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IEEE International Conference on Big Data

作者： Krzysztof Hanzel Damian Grzechca Adam Ziebinski Lukas Chruszczyk Artur Janus Department of Telecommunications and Teleinformatics Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice Poland Department of Electronics Electrical Engineering and Microelectronics Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice Poland Department of Distributed Systems and Informatics Devices Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice Poland Student of Electrical Engineering Faculty of Electrical Engineering Silesian University of Technology Gliwice Poland

The paper describes the energy consumption from the battery based on the current measurements for various cases, i.e., speed (PWL adjustment) and loads. The main purpose of the research is to have additional and reliable information about power consumption and battery life estimation for autonomous guided vehicles (AGV). The authors propose a two-step algorithm. In the first step, a linear classifier was proposed. Then, the KNN classifier was tested; however, it did not give satisfactory results, so it was finally decided to use the random forest to estimate the load and PWL. The time domain current measurement is evaluated, and the beforementioned algorithms process the selected statistical measures. It has been proven that a two-step algorithm allows for achieving high accuracy. Based on the current observation, the paper is a good starting point for further investigation of the AGV because it is usually implemented in the AGV – so it does not require additional hardware. Moreover, it can lead to better energy management and increase battery lifetime.

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Accuracy Evaluation of Stereo Visual SLAM with Unnecessary Feature Point Elimination Using Blur Processing for AR Surgical Support System

Accuracy Evaluation of Stereo Visual SLAM with Unnecessary F...

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International Conference on Intelligent Informatics and Biomedical sciences (ICIIBMS)

作者： Masanao Koeda Yuto Kaihara Atsuro Sawada Akihiro Hamada Toshihiro Magaribuchi Osamu Ogawa Katsuhiko Onishi Hiroshi Noborio Faculty of Computer Science and Systems Engineering Okayama Prefectural University Okayama Japan Department of Urology Graduate School of Medicine Kyoto University Kyoto Japan Faculty of Information Science and Arts Osaka Electro-Communication University Osaka Japan

In this study, we describe an evaluation of the positional accuracy of stereo Visual SLAM with noisy feature points removed by blurring. We constructed an experimental environment that intentionally degrades the accuracy of Visual SLAM. In this environment, we placed the objects that are likely to generate noisy feature points. We evaluated the accuracy of the estimated positions of the original video and the video in which blurred regions of noise in the original video were provided to Visual SLAM. Consequently, we confirmed that the accuracy was improved compared to that of the original video.

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Efficient Malicious Traffic Classification Methods based on Semi-supervised Learning 9

Efficient Malicious Traffic Classification Methods based on ...

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9th International Conference on Dependable systems and Their Applications, DSA 2022

作者： Hu, Xiaoyi Ning, Jinhui Yin, Jie Yang, Jie Adebisi, Bamidele Gacanin, Haris College of Telecommunications and Information Engineering Njupt Nanjing China Jiangsu Police Institute Department of Computer Information and Cyber Security Nanjing China Manchester Metropolitan University Faculty of Science and Engineering Manchester United Kingdom Institute for Communication Technologies and Embedded Systems Rwth Aachen University Aachen Germany

ISBN: (数字)9781665488778

ISBN: (纸本)9781665488778

The proliferation of mobile communication systems, arrival of high-speed broadband networks and more complex network topologies have exacerbated cyber-threats. Cyber-warfare has become an aspect of modern war-fare that can no longer be overlooked. In recent years, network intrusions launched using the Internet have seriously undermined the security systems of many nations. Classifying malicious network traffic is the first step in network intrusion detection. In this paper, we propose three models using semi-supervised learning-based malicious traffic classification (MTC) methods that effectively improve the classification of traffic using a small proportion of labeled traffic data. Employing three different deep neural networks as feature extraction networks respectively, the proposed models use transductive transfer learning and domain adaptive ideas, and ladder networks as classification layers. Experimental results are provided to validate the proposed methods. © 2022 IEEE.

关键词： Complex networks

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Open-Ended Coaxial Probe Based Chloride Concentration Prediction in Water Using Machine Learning Algorithms

Open-Ended Coaxial Probe Based Chloride Concentration Predic...

