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High gain multi-band circularly polarized bi-layered metasurface patch array antenna with dual-orthogonal feeds

Optical and Quantum Electronics 2025年第2期57卷 1-17页

作者： PourHosseini, Mona Jarchi, Saughar Rezaei, Pejman Ghattan Kashani, Zahra Faculty of Technical and Engineering Imam Khomeini International University Qazvin Iran Electrical and Computer Engineering Faculty Semnan University Semnan Iran Faculty of Electrical Engineering K.N. Toosi University of Technology Tehran Iran

In this article, a renovated patch array antenna is developed that achieves features such as high gain and circular polarization by introducing a bi-layered eight-shaped metasurface on top of the feed. The antenna is designed as a dual-feed array structure, utilizing silicon materials for the substrates and featuring two gold patches operating at frequencies of different dimensions with two orthogonal feeds optimized to achieve wide bandwidth operation. Along with modifying the gaps and arcs in the antenna, circular polarization can be made possible with an appropriate pattern of the unit cell. Finally, a designed eight-shaped metasurface has been used to control wave propagation, improve polarization switching properties, and increase the bandwidth of impedance and also axial ratio. The substrates of the renovated patch antennas and that of the bi-layered metasurface are respectively made of silicon and silicon dioxide to reduce undesired waves. Different Components are separated from each other with silicon material without an air gap. The impedance bandwidth covers a wide range of frequencies from 3.24 to 5.44 THz, about 50%. The results show that the axial ratio is in four ranges (3.48–3.74 THz), (4.08–4.16 THz), (4.23–4.27 THz) and (5.05-6 THz), which are in good coverage with the impedance bandwidth. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Circular polarization

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Assessing the Effectiveness of Machine Learning Algorithms in Vehicle Insurance Fraud Prediction: A Comparative Approach 3

Assessing the Effectiveness of Machine Learning Algorithms i...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Satone, Aditya P. Daronde, Subodh Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Biomedical Engineering Maharashtra Wardha442001 India

ISBN: (纸本)9798331509675

Due to the wealth of information accessible on many insurance issues, including travel and auto insurance, individuals are becoming more and more impacted by these kinds of insurance in today's society. But this tendency also makes room for dishonest practices. Insurance fraud is harmful and unlawful, regardless of who commits it - the vendor or the customer. Fraud can come from both the customer and the vendor. The consumer may commit fraud by filing exaggerated claims or using out-of-date policies, while the vendor may market insurance from phoney firms or neglect to submit premiums. This paper presents a comparative analysis of various classification algorithms, including Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), Linear Regression (LR), and Polynomial Regression, for detecting insurance fraud. The performance of these algorithms is evaluated using key metrics such as Precision, Recall, and F1-Score. The analysis reveals that the bestperforming model is the Random Forest, with a Mean Squared Error (MSE) of 6.50, outperforming the other techniques. © 2025 IEEE.

关键词： Random forests

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Auto-3D-house Design from Structured User Requirements

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Machine Intelligence Research 2025年第2期22卷 368-385页

作者： Minkui Tan Qi Chen Zixiong Huang Qi Wu Yuanqing Li Jiaqiu Zhou School of Software Engineering South China University of TechnologyGuangzhou 510006China Faculty of Engineering Computer and Mathematical ScienceUniversity of AdelaideAdelaide SA 5005Australia School of Automation Science and Engineering South China University of TechnologyGuangzhou 510006China

We study the task of automated house design,which aims to automatically generate 3D houses from user ***,in the automatic system,it is non-trivial due to the intrinsic complexity of house designing:1)the understanding of user requirements,where the users can hardly provide high-quality requirements without any professional knowledge;2)the design of house plan,which mainly focuses on how to capture the effective information from user *** address the above issues,we propose an automatic house design framework,called auto-3D-house design(A3HD).Unlike the previous works that consider the user requirements in an unstructured way(e.g.,natural language),we carefully design a structured list that divides the requirements into three parts(i.e.,layout,outline,and style),which focus on the attributes of rooms,the outline of the building,and the style of decoration,*** the processing of architects,we construct a bubble diagram(i.e.,graph)that covers the rooms′attributes and relations under the constraint of *** addition,we take each outline as a combination of points and orders,ensuring that it can represent the outlines with arbitrary ***,we propose a graph feature generation module(GFGM)to capture layout features from the bubble diagrams and an outline feature generation module(OFGM)for outline ***,we render 3D houses according to the given style requirements in a rule-based *** on two benchmark datasets(i.e.,RPLAN and T3HM)demonstrate the effectiveness of our A3HD in terms of both quantitative and qualitative evaluation metrics.

关键词： Automated house design user requirements understanding outline processing layout generation graph feature generation.

