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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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Automated heart disease prediction using improved explainable learning-based technique

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Neural Computing and Applications 2024年第26期36卷 16289-16318页

作者： Bizimana, Pierre Claver Zhang, Zuping Hounye, Alphonse Houssou Asim, Muhammad Hammad, Mohamed El-Latif, Ahmed A. Abd School of Computer Science and Engineering Central South University Changsha410083 China School of Mathematics and Statistics Central South University Changsha410008 China EIAS Data Science Lab College of Computer and Information Sciences and Center of Excellence in Quantum and Intelligent Computing Prince Sultan University Riyadh11586 Saudi Arabia School of Computer Science and Technology Guangdong University of Technology Guangzhou510006 China Department of Mathematics and Computer Science Faculty of Science Menoufia University Shebin El-Koom Egypt Information Technology Department Faculty of Computers and Information Menoufia University Shebin El-Koom Egypt

Heart disease (HD) stands as a major global health challenge, being a predominant cause of death and demanding intricate and costly detection methods. The widespread impact of heart failure, contributing to increased rates of morbidity and mortality, underscores the urgency for accurate and timely prediction and diagnosis. This is crucial for effective prevention, early detection, and treatment, thereby reducing the threat to individual health. However, the early and precise prediction of HD remains a significant challenge. The complexity of medical data poses a considerable challenge for healthcare professionals, who are required to interpret and utilize this information swiftly for effective intervention. Addressing this gap, our study introduces a novel Improved Explainable Learning-Based Technique (IELBT) for HD prediction. This technique harnesses a strategic combination of feature selection, Venn diagrams, data normalization methods, optimized parameters, and machine learning algorithms, specifically tailored for predicting HD. We evaluated the performance of our model using the Alizadeh Sani HD dataset, aiming to accurately detect the presence or absence of the condition. Our results demonstrate that the IELBT, employing a support vector machine with a robust scaling approach, optimal parameterization, and a data split ratio of 70:30, achieves an impressive accuracy rate of 96.00%. Beyond achieving high accuracy, the IELBT outperforms similar models in existing literature and provides significant interpretability and explanation, essential elements in the field of medical diagnosis. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Forecasting

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MedBot - An NLP based ChatBot for Diabetes Prediction 15

MedBot - An NLP based ChatBot for Diabetes Prediction

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

作者： Sreekar, K. Aswin, V. Narayanan, Vishnu Thangavel, Senthil Kumar Somasundaram, K. Shanmugam, Selvanayaki Kolandapalayam Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Coimbatore India Ashland University Department of Mathematics and Computer Science OH United States

ISBN: (纸本)9798350370249

In this age of technology, with digital devices being accessible by almost everyone, have made our lives much better. With the advent of Machine Learning and advanced algorithms at our tips, the applications are immensurate. Such an application can be found in the health-care sector. The need for better and accessible health-care is of immense precedence. In this paper, we employed Machine Learning algorithms like Logistic Regression and other state of the art techniques to predict diabetes, given patient data. Diabetes is an incessant condition that is affecting millions worldwide, every year. Early detection of diabetes and prompt diagnosis is crucial. We have addressed that using a chat-bot to predict diabetes, which we believe is a portable and accessible solution for first-level assessment. We have employed PIMA Indian Diabetes Dataset into our chat-bot, providing interactive medical advice, and enhancing patient engagement, using Telegram API, resulting in an accuracy of 77%. © 2024 IEEE.

关键词： Electronic health record

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AIR-SCAN: Aircraft Identification and Recognition using Deep Learning Scanning 11

AIR-SCAN: Aircraft Identification and Recognition using Deep...

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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Kumar, Aman Singh, Soumyendra Mohan G, Bharathi Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering India Amrita School of Engineering Amrita Vishwa Vidyapeetham Department of Mathematics India

ISBN: (纸本)9798350350357

The research work explores advanced techniques in object detection frameworks, specifically focusing on Faster R-CNN and YOLOv8, to enhance the accuracy of military aircraft detection. Leveraging the multi-stage Faster R-CNN architecture, we investigate the impact of adjusting anchor ratios, systematically evaluating their influence on the model's ability to detect objects with diverse shapes. Departing from the conventional anchor-based approach, YOLOv8 introduces anchor-free detection, eliminating predefined anchors and increasing robustness to object scale variations. Our experimentation includes anchor ratio configurations and explores the innovative components of YOLOv8, such as the CSPDarknet53 backbone and the C2f module. It aims to contribute insights into optimizing military aircraft detection accuracy, catering to the dynamic and critical nature of aerial threats. By providing a comparative analysis of Faster R-CNN and YOLOv8, we shed light on their strengths and weaknesses, offering valuable considerations for deployment in military contexts. The paper's findings contribute to the continual advancement of computer vision applications in national security, emphasizing the importance of automated detection for efficient and timely threat identification. © 2024 IEEE.

