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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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Heart Disease Prediction Using Novel Ensemble and Blending Based Cardiovascular Disease Detection Networks: EnsCVDD-Net and BlCVDD-Net

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IEEE Access 2024年 12卷 109230-109254页

作者： Khan, Hira Javaid, Nadeem Bashir, Tariq Akbar, Mariam Alrajeh, Nabil Aslam, Sheraz Department of Electrical and Computer Engineering COMSATS University Islamabad Islamabad44000 Pakistan Department of Computer Science COMSATS University Islamabad Islamabad44000 Pakistan International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Yunlin Douliu64002 Taiwan Department of Biomedical Technology College of Applied Medical Sciences King Saud University Riyadh11633 Saudi Arabia Cyprus University of Technology Limassol3036 Cyprus

Cardiovascular Diseases (CVDs) have emerged as a significant physiological condition, being a primary contributor to mortality. Timely and precise diagnosis of heart disease is crucial to safeguard patients from additional harm. Recent studies show that the usage of data driven approaches, such as Deep Learning (DL) and Machine Learning (ML) techniques, in the field of medical science is highly useful in accurately diagnosing heart disease in less time. However, statistical learning and traditional ML approaches require feature engineering to generate robust and effective features from data, which are then used in the prediction models. In the case of large complex data, both processes pose many challenges. Whereas, DL techniques are capable of learning features automatically from the data and are effective at handling large and intricate datasets while outperforming the ML models. This study focuses on the accurate prediction of CVDs, considering the patient’s health and socio-economic conditions while mitigating the challenges presented by imbalanced data. The Adaptive Synthetic Sampling Technique is used for data balancing, while the Point Biserial Correlation Coefficient is used as a feature selection technique. In this study, two DL models, Ensemble based Cardiovascular Disease Detection Network (EnsCVDD-Net) and Blending based Cardiovascular Disease Detection Network (BlCVDD-Net), are proposed for accurate prediction and classification of CVDs. EnsCVDD-Net is made by applying an ensemble technique to LeNet and Gated Recurrent Unit (GRU), and BlCVDD-Net is made by blending LeNet, GRU and Multilayer Perceptron. SHapley Additive exPlanations is used to provide a clear understanding of the influence different factors have on CVD diagnosis. The network’s performance is evaluated on the basis of various performance metrics. The results indicate that the EnsCVDD-Net outperforms all base models with 88% accuracy, 88% F1-score, 91% precision, 85% recall, and 777s execu

关键词： Heart

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A Novel Spark-Based Algorithm for Mining Frequent Utility Patterns 6

A Novel Spark-Based Algorithm for Mining Frequent Utility Pa...

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6th International Conference on Artificial Intelligence and Big Data, ICAIBD 2023

作者： Wu, Jimmy Ming-Tai Zhou, Huiying Lin, Jerry Chun-Wei Wang, Ke Liu, Shuo Li, Ranran Shandong University of Science and Technology College of Computer Science and Engineering Qingdao China Western Norway University of Applied Sciences Department of Computer Science Electrical Engineering and Mathematical Sciences Bergen Norway

ISBN: (纸本)9781665491259

We are now living in the era of big data thanks to technological innovation. It is very important to discover valuable information in massive data. High utility itemset mining and frequent itemset mining are commonly used techniques in data mining, but they only consider a single factor, ignoring some frequent and efficient itemsets. So as to mine more valuable information, a method for mining skyline frequent utility patterns is proposed. Facing multi-dimensional and large-scale datasets, how to quickly find skyline points is a new challenge. In this article, a parallel algorithm is proposed within the framework of Spark, using Transformations and Actions operators to mine skyline patterns. Through the analysis of experimental data, it is proved that the proposed method has high computing efficiency and high search efficiency, and can effectively mine skyline frequent utility itemsets. © 2023 IEEE.

关键词： Large dataset

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Blockchain and Quantum-based Collaborative Communication Framework for Telehealth

Blockchain and Quantum-based Collaborative Communication Fra...

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2024 IEEE International Conference on E-Health Networking, Application and Services, HealthCom 2024

作者： Gajjar, Harshal Jivani, Dirgha Trivedi, Chinmay Gupta, Rajesh Jadav, Nilesh Kumar Tanwar, Sudeep Dutta, Ashit Kumar Rodrigues, Joel J.P.C. Department of Computer Science and Engineering Institute of Technology Nirma University Gujarat India Department of Computer Science and Information Systems College of Applied Sciences AlMaarefa University Saudi Arabia Amazonas State University AM Manaus Brazil

ISBN: (纸本)9798350350548

This paper introduces a novel telehealth communication system, designed to enhance the security and integrity of medical data exchange. In the rapidly evolving digital healthcare landscape, the protection of sensitive patient information is paramount. To address this, our system uniquely combines quantum cryptography, specifically the BB84 protocol, with blockchain technology, offering a dual-layered security framework. The Quantum Layer, underpinned by the BB84 protocol, establishes quantum-secure communication channels, effectively encrypting data exchanges between patients, doctors, and hospitals. This layer guarantees that medical information remains confidential and safe from potential quantum-level eavesdropping threats. The subsequent Blockchain Layer further strengthens the system by storing these encrypted communications in an immutable blockchain ledger. This approach not only secures the data against unauthorized alterations but also provides a transparent and permanent record of all transactions, thereby enhancing the auditability of medical communications. © 2024 IEEE.

