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5th IEEE Eurasia Conference on Biomedical engineering, Healthcare and Sustainability, ECBIOS 2023

作者： Hsieh, Sheng-Hsien Lin, Ming-Yi Chen, Ching-Han National Pingtung University of Science and Technology Program of Artificial Intelligence and Mechatronics Pingtung Taiwan Yuan Ze University Department of Computer Science and Engineering Taoyuan Taiwan National Central University Department of Computer Science and Engineering Taoyuan Taiwan

ISBN: (纸本)9798350320978

We present a high-accuracy 3D facial reconstruction system with the following features: real-time 3D facial reconstruction using exposure synchronization multi-camera, feature alignment to quantify facial differences, a software system based on a naked eye 3D display, and medical records for a 3D face database. The proposed system is novel and practical, and the algorithm and the hardware/software architecture improve the current non-quantitative communication status in medical aesthetics. The system achieves preoperative expectations, real-time recording and feedback during surgery, and postoperative tracking analysis, thus making doctor-patient communication more efficient. The facial data database records personal and quantitative beauty data and its changes, providing accurate medical and aesthetic treatment analysis for individuals. In addition, the collected data of different genders, ages, injection sites, and dosages contribute to the development of more accurate medical materials. With artificial intelligence and cloud architecture, we provide reliable data for medical aesthetics customers, helping them understand their medical aesthetic needs. Doctors also provide stable medical care quality to consumers through cloud data, and medical aesthetic consultants do not need to exaggerate medical effects excessively. They can inform customers of specific differences before and after surgery through precise data and provide visual communication and customer expansion to improve consultation conversion rates. © 2023 IEEE.

关键词： Visual communication

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Leveraging Recurrent Neural Networks in Virtual Reality-based Digital Twin Models for Enhanced Predictive Maintenance of Industrial Machines

Leveraging Recurrent Neural Networks in Virtual Reality-base...

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IEEE International Conference on Multi Topic

作者： Hamad Abdul Razzaq Muhammad Jazzel Mehmood Farhan Khan Computer Engineering Program DSSE Habib University Karachi Pakistan

ISBN: (数字)9798331507213

ISBN: (纸本)9798331507220

Digital twin technology has given rise to smart manufacturing in Industry 4.0. Especially with the help of virtual reality, digital twin technology aims to provide an immersive experience by integrating the physical and cyberspace worlds synchronously in real time. One of its key features is the ability to perform predictive maintenance, to help prevent a product from any possible faults in the future. The existing literature has explored the traditional predictive maintenance strategies for forecasting the future state of machines such as simple regression and ARIMA models. However, the potential of machine learning, especially recurrent neural networks (RNNs) is not yet fully investigated for predictive maintenance in digital twin models. The complex nature of industrial operations gives rise to non-linearities in its modeling which can be addressed with the help of neural networks. Therefore, this research aims to investigate the potential of RNNs in the predictive maintenance of an industrial machine use case by performing comparative analysis with simple regression and ARIMA Models. It also highlights the significant improvement RNNs make over the aforementioned strategies. This research also proposes an extension of the existing virtual reality-based digital twin architecture to incorporate automated predictive maintenance of the machine. Moreover, the proposed digital twin architecture acts as a basis for automated predictive maintenance of any product.

关键词： Solid modeling Recurrent neural networks Machine learning Predictive models Real-time systems Digital twins Fourth Industrial Revolution Forecasting Predictive maintenance Smart manufacturing

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Diversity-Optimized Group Extraction in Social Networks

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IEEE Transactions on Computational Social Systems 2024年第1期11卷 756-769页

作者： Hsu, Bay-Yuan Chen, Yi-Ling Ho, Ya-Chi Chang, Po-Yuan Chang, Chih-Chieh Shia, Ben-Chang Shen, Chih-Ya National Tsing Hua University Department of Industrial Engineering and Engineering Management Hsinchu30013 Taiwan National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei10607 Taiwan Hsinchu31040 Taiwan Fu Jen Catholic University Department of Business Administration New Taipei City24205 Taiwan National Taiwan University of Science and Technology Management Undergraduate Program Taipei10607 Taiwan National Tsing Hua University Department of Computer Science Hsinchu30013 Taiwan

In this article, we propose to study a novel research problem to boost group performance, that is, social-aware diversity-optimized group extraction (SDGE), which takes into consideration the two important factors: 1) group diversity and 2) social tightness. We prove the NP-hardness of SDGE and propose an effective algorithm, named group shrinking for diversity maximization (GSDM) with a performance guarantee, that is, GSDM is a three-approximation algorithm to the SDGE problem studied in this article. We further propose three effective pruning strategies that are able to boost the efficiency of GSDM but do not deteriorate its performance. We conduct extensive experiments on multiple large-scale real datasets to evaluate the performance of GSDM. The experimental results show that our proposed GSDM outperforms the other baseline approaches significantly, in terms of solution quality and efficiency. Moreover, the experimental results also confirm that our proposed pruning strategies indeed boost the efficiency of the algorithm. © 2014 IEEE.

