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IDSDeep-CCD: intelligent decision support system based on deep learning for concrete cracks detection

Multimedia Tools and Applications 2025年第12期84卷 10013-10026页

作者： Abualigah, Sayel M. Al-Naimi, Ahmad F. Sachdeva, Gagan AlAmri, Omran Abualigah, Laith Department of Computer Information Systems Jordan University of Science and Technology Irbid Jordan Gagan Sachdeva Dubai United Arab Emirates Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Computer Science Department Al al-Bayt University Mafraq25113 Jordan Research Center University of Tabuk Tabuk71491 Saudi Arabia MEU Research Unit Middle East University Amman11831 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia Applied Science Research Center Applied Science Private University Amman11931 Jordan College of Engineering Yuan Ze University Taoyuan32003 Taiwan

In an era where Intelligent Decision Support Systems (IDSS) are integral to managing the vast data from Internet of Everything (IoE) systems, this study introduces IDSDeep-CCD, a novel IDSS approach for detecting concrete cracks, a critical issue in civil infrastructure maintenance. Traditional visual inspection methods for crack detection are time-consuming and error-prone. To address this, IDSDeep-CCD employs deep learning, utilizing an open-source dataset of concrete crack images for enhanced accuracy. The system's two-stage approach, featuring feature extraction via pre-trained models and a fully connected network for classification, significantly outperforms existing methods. Our results demonstrate a remarkable 99.9% accuracy rate in crack detection without data augmentation, marking a notable advancement in automatic detection technology. This breakthrough offers practical benefits for the industry by enabling more precise and efficient identification of concrete cracks, paving the way for timely and accurate remedial actions. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep neural networks

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Learning-based adaptive optimal output regulation of linear and nonlinear systems:an overview

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Control Theory and technology 2022年第1期20卷 1-19页

作者： Weinan Gao Zhong-Ping Jiang Department of Mechanical and Civil Engineering College of Engineering and Science Florida Institute of Technology150 W.University Blvd.MelbourneFL32901USA Department of Electrical and Computer Engineering Tandon School of EngineeringNew York UniversitySix MetroTech CenterBrooklynNY11201USA

This paper reviews recent developments in learning-based adaptive optimal output regulation that aims to solve the problem of adaptive and optimal asymptotic tracking with disturbance *** proposed framework aims to bring together two separate topics—output regulation and adaptive dynamic programming—that have been under extensive investigation due to their broad applications in modern control *** this framework,one can solve optimal output regulation problems of linear,partially linear,nonlinear,and multi-agent systems in a data-driven *** will also review some practical applications based on this framework,such as semi-autonomous vehicles,connected and autonomous vehicles,and nonlinear oscillators.

关键词： Adaptive optimal output regulation Adaptive dynamic programming Reinforcement learning Learning-based control

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Optimized Phishing Detection with Recurrent Neural Network and Whale Optimizer Algorithm

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computers, Materials & Continua 2024年第9期80卷 4895-4916页

作者： Brij Bhooshan Gupta Akshat Gaurav Razaz Waheeb Attar Varsha Arya Ahmed Alhomoud Kwok Tai Chui Department of Computer Science and Information Engineering Asia UniversityTaichung413Taiwan Symbiosis Centre for Information Technology(SCIT) Symbiosis International UniversityPuneMaharashtra411057India Center for Interdisciplinary Research University of Petroleum and Energy Studies(UPES)Dehradun248007India Ronin Institute MontclairNJ 07043USA Management Department College of Business AdministrationPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Business Administration Asia UniversityTaichung413Taiwan Department of Electrical and Computer Engineering Lebanese American UniversityBeirut1102Lebanon Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Department of Electronic Engineering and Computer Science Hong Kong Metropolitan University(HKMU)HomantinHong KongChina

Phishing attacks present a persistent and evolving threat in the cybersecurity land-scape,necessitating the development of more sophisticated detection *** machine learning approaches to phishing detection have relied heavily on feature engineering and have often fallen short in adapting to the dynamically changing patterns of phishingUniformResource Locator(URLs).Addressing these challenge,we introduce a framework that integrates the sequential data processing strengths of a Recurrent Neural Network(RNN)with the hyperparameter optimization prowess of theWhale Optimization Algorithm(WOA).Ourmodel capitalizes on an extensive Kaggle dataset,featuring over 11,000 URLs,each delineated by 30 *** WOA’s hyperparameter optimization enhances the RNN’s performance,evidenced by a meticulous validation *** results,encapsulated in precision,recall,and F1-score metrics,surpass baseline models,achieving an overall accuracy of 92%.This study not only demonstrates the RNN’s proficiency in learning complex patterns but also underscores the WOA’s effectiveness in refining machine learning models for the critical task of phishing detection.

