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Cybernetics and Innovations (ICCI), International Conference on

作者： Patiyuth Pramkeaw Pimyada Suksomboon Warit Leangruttananon Thittaporn Ganokratanaa Davids Makararpong Narumol Chumuang Worawut Yimyam Chayawee Angsusingha Adil Farooq Mahasak Ketcham Media Technology Program King Mongkut's University of Technology Thonburi Bangkok Thailand Faculty of Science King Mongkut's University of Technology Thonburi Bangkok Thailand Thailand Business school The University of the Thai Chamber of Commerce Bangkok Thailand Digital Media Technology Muban Chombueng Rajabhat University Ratchaburi Thailand Business Information Management Phetchaburi Rajabhat University Phetchaburi Thailand The national broadcasting and Telecommunications Commission Bangkok Thailand Department of Electronic Engineering Maynooth University Ireland Management of Information Systems King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798331533267

ISBN: (纸本)9798331533274

This research presents the development and evaluation of a breath alcohol measurement device utilizing the MQ-3 alcohol sensor and an Arduino Nano-based control system. The primary objective was to design an accurate and reliable system for detecting breath alcohol concentration, with real-time feedback provided through an OLED display and an active buzzer for alerts. The device operates by measuring the alcohol concentration in the breath and converting the sensor data into parts per million (ppm). When the alcohol concentration exceeds the predefined threshold of 5% ABV, the system activates an audible alarm via the buzzer and displays the corresponding ppm value on the OLED screen. A series of tests were conducted with eight volunteers who consumed varying amounts of alcohol. The results demonstrated the system's high accuracy and responsiveness, with the ability to detect alcohol concentrations ranging from safe levels to dangerous thresholds. The device was tested multiple times to ensure consistency and minimize errors, with the final results confirming its effectiveness in providing reliable alcohol measurements. This system holds significant potential for applications in monitoring alcohol levels in drivers and individuals involved in safety-critical activities. The research underscores the importance of real-time alcohol detection systems in enhancing public safety and ensuring compliance with safety regulations.

关键词： Accuracy Measurement uncertainty Organic light emitting diodes Control systems Real-time systems Public security Nanoscale devices Monitoring Vehicles Testing

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NFT-Based Blockchain-Oriented Security Framework for Metaverse Applications

arXiv

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arXiv 2023年

作者： Manzoor, Khadija Noor, Umara Rashid, Zahid Department of Computer Science Faculty of Computing and Information Technology International Islamic University Islamabad Pakistan Technology Management Economics and Policy Program College of Engineering Seoul National University 1 Gwanak-Ro Gwanak-Gu Seoul08826 Korea Republic of

The Metaverse is rapidly evolving, bringing us closer to its imminent reality. However, the widespread adoption of this new automated technology poses significant research challenges in terms of authenticity, integrity, interoperability, and efficiency. These challenges originate from the core technologies underlying the Metaverse and are exacerbated by its complex nature. As a solution to these challenges, this paper presents a novel framework based on Non-Fungible Tokens (NFTs). The framework employs the Proof-of-Stake consensus algorithm, a blockchain-based technology, for data transaction, validation, and resource management. PoS efficiently consume energy and provide a streamlined validation approach instead of resource-intensive mining. This ability makes PoS an ideal candidate for Metaverse applications. By combining NFTs for user authentication and PoS for data integrity, enhanced transaction throughput, and improved scalability, the proposed blockchain mechanism demonstrates noteworthy advantages. Through security analysis, experimental and simulation results, it is established that the NFT-based approach coupled with the PoS algorithm is secure and efficient for Metaverse applications. © 2023, CC BY.

关键词： Blockchain

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Spam Text Detection using Machine Learning Model

Spam Text Detection using Machine Learning Model

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International Technical Conference o Circuits/Systems, Computers and Communications (ITC-CSCC)

作者： Mahasak Ketcham Thittaporn Ganokratanaa Patiyuth Pramkeaw Narumol Chumuang Dept. of Information Technology Management King Mongkut's University of Technology North Bangkok Bangkok Thailand Applied Computer Science Program King Mongkut's University of Technology Thonburi Bangkok Thailand Media Technology Program King Mongkut's University of Technology Thonburi Bangkok Thailand Dept. of Digital Media Technology Muban Chombueng Rajabhat University (MCRU) Thailand

This research presents a classification between spam and non-spam messages by removing duplicate sentences or words. meaningless word and various marks The data is then classified by machine learning techniques and compared by differences between the data sets. Quantitative transformations were performed on each model to find the most efficient model. which can filter spam messages efficiently and quickly By getting the best comparison results in terms of information. quantitative conversion and model use The experimental results showed that Of all the tests, the model that performed best was Random Forest, with an average accuracy of 97

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The Smart Analysis of Performing Scalable Inference for Big Data Analytics

The Smart Analysis of Performing Scalable Inference for Big ...

