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28th International Conference on Knowledge Based and Intelligent information and engineering systems, KES 2024

作者： Kobiela, Dariusz Groth, Jan Hajdasz, Michal Erezman, Mateusz Gdansk University of Technology Faculty of Electronics Telecommunications and Informatics Department of Software Engineering Gabriela Narutowicza 11/12 Gdansk80-233 Poland Gdansk University of Technology Faculty of Electronics Telecommunications and Informatics Department of Algorithms and Systems Modelling Gabriela Narutowicza 11/12 Gdansk80-233 Poland Gdansk University of Technology Faculty of Electronics Telecommunications and Informatics Gabriela Narutowicza 11/12 Gdansk80-233 Poland

The goal of the research was to demonstrate the full data science lifecycle through a use case of the MobileNetv2 model for vehicle image classification task using various validation and test sets, each with different difficulty level. Diverse model variations were employed, each designed to recognize images of ground vehicles and classify them into one of five possible classes: car, truck, motorcycle, bicycle, or bus. In terms of validation accuracy, the highest results were obtained by the model trained with uniformly designed train and val sets (with data normalization and augmentation), where train set also contained validation set. This model also obtained the highest accuracy results on both test sets. The superiority of MODEL 3 BASELINE is confirmed by other metrics as well: test loss, f1-score, AUC and confusion matrices (for both test sets). Results between MODEL 1 BASELINE and MODEL 2 BASELINE differed according to the test set 1 and 2 and other metrics and it was not possible to declare the superiority of one method of datasets preparation over another (original class distribution [no data normalization and no data augmentation] versus uniformly designed [with data normalization and augmentation]). The article also presents challenges and findings - the problems, key issues, and their solutions that arose during the process of data collection and tagging, as well as the preparation and evaluation of the model. © 2024 The Authors.

关键词： Classification (of information)

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Extreme Value Analysis for Time-variable Mixed Workload

Periodica polytechnica Electrical engineering and computer s...

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Periodica polytechnica Electrical engineering and computer science 2022年第1期66卷 1-11页

作者： Bozóki, Szilárd Pataricza, András Department of Measurement and Information Systems Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Magyar tudósok krt. 2 BudapestH-1117 Hungary

Proper timeliness is vital for a lot of real-world computing systems. Understanding the phenomena of extreme workloads is essential because unhandled, extreme workloads could cause violation of timeliness requirements, service degradation, and even downtime. Extremity can have multiple roots: (1) service requests can naturally produce extreme workloads;(2) bursts could randomly occur on a probabilistic basis in case of a mixed workload in multiservice systems;(3) workload spikes typically happen in deadline bound tasks. Extreme Value Analysis (EVA) is a statistical method for modeling the extremely deviant values corresponding to the largest values. The foundation mathematics of EVA, the Extreme Value Theorem, requires the dataset to be independent and identically distributed. However, this is not generally true in practice because, usually, real-life processes are a mixture of sources with identifiable patterns. For example, seasonality and periodic fluctuations are regularly occurring patterns. Deadlines can be purely periodic, e.g., monthly tax submissions, or time variable, e.g., university homework submission with variable semester time schedules. We propose to preprocess the data using time series decomposition to separate the stochastic process causing extreme values. Moreover, we focus on the case where the root cause of the extreme values is the same mechanism: a deadline. We exploit known deadlines using dynamic time warp to search for the recurring similar workload peak patterns varying in time and amplitude. © 2022 Budapest University of Technology and Economics. All rights reserved.

关键词： Time series

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DOA-Estimations-Based 3D Geolocations Using Factor Graphs 11

DOA-Estimations-Based 3D Geolocations Using Factor Graphs

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2023 IEEE 11th International Conference on Information, Communication and Networks, ICICN 2023

作者： Qian, Shen Cheng, Meng Kanagawa University Faculty of Informatics Department of Applied Systems and Mathematics Yokohama Japan Shanghai Normal University The College of Information Mechanical and Electrical Engineering Shanghai China