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International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Hanima Kannan C H Veena Venugopal Deepa Gupta Parul Mathur Dhanesh G Kurup Priyanka Mathur Department of Electronics and Communication Engineering RF and Wireless Systems Laboratory Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Department of Physical Science Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India School of Engineering and Applied Science Benette University Greater Noida UP India

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

Monitoring chloride levels in water is essential for environmental health, industrial processes, and safe drinking water, as both high and low concentrations can cause harm for living being. This paper presents a novel approach to detecting chloride concentrations using Open-Ended Coaxial Probe (OECP) sensing combined with machine learning algorithms. OECP sensing offers real-time, non-destructive, and precise monitoring, enabling early detection of contaminants. Scattering parameter ( $S$ 11) data across a broad frequency range was collected with varying chloride concentrations in controlled laboratory experiments. Thirteen machine learning algorithms were trained to predict chloride concentrations, with different datasets for training and testing to ensure robustness. Among these, CatBoost demonstrated the best performance. After fine tuning and cross-validation, the CatBoost model achieved a test Mean Square Error (MSE) of 0.00627 and Coefficient of Determination (R2) of 0.9936, showcasing its accuracy for applications in water monitoring, corrosion prevention, and safeguarding public health.

关键词： Machine learning algorithms Water quality Predictive models Real-time systems Sensors Scattering parameters Probes Public healthcare Tuning Testing

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NOMA Cooperative Spectrum Sharing Average Capacity Improvement in 5G Network

NOMA Cooperative Spectrum Sharing Average Capacity Improveme...

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sciences and Techniques of Automatic Control and computer Engineering MI-STA, IEEE International Maghreb Meeting of the Conference on

作者： Mohamed Hassan Rashid A. Saeed Rania A. Mokhtar Khalid Hamid Elmustafa Sayed Ali Nidhal Odeh Manwinder Singh Othman O. Khalifa Shayla Islam Department of Wireless Communication Lovely Professional University Phagwara Punjab India Department of Computer Engineering College of Computers and Information Technology Taif University Taif Saudi Arabia Department of Communication Systems Engineering University of Science & Technology Omdurman Sudan Department of Electronics Engineering Sudan University of Science and Technologu (SUST) Khartoum Sudan School of Electrical Computer and Telecommunications Engineering University of Wollongong Australia Department of Electrical and Computer Engineering International Islamic University Malaysia Gombak Malaysia & Libyan Center for Engineering Resarch and Information Technology Bani Waleed Libya Department of Computer Science Institute of Computer Science and Digital Innovation UCSI University Malaysia Kuala Lumpur Malaysia

One of the most effective approaches to satisfying the requirements of the next generation of wireless services is non-orthogonal multiple access (NOMA). Spectrum efficiency and average capacity are two aspects of wireless communication that have become increasingly important as the number of mobile services in use has increased. Using both traditional MIMO and Massive MIMO (M-MIMO), this study proposes two new methods for increasing the average capacity of a 5G network's downlink (DL) NOMA power domain (PD) in conjunction with a Cooperative Cognitive Radio Network (CCRN). The initial method involves NOMA users competing for available rivalry channels (R-CH) on the CCRN. The second technique creates a dedicated channel (D-CH) for NOMA users. The MATLAB program is utilized to evaluate the proposed methods in three scenarios with varying distances, power location coefficients, and transmission power. Successive interference cancellation (SIC) and unstable channel conditions are also considered while assessing the analysis of the proposed system under the assumption of Rayleigh fading. Using DL NOMA PD systems, The results reveal that the higher average capacity of best at transmit power DL NOMA with CCRN (dedicated channel) improved U4 average capacity performance by M-MIMO DL NOMA with CCRN (D-CH) improved by when compared with convention DL NOMA. Formulas in closed form were found for average capacity. The resulting formulas are verified using a Monte Carlo simulation.

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BER Improvement of Cooperative Spectrum Sharing of NOMA in 5G Network

BER Improvement of Cooperative Spectrum Sharing of NOMA in 5...