关键词： Costs

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Assessing Distributed Energy Resource Hosting Capacity Under Different Grid-Tie Inverter Control Strategies 8

Assessing Distributed Energy Resource Hosting Capacity Under...

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8th Workshop on Communication Networks and Power Systems, WCNPS 2023

作者： Santos, Brenda L. M. Barros, Luciano S. Barbosa, Daniel Moreira, Fernando A. Federal University of Bahia Graduate Program in Electrical Engineering Salvador Brazil Federal University of Paraíba Computer Systems Department João Pessoa Brazil Federal University of Bahia Department of Electrical and Computer Engineering Salvador Brazil

ISBN: (纸本)9798350343571

Despite the potential benefits that the integration of distributed energy resources (DERs) can bring to the system, it may cause problems related to power quality constraints, such $as$ reverse power flow in substation and overvoltages. An effective approach to address these problems involves the adoption of reactive and active power control in grid-tie inverters associated with DER. Therefore, this paper assesses the impacts of grid-tie inverter control modes, including both Volt-Var and Volt-Watt strategies, on the DER hosting capacity. In order to improve the overall system operation, modifications in the Volt-Var and Volt-Watt curves were proposed. It is noteworthy that these control strategies can have adverse effects on certain distribution system performance indicators, such $as$ voltage deviation and power losses;for this reason, these indicators are also evaluated in this study. A stochastic approach was adopted to deal with the uncertainties associated with DERs and loads. Finally, from tests conducted in the IEEE 33-bus test system, it was concluded that the proper adjustment of the Volt-Var and Volt-Watt control curves significantly influences DER hosting capacity, $as$ well $as$ voltage deviation and power losses. © 2023 IEEE.

关键词： Reactive power

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Exploring the Impact of Early Detection on DL-Based NIDSs Models 14

Exploring the Impact of Early Detection on DL-Based NIDSs Mo...

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14th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2023

作者： Wang, Minxiao Yang, Ning Weng, Ning Southern Illinois University Computer Engineering Program School of ECBE CarbondaleIL United States Southern Illinois University Information Technology Program School of Computing CarbondaleIL United States

ISBN: (纸本)9798350304138

Deep learning (DL) has been proposed as a promising solution for network intrusion detection systems (NIDSs). While most DL-based NIDSs focus on high accuracy, they often overlook the critical issue of NIDSs response time for intrusion behavior. Recently some existing works presented time-aware evaluation metrics or design DL-based solutions for early intrusion detection, yet they primarily focus on the post-performance of NIDSs, without thoroughly investigating the impact of early intrusion detection on DL-based NIDSs due to the black-box nature of DL models. To address this research gap, we explore the impact of early detection on DL-based models' detection accuracy and input features. In our empirical study, we implement two different existing deep learning-based NIDSs methods and evaluate them on three published benchmark NIDSs datasets. Based on the experiment results on DL-based NIDSs detection accuracy, we observe that the early detection scenario will lead to the NIDSs model's detection accuracy decreasing up to 50%. To understand the reasons behind it, we adopt the SHapley Additive exPlanations (SHAP) to analyze the importance of features and we observe the varying feature distributions. The feature importance and feature distribution results show that early intrusion detection may lead to a degradation of feature quality. © 2023 IEEE.

关键词： Intrusion detection

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Meal Image Recognition and Healthy Meal Combination Recommendation System Integrated with Generative Artificial Intelligence 6

Meal Image Recognition and Healthy Meal Combination Recommen...

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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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Stunting Classification in Toddlers: Implementation and Evaluation of the Decision Tree Algorithm

Stunting Classification in Toddlers: Implementation and Eval...

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2024 International Conference on Information Technology and Computing, ICITCOM 2024

作者： Alita, Debby Ahmad, Imam Jafar Rumandan, Rhaishudin Erkamim, Moh Widyasmoro Universitas Teknokrat Indonesia Faculty of Engineering and Computer Science Bandarlampung Indonesia Institut Agama Islam Negeri Ambon Islamic Education Management Study Program Ambon Indonesia Universitas Tunas Pembangunan Smart City Information Systems Study Program Surakarta Indonesia Universitas Muhammadiyah Yogyakarta Faculty of Engineering Department of Electrical Engineering Yogyakarta Indonesia

ISBN: (纸本)9798350379839

Stunting in toddlers is a chronic nutritional issue that affects the physical and cognitive development of children, with serious long-term consequences such as reduced cognitive function and an increased risk of chronic diseases in adulthood. Therefore, early identification and prevention efforts for stunting are crucial. Classifying toddlers into categories of at-risk for stunting or not is essential to provide timely and appropriate interventions. This study employs data mining techniques using the decision tree algorithm to expedite the stunting detection process and improve the accuracy of nutritional status classification in children. The results indicate that the constructed decision tree model can classify children's nutritional status with an accuracy of 83.26%. The decision tree achieves high accuracy in classifying stunting in toddlers due to its ability to handle complex data and identify significant patterns within the data. © 2024 IEEE.