ISBN: (纸本)9798350314014

This paper proposes direction of arrival (DOA)-estimation-based three-dimensional (3D) positioning techniques using factor graphs. The multiple signal classification (MUSIC), root-MUSIC, and estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithms are used to estimate the DOA of the signals received by multiple sensors. A factor graph (FG) detector updates and refines the measured DOA for obtaining the target positions. Since only the mean and the variance of the DOA are needed for the factor graph process, the burden of the data transmission of the uplink from the sensors to the fusion center and the complexity of the positioning is reduced significantly. The effectiveness of the proposed method is demonstrated through simulations. No matter which DOA estimation method is used, MUSIC, root-MUSIC, or ESPRIT, FG algorithms achieve higher positional accuracy compared to the linear least square (LS) positioning solution in terms of the root mean square error (RMSE). © 2023 IEEE.

关键词： Multiple signal classification

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Deep learning-based blood pressure measurement on a low-power microcontroller 14

Deep learning-based blood pressure measurement on a low-powe...

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14th IEEE International Conference on Cognitive Infocommunications, CogInfoCom 2023

作者： Tóth, Bálint Torma, Szabolcs Nagy, Ákos Szegletes, Luca Department of Measurement and Information Systems Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Műegyetem rkp. 3. BudapestH-1111 Hungary Department of Automation and Applied Informatics Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Műegyetem rkp. 3. BudapestH-1111 Hungary

ISBN: (纸本)9798350325652

Blood pressure is one of the most vital signals characterizing the health status of the human body. Based on this physiological data, many current and future diseases can be detected. Therefore, having a device that can continuously measure blood pressure without a cuff or medical intervention would be highly beneficial. This study aims to investigate noninvasive blood pressure measurement with the help of a low-power microcontroller, namely STM32F446RE. These measuring methods are based on deep neural networks and utilize externally measured photoplethysmography (PPG) and electrocardiogram (ECG) signals. The performance of the proposed models is compared to similar solutions from the literature and inspected for memory usage and runtime. The results of this work show that a convolutional layer-based model can be used for proper blood pressure estimation in such an energy-efficient device. © 2023 IEEE.

关键词： Blood pressure

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A novel approach to predicting liquefaction-induced settlements using Kolmogorov–Arnold Networks (KANs)

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Discover Geoscience 2024年第1期2卷 1-14页

作者： Karakaş, Seyidcem Garanti BBVA Payment Systems İstanbul Turkey Faculty of Computer and Informatics Engineering Istanbul Technical University Istanbul Turkey

Soil liquefaction refers to the loss of strength and bearing capacity of soil under dynamic loads. Structures built on liquefied soils experience settlement. In this study, the newly developed Kolmogorov–Arnold Networks (KAN) method, an innovative artificial neural network technique, was utilized to predict liquefaction-induced settlement. The KAN method, which extends beyond the traditional Multi-Layer Perceptron (MLP) approach, was compared with the Random Forest method for benchmarking purposes. Using data derived from laboratory and field studies, models were constructed using both methods, and their performances were analyzed. According to the results, the KAN model outperformed the RF model in terms of R2, MAE, MSE, and RMSE metrics. Additionally, the feature importance analysis on the KAN model identified cyclic stress ratio (csr) and corrected SPT blow count (n1_60) as the most significant variables. These results underscore the potential of the KAN method in enhancing predictive accuracy and reliability in geotechnical applications, paving the way for its broader acceptance and implementation in real-world scenarios.

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Estimating Mutual Information for Link Adaptation in Generalized Spatial Modulation systems with Neural Networks 26

Estimating Mutual Information for Link Adaptation in General...

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26th International ITG Workshop on Smart Antennas, WSA 2023 and 13th Conference on systems, Communications, and Coding, SCC 2023

作者： Bailon, Daniel Nicolas Kühn, Volker Shavgulidze, Sergo Freudenberger, Jürgen Institute for System Dynamics HTWG Konstanz University of Applied Sciences Germany Institute of Communications Engineering Faculty of Computer Science and Electrical Engineering University of Rostock Germany Faculty of Informatics and Control Systems Georgian Technical University Georgia Germany

