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12th International Symposium on Information and Communication Technology, SOICT 2023

作者： Pham-Hai, Dang Duong-Bao, Ninh He, Jing Nguyen Thi, Luong Lee, Seon-Woo Nguyen-Huu, Khanh Department of Electronics and Telecommunications Dalat University Dalat Viet Nam College of Computer Science and Electronic Engineering Hunan University Changsha China Faculty of Information Technology Dalat University Dalat Viet Nam Division of Software Hallym University Chuncheon Korea Republic of

ISBN: (纸本)9798400708916

Indoor positioning systems are receiving much attention due to their practical applications in our daily lives. WiFi Fingerprinting is the technique that is commonly used for indoor positioning due to its utilization of the WiFi infrastructure which is ubiquitously deployed in modern buildings. However, this technique suffers from the computational burden when determining the user's position in big areas that consist of lots of fingerprints and reference points. To reduce the search space as well as computing time, the clustering algorithm can be applied. In this paper, we evaluate the efficacy of k-means clustering in the WiFi Fingerprinting-based positioning system. Moreover, k-means clustering with outlier removal is applied in several public datasets, in which the WiFi data was collected in different environmental conditions, to show how much the clustering algorithm can help in reducing the computing time as well as improving the positioning accuracy. From the experimental results, compared to popular deterministic-based algorithms, the clustering algorithm can significantly reduce the computing time by up to 79.07%. Nevertheless, it cannot surpass the positioning accuracy of the compared algorithms. © 2023 ACM.

关键词： Indoor positioning systems

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Synthetic Minority Over-sampling Technique for Student Performance Prediction: A Comparative Analysis of Ensemble and Linear Models

Synthetic Minority Over-sampling Technique for Student Perfo...

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International Conference on computer and Information Technology (ICCIT)

作者： Khaled Mahmud Sujon Rohayanti Hassan Nusrat Jahan Department of Software Engineering Faculty of Computing Universiti Teknologi Malaysia (UTM) Johor Malaysia Faculty of Computing Universiti Teknologi Malaysia (UTM) Johor Malaysia Department of Computer Science and Engineering Chittagong University of Engineering & Technology(CUET) Chittagong Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

This study explores the impact of the Synthetic Minority Oversampling Technique (SMOTE) on predictive accuracy in student performance prediction using both linear (Logistic Regression, LR) and ensemble (Random Forest, RF) models. We compare the performance of these models, using a real-time dataset from Universiti Teknologi Malaysia (UTM). While previous studies have examined these models separately, this research uniquely investigates the simultaneous application of SMOTE to both. Our results show that LR’s accuracy improves from 91% to 95% with SMOTE, while RF maintains a consistent 98% accuracy. These findings demonstrate that SMOTE significantly enhances the performance of linear models in handling imbalanced data, providing useful insights for curriculum development in higher education. This research contributes to the literature by guiding higher education institutions in selecting the most effective predictive models.

关键词： Radio frequency Analytical models Logistic regression Accuracy Computational modeling Education Predictive models Data models Real-time systems Information technology

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An Asymmetrical Amplitude Digitized Modulation Scheme for TP-IBMC in Unbalanced Three-Phase IPT Systems

An Asymmetrical Amplitude Digitized Modulation Scheme for TP...

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IEEE Annual Southern Power Electronics Conference (SPEC)

作者： Zhihao He Duleepa J. Thrimawithana Martin Neuburger Grant A. Covic Department of Electrical Computer and Software Engineering University of Auckland Auckland New Zealand Faculty of Mobility and Technology Esslingen University of Applied Sciences Goppingen Germany

ISBN: (数字)9798350351156

ISBN: (纸本)9798350351163

This paper proposes a three-phase asymmetrical amplitude digitized modulation (TP-AADM) scheme for a three-phase integrated boost multi-level converter (TP-IBMC) utilized on an inductive power transfer (IPT) system with a three-to-three coupler and the double-sided LCL tuning network. It can control these output voltages driving the coupler independently in order to generate balanced currents in the secondary coils or the rectifier with the misalignment situation while the conventional solutions require three separated H-bridge converters. A simulation model in accordance with the SAEJ2954 WPT3/Z3 indicates the performance in the misaligned IPT system.

关键词： Multilevel converters Modulation Rectifiers Couplers Wireless power transfer Rendering (computer graphics) Regulation Voltage control Tuning Standards

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Optimized Flame Detection Using a YOLOv5 Neural Network

Optimized Flame Detection Using a YOLOv5 Neural Network

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2024 International Conference on Computing, Robotics and System Sciences, ICRSS 2024

作者： Karim, Shahid Ibrar, Muhammad Bilal, Anas Qadir, Akeel Bibi, Irfana School of Information Engineering Xi'an Eurasia University Xi'an710065 China Software College Shenyang Normal University Shenyang China College of Information Science and Technology Hainan Normal University Haikou571158 China Faculty of Computing and Information Technology University of Punjab Department of Computer Science Punjab Lahore Pakistan

ISBN: (纸本)9798331511296

With the rising prevalence of smart homes, there's an increasing demand for comprehensive automation solutions to mitigate fire risks, especially when homeowners are absent or in homes with elderly residents. This study focuses on enhancing flame detection techniques, particularly for smaller, hard-to-detect fire sources. A customized Yolov5 neural network was developed to improve detection precision. This enhancement was facilitated using a dataset of over 2,000 images from lab camera observations and online platforms. These images were then manually labeled and clustered for anchor dimensions using the K-means++ algorithm. The CIOU and diou - nms techniques were integrated to optimize the network's performance. Upon training, the refined model was tested on the Jet Nano, revealing its high efficacy in flame detection. © 2024 IEEE.