关键词： Classification Data Mining Decision Tree Stunting

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Real-time Computation of E-field in Transcranial Magnetic Stimulation for Neuronavigation and Optimization

Real-time Computation of E-field in Transcranial Magnetic St...

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2025 United States National Committee of URSI National Radio Science Meeting, USNC-URSI NRSM 2025

作者： Hasan, Nahian I. Dannhauer, Moritz Wang, Dezhi Deng, Zhi-De Gomez, Luis J. Elmore Family School of Electrical and Computer Engineering Purdue University WLIN United States National Institute of Mental Health Intramural Research Program National Institutes of Health United States

ISBN: (纸本)9781946815200

Transcranial Magnetic Stimulation (TMS) is a non-invasive brain stimulation technique used for the treatment of depression, as well as various neurological and psychiatric disorders. There has been ongoing interest in developing fast E-field solvers that can be easily incorporated into neuronavigation systems to facilitate real-time E-field in targeted brain regions, optimizing dosing parameters. © 2025 USNC-URSI.

关键词： Neurons

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3D Geometric Shape Assembly via Efficient Point Cloud Matching 41

3D Geometric Shape Assembly via Efficient Point Cloud Matchi...

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41st International Conference on Machine Learning, ICML 2024

作者： Lee, Nahyuk Min, Juhong Lee, Junha Kim, Seungwook Lee, Kanghee Park, Jaesik Cho, Minsu Department of Computer Science and Engineering POSTECH Pohang Korea Republic of Graduate School of Artificial Intelligence POSTECH Pohang Korea Republic of Department of Computer Science and Engineering Seoul National University Seoul Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Seoul Korea Republic of

Learning to assemble geometric shapes into a larger target structure is a pivotal task in various practical applications. In this work, we tackle this problem by establishing local correspondences between point clouds of part shapes in both coarse- and fine-levels. To this end, we introduce Proxy Match Transform (PMT), an approximate high-order feature transform layer that enables reliable matching between mating surfaces of parts while incurring low costs in memory and compute. Building upon PMT, we introduce a new framework, dubbed Proxy Match TransformeR (PMTR), for the geometric assembly task. We evaluate the proposed PMTR on the large-scale 3D geometric shape assembly benchmark dataset of Breaking Bad and demonstrate its superior performance and efficiency compared to state-of-the-art methods. Project page: https://***/pmtr. Copyright 2024 by the author(s)

关键词： 3D modeling

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Unveiling Stock Market Trends Through Predictive Analytics and Sentiment Analysis: InsightfulEquity 5

Unveiling Stock Market Trends Through Predictive Analytics a...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Gupta, Amit Kumar Kumar, Vipin Verma, Ankit Yadav, Prakriti Kumar, Nikhil Sain, Mangal Kiet Group of Institutions Department of Computer Applications Delhi NCR India Integrated Program in Management Indian Institute of Management Indore Madhya Pradesh Indore India Dongseo University Division of Computer and Information Engineering Busan Korea Republic of

ISBN: (纸本)9798350383522

In the world of economic markets, it is nevertheless now not clean to as it should be decide stock expenses. Our research objectives to go deeper into financial savings forecasting by way of developing innovative forecasting strategies and superior emotional guide assessment techniques. By combining those techniques, we hope to better recognize the complicated dating between stock prices and social media sentiment. The three fashions blanketed in our evaluation are Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM) networks, and Linear Regression. In the present examine, we use statistical trying out, regression analysis, and correlation models to have a look at the predictive properties of these fashions at special time points and reveal unique accuracy indices for every. On the other hand, every other extensively used method of analyzing marketplace sentiment is sentiment evaluation, which explains to us how stock price volatility reacts to human beings's sentiment In economic evaluation, our examine highlights the significance of predictive modeling and sensitivity analysis through showing that they are correlated. Using this fusion, we can make better predictions in the stock market, helping participants make informed decisions based on well-analyzed data. © 2024 IEEE.

关键词： Financial Analytics Machine Learning Natural Language Processing Sentiment Analysis Stock Market Prediction

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