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European Conference on Antennas and Propagation, EuCAP

作者： Nehir Berk Onat Ignacio Roldan Francesco Fioranelli Alexander Yarovoy Yanki Aslan Department of Microelectronics Faculty of Electrical Engineering Mathematics and Computer Science Microwave Sensing Signals and Systems Group Delft University of Technology Delft The Netherlands

Efficient prediction of embedded element patterns (EEPs) is including the mutual coupling (MC) effects in the optimization of irregular planar arrays is studied for the first time in the literature. An ANN-based methodology is used to predict the pattern of each element in the whole visible space for a flexible planar array topology in milliseconds. The technique is proposed is validated on a 4-element planar non-uniform sub-array structure. Excellent accuracy on the EEP prediction while providing great efficiency in computational time and load in comparison to the full-wave simulations is demonstrated.

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A Novel Heatsink Attached mm-Wave Active Patch Antenna With Adjustable Frequency and Cooling

A Novel Heatsink Attached mm-Wave Active Patch Antenna With ...

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European Conference on Antennas and Propagation, EuCAP

作者： Feza Turgay Celik Yanki Aslan Department of Microelectronics Faculty of Electrical Engineering Mathematics and Computer Science Microwave Sensing Signals and Systems Group Delft University of Technology Delft The Netherlands

In this study, the thermal management problem of the modern communication systems with small array sizes is addressed. A novel dual-functional active antenna design strategy is introduced for adjustable frequency of operation and cooling extension at millimeter-wave bands. The concept is based on placing different types of heatsinks on the same patch antenna. The electromagnetic and thermal behavior of the proposed heatsink structures are presented via simulations. Reconfigurable operation at 24, 26, and 28 GHz frequencies with 23 to 28 degrees of extra cooling in the chip as compared to the conventional patch is achieved.

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Implementation of AI-Based Support Vector Machine Algorithm for Fraud Detection in Digital Payment systems

Implementation of AI-Based Support Vector Machine Algorithm ...

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International Seminar on Research of Information Technology and Intelligent systems (ISRITI)

作者： Edy Prayitno Rikie Kartadie Uning Lestari Munengsih Sari Bunga Accounting Information Systems Faculty of Information Technology Universitas Teknologi Digital Indonesia Yogyakarta Indonesia Computer Engineering Study Program Faculty of Information Technology Universitas Teknologi Digital Indonesia Yogyakarta Indonesia Informatics Study Program Faculty of Science and Information Technology Universitas Akprind Indonesia Yogyakarta Indonesia Department of Computer Engineering Smart City Information Systems Politeknik Negeri Indramayu Indramayu Indonesia

ISBN: (数字)9798331519643

ISBN: (纸本)9798331519650

The increasing use of digital payment systems has led to a rise in fraudulent activities, presenting a significant challenge in ensuring secure transactions. This research focuses on implementing the Support Vector Machine (SVM) algorithm with a Radial Basis Function (RBF) kernel to detect fraud in digital payment systems. One of the main challenges addressed in this study is the severe class imbalance in the dataset, where fraudulent transactions account for only 0.17% of total transactions. To overcome this, the SMOTE (Synthetic Minority Over-sampling Technique) method was applied to balance the dataset, allowing the model to better recognize fraudulent patterns. The results indicate that the SVM model achieved an accuracy of 99.93%, with a precision of 86.23% and a recall of 75.51%. These results demonstrate that SVM, combined with SMOTE and RBF kernel, is highly effective in detecting fraudulent transactions while minimizing false positives. This research provides a strong foundation for improving fraud detection models in the context of digital payment systems, offering enhanced security and trust for users. Further research could explore hybrid models and real-time data analysis to improve performance.

关键词： Support vector machines Seminars Adaptation models Accuracy Data models Real-time systems Fraud Security Kernel Intelligent systems

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A Deep Learning Approach to Recognizing New Thai Currency Using Transfer Learning for Visually Impaired Individuals

A Deep Learning Approach to Recognizing New Thai Currency Us...