关键词： Smart homes

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On the Stability of Strategic Energy Storage Operation in Wholesale Electricity Markets (Extended Version)

arXiv

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arXiv 2024年

作者： Navon, Aviad Belikov, Juri Orda, Ariel Levron, Yoash The Andrew and Erna Viterbi Faculty of Electrical and Computer Engineering Technion—Israel Institute of Technology Israel The Department of Software Science Tallinn University of Technology Estonia

High shares of variable renewable energy necessitate substantial energy storage capacity. However, it remains unclear how to design a market that, on the one hand, ensures a stable and sufficient income for storage firms, and, on the other hand, maintains stable and affordable electricity costs for the consumers. Here, we use a game theoretic model to study storage competition in wholesale electricity markets. A main result is that these types of games are not necessarily stable. In particular, we find that under certain conditions, which imply a combination of a high share of variable renewable energy sources and low flexibility of conventional power plants, the system will not converge to an equilibrium. However, we demonstrate that a price cap on storage price bids can ensure convergence to a stable solution. Moreover, we find that when the flexibility of conventional power plants is low, while the storage usage for energy balancing increases with renewable energy generation, the profitability of using storage for the sole purpose of energy arbitrage decreases. Copyright © 2024, The Authors. All rights reserved.

关键词： Game theory

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A comprehensive study of the Design and Security of the IoT layer Attacks 6

A comprehensive study of the Design and Security of the IoT ...

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6th IEEE International Conference on Communication and Electronics Systems, ICCES 2021

作者： Rajan, M.Sundar Arunkumar, J.R. Ramasamy, Anusuya Sisay, Berhanu Arbaminch Institute of Technology Arbaminch University Faculty of Electrical and Computer Engineering Ethiopia Arbaminch Institute of Technology Arbaminch University Faculty of Computing and Software Engineering Ethiopia

ISBN: (纸本)9781665435871

IoT is the network of thousands of individuals, resources and expertise interconnecting and sharing. IoT implementations are strongly validated by increased usability, reliability and automation. Authentication and recovery are important in order to set up an automated IoT environment for the protection and privacy of attacks. This research work presents a survey on the design and security of the IoT layer attack. In this thesis, security and privacy concerns will be detailed. This paper also performs a comprehensive overview on the security threats and sources of threat in IoT applications addressing current IoT security principles and IoT protection and privacy potential proposals. © 2021 IEEE.

关键词： Radio frequency identification (RFID)

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Mi-Go: Test Framework which uses YouTube as Data Source for Evaluating Speech Recognition Models like OpenAI’s Whisper

arXiv

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arXiv 2023年

作者： Wojnar, Tomasz Hryszko, Jaroslaw Roman, Adam Jagiellonian University Faculty of Mathematics and Computer Science Division of Software Engineering Lojasiewicza 6 Krakow30-348 Poland

This article introduces Mi-Go, a novel testing framework aimed at evaluating the performance and adaptability of general-purpose speech recognition machine learning models across diverse real-world scenarios. The framework leverages YouTube as a rich and continuously updated data source, accounting for multiple languages, accents, dialects, speaking styles, and audio quality levels. To demonstrate the effectiveness of the framework, the Whisper model, developed by OpenAI, was employed as a test object. The tests involve using a total of 124 YouTube videos to test all Whisper model versions. The results underscore the utility of YouTube as a valuable testing platform for speech recognition models, ensuring their robustness, accuracy, and adaptability to diverse languages and acoustic conditions. Aditionally, by contrasting the machine-generated transcriptions against human-made subtitles, the Mi-Go framework can help pinpoint potential misuse of YouTube subtitles, like Search Engine Optimization. Copyright © 2023, The Authors. All rights reserved.

关键词： Speech recognition

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A Proposal for a New E-Waste Image Dataset Based on the UNU-KEYS Classification

A Proposal for a New E-Waste Image Dataset Based on the UNU-...