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sciences and Techniques of Automatic Control and computer Engineering MI-STA, IEEE International Maghreb Meeting of the Conference on

作者： Mohamed Hassan Rashid A. Saeed Rania A. Mokhtar Khalid Hamid Elmustafa Sayed Ali Nidhal Odeh Manwinder Singh Othman O. Khalifa Shayla Islam Department of Wireless Communication Lovely Professional University Phagwara Punjab India Department of Computer Engineering College of Computers and Information Technology Taif University Taif Saudi Arabia Department of Communication Systems Engineering University of Science & Technology Omdurman Sudan Department of Electronics Engineering Sudan University of Science and Technologu (SUST) Khartoum Sudan School of Electrical Computer and Telecommunications Engineering University of Wollongong Australia Department of Electrical and Computer Engineering International Islamic University Malaysia Gombak Malaysia Libyan Center for Engineering Resarch and Information Technology Bani Waleed Libya Department of Computer Science Institute of Computer Science and Digital Innovation UCSI University Malaysia Kuala Lumpur Malaysia

Non-orthogonal multiple access is one of the best methods for addressing the needs of 5G wireless services (NOMA). The proliferation of mobile devices in recent years has increased the importance of cooperative spectrum sharing and utilization in wireless communication due to increases in the bit error rate (BER) brought on by collisions and interference. In this study, MIMO and Massive MIMO (M-MIMO) in the downlink (DL) NOMA power domain (PD) in conjunction with the Cooperative Cognitive Radio Network are proposed as two new ways for improving and evaluating BER in the 5G network (CCRN). Customers of NOMA compete for available channels on the CCRN in this first strategy. The second approach creates a dedicated channel for NOMA users. Three scenarios with varying distances, power location coefficients, and transmission power are used to evaluate the proposed methods in the MATLAB software program. It is assumed that four users will share a 90 MHz BW using QPSK modulation in all three scenarios. When evaluating the performance of the suggested system under the presumption of frequency-selective Rayleigh fading, concurrent interference cancellation (SIC) and unstable channel conditions are also considered.

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Proposed Avatar Model for Sustainable Smart Tourism Growth: Systematic Literature Review

Proposed Avatar Model for Sustainable Smart Tourism Growth: ...

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International Conference on ICT For Smart Society (ICISS)

作者： Siti Elda Hiererra Satrya Mahardhika Suhono Harso Supangkat Fredy Purnomo Meyliana Arief Ramadhan Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Visual Communication Design Department School of Design Bina Nusantara University Jakarta Indonesia Smart Cities and Communities Innovation Centre Institute of Technology Bandung Bandung Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia School of Computing Telkom University Bandung Indonesia

The development of avatars can significantly impact the sustainable growth of smart tourism. Avatars, virtual representations of individuals, can enhance the tourism experience by providing personalized and immersive. This article uses the systematic literature review (SLR) method to investigate the avatar model development and sustainable smart tourism growth topics. This study aims to identify and analyze the main factors of avatar development and whether the proposed model of the avatar is suitable to support sustainable smart tourism. Two research questions (RQ) were determined and converted into the search term. This study's research results in 30 articles as references. The result of this study is the six main factors of avatar development and a proposed Avatar Model, which can be a reference for the stakeholders (game developer, visual designer, animator, tourism business owner, etc.) to improve their service to users/tourists. The limitations of this study are the database range and keyword search usage. Theoretical implication for academics is in sustainable smart tourism and avatar development areas. The future work is research can be conducted in empiric method with the respondent is Gen Z directly, and also how the stakeholders can easily adopt the proposed avatar model.

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Effects of Meteorological Parameters on Microwave Propagation Modeling During Dust Storm

Effects of Meteorological Parameters on Microwave Propagatio...

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International Conference on Mechatronics (ICOM)

作者： E.I. Eltahir Elfatih A. A. Elsheikh Md Rafiqul Islam Mohamed Hadi Habaebi Aisha Hassan A. Hashim Mosab Hamdan Department of Electrical and Computer Engineering International Islamic University Malaysia (IIUM) Kuala Lumpur Malaysia Department of Electrical Engineering King Khalid University(KKU) Abha Saudi Arabia Walton Institute for Information and Communication Systems Science South East Technological University Waterford Ireland

