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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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Digital-Bracers: A Design Process for an Interactive Monitoring Generative System That Helps People with Hand Movements

Digital-Bracers: A Design Process for an Interactive Monitor...

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2023 International Symposium on World Ecological Design, ISWED 2023

作者： Yao, Yousheng Xie, Yuan Ying, Fangtian Wang, Haoran Huang, Zhihao Zheng, Junpeng Faculty of Humanities and Arts Macau University of Science and Technology China Hexiangning College of Art and Design Zhongkai University of Agriculture and Engineering China College of Computer and Cyber Security Fujian Normal University China

ISBN: (纸本)9781643685069

In the post-New Crown epidemic era, people need to face a period of physical recovery after being infected with New Crown, especially when there are frequent cases of deaths due to over-aggressive or excessive physical exercise after healing, or due to people's concerns about safety hazards, or loss of interest and adherence to fitness due to exercising alone, among other problems. In this paper, we design a complete interactive monitoring generation system for wrist brace components, Digital-Bracers, which consists of both hardware and software. The hardware has integrated textile heart rate monitoring and integrated sensors in a three-lead configuration for a textile-based digital wrist brace that conforms to human comfort. The software is a visualized app with pulse rate, speed, and frequency, oscillation rhythm, content generation social connectivity, etc. The software and hardware are connected via WI-FI or Bluetooth. Through exploration of materials, ergonomics, and textile wrist guard construction, we designed an experimental process and tested Digital-Bracers to determine the system's performance through peak heart rate detection and noise interference. The contribution of the Digital-Bracers Interactive Monitoring and Generation System is the ability to monitor one's own health in real-time during hand exercise to avoid potential risks, while at the same time, The Digital-Bracers Interactive Monitoring System contributes to the real-time monitoring of one's health during hand movements to avoid potential risks, and to the generation of a variety of different sounds to bring a sense of fulfillment and enjoyment to the hand movements. © 2024 The Authors.

关键词： Textiles

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Equivariant frames and the impossibility of continuous canonicalization 24

Equivariant frames and the impossibility of continuous canon...

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Proceedings of the 41st International Conference on Machine Learning

作者： Nadav Dym Hannah Lawrence Jonathan W Siegel Faculty of Mathematics Faculty of Computer Science Technion Israel Department of Electrical Engineering and Computer Science MIT MA Department of Mathematics Texas A&M University TX

Canonicalization provides an architecture-agnostic method for enforcing equivariance, with generalizations such as frame-averaging recently gaining prominence as a lightweight and flexible alternative to equivariant architectures. Recent works have found an empirical benefit to using probabilistic frames instead, which learn weighted distributions over group elements. In this work, we provide strong theoretical justification for this phenomenon: for commonly-used groups, there is no efficiently computable choice of frame that preserves continuity of the function being averaged. In other words, unweighted frame-averaging can turn a smooth, non-symmetric function into a discontinuous, symmetric function. To address this fundamental robustness problem, we formally define and construct weighted frames, which provably preserve continuity, and demonstrate their utility by constructing efficient and continuous weighted frames for the actions of SO(d), O(d), and Sn on point clouds.

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Air Particulate Matters Auto-Rule-Based Labeling to Support Long-Distance Run Environment Data Classification 13

Air Particulate Matters Auto-Rule-Based Labeling to Support ...

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13th IEEE Symposium on computer Applications and Industrial Electronics, ISCAIE 2023

作者： Wandy, Wandy Adi, Kusworo Ayu, Media Anugerah Diponegoro University Doctoral Program of Information Systems School of Postgraduate Studies Semarang Indonesia Diponegoro University Faculty of Science and Mathematics Department of Physics Semarang Indonesia Sampoerna University Faculty of Engineering and Technology Department of Computer Science South Jakarta Indonesia

ISBN: (纸本)9798350347319

Sports science is an interdisciplinary and multidisciplinary science that strives to increase athletic performance and endurance. Sport science recognizes and prevents injuries. Sensors and statistics formalize Sports science. Runners need coaches and teams to support them before, during, and after the run race. Coaches generate running training plans to boost performance. Running race performances may be impacted by air pollution exposure while training, so coaches should consider limiting air pollution exposure when training. One of the external factors is Particulate Matter (PM2.5 and PM10). Sensors connecting to the Internet can record external factors and produce csv data. The foundation of supervised machine learning is the labeling process. Labeling a set of data is one of the laborious and time-consuming phases in every machine-learning application because it requires verifying the accuracy of the labels and making any necessary revisions. This research aimed to find a solution to automatically label numerous air particulate matter raw data using a rule based on parameters to reduce manual work, human errors and faster processes. This labeled data will later be used for supervised machine learning classification to support the coach in generating training programs for the runners in a Sports Information System. Based on Indonesia Air Quality index rule-based approach, labeled text data in csv has been generated and tested with PM2.5 and PM10 parameters in three scenarios with a 100% success rate. It was possible to automate the labeling process, and it explained how automation results in fast and accurate results. © 2023 IEEE.

