检索结果-内蒙古大学图书馆

On Error-Based Step Size Control for Discontinuous Galerkin Methods for Compressible Fluid Dynamics

应用数学与计算数学学报 2025年第1期7卷 3-39页

作者： Hendrik Ranocha Andrew R.Winters Hugo Guillermo Castro Lisandro Dalcin Michael Schlottke-Lakemper Gregor J.Gassner Matteo Parsani Applied Mathematics University of HamburgBundesstr.5520146 HamburgGermany Department of Mathematics Applied Mathematics Linköping UniversityLinköpingSweden Extreme Computing Research Center(ECRC) Computer Electrical and Mathematical Science and Engineering Division(CEMSE)King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia Applied and Computational Mathematics RWTH Aachen UniversityAachenGermany High-Performance Computing Center Stuttgart University of StuttgartStuttgartGermany Department of Mathematics and Computer Science University of CologneCologneGermany Center for Data and Simulation Science University of CologneCologneGermany Extreme Computing Research Center(ECRC) Computer Electrical and Mathematical Science and Engineering Division(CEMSE)King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia Physical Science and Engineering Division(PSE) King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia

We study a temporal step size control of explicit Runge-Kutta(RK)methods for com-pressible computational fluid dynamics(CFD),including the Navier-Stokes equations and hyperbolic systems of conservation laws such as the Euler *** demonstrate that error-based approaches are convenient in a wide range of applications and compare them to more classical step size control based on a Courant-Friedrichs-Lewy(CFL)*** numerical examples show that the error-based step size control is easy to use,robust,and efficient,e.g.,for(initial)transient periods,complex geometries,nonlinear shock captur-ing approaches,and schemes that use nonlinear entropy *** demonstrate these properties for problems ranging from well-understood academic test cases to industrially relevant large-scale computations with two disjoint code bases,the open source Julia pack-ages *** with *** and the C/Fortran code SSDC based on PETSc.

关键词： Explicit Runge-Kutta(RK)methods Step size control Compressible fluid dynamics Adaptivity in space and time Shock capturing

来源：评论

学校读者我要写书评

暂无评论

CNN-Based Deep Learning Model for Early Identification and Categorization of Melanoma Skin Cancer Using Medical Imaging

引用

SN computer science 2024年第7期5卷 1-20页

作者： Pandimurugan, V. Ahmad, Sultan Prabu, A.V. Rahmani, Mohammad Khalid Imam Abdeljaber, Hikmat A. M. Eswaran, Malathi Nazeer, Jabeen Computer Science and Engineering Acharya Institute of Technology Bangalore India Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia University Center for Research and Development (UCRD) Department of Computer Science and Engineering Chandigarh University Gharuan Punjab Mohali 140413 India Department of Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram AP Guntur 522502 India College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia Department of Computer Science Faculty of Information Technology Applied Science Private University Amman Jordan MEU Research Unit Middle East University Amman 11831 Jordan Department of Computer Technology Kongu Engineering College Perundurai Tamil Nadu Erode India

In human life, skin cancer is a curse. If not appropriately diagnosed, it spreads around all body parts in the earlier stage. The melanoma skin cancer death rate is 75% all over the world. There is an urgent need for a cure to be in place. Swarm and evolutionary computation for healthcare deals with real-world healthcare applications. One of the best ways to do this is to find the genes that need to be turned off such that only cancer cells can die. The current therapies used to cure cancer focus on turning off cancer cells but, at times, affect healthy tissues. Using the power of Artificial Intelligence, one can easily mark out the cancer-affected area and provide treatment accordingly. Segmentation techniques can be used to mark cancer lesions, and advanced deep learning algorithms in computer vision can help classify which class of cancer lesions. Automatic detection of melanoma by using a dermoscopic sample is tricky to find the stage and percentage of lesions affected by using deep convolutional neural networks with the help of machine vision tools. To predict skin lesions, we developed a model with three layers, each having output channels of 16, 32, and 64, respectively. For this research, different samples were collected from the international skin imaging collaboration database (ISIC2019, ISIC2020, ISIC2021). The study computed the most important parameters for classifying and identifying the lesion with accuracy, precision, recall, and specificity values. The proposed DCNN model classifier attained an accuracy of 88.82%, 93.45%, and 95.15% with the respective datasets of ISIC2019, ISIC2020, and ISIC2021, indicating high performance compared with the other state-of-the-art networks. The proposed model is a better framework for automating early detection and classification of melanoma skin cancer to protect many lives and avoid mishaps. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Classification Convolution neural network Deep learning Medical imaging Melanoma Skin cancer Swarm and evolutionary computation

来源：评论

学校读者我要写书评

暂无评论

Spread Analysis and Prediction of Covid-19 in India using Machine Learning

Spread Analysis and Prediction of Covid-19 in India using Ma...

