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Semi-analytical finite element method applied for characterizing micropolar fibrous composites

Applied Mathematics and Mechanics(English Edition) 2024年第12期45卷 2147-2164页

作者： J.A.OTERO Y.ESPINOSA-ALMEYDA R.RODRIGUEZ-RAMOS J.MERODIO School of Engineering and Sciences Monterrey Institute of TechnologyEstado de Mexico 52926Mexico Institute of Engineering and Technology Autonomous University of Ciudad JuarezCiudad JuarezChihuahua 32310Mexico Postgraduate Program in Computational Modeling in Science and Technology Federal Fluminense UniversityRıo de Janeiro 27255-125Brazil Faculty of Mathematics and Computer Science University of HavanaLa Habana 10400Cuba Department of Mathematics Applied to ICT School of Informatics Systems EngineeringTechnical University of MadridMadrid 28031Spain

A semi-analytical finite element method(SAFEM),based on the two-scale asymptotic homogenization method(AHM)and the finite element method(FEM),is implemented to obtain the effective properties of two-phase fiber-reinforced composites(FRCs).The fibers are periodically distributed and unidirectionally aligned in a homogeneous *** framework addresses the static linear elastic micropolar problem through partial differential equations,subject to boundary conditions and perfect interface contact *** mathematical formulation of the local problems and the effective coefficients are presented by the *** local problems obtained from the AHM are solved by the FEM,which is denoted as the *** numerical results are provided,and the accuracy of the solutions is analyzed,indicating that the formulas and results obtained with the SAFEM may serve as the reference points for validating the outcomes of experimental and numerical computations.

关键词： semi-analytical approach fiber-reinforced composite(FRC) effective property finite element method(FEM) asymptotic homogenization method(AHM) micropolar elasticity

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A Novel Approach for Hashtag Topic Classification with Multimodal Features and VLMs

A Novel Approach for Hashtag Topic Classification with Multi...

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2024 International Conference on Horizons of Information Technology and engineering, HITE 2024

作者： Zafar, Hafiza Tayyaba Tahir, Bilal Mehmood, Muhammad Amir University of Engineering and Technology Al-Khawarizmi Institute of Computer Science Lahore54890 Pakistan Islamic University of Madinah Associated with Faculty of Computer and Information Systems Madinah42351 Saudi Arabia

ISBN: (纸本)9798331516055

The rapid expansion of Instagram content marked by a large volume of photos and comments demands effective methods for discovering topics. While hashtags facilitate the grouping of posts, categorizing these hashtags into specific topics is challenging task due to the multimodal and noisy nature of content on the platform. In this paper, we introduce a novel approach that leverages multimodal features and Vision-Language Models (VLMs) for the topic classification of hashtags. To achieve this, we develop a dataset InstaHash which includes 464 hashtags and 12,345 posts spanning five topic categories: army, politics, sports, judiciary, and religion. We extract text features using the TF-IDF algorithm and visual features using HSV, SIFT, ResNet50, and VGG16 algorithms. Additionally, we introduce 34 binary features derived from the VLM of LLAVA through prompt engineering. These prompt engineering features examine the six aspects of images such as people, symbols and objects, buildings, events, uniforms, and embedded text. We train four classifiers of Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), and Convolutional Neural Network (CNN) for topic classification of hashtags. Our analysis reveals that combining text and visual features yields an accuracy of 0.89 for topic classification with the CNN classifier. However, by incorporating additional prompt features into the model, we achieve the highest accuracy of 0.96. Moreover, we notice that prompt features related to aspects of people and symbols are the most important features for the topic classification of hashtags. © 2024 IEEE.

关键词： Visual languages

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Prediction of Autoimmune Disease using Ensemble Machine Learning Approaches 2

Prediction of Autoimmune Disease using Ensemble Machine Lear...

