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Enhancing structural health monitoring with AI-ML algorithms: a focus on crack detection and prediction

Asian Journal of Civil engineering 2025年第5期26卷 1907-1918页

作者： Bader, Ahmad Shtayat, Amir Al-Mistarehi, Bara’ Communication and Computer Department Faculty of Engineering Jadara University Irbid Jordan Department of City Planning and Design Jordan University of Science and Technology Irbid 22110 Jordan Department of Civil Engineering Jordan University of Science and Technology Irbid Jordan

SHM is a very important process in terms of the safety and durability of infrastructure. Traditional SHM often faces problems detecting minor structural defects and handling large datasets. Therefore, certain more advanced approaches are called for. The paper discussed the applications of AI and ML algorithms, such as CatBoost and the African Vultures Optimization Algorithm, for such challenges. The research is based on a unique dataset of 8,541 rows and diverse features, developing a predictive framework that enhances crack detection and forecast capabilities. The approach mainly deals with heterogeneous data using the CatBoost algorithm, given its capability for high-accuracy predictions, while AVOA optimizes feature selection, reduces the computational cost, and guarantees no loss in model performance. This methodology has resulted in a significant enhancement of the prediction accuracy, which states the importance of AI-ML integration in SHM. The key results demonstrate the effectiveness of the model in detecting structural anomalies and crack propagation to enable proactive maintenance strategies. This study’s contributions have gone toward advancing SHM with scalable and efficient AI-ML frameworks, enabling real-time monitoring for better infrastructure management. Such development might have a transforming potential to cut down on maintenance costs and enhance operational safety, thus further encouraging sustainable infrastructure systems. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： African vultures optimization algorithm CatBoost Crack Detection Feature selection Machine learning Structural health monitoring

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Deep Learning-Based Trees Disease Recognition and Classification Using Hyperspectral Data

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computers, Materials & Continua 2023年第10期77卷 681-697页

作者： Uzair Aslam Bhatti Sibghat Ullah Bazai Shumaila Hussain Shariqa Fakhar Chin Soon Ku Shah Marjan Por Lip Yee Liu Jing College of Information and Communication Engineering Hainan UniversityHaikou570228China Department of Computer Engineering Balochistan University of Information TechnologyEngineeringand Management Sciences(BUITEMS)QuettaPakistan Department of Computer Science Sardar Bahadur Khan Women’s UniversityQuettaPakistan Department of Computer Science Universiti Tunku Abdul RahmanKampar31900Malaysia Department of Software Engineering Balochistan University of Information TechnologyEngineeringand Management Sciences(BUITEMS)QuettaPakistan Faculty of Computer Science and Information Technology University of MalayaKuala Lumpur50603Malaysia

Crop diseases have a significant impact on plant growth and can lead to reduced *** methods of disease detection rely on the expertise of plant protection experts,which can be subjective and dependent on individual experience and *** address this,the use of digital image recognition technology and deep learning algorithms has emerged as a promising approach for automating plant disease *** this paper,we propose a novel approach that utilizes a convolutional neural network(CNN)model in conjunction with Inception v3 to identify plant leaf *** research focuses on developing a mobile application that leverages this mechanism to identify diseases in plants and provide recommendations for overcoming specific *** models were trained using a dataset consisting of 80,848 images representing 21 different plant leaves categorized into 60 distinct *** rigorous training and evaluation,the proposed system achieved an impressive accuracy rate of 99%.This mobile application serves as a convenient and valuable advisory tool,providing early detection and guidance in real agricultural *** significance of this research lies in its potential to revolutionize plant disease detection and management *** automating the identification process through deep learning algorithms,the proposed system eliminates the subjective nature of expert-based diagnosis and reduces dependence on individual *** integration of mobile technology further enhances accessibility and enables farmers and agricultural practitioners to swiftly and accurately identify diseases in their crops.

关键词： Plant disease Inception v3 CNN crop diseases

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NEAR: Non-Supervised Explainability Architecture for Accurate Review-Based Collaborative Filtering

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IEEE Transactions on Knowledge and Data engineering 2024年第2期36卷 750-765页

作者： Pugoy, Reinald Adrian Kao, Hung-Yu National Cheng Kung University Intelligent Knowledge Management Lab Department of Computer Science and Information Engineering Tainan City701 Taiwan University of the Philippines Open University Faculty of Information and Communication Studies Los Baños4031 Philippines