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International Conference on Signal Processing and Communication systems, ICSPCS

作者： Irfan Ahmad Abubakar Sharif Aneeka Azmat Muhammad Usman Jamil Maynooth International Engineering College Faculty of Science and Engineering Maynooth University Ireland Maynooth International Engineering College Faculty of Science and Engineering Maynooth University Fuzhou James Watt School of Engineering University of Glasgow United Kingdom College of Electrical Engineering and Computer Science Institute of Information Systems and Application National Tsinghua University Hsinchu Taiwan School of Mechanical and Electrical Engineering Chuzhou University Chuzhou China

ISBN: (数字)9798350389630

ISBN: (纸本)9798350389647

Assistive mobile applications play a pivotal role for visually impaired individuals worldwide. These applications often face challenges in currency recognition due to varying perspectives, inconsistent illumination, and background clutter. This issue is especially pressing in developing countries like Thailand, where there is a notable gap in robust currency recognition systems, particularly for the new Thai currency notes. This study employs a deep learning approach using a convolutional neural network (CNN) to automate the recognition of the new Thai currency notes. Using transfer learning, we fine-tuned the CNN using the Xception model, renowned for its depth-wise separable convolution. The network trained on a meticulously curated dataset comprising 3600 images (without data augmentation) of five different denominations of the new Thai currency (20, 50, 100, 500, and 1000 baht) notes, captured under various conditions. The resulting model achieved an average training accuracy of 99.5% and a validation accuracy of 99.8%. Given its robustness and high accuracy, the model can be integrated into an Android application. Such an application would offer a user-friendly and reliable tool for visually impaired individuals to effortlessly identify the new Thai currency notes in their everyday transactions.

关键词： Deep learning Training Accuracy Face recognition Transfer learning Pressing Robustness Mobile applications Convolutional neural networks Currencies

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A framework for dynamic smart traffic light management system

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International Journal of Information Technology (Singapore) 2021年第5期13卷 1769-1776页

作者： Alharbi, Awad Halikias, George Sen, Adnan Ahmed Abi Yamin, Mohammad School of Mathematics Computer Science and Engineering Department of Electrical and Electronic Engineering University of London London United Kingdom Faculty of Computer and Information Systems Islamic University Madinah Saudi Arabia Faculty of Economics and Administration King Abdulaziz University Jeddah Saudi Arabia

Traffic congestion at intersections is one of the major problems which spreads into many other problems impacting health and social life. This problem can be fully or partly solved by applying available technology and tools. In this article, we propose a new framework of smart traffic lights (STL) to dynamically control and manage Traffic Lights. The proposed adaptive method determines priority based on the level of the traffic congestion, assessed by processing of images taken by the long range digital cameras, based on Fog computing, installed at the intersection. The number of vehicles can also be counted by sensor networks and RFID. According to analytical derivation and simulation, STL provide efficient solution and enhancement for managing traffic at intersections as compared to traditional methods. © 2021, Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Congestion Image processing RFID Sensor networks Smart traffic light Traffic Traffic management

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Comparison of The Accuracy of Predicting The Number Of Positive Covid-19 Between The Neural Network and LSTM Methods

Comparison of The Accuracy of Predicting The Number Of Posit...

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International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Eko Riyanto Rifka Dwi Amalia Informatics Engineering STMIK HIMSYA Semarang Indonesia Information Systems Faculty of Computer Science Universitas Pembangunan Nasional Veteran Jakarta Jakarta Indonesia

The COVID-19 pandemic (Coronavirus) is likely to be one of the most serious global problems in the last year. Countries do not have similar experiences with the spread of the virus and its impact from various fields. Estimating the number of COVID-19 cases in the past can help in decision-making about action and plan for virus prevention. The study aims to provide accurate predictive model in predicting confirmed cases of COVID-19 in Semarang city. In predicting the number of people exposed to Covid-19 positive need to be accurate and accurate methods, in some previous studies the two methods most recommended are neural network and LSTM (Long Short Term Memory) which have high levels of accuracy. Based on research that has been carried out that the results of accurate prediction comparison between RNN and LSTM obtain the smallest MSE result is the method.

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Optimal Kernel Choice for Score Function-based Causal Discovery 41

Optimal Kernel Choice for Score Function-based Causal Discov...

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41st International Conference on Machine Learning, ICML 2024

作者： Wang, Wenjie Huang, Biwei Liu, Feng You, Xinge Liu, Tongliang Zhang, Kun Gong, Mingming School of Mathematics and Statistics The University of Melbourne Australia Department of Machine Learning Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates University of California San Diego United States School of Computing and Information Systems The University of Melbourne Australia Huazhong University of Science and Technology China School of Computer Science Faculty of Engineering The University of Sydney Australia Department of Philosophy Carnegie Mellon University United States

Score-based methods have demonstrated their effectiveness in discovering causal relationships by scoring different causal structures based on their goodness of fit to the data. Recently, Huang et al. (2018) proposed a generalized score function that can handle general data distributions and causal relationships by modeling the relations in reproducing kernel Hilbert space (RKHS). The selection of an appropriate kernel within this score function is crucial for accurately characterizing causal relationships and ensuring precise causal discovery. However, the current method involves manual heuristic selection of kernel parameters, making the process tedious and less likely to ensure optimality. In this paper, we propose a kernel selection method within the generalized score function that automatically selects the optimal kernel that best fits the data. Specifically, we model the generative process of the variables involved in each step of the causal graph search procedure as a mixture of independent noise variables. Based on this model, we derive an automatic kernel selection method by maximizing the marginal likelihood of the variables involved in each search step. We conduct experiments on both synthetic data and real-world benchmarks, and the results demonstrate that our proposed method outperforms heuristic kernel selection methods. Copyright 2024 by the author(s)

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Statistical Effects of Propagation Environment and Transmit Array Topology on Cell-Edge User Service Quality at mm-Waves

Statistical Effects of Propagation Environment and Transmit ...