ISBN: (纸本)9783800760510

Spatial modulation (SM) is a low-complexity multiple-input/multiple-output transmission technique that combines index modulation and quadrature amplitude modulation for wireless communications. In this work, we consider the problem of link adaption for generalized spatial modulation (GSM) systems that use multiple active transmit antennas simultaneously. Link adaption algorithms require a real-time estimation of the link quality of the time-variant communication channels, e.g., by means of estimating the mutual information. However, determining the mutual information of SM is challenging because no closed-form expressions have been found so far. Recently, multilayer feedforward neural networks were applied to compute the achievable rate of an index modulation link. However, only a small SM system with two transmit and two receive antennas was considered. In this work, we consider a similar approach but investigate larger GSM systems with multiple active antennas. We analyze the portions of mutual information related to antenna selection and the IQ modulation processes, which depend on the GSM variant and the signal constellation. © VDE VERLAG GMBH · Berlin · Offenbach.

关键词： Complex networks

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Optimization Techniques for Semi-Automated 3D Rigid Registration in Multimodal Image-Guided Deep Brain Stimulation

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Current Directions in Biomedical engineering 2023年第1期9卷 355-358页

作者： Al-Jaberi, Fadil Fachet, Melanie Hoeschen, Christoph Moeskes, Matthias Skalej, Martin Otto von Guericke University Faculty of Electrical Engineering and Information Technology Institute of Medical Technology Chair of Medical Systems Technology Magdeburg Germany Otto von Guericke University Medical Faculty Institute of Biometry and Medical Informatics Magdeburg Germany Martin Luther University Halle-Wittenberg Medical Faculty Neuroradiology Halle Germany

Multimodal image registration is vital in Deep Brain Stimulation (DBS) surgery. DBS treats movement disorders by implanting a neurostimulator device in the brain to deliver electrical impulses. Image registration between computed tomography (CT) and cone beam computed tomography (CBCT) involves fusing images with a specific field of view (FOV) to visualize individual electrode contacts. This contains important information about the location of segmented contacts that can reduce the time required for electrode programming. We performed a semi-automated multimodal image registration with different FOV between CT and CBCT images due to the tiny structures of segmented electrode contacts that necessitate high accuracy in the registration. In this work, we present an optimization workflow for multi-modal image registration using a combination of different similarity metrics, interpolators, and optimizers. Optimization-based rigid image registration (RIR) is a common method for registering images. The selection of appropriate interpolators and similarity metrics is crucial for the success of this optimization-based image registration *** rely on quantitative measures to compare their performance. Registration was performed on CT and CBCT images for DBS datasets with an image registration algorithm written in Python using the Insight Segmentation and Registration Toolkit (ITK). Several combinations of similarity metrics and interpolators were used, including mean square difference (MSD), mutual information (MI), correlation and nearest neighbors (NN), linear (LI), and B-Spline (SPI), respectively. The combination of a correlation as similarity metric, B-Spline interpolation, and GD optimizer performs the best in optimizing the 3D RIR algorithm, enhancing the visualization of segmented electrode contacts. Patients undergoing DBS therapy may ultimately benefit from this. © 2023 the author(s), published by Walter de Gruyter Berlin/Boston.

关键词： Image registration

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Development of Kansei-Based Visualization Pattern for E-Learning Website 4th

Development of Kansei-Based Visualization Pattern for E-Lear...

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4th International Conference on Advances in Computational Science and engineering, ICACSE 2023

作者： Aning, Alberto Mohd Naim, Nur Faraha Baharum, Aslina Faculty of Computing and Informatics Universiti Malaysia Sabah Sabah Kota Kinabalu88450 Malaysia Department of Computing and Information Systems School of Engineering and Technology Sunway University Selangor Bandar Sunway47500 Malaysia

