palm oil industry. © 2024, The Authors. All rights reserved.

关键词： Fruits

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Performance Evaluation of Coffee Bean Binary Classification Through Deep Learning Techniques 1

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International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2022

作者： Islamy, Fajrul Muchtar, Kahlil Arnia, Fitri Dawood, Rahmad Febriana, Alifya Elwirehardja, Gregorius Natanael Pardamean, Bens Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh23111 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department Master of Computer Science Program BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

ISBN: (数字)9783031290787

ISBN: (纸本)9783031290770

Coffee beans are one of the high-value commodities in Indonesia, but the sorting method for the quality of coffee beans still uses visual methods and sieves with mechanical machines. This study aims to provide an alternative to classifying coffee beans using Deep Learning algorithms, such as the ResNet-18 and MobileNetV2. The dataset used is USK-COFFEE which consists of two classes, the normal coffee bean class, and the defect coffee bean class. The models were trained using the balanced and imbalanced datasets, meaning that two scenarios were performed. From the results of this study, the accuracy value for the ResNet-18 model was 91.50% when trained on the balanced dataset and 90.87% on the imbalanced dataset. It outperformed the MobileNetV2 architecture, which only achieved 0.8125 and 0.8112 accuracy scores when trained on the balanced and imbalanced datasets, respectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Computer vision

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Determination, distribution, and health risk assessment of 12 heavy metals in various edible oils in Taiwan

JSFA reports

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JSFA reports 2024年第3期4卷 175-183页

作者： Lin, Shu-Ting Liao, Kai-Wei Xuan, Tan-Chui Chiou, Tai-Ying Lin, Zih-Ee Lee, Wei-Ju School of Food Safety Taipei Medical University Taipei Taiwan Master Program in Food Safety Taipei Medical University Taipei Taiwan School of Regional Innovation and Social Design Engineering Kitami Institute of Technology Hokkaido Kitami Japan Waseda Research Institute for Science and Engineering Waseda University Tokyo Japan

Background: Minimizing the risk of heavy metal pollution is of utmost importance to ensure consumer safety. In this research, we utilized inductively coupled plasma-mass spectrometry to determine the presence of 12 heavy metals in Taiwanese commercial edible oils, both refined and unrefined. Results: Our analytical findings revealed significantly lower levels of heavy metals in refined oils compared with unrefined oils. Through principal component analysis and cluster analysis of 19 types of edible oils, we identified Cd, Ba, Fe, Pb, and Co as the primary variables influencing heavy metal levels in edible oils. Notably, the heavy metal contents in the edible oils did not surpass international standards. Risk assessment results indicated that unrefined oils carried higher risk levels than refined oils. The incremental lifetime cancer risk values calculated for unrefined oils exceeded 10⁻⁶ in all age groups except for the 19 ~ 65 years category, while the noncarcinogenic risks remained well below 1, regardless of refined or unrefined oils. Conclusions: Our study sheds light on the distribution characteristics of heavy metal contents in edible oils, highlighting higher levels of heavy metals and associated risks in non-refined oils compared with refined oils. Ultimately, the presence of heavy metals in edible oils poses low health risks to the human body. © 2024 The Authors. JSFA Reports published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

关键词： edible oil health risk assessment heavy metal ICP-MS pressed oil refined oil

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Multivariate Time-Series Deep Learning for Joint Prediction of Temperature and Relative Humidity in a Closed Space 8

Multivariate Time-Series Deep Learning for Joint Prediction ...

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8th International Conference on Computer science and Computational Intelligence, ICCSCI 2023

作者： Gunawan, Fergianto E. Budiman, Arief S. Pardamean, Bens Juana, Endang Romeli, Sugiarto Cenggoro, Tjeng W. Purwandari, Kartika Hidayat, Alam A. Redi, Anak A.N.P. Asrol, Muhammad Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Electrical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta11440 Indonesia PT Impack Pratama Industri Jakarta14350 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Mathematics Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

An accurate predictive model of temperature and humidity plays a vital role in many industrial processes that utilize a closed space such as in agriculture and building management. With the exceptional performance of deep learning on time-series data, developing a predictive temperature and humidity model with deep learning is propitious. In this study, we demonstrated that deep learning models with multivariate time-series data produce remarkable performance for temperature and relative humidity prediction in a closed space. In detail, all deep learning models that we developed in this study achieve almost perfect performance with an R value over 0.99. © 2023 The Authors. Published by Elsevier B.V.

关键词： closed space deep learning humity prediction indoor temperature prediction

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Fabrication of versatile poly(xylitol sebacate)-co-poly(ethylene glycol) hydrogels through multifunctional crosslinkers and dynamic bonds for wound healing

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Acta Biomaterialia 2023年 170卷 344-359页

作者： Yeh, Ying-Yu Lin, Yi-Yun Wang, Ting-Teng Yeh, Yu-Jia Chiu, Ting-Hsiang Wang, Reuben Bai, Meng-Yi Yeh, Yi-Cheun Institute of Polymer Science and Engineering National Taiwan University Taipei Taiwan Institute of Biomedical Engineering National Taiwan University Taipei Taiwan Biomedical Engineering Program Graduate Institute of Applied Science and Technology National Taiwan University of Science and Technology Taipei Taiwan Institute of Food Safety and Health National Taiwan University Taipei Taiwan Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan Adjunct Appointment to the Department of Biomedical Engineering National Defense Medical Center Taipei Taiwan Program National Taiwan University Taipei Taiwan GIP-TRIAD Master's Degree in Agro-Biomedical Science National Taiwan University Taipei Taiwan

Poly(polyol sebacate) (PPS) polymer family has been recognized as promising biomaterials for biomedical applications with their characteristics of easy production, elasticity, biodegradation, and cytocompatibility. Poly(xylitol sebacate)-co-poly(ethylene glycol) (PXS-co-PEG) has been developed to fabricate PPS-based hydrogels;however, current PXS-co-PEG hydrogels presented limited properties and functions due to the limitations of the crosslinkers and crosslinking chemistry used in the hydrogel formation. Here, we fabricate a new type of PXS-co-PEG hydrogels through the use of multifunctional crosslinkers as well as dynamic bonds. In our design, polyethyleneimine-polydopamine (PEI-PDA) macromers are utilized to crosslink aldehyde-functionalized PXS-co-PEG (APP) through imine bonds and hydrogen bonds. PEI-PDA/APP hydrogels present multiple functional properties (e.g., fluorescent, elastomeric, biodegradable, self-healing, bioadhesive, antioxidant, and antibacterial behaviors). These properties of PEI-PDA/APP hydrogels can be fine-tuned by changing the PDA grafting degrees in the PEI-PDA crosslinkers. Most importantly, PEI-PDA/APP hydrogels are considered promising wound dressings to promote tissue remodeling and prevent bacterial infection in vivo. Taken together, PEI-PDA/APP hydrogels have been demonstrated as versatile biomaterials to provide multiple tailorable properties and desirable functions to expand the utility of PPS-based hydrogels for advanced biomedical applications. Statement of significance: Various strategies have been developed to fabricate poly(polyol sebacate) (PPS)-based hydrogels. However, current PPS-based hydrogels present limited properties and functions due to the limitations of the crosslinkers and crosslinking chemistry used in the hydrogel formation. This work describes that co-engineering crosslinkers and interfacial crosslinking is a promising approach to synthesizing a new type of poly(xylitol sebacate)-co-poly(ethylene glycol) (PXS-co-

关键词： Polyols

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