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Emerging Technologies for Dependable Internet of Things (ICETI), International Conference on

作者： Belal Al-Fuhaidi Zainab Farae Wedad Al-Sorori Naseebah Maqtary Yahya Al-Ashmoery Farouk Al-Fuhaidy Sadik Al-Taweel Faculty of Computing and IT University of Science and Technology Sana’a Yemen Department of IT Al-Razi University Dept. Mathe Matics & Computer Faculty of Science Sana’a University Sana'a Yemen Department of Electrical Engineering Faculty of Engineering Ibb University Ibb Yemen

ISBN: (数字)9798331533557

ISBN: (纸本)9798331533564

Intrusion detection systems (IDSs) are a necessary principle in WSN security, which can successfully prevent various hackers' and intruders' attempts to hack the network. In this research, we address the problem of achieving high accuracy in detecting intrusions in WSNs due to specific characteristics of WSN data, including the appropriate dataset, the drawbacks of feature selection, and choosing the proper algorithms for the classification process. In this paper, we proposed the anomaly-based IDS model using the DNN algorithm and mutual information (MI) technology to select features. The proposed model has been implemented using the Python language used in the Anaconda platform and relying on the standard NSL-KDD dataset. The experimental results showed the capability of the proposed model to achieve high-performance accuracy in intrusion detection using the DNN algorithm compared to the state-of-the-art. The proposed model outperforms other previous relevant works by 3.65% enhancement in terms of accuracy.

关键词： Wireless sensor networks Accuracy computer hacking Intrusion detection NSL-KDD Classification algorithms Security Internet of Things Standards Mutual information

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Neural Network-based System Identification: A Comprehensive FPGA Design and Implementation

Neural Network-based System Identification: A Comprehensive ...

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Artificial Intelligence and Mechatronics Systems (AIMS), International Conference on

作者： Saif Hasan Abdulnabi Yousif Samer Mudhafar Ali Abdulhassan Kadhim Mohammad Baqer Mahdi Hassan Hadi Sojar Department of Electronic and Communications Engineering Faculty of Engineering University of Kufa Najaf Iraq Department of Electrical Engineering College of Engineering University of Baghdad Al-Jadriya Baghdad Iraq Department of Computer Techniques Engineering Information Technology College Imam Ja'afar Al-Sadiq University Najaf Iraq Computer Techniques Engineering Department College of Technical Engineering The Islamic University Najaf Iraq

Neural networks are more commonly used to identify systems for system diagnostics without fully implementing and building the system. The project aims to design and implement the neural network identification system and implement it based on a Field Programmable Gate Array (FPGA) by interfacing between MATLAB / Simulink and Xilinx programs. The work was done thanks to God through two methods: the first through the simulation method through the coupling between MATLAB and Xilinx. While the second method was practically done by loading the simulation designs onto the FPGA. Work performance is measured by subtracting the value resulting from the actual system operation of the identification signal system and the simplified system and the difference between the simulation result and practical result. The use of FPGA with system identification in this project gives us a big advantage that simulation results are equal to practical results (difference is zero).

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Green Communication in 6G 8

Green Communication in 6G

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8th International Conference on Mechatronics engineering, ICOM 2022

作者： Hoque, Mahfara Binta Farhad, Syeda Sohana Dewanjee, Supriya Alom, Zulfikar Mokhtar, Rania A. Saeed, Rashid A. Khalifa, Othman O. Ali, E.S. Azim, M. Abdul Department of Computer Science Asian University for Women Chattogram Bangladesh Department of Computer Engineering College of Computers and Information Technology Taif University P.O. Box 11099 Taif21944 Saudi Arabia Department of Electrical and Electronics Engineering Faculty of Engineering International Islamic University Malaysia Malaysia The Libyan Center for Engineering Research and Information Technology Bani Walid Libya Sudan

ISBN: (数字)9781839538063

ISBN: (纸本)9781839538063

Green communication networking is a part of sustainable development. It aims to reduce energy consumption and serve the network to a vast number of servers cost-effectively. Green communication integrates artificial intelligence to solve complex mathematical problems for enhancing energy efficiency, flexibility, security, and quality of life services. One of the most remarkable characteristics of today's telecoms sector is wireless communication, used for more than a century. Infrared, radio frequency, satellite, and other electromagnetic waves are used in wireless communication technologies to send data over the air. More data is necessary for the 6G experience. More data is necessary and more excellent ambient sensing and awareness. Sensing systems in automated cars, for example, are exceedingly complicated and rely on cellular networks. Mobile and wireless networks, such as 6G, can use green communication to balance resource usage and conserve energy. In this study, 6G for green communication has been discussed to justify the necessity of 6G. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Wireless networks

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ScrumOntoBDD: Agile software development based on scrum, ontologies and behaviour-driven development

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Journal of the Brazilian computer Society 2021年第1期27卷 1-45页

作者： Lopes de Souza, Pedro Lopes de Souza, Wanderley Ferreira Pires, Luís Department of Computing Federal University of São Carlos São CarlosSP Brazil Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede Netherlands