引用

2023 International Conference on Advancement in Computation and computer Technologies, InCACCT 2023

作者： Yenkikar, Anuradha Babu, C. Narendra Ramaiah University of Applied Sciences Department of Computer Science and Engineering Karnataka Bangalore India Pune Institute of Computer Technology Pune India

ISBN: (纸本)9798350396485

Since its breakout, the coronavirus (covid-19) has wreaked devastation all over the world. There hasn't been a nation that hasn't been affected by it, and India is no exception. Finding a treatment for this sickness and stopping its spread is one of the hardest problems humanities has ever faced. World Health organization (WHO) figures show that the mortality rate and the number of people with covid-19 are both increased exponentially in some countries during the peak waves. In this study, we use machine learning approaches to forecast the number of covid-19 confirmations, recoveries, and mortality cases over a period of time and assess the coronavirus outbreak in India. The polynomial regression (PR), support vector regression (SVR), and an autoregressive integrated moving average (ARIMA) model are three techniques that are used. The findings demonstrate that, in terms of prediction outcomes, the ARIMA model provides the least Root Mean Squared Error (RMSE), closely followed by polynomial regression. SVR doesn't perform well since predictions are either too low or too high. Overall, the proposed system can significantly aid in comprehending the pattern of spread in other nations and assist governmental bodies in taking action to lessen its effects in future. © 2023 IEEE.

关键词： Mean square error

来源：评论

学校读者我要写书评

暂无评论

Deep learning and ensemble methods for anomaly detection in ICS security

引用

International Journal of Information Technology (Singapore) 2025年第3期17卷 1761-1775页

作者： Hossain, Md. Alamgir Hasan, Tahmid Karovic, jr, Vincent Abdeljaber, Hikmat A. M. Haque, Md. Alimul Ahmad, Sultan Zafar, Aasim Nazeer, Jabeen Mishra, B.K. Department of Computer Science and Engineering State University of Bangladesh South Purbachal Kanchan Dhaka 1461 Bangladesh Department of Computer Science and Engineering Prime University Dhaka Bangladesh Department of Information Systems Faculty of Management Comenius Bratislava Odbojárov 10 Bratislava 25 82005 Slovakia Department of Computer Science Faculty of Information Technology Applied Science Private University Amman 11931 Jordan MEU Research Unit Middle East University Amman 11831 Lebanon Department of Computer Science Veer Kunwar Singh University Bihar Ara India Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia University Center for Research and Development (UCRD) Department of Computer Science and Engineering Chandigarh University Gharuan Punjab Mohali 140413 India Department of Computer Science Aligarh Muslim University U.P Aligarh 202002 India P.G. Department of Physics Veer Kunwar Singh University Bihar Ara India

This research addresses the escalating threats to industrial control systems by introducing a novel approach that combines deep learning for feature selection with a robust ensemble-based classification technique to enhance anomaly detection. Our method utilizes a tailored autoencoder architecture to efficiently select features, followed by a Random Forest classifier to ensure reliable and generalizable detection of anomalies. Evaluated on the HAI Security Dataset, the proposed approach demonstrates exceptional performance, with significant improvements in detecting anomalous activities in ICS. The results highlight the method’s potential to enhance ICS security by providing a scalable and adaptable framework that evolves with emerging threats. This study not only advances cybersecurity methodologies for industrial systems but also lays the groundwork for future research, emphasizing the need for innovative and effective security solutions in the face of evolving cyber challenges. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Anomaly detection in ICS Cyber-resilience in critical infrastructure Deep learning for feature selection Industrial control systems security Random forest classifier for ICS

来源：评论

学校读者我要写书评

暂无评论

Constant-Loop Dominators for Single-Path Code Optimization 21

Constant-Loop Dominators for Single-Path Code Optimization

引用

21st International Workshop on Worst-Case Execution Time Analysis, WCET 2023

作者： Maroun, Emad Jacob Schoeberl, Martin Puschner, Peter Institute of Computer Engineering TU Wien Vienna Austria Department of Applied Mathematics and Computer Science Technical University of Denmark Lyngby Denmark

ISBN: (纸本)9783959772938

Single-path code is a code generation technique specifically designed for real-time systems. It guarantees that programs execute the same instruction sequence regardless of runtime conditions. Single-path code uses loop bounds to ensure all loops iterate a fixed number of times equal to their upper loop bound. When the lower and upper bounds are equal, the loop must iterate the same number of times, which we call a constant loop. In this paper, we present the constant-loop dominance relation on control-flow graphs. It is a variation of the traditional dominance relation that considers constant loops to find basic blocks that are always executed the same number of times. Using this relation, we present an optimization that reduces the code needed to manage single-path code. Our evaluation shows significant performance improvements, with one example of up to 90%, with mostly minor effects on code size. © Emad Jacob Maroun, Martin Schoeberl, and Peter Puschner.