关键词： Phishing detection Recurrent Neural Network(RNN) Whale Optimization Algorithm(WOA) cybersecurity machine learning optimization

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Statistical Data Mining with Slime Mould Optimization for Intelligent Rainfall Classification

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computer Systems Science & engineering 2023年第10期47卷 919-935页

作者： Ramya Nemani G.Jose Moses Fayadh Alenezi K.Vijaya Kumar Seifedine Kadry Jungeun Kim Keejun Han Department of Mathematics Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science and Engineering University Institute of Engineering and Technology(UIET)Guru Nanak UniversityHyderabadIndia Department of Electrical Engineering College of EngineeringJouf UniversitySaudi Arabia Department of Computer Science and Engineering GITAM School of TechnologyVishakhapatnam CampusGITAM(Deemed to be a University)VishakhapatnamIndia Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Software Kongju National UniversityCheonan31080Korea Division of Computer Engineering Hansung UniversitySeoul02876Korea

Statistics are most crucial than ever due to the accessibility of huge counts of data from several domains such as finance,medicine,science,engineering,and so *** data mining(SDM)is an interdisciplinary domain that examines huge existing databases to discover patterns and connections from the *** varies in classical statistics on the size of datasets and on the detail that the data could not primarily be gathered based on some experimental strategy but conversely for other ***,this paper introduces an effective statistical Data Mining for Intelligent Rainfall Prediction using Slime Mould Optimization with Deep Learning(SDMIRPSMODL)*** the presented SDMIRP-SMODL model,the feature subset selection process is performed by the SMO algorithm,which in turn minimizes the computation *** rainfall *** neural network with long short-term memory(CNN-LSTM)technique is *** last,this study involves the pelican optimization algorithm(POA)as a hyperparameter *** experimental evaluation of the SDMIRP-SMODL approach is tested utilizing a rainfall dataset comprising 23682 samples in the negative class and 1865 samples in the positive *** comparative outcomes reported the supremacy of the SDMIRP-SMODL model compared to existing techniques.

关键词： Statistical data mining predictive models deep learning rainfall prediction parameter tuning

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A computer-aided diagnosis system for breast pathology: a deep learning approach with model interpretability from pathological perspective

A computer-aided diagnosis system for breast pathology: a de...

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作者： Hsu, Wei-Wen Wu, Yongfang Chen, Chung-Hao Hao, Chang Hou, Yu-Ling Gao, Xiang Shao, Yun Zhang, Xueli He, Tao Tai, Yanhong Department of Computer Science and Information Technology National Taitung University Taitung Taiwan Department of Pathology The Fifth Medical Center of Chinese PLA General Hospital Beijing China Department of Electrical and Computer Engineering Old Dominion University NorfolkVA United States Institute of Big Data Technology Bright Oceans Corporation Beijing China

In this study, a computer-aided diagnosis (CAD) system for whole-slide images (WSIs) with breast cancer was proposed to achieve lesion detection and classification. Different from the previously proposed works that performed segmentation directly and applied the segmentation metrics for performance evaluation, here we computed the accuracy in patch-level, region-level, and slide-level to access our proposed framework to meet the clinical requirements, instead of simply regarding it as an AI segmentation task. To achieve that for WSIs, lesion detection was conducted at high magnification (× 40) first for region proposal by patching using the modified AlexNet model;and then, several patches were sampled from the detected regions at lower magnification (× 10) to observe cells’ patterns for lesion classification by the model of ResNet50. With such fashion, the deep features being distinguishing in lesion classification from the convolutional neural networks (CNN) were further analyzed in this work to provide comprehensive interpretability for the proposed CAD system based on pathological knowledge. For our experiments, a total of 186 slides of WSIs were collected and classified into three categories: Non-Carcinoma, Ductal Carcinoma in Situ (DCIS), and Invasive Ductal Carcinoma (IDC). The slide-level accuracy rate for the three-category classification reached 90.8% (99/109) through a fivefold cross-validation and achieved 94.8% (73/77) on the testing set. In addition, the learned features reflect the clinical insights in lesion classification for explainable AI, which enhanced the reliability and validity of our proposed system. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023.

关键词： computer aided diagnosis

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Accuracy Analysis of Group Delay Variation Measurement in Anechoic Chambers 19

Accuracy Analysis of Group Delay Variation Measurement in An...