When developing a Learning Management System (LMS) using Scrum, we noticed that it was quite often necessary to redefine some system behaviour scenarios, due to ambiguities in the requirement specifications, or due to misinterpretations of stories reported by the Product Owners (POs). The definition of test suites was also cumbersome, resulting in test suites that were incomplete or did not at all comply with the system requirements. Based on this experience and to deal with these problems, in this paper, we propose the ScrumOntoBDD approach to agile software development, which combines Scrum, ontologies and Behaviour-Driven Development (BDD). This approach is centred on the concepts and techniques of Scrum and BDD and focuses on the planning and analysis phases of the software life cycle, since the BDD tools currently provide little support to these phases, while most of the problems during the LMS development were found exactly there. We claim that our approach improves the software development practices in this respect. Furthermore, ScrumOntoBDD employs ontologies in order to reduce ambiguities intrinsic to the use of a natural language as a BDD ubiquitous language. In this paper, we illustrate and systematically evaluate our approach, showing that it is beneficial since it improves the communication between members of an agile development team. © 2021, The Author(s).

关键词： Ontology

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Person's multiple intelligence classification based on tweet post using SentiStrength and processed on the Apache Spark framework 1

Person's multiple intelligence classification based on tweet...

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1st South East Asia Science, Technology, engineering and Mathematics International Conference, SEA- STEM 2020

作者： Siregar, B. Misyuari, M.N. Nababan, E.B. Fahmi Department of Information Technology Faculty of Computer Science and Information Technology Universitas Sumatera Utara Indonesia Department of Electrical Engineering Faculty of Engineering Universitas Sumatera Utara Indonesia

A person's self-development is influenced by his ability in solving problems and adapting to his environment. This ability is commonly known as intelligence. Every person has a different dominant intelligence. Honed intelligence can result in managed self-development and self-mastery. A person can discover his talent and expertise to be more advanced in his field. However, discovering one's dominant intelligence requires several variables to assess his behaviours. This research makes use of a person's activities on the social media Twitter. Twitter is a platform on which its users share thoughts. Posted tweets are used as objects to determine the corresponding user's dominant intelligence. The user's tweet will go through a sentiment analysis and intelligence type classification by applying the SentiStrength method to the Apache Spark framework. This research was conducted on 20 Twitter user accounts with the types of intelligence consisting of musical-rhythmic, visual-spatial, verbal-linguistic, logical-mathematical, bodily-kinesthetics, interpersonal, intrapersonal, naturalistic, and spiritual intelligence. The 72% average accuracy is obtained by calculating the correlation between manual testing and the system using the Spearman's rank correlation coefficient (α = 0.05 and the Spearman's value of 0.700). © Published under licence by IOP Publishing Ltd.

关键词： Sentiment analysis

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Identification of Indonesian clickbait news headlines with long short-term memory recurrent neural network algorithm 1

Identification of Indonesian clickbait news headlines with l...

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1st South East Asia Science, Technology, engineering and Mathematics International Conference, SEA- STEM 2020

作者： Siregar, B. Habibie, I. Nababan, E.B. Fahmi Department of Information Technology Faculty of Computer Science and Information Technology Universitas Sumatera Utara Indonesia Department of Electrical Engineering Faculty of Engineering Universitas Sumatera Utara Indonesia

Online news portals are one of the main sources of information currently most accessed by the public. With the number of Internet users increasing day by day, online news portal business entrepreneurs must find ways to remain able to compete to attract netizens' attention to open their news portals. One of the techniques used is to implement clickbait to increase visitor traffic or pageviews. The characteristic of clickbait headlines is that they hide the facts or actual news content in the title section. Clickbait's goal is to get curious Internet users to click on the link with the headline. This research aims to detect Indonesian-language clickbait headlines automatically by utilizing Indonesian language news headline data obtained from several online news portals with the highest amount of traffic on the Internet. This research uses the Recurrent Neural Network method based on Long Short-Term Memory. The model is built using word embedding to represent text data into vector data. The result of this research is a model with a testing accuracy of 83% and a system that is able to classify headlines whether they are clickbait or not. © Published under licence by IOP Publishing Ltd.

关键词： Brain

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Invisible Servoing: a Visual Servoing Approach with Return-Conditioned Latent Diffusion

arXiv

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arXiv 2024年

作者： Gerges, Bishoy Bazzana, Barbara Botteghi, Nicolò Aboudorra, Youssef Franchi, Antonio Robotics and Mechatronics Group Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede7500AE Netherlands Department of Applied Mathematics University of Twente Enschede7500AE Netherlands Department of Computer Control and Management Engineering Sapienza University of Rome Rome00185 Italy

In this paper, we present a novel visual servoing (VS) approach based on latent Denoising Diffusion Probabilistic Models (DDPMs). Opposite to classical VS methods, the proposed approach allows reaching the desired target view, even when the target is initially not visible. This is possible thanks to the learning of a latent representation that the DDPM uses for planning and a dataset of trajectories encompassing target-invisible initial views. The latent representation is learned using a Cross-Modal Variational Autoencoder, and used to estimate the return for conditioning the trajectory generation of the DDPM. Given the current image, the DDPM generates trajectories in the latent space driving the robotic platform to the desired visual target. The approach is applicable to any velocity-based controlled platform. We test our method with simulated and real-world experiments using generic multi-rotor Uncrewed Aerial Vehicles (UAVs). A video of our experiments can be found at https://***/yu-aTxqceOA. Copyright © 2024, The Authors. All rights reserved.

