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2nd UPY International Conference on Applied science and Education, UPINCASE 2020

作者： Santoso, Banu Sari, Marti Widya Department of Computer Engineering Faculty of Computer Science Universitas Amikom Yogyakarta Yogyakarta Indonesia Department of Informatics Faculty of Science and Technology Universitas PGRI Yogyakarta Yogyakarta Indonesia

Parking areas in urban centers are increasingly limited, but not in line with technological developments in addressing the problem of parking service needs in the urban public service center by implementing smart city technology. Many times the parking service waiting time is too long, due to a complicated procedure. Waiting time for services that are too long makes the parking queue increase in length, especially when registering for the end of a new parking lot. Besides, the storage of visitors' parking slip accumulation is still manual and not paperless. Therefore, in this study, a solution was proposed in the form of a system to accelerate administrative services for parking visitors by integrating Radio Frequency Identification (RFID) and Wireless Sensor Network (WSN) technologies. This system utilizes the database so that the results of data processing can be used by management to obtain visitor data information on parking online and quickly. The results obtained from this study are a prototype of a parking visitor monitoring system using WSN technology based on Zigbee and RFID protocols. Meanwhile, the results of visitor parking information presented in data processing can be used by management to find out statistical data relating to parking services. © Published under licence by IOP Publishing Ltd.

关键词： Digital storage

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Applying an integrated production system based on social manufacturing to develop a medical device 2

Applying an integrated production system based on social man...

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2nd UPY International Conference on Applied science and Education, UPINCASE 2020

作者： Sari, Marti Widya Herianto Dharma, Igb Budi Tontowi, Alva Edy Department of Mechanical and Industrial Engineering Faculty of Engineering Universitas Gadjah Mada Yogyakarta Indonesia Department of Informatics Faculty of Science and Technology Universitas PGRI Yogyakarta Yogyakarta Indonesia

In the current Covid-19 pandemic, many manufacturing industries have been affected, including the health industry. The need for medical devices during this pandemic was very high, causing scarcity of goods on the market, and high selling prices, because most medical devices had to be imported. The concept of social manufacturing can be one way to revive the health industry in Indonesia. The purpose of this research is to develop an integrated production system through social manufacturing by involving small and medium companies and measuring the competitive level of production results from the quality side. The method used in this research is starting from literature study, field observation, production system design, product and system testing, and analysis of social manufacturing based production system test results. The results of this study are a social manufacturing-based production system for medical device products in the form of a Sanitation Chamber, and also the measurement of product quality in terms of performance (83.20), features (80.40), reliability (79.56), conformance (81.11), durability (84.67), service ability (78.33), aesthetics (81.83) and perceived quality (79.83). © Published under licence by IOP Publishing Ltd.

关键词： Manufacture

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Forwarding Strategy Performance Using NFD Pipeline Measurements on Vehicular Named Data Networking (V-NDN)

Forwarding Strategy Performance Using NFD Pipeline Measureme...

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International Conference on Wireless and Telematics (ICWT)

作者： Syaiful Ahdan Nana Rachmana Syambas Iskandar School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Faculty of Engineering & Computer Science Universitas Teknokrat Indonesia (UTI) Lampung Indonesia

The Named Data Networking (NDN) paradigm has been used as a promising vehicle communication model, namely the Vehicular Named Data Networking (V-NDN) model. In NDN, delivery of interest in the NDN network is done by selecting an existing interface. The problem is as to which interface is right in terms of fulfilling the request that suits the user. In this study, we compare the effectiveness of each forwarding strategy by creating new scenarios, such as variations in the number of interests and simulation time. We also measure the effectiveness of the forwarding strategy using the parameters specified in the NFD Forwarding Pipeline. The conclusion from the experiment is that superior forwarding strategy recommendations are based on content store (CS) management and data compatibility obtained based on consumer demand for producers.