ISBN: (纸本)9789819729760

Through their websites, a lot of businesses and people directly offer their services. The way in which these services are offered may affect users emotionally. A website that elicits pleasant emotions in its users can make them feel happy, engaged, trustworthy, or satisfied. Well-known for its ability to analyze customer emotional demands and define the connection between emotions and product design aspects, Kansei engineering (KE) is a user-centered technology. The development of e-learning software, e-learning authoring tools, learning management systems, and other related technologies is currently a major investment made by universities, training centers, and government bodies. Even though there are many advantages for educators using e-learning technology, getting students to use these services can be difficult, especially for the e-learning websites of Malaysia’s higher education institutions (HEIs). Few standards exist for user interface (UI) design that encourages emotional connection on the e-learning platforms of Malaysian HEIs. Students were disengaging from e-learning platforms in substantial numbers because of this issue. Hence, this paper aims to explore users’ needs and emotions while using an HEI website and produce a new guideline for HEIs e-learning website. Using the card sorting technique, a visualization pattern has been presented as a design guideline for an e-learning graphical user interface (GUI) website for HEIs in Malaysia. Positive outcomes have been obtained by implementing the suggested guideline on the GUI of the prototype website. The purpose of this guideline is to help HEIs as well as website developers and designers create e-learning websites that will keep students interested in e-learning over time. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Students

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Separated Artificial Neural Network Based Distribution System State Estimation

Separated Artificial Neural Network Based Distribution Syste...

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2023 IEEE PES Generation, Transmission and Distribution International Conference and Exposition, GTD 2023

作者： Pasic, Lejla Pasic, Azra Pasic, Alija Vokony, Istvan Biro, Jozsef Budapest University of Technology and Economics Faculty of Electrical Engineering and Informatics BudapestH-1111 Hungary Department of Telecommunications and Media Informatics Budapest Hungary Department of Electric Power Engineering Budapest Hungary ELKH-BME Information Systems Research Group Budapest Hungary

ISBN: (纸本)9781728170251

Distribution system state estimation is becoming an increasingly important feature in the modern power grid, but the lack of real measurements makes its implementation particularly difficult. A solution for this problem could be the implementation of Artificial Neural Networks for direct distribution system state estimation. In our work, we created an ANN-based distribution system state estimation which can function based on sparse data, the accuracy of the ANN-based state estimation can be further improved utilizing a separated distribution system state estimation (S-ANN), which estimates the state of the network separately according to measurement type based on the same input data. With this method, the root mean square error and standard deviation of the state estimation can be significantly decreased. © 2023 IEEE.

关键词： State estimation

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Unveiling the Truth: Detecting Fake News Using SVM and TF-IDF

Unveiling the Truth: Detecting Fake News Using SVM and TF-ID...

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Advancement in Data Science, E-learning and Information systems (ICADEIS), International Conference on

作者： Randi Rizal Ade Faturahman Ali Impron Irfan Darmawan Erna Haerani Alam Rahmatulloh Department of Informatics Faculty of Engineering Siliwangi University Tasikmalaya Indonesia Departement of Informatics Faculty of Engineering and Agriculture Universitas Muhammadiyah Sampit Central Kalimantan Indonesia Department of Information Systems Faculty of Industrial Engineering Telkom University Bandung Indonesia Department of Information Systems Faculty of Engineering Siliwangi University Tasikmalaya Indonesia

ISBN: (数字)9798331513320

ISBN: (纸本)9798331513337

The spread of fake news on social media platforms such as Facebook, Instagram, and X has become a significant issue in the digital era due to its negative societal and informational impacts. The rapid dissemination of unverified information often leads to misinformation and confusion, highlighting the critical need for reliable detection methods. This study aims to classify and detect fake news by optimizing the Support Vector Machine (SVM) machine learning algorithm using the Term Frequency-Inverse Document Frequency (TFIDF) technique. Two publicly available Kaggle datasets, containing labeled fake and real news, were utilized as the foundation for this research. The research results demonstrate that the SVM algorithm, when optimized with TF-IDF, achieved the highest accuracy of 99.6% compared to other commonly used machine learning algorithms such as Random Forest and Logistic Regression. This highlights the effectiveness of TF-IDF in improving the feature extraction process, enabling the model to better differentiate between fake and real news. These findings underscore the potential of SVM as a reliable tool for combating misinformation. To further enhance fake news detection, future research should explore the use of more comprehensive, multilingual, and complex datasets to better reflect real-world scenarios. Additionally, employing transformer-based approaches, such as BERT, GPT, or similar models, could provide a deeper understanding of linguistic context and semantic relationships, ultimately improving the robustness and accuracy of fake news detection systems.

关键词： Support vector machines Logistic regression Machine learning algorithms Accuracy Social networking (online) Semantics Classification algorithms Web sites Fake news Random forests

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