There is a critical issue in explainable recommender systems that compounds the challenges of explainability yet is rarely tackled: the lack of ground-truth explanation texts for training. It is unrealistic to expect every user-item pair in a dataset to have a corresponding target explanation. Hence, we pioneer the first non-supervised explainability architecture for review-based collaborative filtering (called NEAR) as our novel contribution to the theory of explanation construction in recommender systems. While maintaining excellent recommendation performance, our approach reformulates explainability as a non-supervised (i.e., unsupervised and self-supervised) explanation generation task. We formally define two explanation types, both of which NEAR can produce. An invariant explanation, fixed for all users, is based on the unsupervised extractive summary of an item's reviews via embedding clustering. Meanwhile, a variant explanation, personalized for a specific user, is a sentence-level text generated by our customized Transformer conditioned on every user-item-rating tuple and artificial ground-truth (self-supervised label) from one of the invariant explanation's sentences. Our empirical evaluation illustrates that NEAR's rating prediction accuracy is better than the other state-of-the-art baselines. Moreover, experiments and assessments show that NEAR-generated variant explanations are more personalized and distinct than those from other Transformer-based models, and our invariant explanations are preferred over those from other contemporary models in real life. © 1989-2012 IEEE.

关键词： Recommender systems

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Enhanced Security Protocol for VLSI Systems: Modified AES Algorithm for Robust Data Transmission 3

Enhanced Security Protocol for VLSI Systems: Modified AES Al...

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3rd International Conference on Integrated Circuits and communication Systems, ICICACS 2025

作者： Lawrence, Jinsha Budagam, Dinesh Kumar Mukilan, P. Veerappan, Kavitha Chandrasekhar, E. Barathi, K. Faculty of Engineering Department of Computer Science and Engineering Coimbatore India Visa Department of Ms in Software Engineering CA United States Dhanalakshmi Srinivasan College of Engineering Department of Electronics and Communication Engineering Coimbatore India Kongu Engineering College Department of Electronics and Communication Engineering Erode India Department of Electronics and Communication Engineering Hyderabad India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9798331508456

The development of compact and efficient devices has been made possible by the growth of Very Large Scale Integration (VLSI) technologies, which has transformed modern electronics. However, there are cautions regarding the security of data, especially when it comes to transmission due to the extensive usage of technology. To address these, this study proposes a modified Advanced Encryption Standard (AES) algorithm as a solution for improved security protocol in VLSI systems. The proposed algorithm are intended to support the encryption procedure, increasing stronger production against cryptographic attacks while preserving the system's effectiveness and speed factors that are critical aspects for VLSI applications. Through extensive simulations and testing, the modified AES algorithm demonstrated significant security enhancements the operational efficiency of the VLSI system. The attained outcomes of the proposed work shows that the Modified AES strategy provides a workable technique to protect data in VLSI-based devices while maintaining data communication integrity and confidentiality. The proposed modified AES algorithm demonstrates a significant improvement in performance with a propagation delay of 7 ns, power consumption of 29 mW, and a computational overhead of 62 bits, leading to enhanced efficiency in cryptographic operations. This study highlights that the modified AES algorithm improve security in modern electronic systems and maintain performance. © 2025 IEEE.

关键词： Network security

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Lung tumor image fusion based on VGG19-GAN 4

Lung tumor image fusion based on VGG19-GAN

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4th International Conference on Biomedicine and Bioinformatics engineering, ICBBE 2024

作者： Zhang, Xuewen Zong, Jingjing Yang, Yusen Sun, Xinmeng School of Computer and Communication Engineering Dalian Jiaotong University Liaoning Dalian116028 China Faculty of Medicine Dalian University of Technology Liaoning Dalian116024 China

ISBN: (纸本)9781510682443

Positron emission tomography(PET) and computed tomography(CT) imaging can obtain the functional metabolic information and anatomical information of the human body, respectively. In response to the problem of how to merge the complementary information of PET and CT, a lung tumor image fusion method based on VGG19 and GAN model is proposed in this paper. The basic idea is to extract the content of the PET image and the style of the CT image separately, and then merge these two to generate the fusion image. The style image is represented using a series of statistical methods that capture features such as color, texture, and style. The content image is represented using a series of deep learning techniques that capture features such as shapes, structures, and semantic information. The experimental results show that the proposed method has better subjective and objective results than the compared algorithms, which can generate higher-quality PET/CT fusion images. This method provides comprehensive diagnostic information, which is helpful for the diagnosis of lung tumors in clinical treatment and reduces misdiagnosis and underdiagnoses. © 2024 SPIE.

关键词： Lung cancer

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Statistical Comparison ARIMA Order Performance In Stock Market 7

Statistical Comparison ARIMA Order Performance In Stock Mark...