关键词： Trajectories

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Quanv4EO: Empowering Earth Observation by means of Quanvolutional Neural Networks

arXiv

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arXiv 2024年

作者： Sebastianelli, Alessandro Mauro, Francesco Ciabatti, Giulia Spiller, Dario Le Saux, Bertrand Gamba, Paolo Ullo, Silvia Φ-lab European Space Agency Frascati Italy Engineering Department University of Sannio Benevento Italy Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy School of Aerospace Engineering Sapienza University of Rome Rome Italy Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy

A significant amount of remotely sensed data is generated daily by many Earth observation (EO) spaceborne and airborne sensors over different countries of our planet. Different applications use those data, such as natural hazard monitoring, global climate change, urban planning, and more. Many challenges are brought by the use of these big data in the context of remote sensing applications. In recent years, employment of machine learning (ML) and deep learning (DL)based algorithms have allowed a more efficient use of these data but the issues in managing, processing, and efficiently exploiting them have even increased since classical computers have reached their limits. This article highlights a significant shift towards leveraging quantum computing techniques in processing large volumes of remote sensing data. The proposed Quanv4EO model introduces a quanvolution method for preprocessing multi-dimensional EO data. First its effectiveness is demonstrated through image classification tasks on MNIST and Fashion MNIST datasets, and later on, its capabilities on remote sensing image classification and filtering are shown. Key findings suggest that the proposed model not only maintains high precision in image classification but also shows improvements of around 5% in EO use cases compared to classical approaches. Moreover, the proposed framework stands out for its reduced parameter size and the absence of training quantum kernels, enabling better scalability for processing massive datasets. These advancements underscore the promising potential of quantum computing in addressing the limitations of classical algorithms in remote sensing applications, offering a more efficient and effective alternative for image data classification and analysis. © 2024, CC BY-NC-ND.

关键词： Remote sensing

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A Model-Based Approach for Voltage and State-of-Charge Estimation of Lithium-ion Batteries

A Model-Based Approach for Voltage and State-of-Charge Estim...

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Sustainable Power and Energy Conference (iSPEC)

作者： Milad Andalibi Seyed Saeed Madani Carlos Ziebert Farshid Naseri Mojtaba Hajihosseini Department of Control and Computer Engineering Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia Institute of Applied Materials-Applied Materials Physics Karlsruhe Institute of Technology Karlsruhe Germany Electrical and Computer Engineering Department Electrical Energy Technology Aarhus University Aarhus Denmark

ISBN: (纸本)9781665485234

Electric vehicles are equipped with a large number of lithium-ion battery cells. To achieve superior performance and guarantee safety and longevity, there is a fundamental requirement for a Battery Management System (BMS). In the BMS, accurate prediction of the State-of-Charge (SOC) is a crucial task. The SOC information is needed for monitoring, controlling, and protecting the battery, e.g. to avoid hazardous over-charging or over-discharging. Nonetheless, the SOC is an internal cell variable and cannot be straightforwardly obtained. This paper presents a Kalman Filter (KF) approach based on an optimized second-order Rc equivalent circuit model to carefully account for model parameter changes. An effective machine learning technique based on Proximal Policy optimization (PPO) is applied to train the algorithm. The results confirm the high robustness of the proposed method to varying operating conditions.

关键词： Lithium-ion batteries Machine learning algorithms Computational modeling Estimation Voltage Safety State of charge

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Development of Gamelan music instruments using HC-SR04 sensor on arduino and operated using android-based applications 1

Development of Gamelan music instruments using HC-SR04 senso...

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1st South East Asia Science, Technology, engineering and Mathematics International Conference, SEA- STEM 2020

作者： Siregar, B. Hesananta, M.B. Seniman Fahmi Department of Information Technology Faculty of Computer Science and Information Technology Universitas Sumatera Utara Indonesia Department of Electrical Engineering Faculty of Engineering Universitas Sumatera Utara Indonesia

Gamelan is one of Indonesia's traditional music which has become a cultural identity and has survived for a long time. Bonang is a gamelan musical instrument that is played by being hit on the protruding upper part. The importance of preserving traditional music culture makes us realize that traditional music is not lost in today's technological era. This research is to design a virtual bonang musical instrument using the Arduino Mega2560 microcontroller and the HC-SR04 sensor as tone identification. The result of this research is a virtual bonang musical instrument prototype that can be operated via an Android-based smartphone. This prototype is also useful for introducing art as part of STEAM to students. © Published under licence by IOP Publishing Ltd.

关键词： Music

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