ISBN: (数字)9798350349788

ISBN: (纸本)9798350349795

Dust storms are complex meteorological events that significantly impact the propagation of microwave signals, presenting challenges to prediction models which often rely on theoretical scattering principles without considering various meteorological parameters. In this study, meteorological data-including pressure, visibility, wind speed, temperature, and humidity-were monitored concurrently with signal levels at a 14.4 GHz microwave link operated in Khartoum, Sudan, over the course of a year. The variations in received signal levels were analyzed in relation to these parameters. Initial theoretical predictions, based on measured visibility and dust characteristics, underestimated the actual attenuation observed. Through detailed analysis, it was observed that humidity directly affects dust particle properties. By reassessing the models to incorporate the influence of humidity and other meteorological factors, the predictive accuracy of microwave signal attenuation during dust storms was significantly increased from 0.096 dB/km to 0.576 dB/km for Rayleigh approximation at the visibility limit of 83 m. This research highlights the importance of including comprehensive meteorological data in prediction models to enhance their accuracy in real-world conditions.

关键词： Temperature measurement Microwave integrated circuits Storms Atmospheric modeling Wind speed Humidity Predictive models

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Student Perceptions of AI-Enhanced Adaptive Learning systems: A Pilot Survey

Student Perceptions of AI-Enhanced Adaptive Learning Systems...

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International IEEE Conference and Workshop: Óbuda on Electrical and Power Engineering (CANDO-EPE)

作者： Klara Ida Katonane Gyonyoru Jozsef Katona Department of Organizational Development and Communication Science Institute of Social Sciences University of Dunaujvaros Dunaujvaros Hungary Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Obuda University Budapest Hungary Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemet Hungary Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest Business University Budapest Hungary

ISBN: (数字)9798331531119

ISBN: (纸本)9798331531126

Artificial Intelligence (AI) significantly enhances adaptive learning by personalizing and tailoring instruction to individual student needs. AI analyzes data in real-time to create personalized learning paths based on students' strengths, weaknesses, and preferences, which keeps students engaged and motivated. A major benefit of AI in adaptive learning is the provision of real-time feedback and assessment, allowing students to correct mistakes promptly and understand concepts more thoroughly. AI-based intelligent tutoring systems are primarily intended to simulate personalized tutoring processes that guide students in complex problem-solving and answering questions. It is convenient in teaching mathematics, sciences, and languages. AI also supports inclusive education, dealing with diversified learning requirements and styles, such as those of learners with disabilities. For the teacher, AI acts as a reflector of student performance so that one can intervene early and make adjustments in the method of instruction by creating effective learning environments. AI technology is a field in constant development and harbors the potential to change the face of adaptive learning, bringing an upswing in educational outcomes. This article will summarize the advantages and features that merit improvement of the AI-embedded adaptive learning systems with student feedback.

关键词： Surveys Adaptive learning Power engineering Data security Real-time systems Mathematics Problem-solving Artificial intelligence Faces

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Game theory-based optimization for efficient IoT task offloading in 6G network base stations

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Measurement: Sensors 2024年 33卷

作者： Keshta, Ismail Soni, Mukesh Deb, Nabamita singh, Shweta Saravanan, K. Raza Khan, Dr Ihtiram Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia Dr. D. Y. Patil Vidyapeeth Pune Dr. D. Y. Patil School of Science & Technology Tathawade Pune India Assistant Professor Department of Information Technology Gauhati University India Department of Information Technology R.M.D. Engineering College Chennai India Computer Science Department Jamia Hamdard Delhi India Electronics and communication department IES college of technology Bhopal India

This study aims to optimise computing for intricate jobs within the overlapping coverage of 6G network base stations. A multi-access edge computing network model is created by solving the issues of task offloading. This model involves many base stations and IoT devices. It takes into account factors such as task delay, energy consumption, societal impacts, and economic incentives. The objective of joint optimization is to maximize the revenues of base stations and the utilities of IoT devices by considering base station price, IoT device base station selection, and job offloading mechanisms. The IoT device base station selection is addressed using a many-to-one matching game model, while pricing and task offloading interactions are managed by a two-stage approach based on Stackelberg game theory. The suggested Optimal Pricing and Best Response Algorithm (OBGT), which is based on Game Theory, successfully reaches equilibrium solutions. It demonstrates rapid convergence in simulations and improves both base station profits and IoT device utility. This study integrates data-driven mobile computing systems assurance for futuristic smart vertical networks, enhancing the efficiency of task offloading and optimization. © 2024 The Authors

关键词： Game theory

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