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7th International Conference on Information Technology, InCIT 2023

作者： Deemee, Chanatip Ngampis, Kirati Noraset, Thanapon Thaipisutikul, Tipajin Sun, Min-Te Kitchat, Kotcharat Mahidol University Faculty of Information and Communication Technology Thailand National Central University Department of Computer Science and Information Engineering Taiwan

ISBN: (纸本)9798350358698

Stock market forecasting is important for financial decision-making and risk management. Among the various time series models, the Autoregressive (p) Integrated (d) Moving Average (q) (ARIMA) model has been widely adopted for its simplicity and effectiveness in capturing temporal patterns. However, selecting appropriate ARIMA orders remains a crucial and challenging task, impacting the accuracy of predictions. This paper presents a comprehensive statistical comparison of ARIMA order performance in the context of stock market forecasting. We examine the impact of different ARIMA model orders. Our study utilizes historic New York Stock Exchange (NYSE) stock price data. Our findings shed light on the complex interplay between ARIMA parameters and predictive accuracy, offering valuable insights for robust financial forecasting. © 2023 IEEE.

关键词： Financial markets

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Enhancing face recognition performance: a comprehensive evaluation of deep learning models and a novel ensemble approach with hyperparameter tuning

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Soft Computing 2024年第20期28卷 12399-12424页

作者： Selvaganesan, Jana Sudharani, B. Shekhar, S. N. Chandra Vaishnavi, K. Priyadarsini, K. Raju, K. Srujan Rao, T. Srinivasa Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R & ampD Institute of Science and Technology Avadi Tamil Nadu Chennai600062 India Guntur Dist. Andhra Pradesh Vadlamudi India Department of Electronics and Communication Engineering Sreenidhi Institute of Science and Technology Telangana Hyderabad501301 India Department of Computer Science and Engineering Sona College of Technology Tamil Nadu Salem636005 India Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India Department of Computer Science and Engineering CMR Technical Campus Medchal Telangana Hyderabad501401 India Department of Electronics and Communication Engineering Aditya Engineering College Surampalem533437 India

In response to growing security concerns and the increasing demand for face recognition (FR) technology in various sectors, this research explores the application of deep learning techniques, specifically pre-trained Convolutional Neural Network (CNN) models, in the field of FR. The study harnesses the power of five pre-trained CNN models—DenseNet201, ResNet152V2, MobileNetV2, SeResNeXt, and Xception—for robust feature extraction, followed by SoftMax classification. A novel weighted average ensemble model, meticulously optimized through a grid search technique, is introduced to augment feature extraction and classification efficacy. Emphasizing the significance of robust data pre-processing, encompassing resizing, data augmentation, splitting, and normalization, the research endeavors to fortify the reliability of FR systems. Methodologically, the study systematically investigates hyperparameters across deep learning models, fine-tuning network depth, learning rate, activation functions, and optimization methods. Comprehensive evaluations unfold across diverse datasets to discern the effectiveness of the proposed models. Key contributions of this work encompass the utilization of pre-trained CNN models for feature extraction, extensive evaluation across multiple datasets, the introduction of a weighted average ensemble model, emphasis on robust data pre-processing, systematic hyperparameter tuning, and the utilization of comprehensive evaluation metrics. The results, meticulously analyzed, unveil the superior performance of the proposed method, consistently outshining alternative models across pivotal metrics, including Recall, Precision, F1 Score, Matthews Correlation Coefficient (MCC), and Accuracy. Notably, the proposed method attains an exceptional accuracy of 99.48% on the labeled faces in the wild (LFW) dataset, surpassing erstwhile state-of-the-art benchmarks. This research represents a significant stride in FR technology, furnishing a dependable and accurate

关键词： Convolutional neural networks

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Bilateral Coupled Epsilon Negative Metamaterial for Dual Band Wireless communications

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computers, Materials & Continua 2022年第4期71卷 1263-1281页

作者： Md Mhedi Hasan Mohammad Tariqul Islam Md Moniruzzaman Mohd Hafiz Uddin Norsuzlin Binti Mohd Sahar Md Samsuzzaman Department of Electrical Electronic and Systems Engineering Faculty of Engineering and Built EnvironmentUniversiti Kebangsaan MalaysiaSelangor43600Malaysia Space Science Centre Institute of Climate ChangeUniversiti Kebangsaan MalaysiaSelangor43600Malaysia Department of Computer and Communication Engineering Faculty of Computer Science and EngineeringPatuakhali Science and Technology UniversityPatuakhali8602Bangladesh