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International Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： Vishakha D Bhandarkar Aruna Sri Rongali Mohamed Dawood Shamout Prof. Sachin Pund Gigih Forda Nama Shrishailappa Patil Applied Mathematics and Humanities Yeshwantrao Chavan College of Engineering India Graduate Doctoral Information Technology/Graduate (Information Technology Emphasis/Information Technology Ph.D.) University of The Cumberlands US Department of Management College of Business Administration University of Sharjah Sharjah United Arab Emirates Department of Industrial Engineering Shri Ramdeobaba College of Engineering and Management Nagpur Maharashtra India Department of Informatics Doctoral Program of Environtmental Science University of Lampung Bandar Lampung Computer engineering Vishwakarma Institute of Technology Pune

This paper examines the reproducibility of massive information analytics under particular factors. The paper proposes the “performing Scalable Inference” technique to cope with scalability troubles and to exploit current big statistics platforms for efficient computing and statistics garage of the statistics. In particular, the paper describes how to perform leak-free, parallelizable visible analytics over massive datasets using present extensive records analytics frameworks such as Apache Flink. This method presents an automated manner to execute analytics that preserves reproducibility and the ability to make adjustments without re-running the entire technique. The paper also demonstrates how these analytics may help several real-world use instances, explore affected person cohorts for studies, and develop stratified patient cohorts for hospital therapy. In the end, the paper observes how the proposed method may be exercised within the real world. Actively scalable inference for massive information analytics is pivotal in optimizing decision-making and allocation of assets. Typically, such inferences are made based on information accumulated from numerous sources, databases, unstructured data, and different digital sources. So one can ensure scalability, a complete cloud-primarily based platform has to be hired. This solution will permit the ***, deploying the essential records series and evaluation algorithms are prime here. It could permit the platform to recognize the styles inside the statistics and discover any ability correlations or traits. Additionally, predictive analytics and system mastering strategies may be incorporated to provide insights into the results of the information. In the long run, by leveraging those techniques, the platform can draw efficient inferences and appropriately compare situations in an agile and green way..

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ECG signals-based security and steganography approaches in WBANs: A comprehensive survey and taxonomy

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Sustainable Computing: Informatics and Systems 2024年 41卷

作者： Masdari, Mohammad Band, Shahab S. Qasem, Sultan Noman Sayed, Biju Theruvil Pai, Hao-Ting Department of Computer Engineering Urmia Branch Islamic Azad University Urmia Iran Future Technology Research Center National Yunlin University of Science and Technology Douliu Taiwan Department of Information Management International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Taiwan Riyadh11432 Saudi Arabia Computer Science Department Faculty of Applied Science Taiz University Taiz6803 Yemen Department of Computer Science Dhofar University PO Box 2509 SalalahPCode 211 Oman Bachelor Program of Big Data Applications in Business National Pingtung University Pingtung Taiwan

Wireless Body Area Networks (WBANs) are integral components of e-healthcare systems, responsible for monitoring patients' physiological states through intelligent implantable or wearable sensor nodes. These nodes collect medical data, which is then transmitted to remote healthcare facilities for thorough evaluation. Securing medical data within WBANs is paramount due to its central role in preserving patient privacy and confidentiality. Notably, Electrocardiogram (ECG) signals have recently gained prominence as pivotal elements within diverse security frameworks. Incorporating ECG signals strategically enhances the security and reliability of WBANs and broader e-healthcare systems, instilling greater trustworthiness. This survey article provides an in-depth exploration of contemporary ECG-based security schemes, adding to the scholarly discourse. The imperative to categorize these security paradigms revolves around their use of ECG signals. This categorization identifies three key domains: the first involves schemes that utilize ECG signals for cryptographic operations, encompassing key generation, agreement, management, and authentication. The second category employs steganography-based techniques, using ECG signals to conceal patients' sensitive medical data. The third category focuses on enhancing ECG signal security during data transmission. Each category is meticulously elaborated, detailing architectural foundations, notable contributions, and intrinsic security services. Furthermore, each section presents a comprehensive overview of the attributes characterizing ECG-based security frameworks. This includes insights into employed datasets, simulation environments, evaluation metrics, and inherent advantages and limitations. Expanding on this, a thorough analysis of distinctive attributes underpinning these security frameworks concludes by shedding light on potential directions for future research. © 2023 Elsevier Inc.