关键词： Electronic health record

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Influence of performance and emission of diesel engine with alumina nano material-based catalyst biodiesel using IoT

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International Journal of Power and Energy Conversion 2024年第4期15卷 338-351页

作者： Venkatesh, B. Khan, Mudassir Babu, J. Chinna Nagaraju, C.H. Mohan, R. Madhan Department of Mechanical Engineering Annamacharya Institute of Technology and Sciences Andhra Pradesh Rajampet India Department of Computer Science College of Computer Science Applied College Tanumah King Khalid University P.O. Box 960-61421 Abha Saudi Arabia Department of Electronics and Communication Engineering Annamacharya Institute of Technology and Sciences Andhra Pradesh Rajampet India Department of Electrical and Electronics Engineering Annamacharya Institute of Technology and Sciences India

In this study, the performance and emissions of Al2O3 alkaline cottonseed biodiesel-powered compression ignition engines were predicted. An experiment was conducted using different biodiesel blends made from cottonseed oil under various loading conditions. Biodiesel was produced using ethanol and Cao-mg/Al2O3 as catalysts through the transesterification process and blended with diesel to produce CBDN10, CBDN20, CBDN30, and CBDN40. The CBDN20 blends showed an evident reduction in fuel consumption by 8% and a 12% improvement in thermal efficiency at high static thrust. The use of these blends resulted in minimal CO, CO2, and NOx emissions from engines. These emissions were monitored by an IoT system and analysed with gas analysers, which revealed CBDN20’s ability to reduce nitrogen oxide and hydrocarbon emissions compared to diesel. Additionally, CBDN20 demonstrated improved thermal efficiency and reduced brake-specific fuel consumption compared to the other blends. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Nitrogen oxides

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Construction of computer Network Information Security Protection System Based on Genetic Algorithm 15

Construction of Computer Network Information Security Protec...

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15th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2023

作者： Xiao, Sanxiang Deng, Yihui Li, Ming School of Computer Science Guangzhou College of Applied Science and Technology Guangdong Guangzhou511300 China Guangzhou College of Applied Science and Technology Experimental Training Center Guangdong Guangzhou511300 China School of Computer Science Guangzhou City Construction College Guangdong Guangzhou511300 China

ISBN: (纸本)9798350324433

The development of the Internet has brought convenience to people, but also caused security risks. How to establish a protection system that can not only effectively prevent computer network(CN)s from being attacked by hackers and viruses, but also effectively protect user privacy, real-time and stability has become an urgent problem to be solved. In this paper, the system performance test and analysis, including system test environment, system test content and so on. This paper mainly analyzes the system performance and improvement maethods of the network information security protection system(SPS) under relevant algorithms by means of experimental simulation and data comparison. Experimental data show that the recall rate after incremental training can reach more than 90%. More than 97% of website operation files are recovered in a short time. In order to further improve the performance of the system, a wider range of web pages should be selected for testing. © 2023 IEEE.

关键词： Genetic algorithms

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Handling Non-IID Data in Federated Learning: An Experimental Evaluation Towards Unified Metrics

Handling Non-IID Data in Federated Learning: An Experimental...

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2023 IEEE International Conference on Dependable, Autonomic and Secure Computing, 2023 International Conference on Pervasive Intelligence and Computing, 2023 International Conference on Cloud and Big Data Computing, 2023 International Conference on Cyber science and technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Haller, Marc Lenz, Christian Nachtigall, Robin Awayshehl, Feras M. Alawadi, Sadi Karlsruhe University of Applied Sciences Karlsruhe Germany Institute of Computer Science Delta The University of Tartu Tartu Estonia Blekinge Institute of Technology Department of Computer Science Karlskrona Sweden

ISBN: (纸本)9798350304602

Recent research has demonstrated that Non-Identically Distributed (Non-IID) data can negatively impact the performance of global models constructed in federated learning. To address this concern, multiple approaches have been developed. Nonetheless, previous research lacks a cohesive overview and fails to uniformly assess these strategies, resulting in challenges when comparing and choosing relevant options for real-world scenarios. This study presents a structured survey of cutting-edge techniques for handling the Non-IID data, accompanied by proposing a metric to develop a standardized approach for assessing data skew and its harmony with the appropriate approach. The findings affirm the metric's suitability as a heuristic for assessing data skew in distributed datasets without having insight into client data, serving both scientific and practical purposes and thus supporting the selection of handling strategies. This preliminary research establishes the foundation for discussing standardizing methodologies for evaluating data heterogeneity in federated learning. © 2023 IEEE.

