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Steel Surface Defect Recognition in Smart Manufacturing Using Deep Ensemble Transfer Learning-Based Techniques

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Computer Modeling in engineering & Sciences 2025年第1期142卷 231-250页

作者： Tajmal Hussain Jongwon Seok department of information and Communication engineering Changwon National UniversityChangwon51140Republic of Korea

Smart manufacturing and Industry 4.0 are transforming traditional manufacturing processes by utilizing innovative technologies such as the artificial intelligence(AI)and internet of things(IoT)to enhance efficiency,reduce costs,and ensure product *** light of the recent advancement of Industry 4.0,identifying defects has become important for ensuring the quality of products during the manufacturing *** this research,we present an ensemble methodology for accurately classifying hot rolled steel surface defects by combining the strengths of four pre-trained convolutional neural network(CNN)architectures:VGG16,VGG19,Xception,and Mobile-Net V2,compensating for their individual *** evaluated our methodology on the Xsteel surface defect dataset(XSDD),which comprises seven different *** ensemble methodology integrated the predictions of individual models through two methods:model averaging and weighted *** evaluation showed that the model averaging ensemble achieved an accuracy of 98.89%,a recall of 98.92%,a precision of 99.05%,and an F1-score of 98.97%,while the weighted averaging ensemble reached an accuracy of 99.72%,a recall of 99.74%,a precision of 99.67%,and an F1-score of 99.70%.The proposed weighted averaging ensemble model outperformed the model averaging method and the individual models in detecting defects in terms of accuracy,recall,precision,and *** analysis with recent studies also showed the superior performance of our methodology.

关键词： Smart manufacturing CNN steel defects ensemble models

Semantic Segmentation of Lumbar Vertebrae Using Meijering U-Net(MU-Net)on Spine Magnetic Resonance Images

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Computer Modeling in engineering & Sciences 2025年第1期142卷 733-757页

作者： Lakshmi S V V Shiloah Elizabeth Darmanayagam Sunil Retmin Raj Cyril department of Computer Science and engineering College of Engineering GuindyAnna UniversityChennai600025India department of information Technology Madras Institute of TechnologyAnna UniversityChrompetChennai600044India

Lower back pain is one of the most common medical problems in the world and it is experienced by a huge percentage of people *** to its ability to produce a detailed view of the soft tissues,including the spinal cord,nerves,intervertebral discs,and vertebrae,Magnetic Resonance Imaging is thought to be the most effective method for imaging the *** semantic segmentation of vertebrae plays a major role in the diagnostic process of lumbar *** is difficult to semantically partition the vertebrae in Magnetic Resonance Images from the surrounding variety of tissues,including muscles,ligaments,and intervertebral discs.U-Net is a powerful deep-learning architecture to handle the challenges of medical image analysis tasks and achieves high segmentation *** work proposes a modified U-Net architecture namely MU-Net,consisting of the Meijering convolutional layer that incorporates the Meijering filter to perform the semantic segmentation of lumbar vertebrae L1 to L5 and sacral vertebra ***-colour mask images were generated and used as ground truth for training the *** work has been carried out on 1312 images expanded from T1-weighted mid-sagittal MRI images of 515 patients in the Lumbar Spine MRI Dataset publicly available from Mendeley *** proposed MU-Net model for the semantic segmentation of the lumbar vertebrae gives better performance with 98.79%of pixel accuracy(PA),98.66%of dice similarity coefficient(DSC),97.36%of Jaccard coefficient,and 92.55%mean Intersection over Union(mean IoU)metrics using the mentioned dataset.

关键词： Computer aided diagnosis(CAD) magnetic resonance imaging(MRI) semantic segmentation lumbar vertebrae deep learning U-Net model

Three-Stage Transfer Learning with AlexNet50 for MRI Image Multi-Class Classification with Optimal Learning Rate

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Computer Modeling in engineering & Sciences 2025年第1期142卷 155-183页

作者： Suganya Athisayamani A.Robert Singh Gyanendra Prasad Joshi Woong Cho School of Computing Sastra Deemed to be UniversityThanjavur613401India department of Computational Intelligence SRM Institute of Science and TechnologyKattankulathur603203India department of AI and Software engineering Kangwon National UniversitySamcheok25913Republic of Korea department of Electronics Information and Communication EngineeringKangwon National UniversitySamcheok25913Republic of Korea

In radiology,magnetic resonance imaging(MRI)is an essential diagnostic tool that provides detailed images of a patient’s anatomical and physiological *** is particularly effective for detecting soft tissue ***,radiologists manually interpret these images,which can be labor-intensive and time-consuming due to the vast amount of *** address this challenge,machine learning,and deep learning approaches can be utilized to improve the accuracy and efficiency of anomaly detection in MRI *** manuscript presents the use of the Deep AlexNet50 model for MRI classification with discriminative learning *** are three stages for learning;in the first stage,the whole dataset is used to learn the *** the second stage,some layers of AlexNet50 are frozen with an augmented dataset,and in the third stage,AlexNet50 with an augmented dataset with the augmented *** method used three publicly available MRI classification datasets:Harvard whole brain atlas(HWBA-dataset),the School of Biomedical engineering of Southern Medical University(SMU-dataset),and The National Institute of Neuroscience and Hospitals brain MRI dataset(NINS-dataset)for *** hyperparameter optimizers like Adam,stochastic gradient descent(SGD),Root mean square propagation(RMS prop),Adamax,and AdamW have been used to compare the performance of the learning ***-dataset registers maximum classification *** evaluated the performance of the proposed classification model using several quantitative metrics,achieving an average accuracy of 98%.