关键词： Authentication

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Enhancing Educational Innovation: A Comparative Analysis of Blockchain Adoption Strategies in Smart Learning Environments

Enhancing Educational Innovation: A Comparative Analysis of ...

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International Conference on Informatics and Computing (ICIC)

作者： Mochamad Heru Riza Chakim Daelami Ahmad Umi Rusilowati Fitra Putri Oganda Yulia Putri Ayu Sanjaya Po Abas Sunarya Economics and Business University of Raharja Indonesia Commercial Administration Study Program Sekolah Tinggi Ilmu Administrasi Banten Indonesia Management Pamulang University Indonesia Information Systems University of Raharja Indonesia Retail Management University of Raharja Indonesia Faculty of Science and Technology University of Raharja Indonesia

The recent improvement of traditional education systems in developing countries is supported by the adoption of the latest blockchain technology. This disruptive technology is key to improving accountability and transparency. This study investigates the main factors that influence educational institutions’ intention to implement blockchain technology in e-learning. By combining blockchain adoption strategies, smart learning environments, and enhancing educational innovation this study develops an updated model of the technology Acceptance Model. This conceptual model was evaluated and validated using online survey-based structural analysis. The results revealed that the blockchain adoption strategy factor has a significant influence on blockchain adoption in smart learning environments. Other findings also had important significant effects on the adoption of blockchain technology. This research presents an updated technology Acceptance Model for blockchain adoption, providing guidance for decision-makers in developing smart learning environments for educational institutions in developing countries.

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Air Quality Prediction Based on Air Pollution Emissions in the City Environment Using XGBoost with SMOTE

Air Quality Prediction Based on Air Pollution Emissions in t...

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International Conference of Computer science and information technology

作者： Adli A Nababan Sutarman Muhammad Zarlis Erna B Nababan Department of Computer Science Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Department of Mathematics Faculty of Mathematics and Natural Sciences Universitas Sumatera Utara Medan Indonesia Department of Information Systems Management BINUS Graduate Program-Master of Information Systems Management Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798350399080

Air pollution makes it worse in populated regions. Indonesia's major cities are also impacted by air pollution. Due to rising traffic, material consumption by vehicles, industrial growth, land burning, and garbage collection, air quality has altered significantly. Accurate measurement and classification of air quality are required. Results of accurate classification aid in forming governmental regulations. To reach the criteria for livable air quality, we intend to manage controls. Some of the characteristics of air pollution include particulate (PM10) or PM25, sulfides (in the form of SO2), carbon monoxide (CO), ozone (O3), and nitrites (NO2). Using the XGBoost algorithm and synthetic minority oversampling (SMOTE) methods based on the Air Pollution Standard Index (ISPU) category, this study seeks to uncover factors that affect air quality. XGBoost, an ensemble machine learning approach built on a decision tree and applying a gradient reinforcement framework, is the classification algorithm in use. Air quality categorization has been tested and shown to work with the XGBoost algorithm. The dataset used in this study is air quality in 2021 which was collected by the Jakarta Environmental Ministry The proposed classification model will be evaluated using the Repeated k-fold cross-validation method for objective results. The results of the study prove that SMOTE and XGBoost have better performance than using only the XGBoost method in predicting air quality, where the overall accuracy value of SMOTE with XGBoost is 99.60%, The overall precision value is 99.60%, the overall recall value is 99.60% and the overall f1-score is 99.60% and the overall ROC AUC value is 99.96%. The overall SMOTE method with the proposed XGBoost has given better performance in air pollution prediction.

关键词： Machine learning algorithms Urban areas Air pollution Prediction algorithms Particle measurements Regulation Classification algorithms

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IoT Attack Detection and Prevention Through Machine Learning System

IoT Attack Detection and Prevention Through Machine Learning...