This work presents a dual band epsilon negative(ENG)metamaterial with a bilateral coupled split ring resonator(SRR)for use in C and X band wireless communication *** traditional split-ring resonator(SRR)has been amended with this engineered *** proposed metamaterial unit cell is realized on the 1.6 mm thick FR-4 printed media with a dimension of 10×10 *** resonating patch built with a square split outer *** interlinked inner rings are coupled vertically to the outer ring to extend its electrical length as well as to tune the resonance *** simulation is performed using CST studio suite 2019 to design and performance *** transmission coefficient(S21)of the proposed unit cell and array configuration exhibits two resonances at 6.7 and 10.5 GHz with wide bandwidth extended from 4.86 to 8.06 GHz and 10.1 to 11.2 GHz,*** permittivity is noted at frequencies between 6.76–9.5 GHz and 10.5–12 GHz,consecutively,with near-zero refractive index and *** optimal EMR value depicts the compactness of the proposed *** 1×2,2×2 and 4×4 arrays are analyzed that shows similar response compared to the unit *** results of the 2×2 array shows the close similarity of S21 response as compared to *** observed properties of the proposed unit cell ascertain its suitability for wireless communications by enhancing the gain and directivity of the antenna system.

关键词： Metamaterial epsilon negative near-zero index split ring resonator wireless communications

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Comparison of Mel Frequency Cepstral Coefficient (MFCC) and Mel Spectrogram Techniques to Classify Industrial Machine Sound 15

Comparison of Mel Frequency Cepstral Coefficient (MFCC) and ...

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15th International Conference on Software, Knowledge, Information Management and Applications, SKIMA 2023

作者： Hafiz, Nur Fatinah Mashohor, Syamsiah Shazril, M.H.S.E.M.A. Rasid, M. Fadlee A. Ali, Azizi Universiti Putra Malaysia Faculty of Engineering Dept. of Computer and Communication Systems Engineering Selangor Serdang43400 Malaysia Selangor Serdang43400 Malaysia

ISBN: (纸本)9798350316551

The utilization of sound recognition to detect the faulty machines are getting the interest to be explored with the technology advancement in IR 4.0. In this paper, we investigated the performance of two acoustic features in representing the sound of a fan in industrial machines, that is acquired from the MIMII dataset. The Mel Frequency Cepstral Coefficient (MFCC) and Mel Spectrogram techniques are explored and compared in this paper. The classification is performed using Convolutional Neural Network (CNN) with the input of images from both techniques. The dataset of the input images are the audio files of 6dB fans sound in industrial machine. The results suggest that Mel Spectrogram is an effective feature for faulty machine sound recognition when used with CNN model. © 2023 IEEE.

关键词： Convolution

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Diagnosis of COVID-19 on Chest X-ray (CXR) Images Using CNN with Transfer Learning and Integrated Stacking Ensemble Learning 1

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4th International conference on Innovative Manufacturing, Mechatronics and Materials Forum, iM3F2023

作者： Low, Wai Sing Chow, Li Sze Solihin, Mahmud Iwan Handayani, Dini Oktarina Dwi Department of Electrical and Electronic Engineering Faculty of Engineering Technology and Built Environment UCSI University Cheras Malaysia Department of Mechanical Engineering Faculty of Engineering Technology and Built Environment UCSI University Cheras Malaysia Computer Science Department Faculty of Information Communication and Technology International Islamic University Malaysia Kuala Lumpur Malaysia

ISBN: (数字)9789819988198

ISBN: (纸本)9789819988181

COVID-19 caused a pandemic outbreak, resulting in many deaths and severe economic damage since 2019. Hence, the diagnosis of COVID-19 has become one of the major fields of research. Although RT-PCR has excellent reliability and precision, it is time-consuming and laborious. Therefore, the chest X-ray was used as an alternative and reliable diagnostic tool for COVID-19. However, it requires a radiologist to analyze the X-ray images, which is limited by the availability of experts and time. Henceforth, many researchers deployed automated computer-aided diagnosis with deep learning neural networks to speed up the diagnosis of COVID-19 with high accuracy and reproducibility. This study applied six state-of-art convolutional neural networks (DenseNet201, MobileNetV2, ResNet101V2, VGG16, InceptionNetV3, and Xception) with transfer learning. An integrated stacking ensemble method was used to concatenate DenseNet201, MobileNetV2, VGG16, and Xception to produce a robust and accurate diagnostic model for COVID-19. The proposed ensembled CNN model in this study produced a test accuracy of 0.9725, sensitivity of 0.9749, and F1-score of 0.9724. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： COVID-19

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