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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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Elevating Ocular Diagnosis: Harnessing the Power of EfficientNet for Eye Disease Classification 3

Elevating Ocular Diagnosis: Harnessing the Power of Efficien...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Mohith, Vankadari Raja, Kalpana Oviya, I.R. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India Yale University Section for Biomedical Informatics and Data Science School of Medicine New Haven United States

ISBN: (纸本)9798350372120

In response to the evolving landscape of healthcare technology this study addresses the need for automated diagnostic tools in the field of ophthalmology. By utilizing deep learning methods specifically leveraging the EfficientNetV2S framework we achieve an overall accuracy rate of 94.6% in classifying images depicting common eye conditions like cataract, diabetic retinopathy, glaucoma, and normal cases. Our model exhibits capabilities across a diverse spectrum of medical conditions due to meticulous preprocessing and fine-tuning procedures. The results highlight the potential for automated technology integration in healthcare settings, which might improve patient care and treatment plans. This paper highlights the use of advanced neural network designs in the diagnosis of ocular illnesses, offering insights at the nexus of learning and medical image processing. © 2024 IEEE.

关键词： Ophthalmology

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Optimized Price Prediction of Cryptocurrencies using Deep Learning on High-Volume Time Series Data

Optimized Price Prediction of Cryptocurrencies using Deep Le...

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International Conference on Information, Intelligence, Systems and Applications (IISA)

作者： Gerasimos Vonitsanos Andreas Kanavos Foteini Grivokostopoulou Spyros Sioutas Computer Engineering and Informatics Department University of Patras Patras Greece Department of Informatics Ionian University Corfu Greece Computer Technology Institute and Press “Diophantus“ Patras Greece

ISBN: (数字)9798350368833

ISBN: (纸本)9798350368840

Statistical models, enhanced by deep learning techniques, have become pivotal in various predictive tasks, including financial forecasting. This paper addresses the challenge of predicting cryptocurrency prices, utilizing a dataset comprising various cryptocurrencies treated as time series. We employ several deep neural network architectures-including Multilayer Perceptrons (MLP), Recurrent Neural Networks (RNN), Long Short-Term Memory networks (LSTM), and Bidirectional LSTM networks-to forecast future prices. Our methodology involves a detailed analysis of cryptocurrency time series to inform the design of these networks. The performance of each model is rigorously compared, highlighting their predictive capabilities in the context of cryptocurrency markets. This study not only contributes to the empirical literature by applying advanced neural networks to high-volume financial data but also provides a comparative analysis that may guide future applications of deep learning in economic forecasting.

关键词： Deep learning Biological system modeling Time series analysis Predictive models Multilayer perceptrons Economic forecasting Big Data applications Cryptocurrency Context modeling

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Informal Indonesian Part-of-Speech Tagger Using Hidden Markov Model and Normalization Algorithm

Informal Indonesian Part-of-Speech Tagger Using Hidden Marko...

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2023 International Conference on Electrical engineering and informatics, ICEEI 2023

作者： Rachman, Fika Hastarita Solihin, Firdaus Fatonah, Nenden Siti Hazhiahadani, Tsania Herawati, Sri Imamah University of Trunojoyo Madura Department of Informatics Engineering Bangkalan Indonesia Esa Unggul University Department Magister of Computer Science Jakarta Indonesia University of Trunojoyo Madura Department of Information Systems Bangkalan Indonesia

ISBN: (纸本)9798350381290

Twitter is a social networking platform that can be used as resources to find information in real time. Twitter is one of the social media that does not have writing criteria, users are free to express their thoughts and send messages. There are many messages and tweets that use abbreviations, foreign words, local and mixed languages, which are characteristic of informal language patterns. Informal language patterns do not rely on Indonesian, making the process of annotating word classes less precise and influential on the level of precision. The use of POS Tagging in previous research can annotate word classes well in formal sentences. The problem in informal sentences is solved by the process of word normalization after the preprocessing stage, where informal words are converted into formal words then Part-of-Speech Tagging is carried out on each word in the sentence such as nouns, verbs, adjectives, etc. The dataset used was 846 words, with a informal word count of 99 words. With the Hidden Markov Model commonly used in machine learning as well as the word normalization process, the system is able to increase the resulting precision value, there is a significant increase of up to 10% in the precision produced. © 2023 IEEE.

关键词： Hidden Markov models

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Low Complexity Deep Learning Based Coordinated Beamforming for mmWave Massive MIMO Vehicular Networks

Low Complexity Deep Learning Based Coordinated Beamforming f...