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2nd International Conference on Augmented Intelligence and Sustainable systems, ICAISS 2023

作者： Khandhan, C. Rukumani Sailogaprakash, S. Sakthivel, K. Seenivasan, D. Mekala, R. Kongu Engineering College Computer Technology-PG Erode India K.S.Rangasamy College of Technology Computer Science and Bussiness Systems Tiruchengode India M.Kumarasamy College of Engineering Computer Science and Bussiness Systems Karur India Bannari Amman Institute of Technology Information Science and Engineering Sathyamangalam India

ISBN: (纸本)9798350325799

The term "autoimmune diseases"refers to a class of illnesses where the immune system attacks the body's own healthy cells. Effective treatment and management of many disorders depend on early detection and precise diagnosis. In this research, Using the clinical and laboratory data were it had been imported from the UCI data repository to develop an ensemble model for the prediction of autoimmune disorders. To create a reliable predictive model, the ensemble model incorporates many machine learning approaches like the Gradient boost classifier, SVC and RF, and the KNN. The suggested model was trained and assessed using a sizable dataset of individuals with autoimmune disorders. When the model's performance was compared to that of individual machine learning methods, it was exposed that the ensemble model gives out them all in terms of accuracy, and sensitivity, and set out with the help of hyper-parameter Tuning. © 2023 IEEE.

关键词： Machine learning

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Strategy for Rapid Diabetic Retinopathy Exposure Based on Enhanced Feature Extraction Processing

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computers, Materials & Continua 2023年第6期75卷 5597-5613页

作者： V.Banupriya S.Anusuya Department of Computer Science and Business Systems M.Kumarasamy College of EngineeringKarur639113TamilnaduIndia Department of Information Technology Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesSIMATSChennai602117Tamil NaduIndia

In the modern world,one of the most severe eye infections brought on by diabetes is known as diabetic retinopathy(DR),which will result in retinal damage,and,thus,lead to *** retinopathy(DR)can be well treated with early *** fundus images of humans are used to screen for lesions in the ***,detecting DR in the early stages is challenging due to the minimal ***,the occurrence of diseases linked to vascular anomalies brought on by DR aids in diagnosing the ***,the resources required for manually identifying the lesions are ***,training for Convolutional Neural Networks(CNN)is more *** proposed research aims to improve diabetic retinopathy diagnosis by developing an enhanced deep learning model(EDLM)for timely DR identification that is potentially more accurate than existing CNN-based *** proposed model will detect various lesions from retinal images in the early ***,characteristics are retrieved from the retinal fundus picture and put into the EDLM for *** dimensionality reduction,EDLM is ***,the classification and feature extraction processes are optimized using the stochastic gradient descent(SGD)*** EDLM’s effectiveness is assessed on the KAG-GLE dataset with 3459 retinal images,and results are compared over VGG16,VGG19,RESNET18,RESNET34,and *** results show that the EDLM achieves higher average sensitivity by 8.28%for VGG16,by 7.03%for VGG19,by 5.58%for ResNet18,by 4.26%for ResNet 34,and by 2.04%for ResNet 50,respectively.

关键词： Diabetic retinopathy deep learning classification retinal fundus

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Research on Polarization Theory Based on Target Polarization Projection 2

Research on Polarization Theory Based on Target Polarization...

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2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

作者： He, Yaomin Yang, Jian Yang, Yimin Yin, Junjun Liu, Chao Academy of Military Sciences Institute of Systems Engineering Beijing China Tsinghua University Department of Electronic Engineering Beijing China University of Science and Technology School of Computer and Communication Engineering Beijing China

ISBN: (纸本)9798331515669

Aiming at the information processing in classical polarization theory, such as polarization decomposition, optimal polarization, polarization filtering, polarization detection, and polarization classification, et al, they are of great significance which supported a large number of polarization applications. Although the theory is relatively rich, there are also some deficiencies, i.e., overlapping theories and inconsistent concepts, which have inconvenient effects on the systematic research of polarization theory. In this regard, this paper proposes the concept and mathematical model of "target polarization projection", in order to form two polarization basic theories with target polarization decomposition. It can achieve a series of polarization information processing such as polarization filtering, polarization detection, and polarization classification, thus providing a clear polarization theory framework and laying a theoretical foundation for the application of polarimetric SAR. © 2024 IEEE.