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Communication, Security and Artificial Intelligence (ICCSAI), International Conference on

作者： Kanishk Agrawal Pavan Kumar Reddy Poli Yash Gupta Garv Juneja Adhip Kishen Bopaiah Dinesh Ashok Arani Department of IET CSE Manglayatan University Aligarh Uttar Pradesh India Managing Information Technology Implementations Program management for fortune 6 Healthcare Company USA Department of Computer Science and Information Systems Birla Institute of Technology and Science Pilani India Department of Computer Science and Engineering Maharaja Surajmal Institute of Technology Guru Gobind Singh Indraprastha University Delhi India ICT Trainer Trinity College Bangalore India Manglayatan University Aligarh Uttar Pradesh India

Attacks on web applications are constantly growing in both frequency and severity. Abundant data on the internet stimulates hackers to attempt different types of cyberattacks. Attack detection using conventional approaches and outdated data processing techniques has become outmoded as a result of this development. The purpose of this study is to investigate IoT attacks and discuss the efficient ML technique implementation strategies for restricting security risks. Among different security techniques, Machine learning (ML) systems demonstrated commendable feasibility in improving network and device security for the Internet of Things. The study with contextual research recognises and comprehends that modified “Support Vector Machine (SVM)” as well as “Random Forest (RF)” ML techniques showed optimal performance in IoT attack detection and prevention.

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Preprocessing Techniques for Enhancing Vision-Based Detection System for Tire Sidewall Extraction

Preprocessing Techniques for Enhancing Vision-Based Detectio...

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International Seminar on Research of information technology and Intelligent Systems (ISRITI)

作者： Jia Hao Loo Veronica Lestari Jauw Chin Seong Lim Khairul-Rijal Sarmin Nunung Nurul Qomariyah Ahmad Nurul Fajar Department of Mechanical Materials and Manufacturing Engineering University of Nottingham Malaysia Malaysia Continental Tyre Technology Centre Malaysia Petaling Jaya Malaysia Computer Science Department School of Computing and Creative Arts Bina Nusantara University Jakarta Indonesia Information System Management Department BINUS Graduate Program-Master Information Systems Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331519643

ISBN: (纸本)9798331519650

This study aims to develop a system for extracting crucial information from tire sidewalls using Optical Character Recognition (OCR). Initially, images of tire were captured manually by smartphone cameras, including Redmi 9T, iPhone 11, and Galaxy S23 Ultra. The captured images are then transferred to a computer for storage. Subsequently, these images were cropped according to the boundaries identified by Hough Circle Transform (HCT). The cropped images were then further pre-processed. During the pre-processing phase, geometrical transformation and image sharpening techniques are applied to enhance the clarity and readability of the text images. The text is then extracted using Google Vision, with the extracted text categorized by size, DOT, brand and pattern. The results indicated that the effectiveness of image pre-processing was constrained by the accuracy of circle detection, which reached a maximum rate of 87.1%. This causes parts of the text to be cut out inaccurately, leading to a suboptimal extraction accuracy of 55.65%. It is also observed that the Redmi 9T camera produced inconsistent results compared to other devices. Specifically, the iPhone 11 and Samsung Galaxy S23 Ultra demonstrated superior extraction accuracies of 69.71% and 66.37%, respectively, whereas the Redmi 9T achieved a lower extraction accuracy of 37.76%.

关键词： Accuracy Optical character recognition Text categorization Lighting Transforms US Department of Transportation Cameras Tires Internet Data mining

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Effect Implementation of information technology Software on Improving Performance Capacity Academic and Non Academic Service Sunan Ampel Islamic University of Surabaya 2

Effect Implementation of Information Technology Software on ...

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2nd Bukittinggi International Conference on Education, BICED 2020

作者： Ilham Eliyana, Anis Rizki Sridadi, Ahmad Usman, Indrianawati Purnomo, Agung Information Systems Study Program Department of Technology Faculty of Science and Technology Sunan Ampel Islamic University of Surabaya Indonesia Department of Management Faculty of Economics and Business Airlangga University Indonesia Bina Nusantara Institute of Creative Technology Malang Ahmad Yani 117 Street Surabaya East Java Indonesia

This research was conducted to examine the effect of applying information technology software to increase the capacity of academic and non-academic performance at the Sunan Ampel State Islamic University in Surabaya as a work support tool. The aim is to apply a software that is able to identify the needs of increasing performance capacity at Sunan Ampel State Islamic University in Surabaya, both academic and non-academic, so that systematic control and evaluation of all academic and non-academic operations can be carried out. The research respondents were 60 students and employees from the Faculty of science and technology of the Sunan Ampel State Islamic University in Surabaya. This research uses a quantitative approach with the analysis technique used is path analysis with SmartPLS software-based data processing and the overall results support the hypothesis. Based on the results of the study stated that information technology has a positive and significant effect on performance, information technology has a positive and significant effect on academic services, information technology has a positive and significant effect on non-academic services, but performance has no effect on academic and non-academic services. The conclusion of the research is the influence of the application of information technology software can improve academic and non-academic performance and services at the Sunan Ampel State Islamic University of Surabaya © Published under licence by IOP Publishing Ltd.

关键词： Regression analysis

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