关键词： MRI tumors classification AlexNet50 transfer learning hyperparameter tuning optimizer

A Novel Self-Supervised Learning Network for Binocular Disparity Estimation

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Computer Modeling in engineering & Sciences 2025年第1期142卷 209-229页

作者： Jiawei Tian Yu Zhou Xiaobing Chen Salman A.AlQahtani Hongrong Chen Bo Yang Siyu Lu Wenfeng Zheng department of Computer Science and engineering Major in Bio Artificial IntelligenceHanyang UniversityAnsan-si15577Republic of Korea School of Electrical and Computer engineering Louisiana State UniversityBaton RougeLA 70803USA department of Computer engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11574Saudi Arabia School of Automation University of Electronic Science and Technology of ChinaChengdu610054China

Two-dimensional endoscopic images are susceptible to interferences such as specular reflections and monotonous texture illumination,hindering accurate three-dimensional lesion reconstruction by surgical *** study proposes a novel end-to-end disparity estimation model to address these *** approach combines a Pseudo-Siamese neural network architecture with pyramid dilated convolutions,integrating multi-scale image information to enhance robustness against lighting *** study introduces a Pseudo-Siamese structure-based disparity regression model that simplifies left-right image comparison,improving accuracy and *** model was evaluated using a dataset of stereo endoscopic videos captured by the Da Vinci surgical robot,comprising simulated silicone heart sequences and real heart video *** results demonstrate significant improvement in the network’s resistance to lighting interference without substantially increasing ***,the model exhibited faster convergence during training,contributing to overall performance *** study advances endoscopic image processing accuracy and has potential implications for surgical robot applications in complex environments.

关键词： Parallax estimation parallax regression model self-supervised learning Pseudo-Siamese neural network pyramid dilated convolution binocular disparity estimation

A Comprehensive Survey on Federated Learning Applications in Computational Mental Healthcare

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Computer Modeling in engineering & Sciences 2025年第1期142卷 49-90页

作者： Vajratiya Vajrobol Geetika Jain Saxena Amit Pundir Sanjeev Singh Akshat Gaurav Savi Bansal Razaz Waheeb Attar Mosiur Rahman Brij B.Gupta Institute of Informatics and Communication University of DelhiDelhi110021India Maharaja Agrasen College University of DelhiDelhi110096India Computer engineering Ronin InstituteMontclairNJ 07043USA department of Research and Innovation Insights2TechinfoJaipur302001India University Centre for Research and Development(UCRD) Chandigarh UniversityChandigarh140413India Management department College of Business AdministrationPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia CCRI&department of Computer Science and information engineering Asia UniversityTaichung413Taiwan Symbiosis Centre for information Technology(SCIT) Symbiosis International UniversityPune411057India Center for Interdisciplinary Research University of Petroleum and Energy Studies(UPES)Dehradun248007India

Mental health is a significant issue worldwide,and the utilization of technology to assist mental health has seen a growing *** aims to alleviate the workload on healthcare professionals and aid *** applications have been developed to support the challenges in intelligent healthcare ***,because mental health data is sensitive,privacy concerns have *** learning has gotten some *** research reviews the studies on federated learning and mental health related to solving the issue of intelligent healthcare *** explores various dimensions of federated learning in mental health,such as datasets(their types and sources),applications categorized based on mental health symptoms,federated mental health frameworks,federated machine learning,federated deep learning,and the benefits of federated learning in mental health *** research conducts surveys to evaluate the current state of mental health applications,mainly focusing on the role of Federated Learning(FL)and related privacy and data security *** survey provides valuable insights into how these applications are emerging and evolving,specifically emphasizing FL’s impact.

关键词： Depression emotional recognition intelligent healthcare systems mental health federated learning stress detection sleep behaviour

Progress of crystalline carbon nitride in synthesis,atomic structure characterization and photocatalysis

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Nano Research 2025年第1期18卷 455-476页

作者： Yueyang Tan Zhongyan Zhang Yayun Pu Zongzhao Sun Limin Huang School of Materials Science and engineering Harbin Institute of TechnologyHarbin 150001China department of Chemistry Southern University of Science and TechnologyShenzhen 518055China School of Traffic and Environment Shenzhen Institute of Information TechnologyShenzhen 518172China College of Chemistry and Chemical engineering Linyi UniversityLinyi 276000China School of Optoelectronic engineering Chongqing University of Posts and TelecommunicationsChongqing 400065China