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2024 Panhellenic Conference on Electronics and Telecommunications, PACET 2024

作者： Rekkas, Vasileios P. Sotiroudis, Sotirios P. Sarigiannids, Panagiotis Karagiannidis, George K. Christodoulou, Christos G. Goudos, Sotirios K. University of Western Macedonia Department of Informatics and Telecommunications Engineering Kozani Greece Aristotle University of Thessaloniki School of Electrical and Computer Engineering Thessaloniki Greece The University of New Mexico Department of Engineering and Computing United States Aristotle University of Thessaloniki ELEDIA@AUTH School of Physics Thessaloniki Greece Bharath University Department of Electronics and Communication Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350318845

Enabling high mobility in millimeter-wave (mmWave) systems is crucial for next-generation wireless communication systems. Challenges arise in applications such as vehicular communications and wireless virtual/augmented reality. MmWave systems grapple with concerns related to narrow beams, signal vulnerability to obstructions that affect coverage, and the necessity for frequent handovers. Additionally, identifying optimal beamforming vectors in large antenna array mmWave systems involves substantial training overhead, impacting efficiency. This paper introduces a novel solution to address challenges in enabling high mobility in mmWave systems. The approach combines deep learning (DL) and coordinated beamforming to support applications such as vehicular communications and wireless virtual/augmented reality. The solution involves multiple coordinating base stations (BSs) serving a mobile user, utilizing a single pilot training sequence. A DL model predicts beamforming vectors based on received signals, providing reliable coverage, low latency, and minimal training overhead for highly mobile mmWave applications. Simulations demonstrate its effectiveness in high-mobility scenarios, approaching the performance of an optimal solution, a genie-aided solution that knows the optimal beamforming vectors without any training overhead. © 2024 IEEE.

关键词： Millimeter waves

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A Comparison Study on Generative Adversarial Networks (GANs) for Satellite Image Upscaling 12

A Comparison Study on Generative Adversarial Networks (GANs)...

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12th IEEE Conference on Systems, Process and Control, ICSPC 2024

作者： Li, Brandon Kah Wei Jasser, Muhammed Basheer Issa, Bayan Ajibade, Samuel-Soma M. Chua, Hui Na Wong, Richard T.K. Hamzah, Muzaffar Sunway University No. 5 Jalan Universiti School of Engineering and Technology Department of Computing and Information Systems Selangor Darul Ehsan Bandar Sunway47500 Malaysia School of Engineering and Technology Selangor Darul Ehsan Bandar Sunway47500 Malaysia University of Aleppo Faculty of Informatics Engineering Syria Istanbul Ticaret University Department of Computer Engineering Istanbul Turkey Universiti Malaysia Sabah Faculty of Computing and Informatics Sabah Kota Kinabalu88450 Malaysia

ISBN: (纸本)9798350391398

Satellite images that are publicly available on the internet such as Google Maps are mostly low-quality and blurry which is not very useful for research or decision-making due to the lack of details. Recently image upscaling has been increasing in popularity to address several real-world problems where one approach is using Convolutional Neural Networks (CNNs). However, CNNs have difficulty in restoring fine details and this has led to the use of Generative adversarial networks (GANs) for image scaling. In this paper, three GAN models are trained which are Enhanced Super-Resolution Generative Adversarial Networks (Real-ESRGAN), Image Restoration Using Swin Transformer (SwinIR-Medium), and Dual Aggregation Transformer (DAT-2). The models are evaluated with well-known image metrics to determine each model's effectiveness in upscaling low-resolution satellite images. The results show that all models can reconstruct the image to some degree where the best out of the three is DAT-2. Further fine-tuning of the best model by changing the loss functions has shown improvements in structural integrity for the upscaled image. © 2024 IEEE.

关键词： Generative adversarial networks

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Developing Engagement in the Learning Management System Supported by Learning Analytics

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computer Systems Science & engineering 2022年第7期42卷 335-350页

作者： Suraya Hamid Shahrul Nizam Ismail Muzaffar Hamzah Asad W.Malik Department of Information System Faculty of Computer Science and Information TechnologyUniversiti MalayaKuala LumpurMalaysia Faculty of Computing and Informatics Universiti Malaysia SabahSabahMalaysia Department of Computing School of Electrical Engineering and Computer Science(SEECS)National University of Sciences and Technology(NUST)IslamabadPakistan

Learning analytics is an emerging technique of analysing student par-ticipation and *** recent COVID-19 pandemic has signiﬁcantly increased the role of learning management systems(LMSs).LMSs previously only complemented face-to-face teaching,something which has not been possible between 2019 to *** date,the existing body of literature on LMSs has not analysed learning in the context of the pandemic,where an LMS serves as the only interface between students and ***,productive results will remain elusive if the key factors that contribute towards engaging students in learning are notﬁrst identiﬁ***,this study aimed to perform an exten-sive literature review with which to design and develop a student engagement model for holistic involvement in an *** required data was collected from an LMS that is currently utilised by a local Malaysian *** model was validated by a panel of experts as well as discussions with *** is our hope that the result of this study will help other institutions of higher learning determine factors of low engagement in their respective LMSs.