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International Symposium on Electrical Apparatus and Technologies (SIELA)

作者： Dimitar I. Iliev Marin B. Marinov Frank Ortmeier Faculty of German Engineering and Industrial Management Technical University of Sofia Sofia Bulgaria Department of Electronics Faculty of Electronic Engineering and Technologies Technical University of Sofia Sofia Bulgaria Faculty of Computer Science Chair of Software Engineering Otto-von-Guericke- University Magdeburg Magdeburg Germany

ISBN: (数字)9798350382365

ISBN: (纸本)9798350382372

This paper introduces the largest and most diverse e-waste dataset to date, consisting of 19,613 pictures, 28,941 annotations, and 77 classes, where each class represents one visually distinctive type of electronic device. Furthermore, our dataset was structured using the United Nations University's UNU-KEYS classification, which ensures the compatibility of our dataset with the different e-waste classifications used worldwide and allowed us to choose more relevant dataset classes. The proposed methodology for image collection, annotation, and labeling is explained. The advantages and disadvantages of this dataset and how it can be improved in the future are presented. This dataset has been made open source with a permissive CC by 4.0 license so that it can be used to stimulate scientific progress in the field of automatic e-waste recognition, with the end goal of solving the global e-waste problem. The dataset is available online at https://***/electronic-waste-detection/e-waste-dataset-r0ojc.

关键词： Annotations Data acquisition Documentation Electronic waste Licenses Planning Proposals

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Performance Improvement in Blood Cell Segmentation with Deep Learning-based Image Fusion Approach

Performance Improvement in Blood Cell Segmentation with Deep...

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International Artificial Intelligence and Data Processing Symposium (IDAP)

作者： Fatma Tuana Dogu Hulya Dogan Ramazan Ozgur Dogan F. Sena Sezen Department of Electrical and Electronics Engineering Faculty of Engineering Karadeniz Technical University Trabzon Turkey Department of Software Engineering Faculty of Engineering Karadeniz Technical University Trabzon Turkey Department of Artificial Intelligence Engineering Faculty of Computer and Information Sciences Trabzon University Trabzon Turkey Department of Pharmacology Faculty of Pharmacy Karadeniz Technical University Trabzon Turkey

ISBN: (数字)9798331531492

ISBN: (纸本)9798331531508

To overcome the limitations of examining blood cells under a microscope, several investigations have been carried out using artificial intelligence techniques. These investigations aim to automate the processes of detecting, counting, segmenting, and classifying blood cells. Typically, these investigations employ two-dimensional (2D) images to apply artificial intelligence and image processing techniques. Nevertheless, these 2D images occasionally lack optimal focus as a result of the microscope’s depth of focus (DOF). Inadequate achievement of optimally focused imaging has a detrimental impact on the efficiency of automated processes. In this context, the objective of this study is founded around two primary roles. The first of these is the performance improvement of blood cell segmentation by developing a deep learning-based image fusion approach. The second is the preparation of new data sets containing images with different focuses for optimally focused imaging of blood cells. In the study, four different performance evaluation criteria, namely Accuracy (ACC), Sensitivity (SEN), Specificity (SPE), and Jaccard, are utilized to demonstrate the efficacy of the deep learning-based image fusion approach in blood cell segmentation. Both the experimental results of the performance evaluation criteria and the visual findings obtained in the study indicate that the performance in blood cell segmentation is improved with a deep learning-based approach.

关键词： Performance evaluation Image segmentation Visualization Accuracy Microscopy Two-dimensional displays Cells (biology) Artificial intelligence Blood Image fusion

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A Novel Trust Assessment System for Online Social Networking Environment Using Learning Assisted Classification Model

A Novel Trust Assessment System for Online Social Networking...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Nithya, S. Deepa, D. Babu, Vunnava Dinesh Pallavi, G. Fawareh, Hamed Kayalvizhy, R.D. Saveetha Engineering College Department of Computer Science Engineering Chennai India St. Joseph's College of Engineering Department of Cse Chennai India Rv Institute of Technology Department of Cse Guntur India Laxminarayan Innovation Technological University Department of Civil Engineering Nagpur India Zarqa University Faculty of Information Technology Software Engineering Dept Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia R.M.K. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Kavaraipettai India

ISBN: (纸本)9798331543617

Trust is essential in the constantly developing environment of online social networks. The Nexus Terroism Impact and Intro Trust facilitate the communication, decision-making, and maintenance of digital communities among individuals. Trust-based Learning with Classification Principle (TLCP) is introduced in this research to improve the assessment of trust in social networks. To ascertain its efficacy, our proposed methodology undergoes a 10-fold cross-validation against the Trust Assessment Methodology (TAM). The traditional Trust Assessment Model (TAM) evaluates and regulates trust in Internet social networking. This method incorporates trust assessment factors to generate a user dependability judgment that is both discriminatory and dynamic. The Trustworthy Account Management (TAM) challenge is designed to enhance digital trust by addressing disinformation, deception, and hostile conduct. The TLCP is able to accurately evaluate trust by utilizing reputation scoring, community analysis, and user behavior metrics. Trust levels are dynamically adjusted as a result of the identification of trustworthy behavioral patterns and danger indicators by machine learning algorithms and social media data. This research examines the connections between trust management and digital interactions and suggests a general solution to alleviate specific trust evaluation issues across online social networks. © 2024 IEEE.

关键词： Adversarial machine learning

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