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33rd International Conference on Organization and technology of Maintenance, OTO 2024

作者： Rücker, Kristian Livada, Časlav Galba, Tomislav Baumgartner, Alfonzo Faculty of Electrical Engineering Josip Juraj Strossmayer University in Osijek Computer Science and Information Technology Osijek Kneza Trpimira 2B Osijek31000 Croatia

ISBN: (纸本)9783031805967

Rapid advances in science and technology have led to the integration of artificial intelligence (AI) in many areas, including augmented reality (AR). This study explores the application of the Stable Diffusion (SD) model to develop an AR application that generates images based on text descriptions. Using Unity, a versatile game development platform, we implemented this AR application, scripting in C# with Visual Studio 2022. The user interface and functionality of the SD model were managed via the UI Stable Diffusion Automatic 1111 tool, allowing seamless integration and execution of SD functions. This work demonstrates the potential of AI-driven models in enhancing AR experiences and introduces a novel approach to dynamic visual content creation and editing. Furthermore, this technology has significant implications for the maintenance of municipal infrastructure and maintenance organizations. By converting text instructions into visual AR overlays, maintenance teams can improve the accuracy and efficiency of tasks such as inspections, repairs, and infrastructure management, streamlining operations and reducing errors. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Augmented reality

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Few-shot relation extraction method based on self-supervised learning prototypical networks 5

Few-shot relation extraction method based on self-supervised...

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5th International Conference on Physics and engineering Mathematics, ICPEM 2024

作者： Qian, Shenyi Sun, Yifan Cheng, Jia Li, Daiyi Fu, Yilong Li, Kunlin Wei, Mengyi College of Computer Science and Technology Zhengzhou University of Light Industry Zhengzhou450000 China Faculty of Information Engineering Zhengzhou College of Finance and Economics Zhengzhou450000 China

ISBN: (数字)9781510689169

ISBN: (纸本)9781510689152

Few-shot relation extraction (FSRE) is a key task in information extraction that aims to train models using a small number of labeled samples to identify relation categories in sentences. Existing research widely adopts meta-learning approaches, with prototypical networks being popular due to their simplicity and efficiency. However, these methods mainly focus on distinguishing samples of different categories, establishing inter-class boundaries based solely on simple calculations of relation category samples, while ignoring the differences between instances and failing to fully utilize instance feature information. To address this issue, this paper proposes self-supervised learning prototypical networks (SSLPN), a self-supervised learning-based prototypical network method for FSRE. This model utilizes self-supervised learning to enhance sample discriminability and effectively learn category representations. SSLPN consists of three stages, and this three-stage architecture synergistically enhances sample discriminability, mitigates overfitting, and optimizes model performance for FSRE tasks. The FewRel1.0 dataset was used to evaluate SSLPN, and the outcomes demonstrated that the proposed method considerably enhanced the FSRE performance, confirming its efficacy. © 2025 SPIE.

关键词： Self-supervised learning

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Dataset Ratio Influence on kNN Classification Results 33rd

Dataset Ratio Influence on kNN Classification Results

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33rd International Conference on Organization and technology of Maintenance, OTO 2024

作者： Gaće, Marin Galba, Tomislav Baumgartner, Alfonzo Livada, Časlav Faculty of Electrical Engineering Josip Juraj Strossmayer University of Osijek Computer Science and Information Technology Osijek Kneza Trpimira 2B Osijek31000 Croatia

ISBN: (纸本)9783031805967

Classification is used in many fields today, and for most of them machine learning algorithms can be used to make a decision. This article investigates the effects of different sizes of training and test datasets on the accuracy of classification using both classical k-nearest neighbors (kNN) and weighted k-nearest neighbors (wkNN) methods. This study systematically evaluates how different ratios of training to test data affect classification performance and provides a comprehensive analysis of the interplay between the ratios of the datasets and the parameter k. The results show that the ratio of training to test datasets exerts a more significant influence on classification accuracy than the choice of the parameter k. In particular, extreme ratios, where the training dataset is either much larger or much smaller than the test dataset, lead to suboptimal classification results. This emphasizes the importance of carefully selecting an appropriate balance between training and test data to ensure robust and reliable classification results. By highlighting the critical role of the ratio between datasets, this paper provides valuable insights for optimizing kNN and weighted kNN methods in practical applications. The results have far-reaching implications for the design and implementation of machine learning models and emphasize the need for a balanced data distribution to achieve high classification accuracy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： k-nearest neighbors