关键词： Standardization

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IDS-INT:Intrusion detection system using transformer-based transfer learning for imbalanced network traffic

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Digital Communications and Networks 2024年第1期10卷 190-204页

作者： Farhan Ullah Shamsher Ullah Gautam Srivastava Jerry Chun-Wei Lin School of Software Northwestern Polytechnical UniversityXian710072ShaanxiChina Knowledge Unit of Systems and Technology University of Management and TechnologySialkot 51040Pakistan Department of Math and Computer Science Brandon UniversityBrandonMB R7A 6A9Canada Department of Computer Science and Math Lebanese American UniversityBeirut1102Lebanon Western Norway University of Applied Sciences BergenNorway Research Centre for Interneural Computing China Medical UniversityTaichungTaiwanChina

A network intrusion detection system is critical for cyber security against llegitimate *** terms of feature perspectives,network traffic may include a variety of elements such as attack reference,attack type,a subcategory of attack,host information,malicious scripts,*** terms of network perspectives,network traffic may contain an imbalanced number of harmful attacks when compared to normal *** is challenging to identify a specific attack due to complex features and data imbalance *** address these issues,this paper proposes an Intrusion Detection System using transformer-based transfer learning for Imbalanced Network Traffic(IDS-INT).IDS-INT uses transformer-based transfer learning to learn feature interactions in both network feature representation and imbalanced ***,detailed information about each type of attack is gathered from network interaction descriptions,which include network nodes,attack type,reference,host information,***,the transformer-based transfer learning approach is developed to learn detailed feature representation using their semantic ***,the Synthetic Minority Oversampling Technique(SMOTE)is implemented to balance abnormal traffic and detect minority ***,the Convolution Neural Network(CNN)model is designed to extract deep features from the balanced network ***,the hybrid approach of the CNN-Long Short-Term Memory(CNN-LSTM)model is developed to detect different types of attacks from the deep *** experiments are conducted to test the proposed approach using three standard datasets,i.e.,UNsWNB15,CIC-IDS2017,and *** explainable AI approach is implemented to interpret the proposed method and develop a trustable model.

关键词： Network intrusion detection Transfer learning Features extraction Imbalance data Explainable AI Cybersecurity

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On Error-Based Step Size Control for Discontinuous Galerkin Methods for Compressible Fluid Dynamics

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应用数学与计算数学学报 2025年第1期7卷 3-39页

作者： Hendrik Ranocha Andrew R.Winters Hugo Guillermo Castro Lisandro Dalcin Michael Schlottke-Lakemper Gregor J.Gassner Matteo Parsani Applied Mathematics University of HamburgBundesstr.5520146 HamburgGermany Department of Mathematics Applied Mathematics Linköping UniversityLinköpingSweden Extreme Computing Research Center(ECRC) Computer Electrical and Mathematical Science and Engineering Division(CEMSE)King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia Applied and Computational Mathematics RWTH Aachen UniversityAachenGermany High-Performance Computing Center Stuttgart University of StuttgartStuttgartGermany Department of Mathematics and Computer Science University of CologneCologneGermany Center for Data and Simulation Science University of CologneCologneGermany Extreme Computing Research Center(ECRC) Computer Electrical and Mathematical Science and Engineering Division(CEMSE)King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia Physical Science and Engineering Division(PSE) King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia

We study a temporal step size control of explicit Runge-Kutta(RK)methods for com-pressible computational fluid dynamics(CFD),including the Navier-Stokes equations and hyperbolic systems of conservation laws such as the Euler *** demonstrate that error-based approaches are convenient in a wide range of applications and compare them to more classical step size control based on a Courant-Friedrichs-Lewy(CFL)*** numerical examples show that the error-based step size control is easy to use,robust,and efficient,e.g.,for(initial)transient periods,complex geometries,nonlinear shock captur-ing approaches,and schemes that use nonlinear entropy *** demonstrate these properties for problems ranging from well-understood academic test cases to industrially relevant large-scale computations with two disjoint code bases,the open source Julia pack-ages *** with *** and the C/Fortran code SSDC based on PETSc.

关键词： Explicit Runge-Kutta(RK)methods Step size control Compressible fluid dynamics Adaptivity in space and time Shock capturing

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Analyze the encryption and decryption methods using the Catalan sequence 38

Analyze the encryption and decryption methods using the Cata...

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38th ECMS International Conference on Modelling and Simulation, ECMS 2024

作者： Zdanski, Maciej Walusiak, Lukasz Rylko, Natalia Faculty of Computer Science and Telecommunications Cracow University of Technology Poland Faculty of Architecture Civil Construction and Applied Arts Academy of Silesia Poland

This paper concerns the project and implementation of a cryptographic tool based on the Catalan sequence. It explains the beginnings of cryptography and how people’s approach to transmitting messages has changed over the years. With the development of society, the need to conceal the meaning of messages from unauthorized individuals has grown. The paper addresses the issue of symmetric affine cipher, its complexity, and the secure transmission of the secret key through individuals using the tool. The key can be entered by the user-sender or generated automatically using the Catalan sequence. In contemporary times, the issue of secure data transmission, and consequently the appropriate methods of encryption and decryption, is crucial. The presented actions not only show an analysis of the methods but also the implementation of these as a functioning tool. ©ECMS Daniel Grzonka, Natalia Rylko, Grazyna Suchacka, Vladimir Mityushev (Editors) 2024.

关键词： Cryptography

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