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Integrating V2X solutions in intelligent green cities: an AI-driven point exchange system approach

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Neural Computing and Applications 2025年 1-33页

作者： Talaat, Fatma M. Shaban, Warda M. ZainEldin, Hanaa Badawy, Mahmoud Elhosseini, Mostafa Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh Egypt Faculty of Computer Science & Engineering New Mansoura University Gamasa35712 Egypt Department of Communication and Electronics Engineering Nile Higher Institute for Engineering and Technology Mansoura Egypt Faculty of Artificial Intelligence and informatics Horus University New Damietta Egypt Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt Computer Science and Information Department Applied College Taibah University Madinah42353 Saudi Arabia College of Computer Science and Engineering Taibah University Yanbu46421 Saudi Arabia

With rapid urbanization, smart cities have become essential for enhancing urban management and sustainability by integrating technological, social, and institutional innovations. Among these innovations, vehicle-to-everything (V2X) communications and electric vehicles (EVs) play a critical role in reducing carbon emissions and optimizing urban mobility. To address the existing gaps in holistic V2X integration, this paper presents a novel eco-assistive fog-based traffic management system (EAFTMS) that leverages a four-tier architecture (IoT, fog, cloud, and application) for scalable, real-time traffic optimization. A key innovation of this system is the AI-driven point exchange system (PES), designed to incentivize sustainable behaviors such as reducing unnecessary vehicle usage and promoting green lifestyle choices. Unlike conventional models, the proposed framework incorporates real-time behavioral monitoring, rewards-based sustainability programs, and V2X-enabled dynamic traffic control. Empirical validation demonstrates that EAFTMS outperforms existing models, including support vector regression (SVR), achieving a 30% reduction in latency, a 40% improvement in response time, a 25% increase in traffic flow efficiency, and a 35% reduction in CO2 emissions. These results highlight the framework’s potential to set new standards in intelligent green cities by offering scalable, practical, and environmentally impactful solutions to urban transportation challenges. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Urban transportation

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A Review of Fintech Management and Security Development

A Review of Fintech Management and Security Development

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Information technology International Seminar (ITIS)

作者： Ali Akbar Ritonga Syaiful Zuhri Harahap Adri Gabriel Sooai Aryo Nugroho Samsir Puput Dani Prasetyo Adi Department of Informatics Management Faculty of Science and Technology Universitas Labuhanbatu Labuhanbatu North Sumatera Indonesia Department of Information System Faculty of Science and Technology Universitas Labuhanbatu Labuhanbatu North Sumatera Indonesia Department of Computer Engineering Faculty of Computer Science Universitas Katolik Widya Mandira Kupang NTT Indonesia Department of Information System Faculty of Computer Science Universitas Narotama Surabaya Indonesia Department of Informatics Engineering Faculty of Computer Science Universitas Labuhanbatu Labuhanbatu North Sumatera Indonesia Department of Telecommunication National Research and Innovation Agency (BRIN) Bandung Indonesia

ISBN: (数字)9798331521295

ISBN: (纸本)9798331521301

This research focuses on the review of Fintech and its development on the IoT Platform and also the risks that can be posed to the IoT network used. Finance is the most essential side of several other sectors which in this case often experience many losses, one of the biggest losses is the existence of hackers and theft of money through the Internet platform or Internet Server. This research reviews how the ins and outs of the flow of funds located on servers or online banks. And ways to overcome hackers and bypass data. Comprehensively reviewed in this research review. This research discusses Cryptocurrency Analysis, Bitcoin, and Ethereum Comparison which are some of the methods used in online-based Finance, which is without currency but has the same value even more than currency if the conversion process is carried out at the bank. In this research, it is comprehensively discussed how methods are carried out in the process of securing data, Cryptocurrency, Bitcoin, and Ethereum are comprehensively discussed to get a lot of data and comparisons and the right steps to deal with crime via the internet. The predicted use of IoT in the Fintech sector is 140 billion connected devices, IoT Loans 110 billion connected devices, and IoT insurance with an estimated 90 billion connected devices, in the approaching 2026.