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Deep Learning Algorithm for Person Re-Identification Based on Dual Network Architecture

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computers, Materials & Continua 2025年第5期83卷 2889-2905页

作者： Meng Zhu Xingyue Wang Honge Ren Abeer Hakeem Linda Mohaisen College of Information Engineering Harbin UniversityHarbin150086China Heilongjiang Provincial Key Laboratory of the Intelligent Perception and Intelligent Software Harbin UniversityHarbin150086China College of Computer and Control Engineering Northeast Forestry UniversityHarbin150040China Heilongjiang Forestry Intelligent Equipment Engineering Research Center Northeast Forestry UniversityHarbin150040China Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia

Changing a person’s posture and low resolution are the key challenges for person re-identification(ReID)in various deep learning *** this paper,we introduce an innovative architecture using a dual attention network that includes an attentionmodule and a joint measurement module of spatial-temporal *** proposed approach can be classified into two main ***,the spatial attention feature map is formed by aggregating features in the spatial ***,the same operation is carried out on the channel dimension to formchannel attention ***,the receptive field size is adjusted adaptively tomitigate the changing person posture ***,we use a joint measurement method for the spatial-temporal information to fully harness the data,and it can also naturally integrate the information into the visual features of supervised ReID and hence overcome the low resolution *** experimental results indicate that our proposed algorithm markedly improves the accuracy in addressing changing human postures and low-resolution issues compared with contemporary leading *** proposed method shows superior outcomes on widely recognized benchmarks,which are the Market-1501,MSMT17,and DukeMTMC-reID ***,the proposed algorithmattains a Rank-1 accuracy of 97.4% and 94.9% mAP(mean Average Precision)on the Market-1501 ***,it achieves a 94.2% Rank-1 accuracy and 91.8% mAP on the DukeMTMC-reID dataset.

关键词： Person reidentification ReID computer vision self-attention spatial-temporal information

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Design and Modeling of a Photonic Crystal Multiplexer Using Artificial Intelligence

Advanced Electromagnetics

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Advanced Electromagnetics 2025年第1期14卷 59-64页

作者： Karami, Pouya Yahya, Salah I. Palash, Behnam Chaudhary, Muhammad Akmal Assaad, Maher Parandin, Fariborz Roshani, Saeed Hazzazi, Fawwaz Roshani, Sobhan Department of Electrical Engineering Kermanshah Branch Islamic Azad University Kermanshah Iran Department of Communication and Computer Engineering Cihan University-Erbil Erbil44001 Iraq Department of Software Engineering Faculty of Engineering Koya University KOY45 Koya Iraq Department of Electrical and Computer Engineering College of Engineering and Information Technology Ajman University 346 Ajman United Arab Emirates Department of Electrical Engineering College of Engineering Prince Sattam bin Abdulaziz University Al-Kharj11492 Saudi Arabia

In this paper, design and modeling of an all-optical 2×1 multiplexer based on 2D photonic crystals and artificial neural networks (ANNs) are presented. The proposed structure aims to maximize the difference between the output powers in logical states, which is critical for enhancing the system ability to distinguish between these states. In this study, an ANN model is employed to accurately predict the normalized output power of the designed photonic crystal multiplexer, providing a time-efficient alternative to conventional simulation methods for analyzing multiplexer behavior across various logical states. The results demonstrate significant improvements in signal separation and overall performance compared to previous works. Additionally, a detailed comparison of the normalized output power for different logic states is provided, highlighting the advantages of the proposed design. © 2025, Advanced Electromagnetics. All rights reserved.