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19th European Conference on Antennas and Propagation, EuCAP 2025

作者： Caizzone, S. Tripathi, V. Elmarissi, W. Gabler, B. Institute of Communications and Navigation German Aerospace Center (DLR) Oberpfaffenhofen Germany School of Computation Information and Technology Technical University of Munich Department of Electrical Engineering Munich Germany Microwaves and Radar Institute German Aerospace Center (DLR) Oberpfaffenhofen Germany

ISBN: (纸本)9788831299107

Precise GNSS antenna characterization is needed in several applications, targeting high-accuracy positioning and timing. While the characterization of phase center variations is nowadays canonically established, the measurement of group delay variations is still a matter of research for its high requirements in terms of measurement accuracy. The present paper shows an analysis of the accuracy performance achievable in anechoic chambers, estimating the uncertainty in the group delay variation produced by imperfections of the measurement system itself and assessing the impact of signal processing countermeasures. © 2025 European Association on Antennas and Propagation.

关键词： anechoic chamber antennas GNSS group delay high accuracy

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Optimizing One-Stage Object Detection for URLLC: A Two-Stage Pipeline with Oculi SPU 25

Optimizing One-Stage Object Detection for URLLC: A Two-Stage...

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25th International Arab Conference on Information technology, ACIT 2024

作者： Moghlebey, Adam Bou Moubayed, Abdallah Injadat, MohammadNoor Lebanese American University Electrical & Computer Engineering Department Byblos Lebanon King Fahd University of Petroleum & Minerals Computer Engineering Department Interdisciplinary Research Center for Intelligent Secure Systems Dhahran Saudi Arabia Zarqa University Data Science & Artificial Intelligence Department Zarqa Jordan University of Business and Technology Jeddah Saudi Arabia

ISBN: (纸本)9798331540012

Pedestrian-related accidents account for an estimated 20-25% of the approximately 1.19 million road fatalities occurring annually, highlighting the urgent need for enhanced detection systems in surveillance and smart transportation to mitigate such incidents. The escalating demand for Ultra-Reliable Low-Latency Communications (URLLC) in modern surveillance and transportation systems has become a solution to that problem but highlighted significant challenges in reducing inference times of machine learning models, particularly on constrained hardware. The maximum permissible latency for most vehicular applications ranges from 6 to 20 milliseconds based on the application. When tested on an 8GB RAM setup using the NTU, LLVIP, and VIRAT datasets, YOLOv8n achieved a minimum inference time of approximately 200 milliseconds per frame, making it unsuitable for URLLC applications despite 8GB RAM being an adequate hardware setup for edge devices. This research introduces a new two-stage pipeline that integrates Oculi's Sensing and Processing Unit (SPU) simulation with the YOLOv8n one-stage object detection model to enhance pedestrian detection, thereby addressing these challenges. By leveraging Regions of Interest (ROIs) generated by Oculi's SPU, our approach optimizes the input data for the YOLO model, substantially reducing computational overhead and thus making it suitable for URLLC applications. This integration can lead to a reduction in inference time by up to 93% in the tested frames when being compared to YOLOv8n pre-trained model. © 2024 IEEE.

关键词： Digital storage

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Leveraging Big Data Intelligence for Electronic Health Data Collection and Mining in Smart Innovative Cities: Approaches and Applications 7

Leveraging Big Data Intelligence for Electronic Health Data ...

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7th Iranian Conference on Advances in Enterprise Architecture, ICAEA 2023

作者： Zanjani, S. Mohammadhosain Zanjani, S. Mohammadali Shahinzadeh, Hossein Moazzami, Majid Ebrahimi, Farshad Nourmahnad, Nasim Islamic Azad University Najafabad Branch Department of Electrical Engineering Najafabad Iran Islamic Azad University Smart Microgrid Research Center Najafabad Branch Najafabad Iran Amirkabir University of Technology Department of Electrical Engineering Tehran Iran University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States

ISBN: (纸本)9798350308068

In recent years, the Internet of Things (IoT) and the development of smart cities have brought about significant advancements in various fields, including medicine. The integration of IoT devices and smart city infrastructure has transformed domains like e-government, e-learning, and ehealth. In particular, the field of e-health, which combines medical informatics, public health, and information technology, has witnessed extensive research to analyze the vast amounts of data generated by these connected systems. This data, often referred to as big data, poses challenges in terms of storage, analysis, visualization, and processing due to its complexity and diversity. The emergence of big data analytics has revolutionized management practices, including healthcare, and offers promising opportunities for progress. By leveraging data analysis techniques and optimizing the architecture of healthcare systems within the context of IoT and smart cities, numerous benefits can be achieved. These include cost reduction, early detection of epidemics, and improved quality of life for individuals. This article aims to provide a comprehensive overview of fundamental concepts, architectural considerations, and challenges in utilizing big data for e-health and healthcare within the context of IoT and smart cities. By fostering a deeper understanding of data-driven approaches for better healthcare outcomes, it seeks to contribute to further advancements in this field. © 2023 IEEE.