关键词： National security

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A Multi-Task Framework Using Mamba for Identity, Age, and Gender Classification from Hand Images 15

A Multi-Task Framework Using Mamba for Identity, Age, and Ge...

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15th International Conference on Information and Knowledge Technology, IKT 2024

作者： Rezasoltani, Amirabbas Hosseini, Alireza Toosi, Ramin Akhaee, Mohammad Ali School of Mathematics Statistics and Computer Science College of Science University of Tehran Tehran Iran School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Adak Vira Iranian Rahjoo Company Tehran Iran

ISBN: (纸本)9798331522254

Biometric authentication is crucial for secure access, surpassing traditional password-based methods vulnerable to breaches. Non-intrusive techniques like hand-based biometrics offer unique advantages, using physiological traits to identify individuals and predict soft biometrics such as age and gender. However, most current systems are designed with a single-purpose focus. In this field, Convolutional Neural Networks (CNNs) are typically utilized but struggle to capture long-range dependencies in images. Transformers, while more effective at handling such relationships, come with high computational costs. To overcome these challenges, this study introduces a novel multi-task learning framework that predicts identity, age, and gender simultaneously. The framework integrates the efficient long-range dependency modeling of Mamba, utilizing Visual State-Space Models (VSS) to capture both local and global patterns with reduced computational complexity. Experiments on the 11k Hands dataset demonstrate superior or competitive performance compared to existing methods. © 2024 IEEE.

关键词： Convolutional neural networks

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Recent Application and Progress of Metal Halide Perovskite Photodetector on Flexible Substrates

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Chinese Journal of Chemistry 2023年第24期41卷 3689-3702页

作者： Shuigen Li Xiangyu Xie Fan Huang Lili Shu Langyu Liu Chasheng Wang Yong Yang Rong Li School of New Energy Science and Engineering Xinyu UniversityXinyuJiangxi338004 China Department of Engineering Mechanics School of Aerospace EngineeringHuazhong University of Science and TechnologyWuhanHubei430074 China School of Mathematics and Computer Science Xinyu UniversityXinyuJiangxi338004 China Orthopedics of People's Hospital of Xinyu XinyuJiangxi338004 China

In recent years,flexible photodetectors(FPDs)have received increasing attention due to their applications in electronic eyes,flexible sensing,terminal devices,and wearable *** addition,metallic halide perovskite materials are considered as future materials for FPDs due to their compatibility with flexible substrates,low cost,simple synthesis methods,and superior optoelectronic *** review provides a comprehensive overview of the relevant cutting-edge research in the field of flexible perovskite photodetectors(FPPDs)from 2020 to ***,the evaluation criteria for FPPDs are discussed and the development of perovskite stability criteria is emphatically ***,the synthesis methods and device construction processes of metal halide perovskite materials commonly used by researchers in the past three years are *** include single crystals and low-dimensional ***,we have elaborated on the research of self-powered FPPD and its contributions in wearability,terminals,and ***,a summary of developments and possibilities in the field of FPPDs from 2020 to 2022 is provided.

关键词： 2D/3D perovskites Perovskite phases Nanostructures photodetectors Self-powered Heterojunction

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An Automated Identification Approach for Partial Discharge Detection Using Density-Based Clustering Without User Inputs

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第1期5卷 310-320页

作者： Rahman, Md Anisur Ang, Li-Minn Seng, Kah Phooi Charles Sturt University School of Computing Mathematics & Engineering BathurstNSW2795 Australia University of the Sunshine Coast School of Science and Engineering Sippy DownsQLD4556 Australia Xi'an Jiaotong Liverpool University School of AI & Advanced Computing Suzhou215123 China Queensland University of Technology School of Computer Science BrisbaneQLD4000 Australia

A recently published state-of-the-art density-based clustering technique called ICFSFDP for partial discharge (PD) detection requires various sensitive user-defined input parameters. This article presents a parameter-free clustering technique called PDAutoClust for PD detection. The PDAutoClust algorithm can produce high-quality clusters for PD datasets without requiring user-defined input parameters. PDAutoClust produces high-quality clustering results by utilizing a vein-based density clustering approach. The vein of a cluster is produced by using multivariate kernel density estimation and a unique neighborhood set. We compared the performance of PDAutoClust against ICFSFDP and seven other state-of-the-art density-based and non-density-based clustering techniques by using four PD datasets in terms of adjusted rand index, normalized mutual information, F1-score, and purity. Another contribution of the article is a novel merging technique used with PDAutoClust to merge small non-viable clusters that a clustering technique may produce. PDAutoClust produces the final clusters for a dataset by merging the non-viable clusters that a clustering technique may produce. We also evaluate the performance of PDAutoClust with merging technique versus PDAutoClust without merging technique using four datasets. Simulation results for PDAutoClust with the merging technique show good performance compared to ICFSFDP and seven other state-of-the-art clustering techniques. We also performed an ablation study to demonstrate the importance of the steps involved in PDAutoClust. © 2020 IEEE.