关键词： Classification (of information)

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Driver Information System based on computer Vision 32

Driver Information System based on Computer Vision

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32nd International Conference on Software, Telecommunications and computer Networks, SoftCOM 2024

作者： Kovacevic, Mihovil Vranjes, Mario Grbic, Ratko Herceg, Marijan TTTech Auto d.o.o Cara Hadrijana 10b Osijek Croatia Osijek Croatia Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia

ISBN: (纸本)9789532901382

In the last decade, rapid advancements in computer technology have significantly impacted various industries, especially the automotive sector. This paper presents a software solution for a system for displaying information about the profile of the driver of the vehicle in front of the ego-vehicle, based on license plate detection (LPD) and recognition. The system consists of two applications, one for detecting vehicle license plates (VLPs) and recognizing text on them, and a web application for displaying driver profile data and reviewing drivers. Various algorithms for LPD and text recognition were explored within this research, whereas YOLOv5 small and PaddleOCR are selected as the most suitable for these tasks, respectively. The experimental results show that the selected YOLOv5 small achieved a precision of 0.9905, recall of 0.9811, and F1 score of 0.9858 for the LPD task while processing 2.69 frames per second (FPS) when tested on an Intel(R) Core(TM) i7-8550U CPU @ 1.80 GHz 1.99 GHz processor. Regarding PaddleOCR, the system's maximum recognition distance for VLPs was determined to be when the vehicle in front of the ego-vehicle is distanced at approximately 8 meters for images with resolution of 1920 × 1080 pixels, where PaddleOCR achieved an accuracy of 77.08% after testing. A Django web application was developed to display driver profile data and enable driver reviews based on license plates and the experimental results confirmed its functionality. © 2024 University of Split, FESB.

关键词： Automotive industry

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Deep Reinforcement Learning-Based Robust Design for an IRS-Assisted MISO-NOMA System

IEEE Transactions on Machine Learning in Communications and ...

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IEEE Transactions on Machine Learning in Communications and Networking 2024年 2卷 424-441页

作者： Waraiet, Abdulhamed Cumanan, Kanapathippillai Ding, Zhiguo Dobre, Octavia A. University of York School of Physics Engineering and Technology YorkYO10 5DD United Kingdom Khalifa University Department of Computer and Communication Engineering Abu Dhabi United Arab Emirates Memorial University Faculty of Engineering and Applied Science St. John'sNLA1C 5S7 Canada

In this paper, we propose a robust design for an intelligent reflecting surface (IRS)-assisted multiple-input single output non-orthogonal multiple access (NOMA) system. By considering channel uncertainties, the original robust design problem is formulated as a sum-rate maximization problem under a set of constraints. In particular, the uncertainties associated with reflected channels through IRS elements and direct channels are taken into account in the design and they are modelled as bounded errors. However, the original robust problem is not jointly convex in terms of beamformers at the base station and phase shifts of IRS elements. Therefore, we reformulate the original robust design as a reinforcement learning problem and develop an algorithm based on the twin-delayed deep deterministic policy gradient agent (also known as TD3). In particular, the proposed algorithm solves the original problem by jointly designing the beamformers and the phase shifts, which is not possible with conventional optimization techniques. Numerical results are provided to validate the effectiveness and evaluate the performance of the proposed robust design. In particular, the results demonstrate the competitive and promising capabilities of the proposed robust algorithm, which achieves significant gains in terms of robustness and system sum-rates over the baseline deep deterministic policy gradient agent. In addition, the algorithm has the ability to deal with fixed and dynamic channels, which gives deep reinforcement learning methods an edge over hand-crafted convex optimization-based algorithms. © 2023 CCBY.

关键词： Heuristic algorithms

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Optimizing Patient Outcomes with AI and Predictive Analytics in Healthcare 65

Optimizing Patient Outcomes with AI and Predictive Analytics...

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65th IEEE Annual International Scientific Conference on Power and Electrical engineering of Riga Technical University, RTUCON 2024

作者： Janjua, Jamshaid Iqbal Ghazal, Taher M. Abushiba, Walid Abbas, Sagheer Lahore Pakistan College of Arts & Science Applied Science University P.O.Box 5055 Manama Bahrain Center for Cyber Security Faculty of Information Science and Technology Selangor Bangi43600 Malaysia College of Engineering Applied Science University Bahrain PMU Department of Computer Science Al Khobar Saudi Arabia

ISBN: (纸本)9798350365771

Healthcare's integration with the emerging artificial intelligence, predictive analytics and health information exchange (HIE) system is currently undergoing a revolution. These emerging technologies are offering better diagnostic accuracies, more personalized treatments, and better patients' outcomes. Artificial Intelligence supported by data from either Electronic Health Records or other big data sources are increasing the accuracy of forecasting critical patient conditions such as disease developments, hospital readmission rates, and mortality risks in patients. These AI-empowered models ensure seamless data sharing between the care providers and promote operational interoperability. Challenges to overcome include issues of privacy of the data, the biases of the prediction algorithms, and the proper integration of the model into the currently established operational environments of healthcare providers. With these technologies combined, it will become possible to optimize patient's outcomes with the provision of data to empower real-time medical interventions. © 2024 IEEE.