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European Conference on Antennas and Propagation, EuCAP

作者： Nicolas Van De Kreeke Yanki Aslan Nehir Berk Onat Alexander Yarovoy Department of Microelectronics Microwave Sensing Signals and Systems Group Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft The Netherlands

The effects of multipath on the statistical cell-edge user service quality is for the first time investigated for mm-wave multi-user communication systems. The focus is given on setting the user spacing constraints and the transmit array topology via thinning, which can be used to enhance wireless security or decrease analog/digital complexity. A hybrid line-of-sight/non-line-of-sight channel is created by using a statistical model following the communication standards. The multipath signal components are included in the model by using non-coherent or coherent modes of operation. It is shown in simulation that selection, by the medium access control layer, of large angular spacings between the simultaneously served users and application of antenna array thinning at the array edges improves the system performance.

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International Conference of computer and Informatics engineering (IC2IE)

作者： Wandy Wandy Kusworo Adi Media Anugerah Ayu Doctoral Program of Information Systems School of Postgraduate Studies Diponegoro University Semarang Indonesia Department of Physics Faculty of Science and Mathematics Diponegoro University Semarang Indonesia Department of Computer Science Faculty of Engineering and Technology Sampoerna University South Jakarta Indonesia

ISBN: (数字)9798331505530

ISBN: (纸本)9798331505547

Full-marathon and Half-marathon distances are categorized as road running. Full-marathon running is becoming increasingly popular, and Half-marathon is increasing worldwide in both sexes and all age groups. Some aspects might relate to Full-marathon and Half-marathon running performance during training and races. Technology also plays an essential role in supporting runners and running races. Technology like artificial intelligence (AI) now supports the running athlete, not only predicting performance and results. It can also be used later to help the coach generate training programs for the athlete. This research aimed to find many aspects of marathons and performance and analyze them to see if artificial intelligence could later support them. It used secondary data and a systematic literature review proposed by Kitchenham. Out of the 58 articles, 21 of them (36.21%) received a score of 1 from Q1. Additionally, 19 articles (32.76%) received a score of 1 from both Q2 and Q3. Among the 58 articles, 9 (15.52%) received a total score of 3, with all three Q1, Q2, and Q3 scores being 1. This indicates that artificial intelligence will likely support the content of these nine articles. Several factors were also discovered to be connected to marathons and athletic performance. These findings suggested that additional investigation into marathons and performance, later backed by artificial intelligence, remained pertinent and essential.

关键词： Training Roads Artificial intelligence Informatics

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Towards Federated Learning-Based Forecasting of Renewable Energy Production

Towards Federated Learning-Based Forecasting of Renewable En...

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Federated Learning Technologies and Applications (FLTA), International Conference on

作者： Viktor Walter Fabian Stricker Andreas Wagner Christian Zirpins Faculty of Management Science and Engineering Karlsruhe University of Applied Sciences (HKA) Karlsruhe Germany Faculty of Computer Science and Business Information Systems Karlsruhe University of Applied Sciences (HKA) Karlsruhe Germany Fraunhofer Institute for Industrial Mathematics (ITWM) Kaiserslautern Germany

ISBN: (数字)9798350354812

ISBN: (纸本)9798350354829

The integration of volatile renewable energy sources requires reliable generation forecasts. Traditional forecasting methods that rely on commercial providers impose costs and de-pendencies on renewable energy operators. This paper proposes a literature survey on federated learning (FL) in the context of renewable energy forecasting and an analysis of open challenges in research and practice and possible solution approaches for re-alizing such a framework. Our focus is on short-term forecasts for day-ahead markets, which are critical for trading and operational efficiency. The FL approach preserves data privacy and improves forecast accuracy by leveraging distributed data from multiple operators. We present an analysis of current FL applications in renewable energy forecasting, identify implementation challenges, and propose solutions to overcome these barriers. This study aims to empower market participants to produce independent, accurate forecasts, thereby improving economical outcomes and operational stability.

关键词： Surveys Industries Renewable energy sources Costs Accuracy Federated learning Production Stability analysis Reliability Forecasting

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