关键词： Seminars Reviews Computer hacking Fintech Bitcoin Blockchains Internet of Things Security Servers Protection

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A real-time system for detecting personal protective equipment compliance using deep learning model YOLOv5

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Procedia Computer science 2024年 245卷 647-656页

作者： Nur Amielia Natasha Mohamad Nazli Nurbaity Sabri Raihah Aminuddin Shafaf Ibrahim Syafiq Yusof Siti Diana Nabilah Mohd Nasir College of Computing Informatics & Mathematics MARA University of Technology Malacca Branch (Jasin Campus) 77300 Malacca Malaysia Yus Industries Sdn Bhd Malaysia Faculty of Science and Engineering University of Hull Hull United Kingdom

The manufacturing and construction sectors report high rates of occupational accidents and diseases. In response, many countries, including Malaysia, have incorporated the use of Personal Protective Equipment (PPE) into their government laws and safety regulations. In Malaysia, this requirement is outlined in Standard Operating Procedure (SOP) manuals in the construction sector, which specify the mandatory use of seven types of PPE: hard hats, safety goggles, high-visibility vests, safety shoes, gloves, respirators, and earplugs. However, monitoring a large number of workers and ensuring consistent use of PPE on construction sites can be a challenging task if done manually by site supervisors. Therefore, this project aims to implement a real-time PPE detection system using a deep learning model to effectively monitor workers’ safety compliance. This system can generate reports that companies in the appropriate sectors can use to monitor compliance with the Occupational Safety and Health Act (OSHA). The prototype system was developed as a web-based platform. The object detection deep learning model achieved a mean Average Precision (mAP) of 91.5% in the metric evaluation, while the accuracy testing yielded a 98.3% accuracy rate. For future work, it is suggested to enhance the detection model for other types of PPE and to make the system more reliable across all sectors by expanding the training dataset.

关键词： Personal Protective Equipment Standard Operating Procedure object detection deep learning YOLOv5

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Subject-independent multi-channel voting for EEG-based emotion recognition using wavelet scattering deep network and advanced signal metrics

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Pattern Analysis and Applications 2025年第3期28卷 1-29页

作者： Elrefaiy, Ahmed Tawfik, Nahed Zayed, Nourhan Elhenawy, Ibrahim Department of Computer Science Faculty of Computers and Informatics Zagazig University Zagazig Egypt Field of Artificial Intelligence & Computer Engineering Technology El Sewedy University of Technology Cairo Egypt Computers and Systems Department Electronics Research Institute Cairo Egypt Mechanical Engineering Department The British University in Egypt Cairo Egypt

Electroencephalography (EEG) signals, reflecting human brain activity, hold potential beyond medical diagnosis, particularly in emotion recognition. Despite the development of machine learning models utilizing EEG data for this purpose, achieving good enough accuracy remains a challenge due to signals complexity and non-stationary nature, especially in extracting effective features that encapsulate temporal and frequency information. This paper introduces a novel hand-crafted feature extraction technique that avoids conventional signal segmentation and analyzes the entire length of EEG signals. This method builds a convolutional network utilizing Wavelet Scattering Transform (WST) blocks, followed by deriving a comprehensive 17-feature set from the raw EEG data and WST scattering coefficients. This integrative set takes advantage of the WST’s ability to produce a signal representation that is stable against noise, invariant to time shifts, and captures both temporal and frequency components while also leveraging the intrinsic properties of the raw data, offering an alternative to the computational deep models. The integration of Linear Discriminant Analysis for dimensionality reduction and the K-Nearest Neighbors algorithm for classification, further refined by a majority voting mechanism across all channels, results in a robust classification framework. The proposed method is evaluated across GAMEEMO and DEAP datasets with two and four emotional classes, using Leave-One-Subject-Out validation, achieving classification accuracy exceeding 97%. The findings support the effectiveness of this approach in EEG-based emotion recognition. Furthermore, an ablation study on the two datasets is implemented to assess each component’s impact, revealing insights into the model’s effectiveness and improvement areas.