Crystalline carbon nitride(CCN)has emerged as a highly promising semiconductor photocatalyst with unique properties,such as enhanced charge migration rate,reduced carrier recombination probability,narrow band gap and improved light-harvesting efficiency,which are suitable for a wide range of applications in solar-to-chemical conversion,energy storage,therapeutic and environmental pollution *** the past few years,there has been an increasing number of reviews on CCN ***,most of these reviews mainly focus on synthesis methods,modification and applications,with less emphasis on the relationship between structures and properties,as well as on indepth exploration of the crystalline *** electronic instability of CCN presents challenges for conventional characterization techniques to directly and thoroughly investigate the relationship between its intrinsic atom structure and photocatalytic *** mini-review not only highlights the progress in CCN-based photocatalysts,with a focus on molten-salt synthesis(including solid-salt-induced crystallization),but also emphasizes the atomic structure characterization by specifically introducing the differential phase contrast(DPC)scanning transmission electron microscopy(STEM)technique,which is essential for enhancing our understanding of the crystal structure and photocatalytic mechanisms of ***,the review outlines the photocatalytic performance and puts forward potential challenges on CCN *** review will provide a clearer understanding of the relationship in developing precise customization strategies for CCN materials and ultimately explain the regularity and specificity of the enhanced performance in targeted photocatalytic systems.

关键词： crystalline carbon nitride synthesis characterization photocatalysis

Congruent Feature Selection Method to Improve the Efficacy of Machine Learning-Based Classification in Medical Image Processing

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Computer Modeling in engineering & Sciences 2025年第1期142卷 357-384页

作者： Mohd Anjum Naoufel Kraiem Hong Min Ashit Kumar Dutta Yousef Ibrahim Daradkeh department of Computer engineering Aligarh Muslim UniversityAligarh202002India College of Computer Science King Khalid UniversityAbha61413Saudi Arabia School of Computing Gachon UniversitySeongnam13120Republic of Korea department of Computer Science and information Systems College of Applied SciencesAlMaarefa UniversityRiyadh13713Saudi Arabia department of Computer engineering and information College of Engineering in Wadi AlddawasirPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia

Machine learning(ML)is increasingly applied for medical image processing with appropriate learning *** applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for *** primary concern of ML applications is the precise selection of flexible image features for pattern detection and region *** of the extracted image features are irrelevant and lead to an increase in computation ***,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image *** process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel *** similarity between the pixels over the various distribution patterns with high indexes is recommended for disease ***,the correlation based on intensity and distribution is analyzed to improve the feature selection ***,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the ***,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of ***,the probability of feature selection,regardless of the textures and medical image patterns,is *** process enhances the performance of ML applications for different medical image *** proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected *** mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.

关键词： Computer vision feature selection machine learning region detection texture analysis image classification medical images

Correlations multiplexing for link prediction in multidimensional network spaces

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Science China(information Sciences) 2018年第11期61卷 48-66页

作者： Yunpeng XIAO Xixi LI Yuanni LIU Hong LIU Qian LI Chongqing engineering Laboratory of internet and information Security Chongqing University of Posts and Telecommunications

In social networks, link establishment among users is affected by diversity correlations. In this paper, we study the formation of links, map correlations into multidimensional network spaces and apply their behavioral and structural features to the problem of link prediction. First, by exploring user behavioral correlation and network structural correlation, we map them into three network spaces: following space, interaction space and structure space. With a hierarchical process, the coupling relationship between the spaces can be reduced and we can analyze the correlation in each space separately. Second, by taking advantage of the latent Dirichlet allocation(LDA) topic model for dealing with the polysemy and synonym problems, the traditional text modeling method is improved by Gaussian weighting and applied to user behavior *** this way, the expression ability of topics can be enhanced, and improved topic distribution of user behavior can be obtained to mine user correlations in the following space and the interaction space. Moreover, the method can be extended using the hidden naive Bayesian algorithm which is good at reducing attribute independence. By quantifying the dependencies between common neighbors, we can analyze user correlations in the structure space and multiplex the correlations of the other two spaces to link prediction. The experimental results indicate that the method can effectively improve link prediction performance.

关键词： social networks multidimensional network spaces link prediction LDA hidden naive Bayes

A query index for continuous queries on RFID streaming data

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Science in China(Series F) 2008年第12期51卷 2047-2061页

作者： Jaekwan PARK Bonghee HONG Chaehoon BAN department of Computer engineering Pusan National University department of internet Application College of Information & Technology

RFID middleware collects and filters RFID streaming data to process applications＇ requests called continuous queries, because they are executed continuously during tag movement. Several approaches to building an index on queries rather than data records, called a query index, have been proposed to evaluate continuous queries over streaming data. EPCglobal proposed an Event Cycle Specification （ECSpec） model, which is a de facto standard query interface for RFID applications. Continuous queries based on ECSpec consist of a large number of segments that represent the query conditions. The problem when using any of the existing query indexes on these continuous queries is that it takes a long time to build the index, because it is necessary to insert a large number of segments into the index. To solve this problem, we propose a transform method that converts a group of segments into compressed data. We also propose an efficient query index scheme for the transformed space. Comparing with existing query indexes, the performance of proposed index outperforms the others on various datasets.

关键词： RFID system middleware continuous query stabbing query query index