关键词： Merging

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Speaker Identification Using CNN-LSTM Model on RAVDESS Dataset: A Deep Learning Approach 4

Speaker Identification Using CNN-LSTM Model on RAVDESS Datas...

引用

4th International Conference on Intelligent Technologies, CONIT 2024

作者： Suryamritha, M. Balaji, Varshini Kannan, Srinidhi Murali, K. Amrita School of Computing Department of Computer Science and Engineering Bengaluru India Department of Mathematics Amrita Vishwa Vidyapeetham India

ISBN: (纸本)9798350349900

Speaker identification is used for identifying an individual based on their voice. Signal processing and deep neural networks are used for feature extraction. This paper presents a method that combines CNN and LSTM for speaker identification. Multiple models such as GMM, CNN, SVM were compared and CNN+LSTM outperformed with an accuracy of 96.52% and F1 measure of 97%. The CNN+LSTM model combines spatial and temporal information extracted by the CNN and LSTM layers which allows to capture of both local and long-term dependencies in the audio data hence making this model very efficient. The above models were evaluated on the RAVDESS (Ryerson AudioVisual Database of Emotional Speech and Song) dataset. The results highlight their potential for practical applications within speaker identification systems. © 2024 IEEE.

关键词： Deep neural networks

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AI-Driven Relocation Tracking in Dynamic Kitchen Environments 14

AI-Driven Relocation Tracking in Dynamic Kitchen Environment...

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14th International Conference on computer and Knowledge engineering, ICCKE 2024

作者： Esfahani, Arash Nasr Hosseini, Hamed Masouleh, Mehdi Tale Kalhor, Ahmad Sajedi, Hedieh Human and Robot Interaction Lab School of Electrical and Computer Engineering University of Tehran Tehran Iran School of Mathematics Statistics and Computer Science University of Tehran Tehran Iran

ISBN: (纸本)9798331511272

As smart homes become more prevalent in daily life, the ability to understand dynamic environments is essential which is increasingly dependent on AI systems. This study focuses on developing an intelligent algorithm which can navigate a robot through a kitchen, recognizing objects, and tracking their relocation. The kitchen was chosen as the testing ground due to its dynamic nature as objects are frequently moved, rearranged and replaced. Various techniques, such as SLAM feature-based tracking and deep learning-based object detection (e.g., Faster R-CNN), are commonly used for object tracking. Additionally, methods such as optical flow analysis and 3D reconstruction have also been used to track the relocation of objects. These approaches often face challenges when it comes to problems such as lighting variations and partial occlusions, where parts of the object are hidden in some frames but visible in others. The proposed method in this study leverages the YOLOv5 architecture, initialized with pre-trained weights and subsequently fine-tuned on a custom dataset. A novel method was developed, introducing a frame-scoring algorithm which calculates a score for each object based on its location and features within all frames. This scoring approach helps to identify changes by determining the best-associated frame for each object and comparing the results in each scene, overcoming limitations seen in other methods while maintaining simplicity in design. The experimental results demonstrate an accuracy of 97.72%, a precision of 95.83% and a recall of 96.84% for this algorithm, which highlights the efficacy of the model in detecting spatial changes. ©2024 IEEE.

关键词： Optical flows

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A DISCRETIZING LEVENBERG-MARQUARDT SCHEME FOR SOLVING NONLIEAR ILL-POSED INTEGRAL EQUATIONS

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Journal of Computational mathematics 2022年第5期40卷 686-710页

作者： Rong Zhang Hongqi Yang School of Mathematics and Computer Science Gannan Normal UniversityGanzhou 341004China Guangdong Province Key Laboratory of Computational Science School of Computer Science and EngineeringSun Yat-sen UniversityGuangzhou 510275China

To reduce the computational cost,we propose a regularizing modified LevenbergMarquardt scheme via multiscale Galerkin method for solving nonlinear ill-posed *** results for the regularizing modified Levenberg-Marquardt scheme for the solution of nonlinear ill-posed problems have been *** on these results,we propose a modified heuristic parameter choice rule to terminate the regularizing modified Levenberg-Marquardt *** imposing certain conditions on the noise,we derive optimal convergence rates on the approximate solution under special source *** results are presented to illustrate the performance of the regularizing modified Levenberg-Marquardt scheme under the modified heuristic parameter choice.

关键词： The regularizing Levenberg-Marquardt scheme Multiscale Galerkin methods Nonlinear ill-posed problems Heuristic parameter choice rule Optimal convergence rate.

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