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Improving fNIRS Signal Quality Using Smoothing Filtering

Improving fNIRS Signal Quality Using Smoothing Filtering

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2023 Progress in Applied Electrical engineering, PAEE 2023

作者： Kawala-Sterniuk, Aleksandra Mikolajewski, Dariusz Leiva, Luis A. Ruotsalo, Tuukka Lysiak, Adam Grochowicz, Barbara Jacek Gorzenariczyk, Edward Luckiewicz, Adrian Wieczorek, Anna Pelc, Mariusz Automatic Control and Informatics Opole University of Technology Faculty of Electrical Engineering Opole Poland Institute of Computer Science Kazimierz Wielki University Bydgoszcz Poland University of Luxembourg Luxembourg Department of Computer Science Luxembourg Luxembourg University of Helsinki Denmark University of Copenhagen Finland Institute of Philosophy Kazimiez Wielki University Bydgoszcz Poland

ISBN: (纸本)9798350316254

Biomedical signals are extremely difficult to analyze, mainly due to the non-stationary nature of these signals. Filtering does not always bring the desired results, because often the desired information is filtered out. In the case of EEG signals, smoothing filters gave very good results. In this paper, various types of smoothing filters for the analysis of infrared spectroscopy signals were compared. © 2023 IEEE.

关键词： Information filtering

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A Dynamic Optimization Framework for Computation Rate Maximization in UAV-Assisted Mobile Edge Computing

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IEEE Transactions on Vehicular technology 2025年

作者： Chen, Yang Pi, Dechang Yang, Shengxiang Xu, Yue Wang, Bi Qin, Shuo Wang, Yintong Xi'an University of Science and Technology Artificial Intelligence and Computer Science Xi'an China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing China De Montfort University School of Computer Science and Informatics LeicesterLE1 9BH United Kingdom Jiangxi University of Science and Technology Faculty of Information Engineering Ganzhou China Xi'an University of Posts and Telecommunications School of Computer Science and Technology Xi'an China Nanjing Xiaozhuang University School of Information Engineering Nanjing China

Mobile edge computing (MEC) significantly boosts the computing power and reduces the energy consumption of Internet of Things (IoT) devices, serving as a valuable complement to cloud computing. The application of unmanned aerial vehicle (UAV) for MEC systems can effectively alleviate the issue of insufficient or damaged communication facilities in remote areas, further expanding the scope of MEC applications. In this article, we present a system model for UAV-assisted wireless-powered MEC systems in a dynamic environment with the objective of maximizing the computation rate of user devices. Due to the complexity of the optimization objective in dynamic environments, we propose a swarm intelligence-based optimization framework with a mechanism for responding to environmental changes, which is intended to enhance population diversity in both static and dynamic environments with the aim of overcoming premature convergence. We integrate particle swarm optimization and harmony search into the proposed framework, naming them DOPSO and DOHS, respectively. Simulation results for two offloading modes in UAV-assisted MEC systems indicate that the proposed framework significantly outperforms other dynamic optimization algorithms. © 1967-2012 IEEE.

关键词： Energy utilization

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Integration of Large Language Models into Higher Education: A Perspective from Learners

Integration of Large Language Models into Higher Education: ...

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International Symposium on Computers in Education (SIIE)

作者： Katerina Zdravkova Fisnik Dalipi Fredrik Ahlgren Faculty of Computer Science and Engineering Ss. Cyril and Methodius University Skopje N. Macedonia Department of Informatics Faculty of Technology Linnaeus University Kalmar Sweden Department of Computer Science and Media Technology Faculty of Technology Linnaeus University Kalmar Sweden

Large language models (LLMs) are being criticized for copyright infringement, inadvertent bias in training data, a danger to human innovation, the possibility of distributing incorrect or misleading information, and prejudice. Due to their popularity among students, the introduction of many comparable apps, and the inability to resist unfair and fraudulent student usage, their educational use needs to be adapted and harmonized. The incorporation of LLMs should be defined not only by pedagogues and educational institutions, but also by students who will actively utilize them to learn and prepare assignments. In order to find out what students from two universities think and suggest about LLMs use in education, they were asked to give their contribution by answering the survey that was conducted at the beginning of the spring semester of academic 2022/23. Their feedback was quantitatively and qualitatively analyzed, showing in a better light what students think about LLMs and how and why they would use them. Based on the analysis, the authors propose an original strategy for integrating LLMs into education. The proposed approach is also adapted for those students who are not interested in using LLMs and for those who prefer the hybrid mode by combining their own research with LLMs generated recommendations. The authors expect that by implementing the proposed strategy, schools will benefit from a better education in which research, creativity, academic honesty, recognition of false information, and the ability to improve knowledge will prevail.

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