关键词： Silicon photonics

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Life Cycle Management of Operational technology – OT 33rd

Life Cycle Management of Operational Technology – OT

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33rd International Conference on Organization and technology of Maintenance, OTO 2024

作者： Dorić, Dražen Glavaš, Hrvoje Varga, Fran Faculty of Electrical Engineering Josip Juraj Strossmayer University of Osijek Computer Science and Information Technology Osijek Kneza Trpimira 2B Osijek31000 Croatia

ISBN: (纸本)9783031805967

Systematic management of all phases of the lifecycle of an industrial control system, recently referred to as operational technology or OT for short, ensures that the system is up to date, compliant with regulations and in line with changing business requirements. This is especially true for critical infrastructure. In this way, operational resilience is improved, risks are minimized and a proactive approach to maintenance is encouraged, with a focus on safety, efficiency and long-term sustainability. The paper provides an overview of the motivations for systematic lifecycle management of OT systems, offers a description and illustration of lifecycle costs in the CAPEX and OPEX phases, emphasizes the changing role of corrective and preventive maintenance from a lifecycle management perspective, and budgeting for various activities, including cybersecurity and OT system upgrades. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Preventive maintenance

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Hybrid Reptile-Snake Optimizer Based Channel Selection for Enhancing Alzheimer’s Disease Detection

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Journal of Bionic engineering 2025年第2期22卷 884-900页

作者： Digambar Puri Pramod Kachare Smith Khare Ibrahim Al-Shourbaji Abdoh Jabbari Abdalla Alameen Department of Electronics and Telecommunication Ramrao Adik Institute of TechnologyD.Y.Patil CampusNavi-MumbaiMaharashtra400706India Maersk Mc-Kinney Moller Institute Faculty of EngineeringUniversity of Southern DenmarkOdenseDenmark Department of Electrical and Electronics Engineering Jazan UniversityJazan45142Saudi Arabia Department of Computer Science University of HertfordshireHatfieldUK Department of Computer Engineering and Information College of Engineering in Wadi AlddawasirPrince Sattam Bin Abdulaziz UniversityWadi Alddawasir11991Saudi Arabia

The global incidence of Alzheimer's Disease(AD)is on a swift *** Electroencephalogram(EEG)signals is an effective tool for the identification of AD and its initial Mild Cognitive Impairment(MCI)stage using machine learning *** of AD using EEG involves multi-channel ***,the use of multiple channels may impact the classification performance due to data redundancy and *** this work,a hybrid EEG channel selection is proposed using a combination of Reptile Search Algorithm and Snake Optimizer(RSO)for AD and MCI detection based on decomposition *** Mode Decomposition(EMD),Low-Complexity Orthogonal Wavelet Filter Banks(LCOWFB),Variational Mode Decomposition,and discrete-wavelet transform decomposition techniques have been employed for subbands-based EEG *** extracted thirty-four features from each subband of EEG ***,a hybrid RSO optimizer is compared with five individual metaheuristic algorithms for effective channel *** effectiveness of this model is assessed by two publicly accessible AD EEG *** accuracy of 99.22% was achieved for binary classification from RSO with EMD using 4(out of 16)EEG ***,the RSO with LCOWFBs obtained 89.68%the average accuracy for three-class classification using 7(out of 19)*** performance reveals that RSO performs better than individual Metaheuristic algorithms with 60%fewer channels and improved accuracy of 4%than existing AD detection techniques.

关键词： Alzheimer's Disease Brain disorder Electroencephalogram Reptile Search Algorithm Snake Optimizer Optimization

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Federated Learning for Low-Latency IoT Communications

Federated Learning for Low-Latency IoT Communications

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Computing and information technology (ICCIT-1441), 2020 International Conference on

作者： Mohammed M Alenazi Computer Engineering Faculty of Computers and Information Technology University Of Tabuk

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

Many sectors have been transformed by the growth of the Internet of Things devices, but it also presents difficulties, particularly in the latency of data exchange. In this research, I suggest a unique distributed federated learning architecture for low-latency Internet of Things communications. I empower IoT devices to jointly train machine learning models without compromising data privacy by utilizing federated learning techniques. To accomplish low-latency IoT communications, the architecture prioritizes communication efficiency optimization. Use Python to create this design and provide experimental data to show how successful.