关键词： Wiener filtering

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Less Labeling Burden: A SAR Ship Detection Data Selection Method Based on K-means 2

Less Labeling Burden: A SAR Ship Detection Data Selection Me...

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2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

作者： Yang, Yimin Yin, Junjun Lang, Ping Yang, Jian He, Yaomin Tsinghua University Department of Electronic Engineering Beijing China University of Science and Technology School of Computer and Communication Engineering Beijing China Academy of Military Sciences Institute of Systems Engineering Beijing China

ISBN: (纸本)9798331515669

In deep learning-based object detection, labeled data is crucial. However, annotating ship targets in Synthetic Aperture Radar (SAR) images is challenging because of the distinct characteristics of SAR images, especially when employing Oriented Bounding Boxes (OBB). These challenges arise from factors such as varying ship orientations, complex backgrounds, and the presence of noise, making accurate annotation more difficult. To reduce the labeling burden while minimizing its impact on ship detection performance, we propose a SAR data selection method. This method ensures that, despite limited quantities, the data contains a more representative range of scenarios when constructing a new dataset. By selecting 10 features of SAR images and performing K-means clustering based on these features, we choose the samples closest to the cluster centers as the selected data. We have validated the effectiveness of the proposed method by applying two typical object detection models to both randomly selected and clustering-selected datasets, thereby demonstrating the efficacy of proposed approach. © 2024 IEEE.

关键词： Radar imaging

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Vulnerability Detection of Ethereum Smart Contract Based on SolBERT-BiGRU-Attention Hybrid Neural Model

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computer Modeling in engineering & sciences 2023年第10期137卷 903-922页

作者： Guangxia Xu Lei Liu Jingnan Dong Cyberspace Institute of Advanced Technology Guangzhou UniversityGuangzhou510006China School of Software Engineering Chongqing University of Posts and TelecommunicationsChongqing400065China School of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing400065China

In recent years,with the great success of pre-trained language models,the pre-trained BERT model has been gradually applied to the field of source code ***,the time cost of training a language model from zero is very high,and how to transfer the pre-trained language model to the field of smart contract vulnerability detection is a hot research direction at *** this paper,we propose a hybrid model to detect common vulnerabilities in smart contracts based on a lightweight pre-trained languagemodel BERT and connected to a bidirectional gate recurrent *** downstream neural network adopts the bidirectional gate recurrent unit neural network model with a hierarchical attention mechanism to mine more semantic features contained in the source code of smart contracts by using their *** experiments show that our proposed hybrid neural network model SolBERT-BiGRU-Attention is fitted by a large number of data samples with smart contract vulnerabilities,and it is found that compared with the existing methods,the accuracy of our model can reach 93.85%,and the Micro-F1 Score is 94.02%.

关键词： Smart contract pre-trained language model deep learning recurrent neural network blockchain security

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A Hybrid Model to Predict the Breast Cancer using Stacking and Bagging Model 3

A Hybrid Model to Predict the Breast Cancer using Stacking a...

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3rd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Yuvalatha, S. Nithyapriya, S. Prabhavathy, S. Priyadharshini, R. Savitha, S. Kayathri, S. Bannari Amman Institute of Technology Department of Computer Science and Business Systems Sathyamangalam India Bannari Amman Institute of Technology Department of Artificial Intelligence and Data Science Sathyamangalam India Christ the King Engineering College Department of Electronics and Communication Engineering Coimbatore India Sri Ranganathar Institute of Engineering Department of Computer Science and Engineering Coimbatore India PSR Engineering College Department of Computer and Science and Engineering Sivakasi India