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AE-MCDM: an autoencoder-based multi-criteria decision-making approach for unsupervised feature selection

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Journal of Supercomputing 2025年第7期81卷 1-44页

作者： Hashemi, Amin Dowlatshahi, Mohammad Bagher Farshidi, Siamak Moradi, Parham Department of Computer Engineering Faculty of Engineering Lorestan University Khorramabad Iran Informal Technology Group Wageningen University & Research Wageningen Netherlands School of Engineering RMIT University Melbourne Australia

Feature selection is a fundamental technique for reducing the dimensionality of high-dimensional data by identifying the most relevant features while discarding redundant or irrelevant ones. In unsupervised settings, where labeled data are unavailable and labeling is costly, effective feature selection becomes even more challenging. This paper proposes AE-MCDM, a novel unsupervised feature selection method that integrates autoencoder-based feature extraction with multi-criteria decision-making (MCDM). The autoencoder captures high-level feature representations, and the connection weights between input features and hidden neurons reflect feature importance. These weights are then processed using MCDM to rank and select the most informative features. Unlike conventional unsupervised feature selection methods, AE-MCDM leverages deep representation learning to enhance feature evaluation. To the best of our knowledge, this is the first attempt to combine autoencoders with MCDM for feature selection. Extensive experiments on various datasets demonstrate that AE-MCDM outperforms existing methods in terms of clustering performance, measured by metrics such as accuracy, precision, recall, and normalized mutual information (NMI), while also achieving competitive computational efficiency. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Labeled data

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Adopting Graph Neural Networks to Understand and Reason About Dynamic Driving Scenarios

IEEE Open Journal of Intelligent Transportation Systems

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IEEE Open Journal of Intelligent Transportation Systems 2025年 6卷 579-589页

作者： Su, Peng Xiang, Conglei Chen, Dejiu KTH Royal Institute of Technology Faculty of Engineering Design Department Stockholm100 44 Sweden KTH Royal Institute of Technology Electrical Engineering and Computer Science School Stockholm100 44 Sweden

With advances in Deep Neural Networks (DNN), Automated Driving Systems (ADS) enable the vehicle to perceive their surroundings in dynamic driving scenarios and perform behaviors by collecting operational data from sensors such as LiDAR and cameras. Current DNN typically detect objects by analyzing and classifying unstructured data (e.g., image data), providing critical information for ADS planning and decision-making. However, advanced ADS, particularly those required to perform the Dynamic Driving Task (DDT) autonomously, are expected to understand driving scenarios across various Operational Design Domains (ODD). This capability requires the support for a continuous comprehension of driving scenarios according to operational data collected by sensors. This paper presents a framework that adopts Graph Neural Networks (GNN) to describe and reason about dynamic driving scenarios via analyzing graph-based data based on collected sensor inputs. We first construct the graph-based data using a meta-path, which defines various interactions among different traffic participants. Next, we propose a design of GNN to support both the classification of the node types of objects and predicting relationships between objects. As results, the performance of the proposed method shows significant improvements compared to the baseline method. Specifically, the accuracy of node classification increases from 0.77 to 0.85, while that of relationships prediction rises from 0.74 to 0.82. To further utilize graph-based data constructed from dynamic driving scenarios, the proposed framework supports reasoning about operational risks by analyzing the observed nodes and relationships in the graph-based data. As a result, the model achieves a MRR of 0.78 in operational risks reasoning. To evaluate the practicality of the proposed framework in real-world systems, we also conduct a real-time performance evaluation by measuring the average process time and the Worst Case Execution Time (WCET). Com

关键词： Graph neural networks

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IoT based Line of Control Monitoring System 2

IoT based Line of Control Monitoring System

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Petkar, Taniya Faculty of Engineering and Technology Department of Computer Science And Medical Engineering Maharashtra Wardha442001 India

ISBN: (纸本)9798331505745

This paper presents a novel line-of-control (LoC) monitoring system that leverages the Internet of Things (IoT) to improve border security. The system creates a strong infrastructure for real-time monitoring throughout critical border regions, especially the Line of Control, by utilizing IoT devices, sensors, and communication technologies. The coverage, timeliness, and data quality of conventional border surveillance systems are frequently compromised. As opposed to this, the suggested system makes use of a wirelessly networked network of intelligent sensors, such as infrared, acoustic, and video cameras. By utilizing sophisticated image processing algorithms and machine learning to analyze the gathered data, the system is able to differentiate between regular activities and any security risks. To help security staff make decisions quickly, real-time updates are transmitted to a centralized control center. Integration with cloud computing also makes data analysis, retrieval, and storage simpler and guarantees data accessibility for forensic investigations and trend research. © 2025 IEEE.

关键词： Network security

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Classification of human-written and AI-generated sentences using a hybrid CNN-GRU model optimized by the spotted hyena algorithm

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Alexandria engineering Journal 2025年 126卷 116-130页

作者： Ragab, Mahmoud Ashary, Ehab Bahaudien Kateb, Faris Hakeem, Abeer Mosli, Rayan Albogami, Nasser N. Nooh, Sameer Information Technology Department Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Electrical and Computer Engineering Department Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia Information Systems Department Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia

The rapid advancement of artificial intelligence (AI) in generating human-like text poses significant challenges in distinguishing between human-written and AI-generated content. Recent advancements in natural language generation have significantly enhanced the quality and variety of AI-generated text, making it almost indistinguishable from human-written content. ChatGPT, a popular AI model, belongs to the generative pre-trained transformer family. While human content is created with a clear intent to convey meaning, AI-generated text aims to replicate human-like language. Classifying human-written and AI-generated sentences is crucial for addressing issues like fake news, plagiarism, and spamming. AI text often follows repetitive patterns, while human writing is more creative and original, making detection significant for combating misinformation. Therefore, this study proposes to classify human-written and AI-generated sentences using a hybrid CNN-GRU model optimized by the Spotted Hyena Algorithm (CHWAIG-DLSHO) approach. The approach involves preprocessing text data through tokenization, lemmatization, and data splitting, followed by word embedding using Latent Dirichlet Allocation (LDA). A hybrid convolutional neural network (CNN) and gated recurrent unit (GRU) model is employed for sentence classification. The spotted hyena optimizer (SHO) model is utilized to fine-tune the hyperparameters of the CNN-GRU model, enhancing its performance. The analysis of the CHWAIG-DLSHO method takes place utilizing AI vs. human text dataset. The performance validation of the CHWAIG-DLSHO method portrayed a superior accuracy value of 99.17 % over existing techniques. © 2025 The Authors

关键词： Convolutional neural networks

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Neuroadaptive Containment Control for Nonlinear Multiagent Systems With Input Saturation: An Event-Triggered Communication Approach

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3163-3173页

作者： Wang, Xin Zhang, Sen Li, Huaqing Zhang, Wei Li, Hongyi Huang, Tingwen Southwest University College of Electronic and Information Engineering Chongqing400715 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

In this article, a neuroadaptive event-triggered containment control strategy combined with the dynamic surface control (DSC) approach is proposed for nonlinear multiagent systems (MASs) with input saturation. Based on the event-triggered communication mechanisms, the updates of neural network weight and controllers are implemented solely under triggering conditions of violation, which markedly reduces unnecessary communication resources and minimizes inefficient control costs compared with the traditional control method. Radial basis function neural networks (RBF NNs) are employed to handle the nonlinear uncertainties of MASs. Simultaneously, an adaptive compensatory mechanism is incorporated within the backstepping process to address the nonlinear effect posed by input saturation. Additionally, we demonstrate the system stability by extending the Lyapunov theorem to jump and continuous scenarios while excluding Zeno behavior, realizing that all followers can enter the convex hull constructed by leaders. Finally, the effectiveness of the proposed methodology is verified through application simulations. © 2013 IEEE.

关键词： Radial basis function networks

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SELL:A Method for Low-Light Image Enhancement by Predicting Semantic Priors

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IEEE Signal Processing Letters 2025年 32卷 1785-1789页

作者： Xiao, Quanquan Jin, Haiyan Su, Haonan Yan, Ruixia Xi'an University of Technology Faculty of Computer Science and Engineering Xi'an710000 China Guizhou Minzu University Guiyang550000 China

In recent years, low-light image enhancement techniques have made significant progress in generating reasonable visual details. However, current methods have not yet fully utilized the full semantic prior of visual elements in low-light environments. Therefore, images generated by these low-light image enhancement methods often suffer from degraded visual quality and may even be distorted. To address this problem, we propose a method to guide low-light image enhancement by predicting semantic priors. Specifically, we train a semantic prior predictor under standard lighting conditions, which is made to learn and predict semantic prior features for low-light images by knowledge distillation on high-quality standard images. Subsequently, we utilize a semantic-aware module that enables the model to adaptively integrate these learned semantic priors, thus ensuring semantic consistency of the enhanced images. Experiments show that the method outperforms several current state-of-the-art methods in terms of visual performance on the LOL-v2 and SICE benchmark datasets. © 1994-2012 IEEE.

关键词： Economic and social effects

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Exploiting Multi-Level Data Uncertainty for Japanese-Chinese Neural Machine Translation

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IEICE Transactions on Information and Systems 2025年第5期E108.D卷 440-443页

作者： Li, Zezhong Ma, Jianjun Ren, Fuji Faculty of Computer Science Zhejiang University of Water Resources and Electric Power China Faculty of Foreign Languages Dalian University of Technology China College of Computer Science and Engineering The Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China China

The performance of Neural Machine Translation (NMT) heavily depends on the severity of data uncertainty existing in the training examples. In terms of its causes, data uncertainty can be categorized into intrinsic and extrinsic uncertainty, both of which can increase the learning difficulty in NMT, and lead to degradation in translation performance. To cope with this challenge, we propose a simple yet effective method to estimate the data uncertainty and incorporate it into the adaptive training of NMT, which can mitigate the hurt brought by the uncertain data. Our method consists of two modules: 1) we propose a mBERT based model for estimating token-level and sentence-level data uncertainties jointly, which is trained with multi-task learning;2) we propose heterogeneous ways of incorporating the derived multi-level uncertainties into the NMT training by using soft-masked embedding and weighted loss. Extensive experiments on Japanese↔Chinese translation show that our proposed methods substantially outperform the strong baselines in terms of BLEU scores, and verify the effectiveness of modeling data uncertainty in NMT. Copyright © 2025 The Institute of Electronics, Information and Communication Engineers.

关键词： Neural machine translation

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