关键词： Electronic health record

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Penetration Testing: A Cost-Benefit Analysis of Best Practices Implementation for Software Startups

Penetration Testing: A Cost-Benefit Analysis of Best Practic...

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Novel Intelligent and Leading Emerging sciences Conference (NILES)

作者： Ahmed Ali Gaafar Khaled M. Fouad Mohamed A. Sadek Faculty of Information Technology and Computer Science Nile University Cairo Egypt Faculty of Computers and Artificial Intelligence Benha University Faculty of Computer Science and Engineering New Mansoura University Nile University

ISBN: (数字)9798350378511

ISBN: (纸本)9798350378528

Despite software startups often not handlingsensitive data, the implementation of robust security measures is crucial to mitigate significant financial and reputational risks. This study investigates the cost-benefit analysis of implementing best practices in penetration testing (Pentest) versus notimplementing them, using Roboost as a case study. It emphasizes that proactive security investments not only protect current assets but also prepare organizations for future growthThe research employs a mixed-methods approach, combining quantitative analysis of financial data with qualitative insights from stakeholder interviews. The findings indicate that while the initial costs of hiring penetration testers and implementing security protocols are substantial, the benefits far outweigh these expenses. Securing new clients and retaining existing ones, which constitute a significant portion of Roboost’ s revenue, underscores the financial prudence of these investments. Our analysis demonstrates that the costs of not implementing security practices, such as potential losses from security breaches, are substantially higher than the investments in proactive measures. This underscores the critical importance of embedding security best practices within organizational frameworks to ensure long-term sustainability and success.

关键词： Statistical analysis Current measurement Software Loss measurement Security Cost benefit analysis Sustainable development Best practices Investment Penetration testing

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Protecting Vulnerable Road users using IoT-CNN for Safety Measures 5

Protecting Vulnerable Road users using IoT-CNN for Safety Me...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Karpagalakshmi, R.C. Sampoornam, M. Maria Vinya, Viyyapu Lokeshwari Monikapreethi, S.K. Yuvaraj, S. Srinivasan, C. Alliance College of Engineering and Design Alliance University Karnataka Bengaluru India J.J College of Engineering and Technology Department of Information Technology Tamil Nadu Tiruchirappalli India Vardhaman College of Engineering Department of Computer Science and Engineering Telangana Ranga Reddy India Rajalakshmi Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Kattankulathur India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350379945

The increasing number of road accidents involving Vulnerable Road Users (VRUs) highlights the urgent need for innovative safety solutions. This study proposes a novel approach that leverages IoT and Convolutional Neural Network (CNN) technologies to enhance VRU safety. By deploying IoT sensors on vehicles and infrastructure, the proposed model can accurately track VRU locations and environmental conditions in real-time. A CNN-based model is developed to robustly detect and classify VRUs under various traffic scenarios, with continuous improvement through learning. The integration of IoT and CNN enables real-time communication and timely warnings, facilitating proactive interventions to prevent accidents. Experimental evaluations demonstrate the effectiveness of the proposed system in improving VRU safety. This research offers a promising solution for reducing road accidents and creating safer urban environments. © 2024 IEEE.

关键词： Convolutional neural networks

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Vector Control of Permanent Magnet Synchronous Motor

Vector Control of Permanent Magnet Synchronous Motor

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Patil, Harshal Lohumi, Yogesh Nandankar, Praful V. Amalraj, J. Leo Barve, Amit Maranan, Ramya Sri Balaji University Maharashtra Pune India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun248002 India Government College of Nagpur Department of Electrical Engineering Maharashtra 441108 India R.M.K College of Engineering and Technology Science and Humanities Tamil Nadu Chennai601 206 India Parul Institute of Technology Parul University Faculty of Engineering and Technology Department of Computer Science and Engineering Gujarat 391760 India Saveetha School of Engineering Simats Department of Research and Innovation Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350375237

Experimental measurements were conducted to validate the vector control (VC) algorithm's functionality with a synchronous motor (SM). Various scenarios were tested, including start-up, reversal, and responses to speed changes. Stability and regulation quality were assessed under load, with deliberate de-excitation testing to prevent motor damage. Transitioning to torque control, responses to torque changes during start-up and reversal were observed. Despite hardware limitations causing waveform discontinuities, the VC structure proved effective for laboratory purposes. Parameter designs of individual regulators were validated through measured data. The system's capability to handle rapid transients was challenged, necessitating longer recording times on the oscilloscope. Overall, the study confirms the functionality of individual system blocks and the practical feasibility of VC for synchronous drives, highlighting the need to address waveform discontinuities and hardware limitations in future implementations. © 2024 IEEE.

关键词： Torque control

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