ISBN: (纸本)9798350317022

Breast cancer is a malignant tumor that develops in the cells of the breast tissue. Breast cancer is one of the major causes of death for women globally. In the examination of medical data, breast cancer prediction is a difficult task. To make decisions and accurately distinguish between benign and malignant tumors, physicians and pathologists need certain automated technologies. In this paper, hybrid ensemble technique (Bagging and Stacking) is used to predict the breast tumors as benign and malignant tumors. In the proposed work, the subset of data is created from the initial Wisconsin (Diagnostic) Data Set by bootstrapping technique. Each bootstrap dataset is used to train the weak learner. The weak learners are K-Nearest Neighbors (KNN) Random Forest (RF), Decision Tree (DT) and Support Vector Machine (SVM). The Logistic Regression (LR) is used as the Meta Learner. The Meta Learner uses the predictions of weak learners as its training data. The proposed hybrid ensemble model obtains an accuracy 98.7%, Precision 98.83%, Recall 98.54%, F1 Score 98.68% and 0.012% error. © 2023 IEEE.

关键词： Bagging Bootstrapping Ensemble Malignant Meta Learner Stacking

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ARCH-COMP22 Repeatability Evaluation Report 9th

ARCH-COMP22 Repeatability Evaluation Report

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9th International Workshop on Applied Verification of Continuous and Hybrid systems, ARCH 2022

作者： Johnson, Taylor T. Vanderbilt University Department of Computer Science Institute for Software Integrated Systems NashvilleTN United States

The repeatability evaluation for the 6th International Competition on Verifying Continuous and Hybrid systems (ARCH-COMP’22) is summarized in this report. The competition took place as part of the workshop Applied Verification for Continuous and Hybrid systems (ARCH) in 2022, affiliated with the 41st International Conference on computer Safety, Reliability and Security (SAFECOMP’22). In its sixth edition, 25 tools had submitted artifacts through a Git repository for the repeatability evaluation, which were applied to solve benchmark instances through 7 competition categories. The majority of participants adhered to the specifications of this year’s repeatability evaluation, which was to submit scripts to automatically install and execute tools in containerized virtual environments (specifically Dockerfiles to execute within Docker containers). Some categories used performance evaluation information from a common execution platform. The repeatability results represent a snapshot of current tools and the types of benchmarks on which they are well suited, and so that others may repeat their analyses. Due to the diversity of problems in verification of continuous and hybrid systems, as well as basing on standard practice in repeatability evaluations, we evaluate the tools with pass and/or failing of being repeatable. © 2022, EasyChair. All rights reserved.

关键词： Hybrid systems

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An Effective Heart Disease Prediction Method using Extreme Gradient Boosting Algorithm Compared with Convolutional Neural Networks 9

An Effective Heart Disease Prediction Method using Extreme G...

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9th International Conference on Advanced Computing and Communication systems, ICACCS 2023

作者： Anitha, Cuddapah Rajkumar, S. Dhanalakshmi, R. Mohan Babu University Computer Science and Systems Engineering Sree Vidyaniketan Engineering College Andhra Pradesh Tirupati India Vellore Institute of Technology School of Computer Science and Engineering Chennai India Saveetha School of Engineering SIMATS Department of Computer Science and Engineering Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350397376

Heart disease is one of the most serious and common problems to human beings around the face;% to 90% of people are affected by heart problems in which, and Several methods exist to identify heart disease in an earlier stage, but few methods fail to manage the accuracy due to the large volume of data. In addition, the methods require more time to process those features. To overcome these research issues, effective intelligent techniques are proposed to predict heart disease in an earlier stage. Additionally, our model's balanced accuracy (89.5%) outperforms the XGBoost classifiers' accuracy rates. Excellent recall values and area under the curve, together with high specificity and test accuracy, are obtained using the CNN classifier. Designers contrast the XGboost, KNN, SVM, and Naive Bayes Classifiers' classification precision on various healthcare datasets. Finally, this leads to a reduction in the overall error rate and also improves the recognition accuracy by more than 90%. © 2023 IEEE.

关键词： Convolutional neural networks

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