关键词： Smart city

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A solution for resource allocation through complex systems in fog computing for the internet of things

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Computing 2024年第7期106卷 2107-2131页

作者： Faraji, Fatimah Javadpour, Amir Sangaiah, Arun Kumar Zavieh, Hadi Department of Computer Engineering Yazd University Safaeih Yazd100190 Iran Harbin Institute of Technology Shenzhen10587 China ADiT-Lab Electrotechnics and Telecommunications Department ISMAI and INESC TEC Instituto Politécnico de Viana do Castelo ISMAI Porto10587 Portugal International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Yunlin 64002 Taiwan Department of Economic and Statistics Guangzhou Higher Education Mega Center Guangzhou University Guangzhou510006 China Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon

To manage the IoT resources to process the massive amount of collected data in complex systems, Allocation of tasks to nodes to increase user satisfaction has been noticed by researchers. In allocating tasks, the necessity of a powerful auxiliary platform is felt. Cloud and fog networks are two suitable options for performing and completing tasks on the Internet of Things. In the cloud network, due to offloading tasks to remote computing centers, problems such as increasing complete time, increasing traffic in the network, and high energy consumption for the Internet of Things network occur. The fog network solves the problem of completing tasks due to reducing the geographical distance between Internet of Things devices and processor nodes. The devices in the fog network cooperate with the Internet of Things network’s diverse and variable nodes with time computing, storage, and processing resources. In this heterogeneous environment, the non-cooperation and dishonesty of malicious fog nodes through misrepresentation of shared resources and non-completion of tasks can disrupt the performance of the Internet of Things network. As a result, two fundamental challenges in the heterogeneous fog network include providing resources to allocate tasks and recognizing valid fog nodes. This paper focuses on creating a resource provider network to allocate tasks to authentic fog nodes with appropriate resources. When an Internet of Things node faces a shortage of resources and link prediction, it can form a resource supply network with neighboring nodes to solve the two stated challenges. For this purpose, in the first stage, efforts are made to select the most appropriate options among the neighboring nodes based on specific criteria. In the second step, reliable nodes are identified based on a trust mechanism, including resource trust and entity trust. In the last step, tasks are divided among reliable devices with sufficient resources. The evaluation of the proposed method i

关键词： Quality of service

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An Investigation into Robust Video Watermarking Techniques 15

An Investigation into Robust Video Watermarking Techniques

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Deshpande, Vivek Vijayalakshmi, V.J. Ojha, Varun Arunadevi, R. Chaudhary, Anisha Manu, S.E. Vishwakarma Institute of Information Technology Department of Computer Engineering Maharashtra Pune India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Coimbatore India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai India Quantum University Research Center Quantum University India School of Engineering and Technology Civil Engineering Karnataka Bangalore India

ISBN: (纸本)9798350370249

this paper investigates techniques for ensuring the robustness of video watermarking algorithms. Many watermarking strategies awareness of performance measures like protection and imperceptibility;however frequently need to remember robustness, which is a vital element in creating a robust watermark that can preserve diverse types of signal noise and intentional attacks. The authors proposed an adaptive technique for the usage of movement estimation-primarily based watermark encoding and interpreting to assure robustness towards diverse noise and alteration assaults, along with white noise, contrast less noise, image filtering, JPEG compression, and video cropping. The authors in comparison the proposed algorithm with existing methods in phrases of height sign-to-noise ratio (PSNR), normalized correlation (NC), and robustness evaluation with four reference points (RP4). Effects showed that the proposed set of rules carried out higher in terms of PSNR, NC, and RP4, providing robust evidence of its robustness in comparison to present algorithms. Usual, the consequences from this paper recommended that the proposed adaptive method is a promising solution for robust video watermarking. The algorithm presents a strong overall performance in phrases of security, imperceptibility, and robustness towards various sorts of noise and intentional attacks. Future studies could discover the utility of this method to a much wider variety of video watermarking situations. © 2024 IEEE.

关键词： White noise

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