关键词： Engagement analysis learning analytics learning management system student engagement

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Robust SmartCityAI Lakehouse: IKN (New Capital City of Indonesia) Case Study 11

Robust SmartCityAI Lakehouse: IKN (New Capital City of Indon...

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11th International Conference on ICT for Smart Society, ICISS 2024

作者： Hermanus, Davy Ronald Hermanus, Danny Robert Hidayat, Fadhil Supangat, Suhono Harso Bina Nusantara University Research Interest Group in Educational Technology Computer Science Department Bandung Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia Minneapolis United States

ISBN: (纸本)9798350389654

The rapid advancement of smart cities, driven by innovative communication and information technologies (ICT), has transformed urban management. This paper introduces the Robust SmartCityAI Lakehouse, a hybrid framework specifically designed to implement smart city solutions in IKN, the New Capital City of Indonesia. The proposed architecture seamlessly integrates diverse data sources and supports a wide range of applications, including real-time AI-driven transportation management, energy optimization, public safety enhancement, waste and water management, environmental monitoring, and air quality control. By leveraging both on-premises processing and scalable cloud infrastructure, this framework enhances urban sustainability and improves the quality of life for citizens. The paper also explores the key benefits and challenges of deploying this architecture, providing practical strategies for its implementation in complex urban environments. © 2024 IEEE.

关键词： Air quality

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Visualization of Syntax and Semantics for Simple Functional Language of Natural Numbers and Boolean Values 10

Visualization of Syntax and Semantics for Simple Functional ...

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10th IEEE Jubilee International Conference on Computational Cybernetics and Cyber-Medical Systems, ICCC 2022

作者： Perháč, Ján Ferencsik, Michal Zhukovskyy, Viktor Zhukovska, Nataliia Shatnyi, Serhii Technical University of Košice Faculty of Electrical Engineering and Informatics Department of Computers and Informatics Košice Slovakia National University of Water and Environmental Engineering Department of Computer Science and Applied Mathematics Rivne Ukraine

ISBN: (数字)9781665481779

ISBN: (纸本)9781665481779

Building an abstract syntax tree is an integral part of compilers. We analyze a few of the popular techniques providing such analysis in the web environment. Based on the analysis of these solutions, we deal with the implementation of our parser solution, which also provides a graphical representation of abstract syntax trees as well as proof trees. This solution is then used in our application designed to interpret the expressions of the simple teaching functional language of natural numbers and boolean values. In addition, the application supports the evaluation (semantics) of expressions (step by step), and also their individual properties: the depth of the syntax tree, syntax tree nodes count, and the constants used in the expression. © 2022 IEEE.

关键词： Semantics

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Design and Implementation the Prayer Reminder Application using KISS Principle based on User Centered Design

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Procedia computer Science 2024年 234卷 1484-1491页

作者： Andhik Ampuh Yunanto Febi Fidhiyanti Putri Desy Intan Permatasari Nailussa'ada Fadilah Fahrul Hardiansyah Umi Sa'adah Adam Shidqul Aziz Department of Informatics and Computer Engineering Politeknik Elektronika Negeri Surabaya Keputih Sukolilo Surabaya East Java 60111 Indonesia

The five daily prayers are an obligation of worship for Muslims in the world. Prayers have a predetermined time schedule according to the teachings of the Prophet Muhammad SAW and have different schedules according to each region. As Muslims, it is obligatory to pray five times a day. However, there are a lot of start-up company which forgot about the employee need such as praying. One of them is called byPulsa Start-up company which wants anticipates delays in praying. Because of this reason, the Ceo at byPulsa need an application to remind the employee about praying. So, this company wants to improve the additional feature into its application and develop its application more less interaction and efficient. So, this research aims to develop the previous application into an application that can remind employees to pray. In this research, Kiss Principle Method and User Centered Design were used to this application to make it more efficient and usable for user. The results show that the user feels more satisfied with the application because it has prayer schedule feature based on user's position. The CSAT score from evaluation has score 80% which higher than previous application. This application can help and guide users in exploring their religion by following online studies and using features such as prayer beads, Islamic book recommendations, even Islamic podcasts. As well as the Kiss principle method used here, it functions to make it easier for users to interact with the application.

关键词： Prayer reminder Kiss Principle User Centered Design Android Application

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