关键词： Data privacy Federated learning Computational modeling computer architecture Data models Internet of Things Data communication Low latency communication information technology Optimization

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Investigation of AI with OpenCV-Python for Detecting Diabetes

Investigation of AI with OpenCV-Python for Detecting Diabete...

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Conference of Recent Trends and Applications of Soft Computing in engineering, RTASCE 2024

作者： Mutar, Hussein A. Yalwi, Ilyas Khudhair Mezaal, Yaqeen S. Th. Salim ALRikabi, Haider Alaidi, Abdul Hadi M. Niama ALRubeei, Ibtihal R. Magdy, Ahmed Electrical Engineering Department College of Engineering Wasit University Wasit Iraq College of Computer Science and Information Technology Wasit University Wasit Iraq College of Engineering University of Information Technology and Communications Baghdad Iraq Electrical Engineering Department Faculty of Engineering Suez Canal University Ismailia Egypt

ISBN: (纸本)9783031828805

Plenty of different diagnosing methods have been extensively utilized to identify diabetes accurately;however, an absolutely precise and definitive diagnosis has not yet been attained. Within the context of this research, our primary objective is to leverage the cutting-edge capabilities of Artificial Intelligence (AI) coupled with OpenCV to assist medical professionals, thereby minimizing the rate of misdiagnosis. Specifically, we harness the power of AI to effectively classify diverse images portraying patients afflicted with Non-Proliferative Diabetic Retinopathy (NPDR), with the ultimate goal of determining the severity level at which they are situated. In conjunction with this, Python, with OpenCV, has a crucial role in extracting pertinent features that may be discernible within the given images. Our methodology involves the collection and preprocessing of the Eye PACS Dataset on Kaggle, followed by feature extraction and model training using some machine learning algorithms, including convolutional Neural Network CNN, decision trees, support vector machines SVM, and neural networks. OpenCV is utilized for image processing tasks, enhancing the feature extraction process, certain individual features present within the images are precluded from being considered as contributing factors in the classification process. Some of these features include but not limited to, the measurement of the luminous blobs which are present in the image, the specific area of existence of red lesion. The evaluation of the models includes the analysis of their performance based on the goal of the prediction task, specifically decimal-based accuracy, precision, recall, and F1-score. This research employs a wide-ranging dataset embracing low, medium and high level of image severity. At last, after lots of simulation, it came to a conclusion that the CNN increases its level of classification accuracy up to 98%. These findings show that the proposed application of AI improves the accuracy

关键词： Convolutional neural networks

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Weak and Extremely Low Frequency Magnetic Fields Alter the Growth Rates of HT-1080 Fibrosarcoma

Weak and Extremely Low Frequency Magnetic Fields Alter the G...

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2025 United States National Committee of URSI National Radio Science Meeting, USNC-URSI NRSM 2025

作者： Dang, Nhat Keller, Jason Bajtos, Marek Gurhan, Hakki Barnes, Frank Department of Electrical Computer and Energy Engineering College of Engineering and Applied Science University of Colorado Boulder BoulderCO United States Department of Electromagnetics and Biomedical Engineering Faculty of Electrical Engineering and Information Technology University of Zilina Zilina Slovakia

ISBN: (纸本)9781946815200

This study presents experimental results showing that weak, extremely low frequency electromagnetic (EM) fields influence the growth rates of HT-1080 fibrosarcoma cells in vitro and offers a theoretical explanation for these effects. The results demonstrate that weak magnetic fields both increase and decrease the growth rates of fibrosarcoma cells by altering the mitochondrial superoxide (O2-) and calcium ions (Ca2+) concentrations in living cells. Over a 4-day exposure period, fibrosarcoma growth rates were modulated in the presence of low-level alternating current (AC) magnetic fields at a 10 μT amplitude at 13.5 Hz to 30 Hz frequency, superimposed on a 45 μT static background field. Effects due to changes in amplitude from 10 to 30 μT at selected frequencies are also investigated with the same superimposed static field. © 2025 USNC-URSI.

关键词： Electromagnetic field effects

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