关键词： Real time systems

来源：评论

学校读者我要写书评

暂无评论

IOT and Machine Learning Based Real-Time Protection Against Cyber-Physical Threat Mitigation 4

IOT and Machine Learning Based Real-Time Protection Against ...

引用

4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Saini, Harnit Mohammad, Faz Singh, Manoj Kumar Sharma, Kapil Kumar Shukla, Meenu Singh, Ramendra Ajay Kumar Garg Engineering College Department of Computer Science Ghaziabad India Gl Bajaj Institute of Technology and Management Department of Applied Computational Science and Engineering Greater Noida India Ims Engineering College Department of Computer Science & Engineering Ghaziabad India Ims Engineering College Department of Mca Ghaziabad India Maharishi University of Information Technology School of Engineering and Technology Noida India Ghaziabad India

ISBN: (纸本)9798350360165

An investigation is carried out into the dynamic relationship between machine learning algorithms used in IoT security, with detailed scrutiny of performance indicators to give a fuller view of what happens. The study pays particular attention to metrics like accuracy, precision, recall, and F1 score when ten varied samples are being examined. As a result, all the algorithms exhibit excellent performance with 95.5 %-98.2 % accuracy values, inspiring high reliability under diverse scenarios. Precision and recall metrics also depict the models' capabilities. Balancing precision and recall can be observed in F1 scores that are between 95.2% and 98.0%, and these numbers show that the approach based on this understanding of trade-offs is characterized by its operationalization. At the other end, results obtained from performance metrics go beyond numerical benchmarks to give a holistic appreciation of the algorithms' strength and areas for possible enhancement. The paper also does not provide only a theoretical approach to machine learning related to security in the IoT environment but also provides information about security considerations for the successful deployment of powerful, adaptive models into real-life scenarios. In this case, findings of the research help in understanding how to make sure that smart and effective measures are implemented during the process of developing security solutions considering all related issues in connection with the IoT technology landscape. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Optimal Logistic Map with DNA Computing Model for Image Encryption 2

Optimal Logistic Map with DNA Computing Model for Image Encr...

引用

2nd International Conference on Computing and Data Analytics, ICCDA 2024

作者： Shiny, K.V. Wilfred Blessing, N.R. Jino, R. Palarimath, Suresh Mathumitha, V. Sutherlin Subitha, G. Bharath Institute of Higher Education and Research Department of Computer Science and Engineering Chennai India College of Computing and Information Sciences University of Technology and Applied Sciences-Ibri Ibri Oman Department of Civil Engineering Chennai India Stella Mary's College of Engineering Department of Computer Science and Engineering Tamil nadu Kanyakumari District India

ISBN: (纸本)9798331542573

The logistic map with optimization with a novel Deoxyribonucleic Acid (DNA) sequence operation-based novel image encryption scheme is generated. The best mask is obtained by enhancing the excellence of DNA which is the significant advantage of this approach. According to the image decryption and encryption process, correct errors and the similarity of DNA sequences are decreased by the DNA cryptogram becoming an input pace for DNA calculation. The maximum PSNR of encrypted images is obtained by the logistic map function to improve the performance of image security Ant Lion optimization (ALO). The image encryption and decryption process generate optima point DNA sequence rules. DNA coding: utilizing the complementary rule, each nucleotide is then changed into its base pair for a random time or times;the times are produced by Chebyshev maps. The experimental investigations provided better UACI, Number of Pixel Exchange Rate (NPCR), and Peak Signal Noise Ratio (PSNR). This strategy is significant because it increases entropy, which is the fundamental property of randomness, withstands multiple statistical and differential attacks, and produces positive experimental outcomes. © 2024 IEEE.

关键词： Signal to noise ratio

来源：评论

学校读者我要写书评

暂无评论

Comparison of Machine Learning Algorithm for Stock Price Prediction Using Sentiment Analysis 5

Comparison of Machine Learning Algorithm for Stock Price Pre...

引用

5th International Conference on Emerging Smart Computing and Informatics, ESCI 2023

ISBN: (纸本)9781665475242

Forecasting companies' stock market prices are one the interesting topics for analysts and researchers. Although a company's stock price can be unpredictable, long-term forecasts can help but it is dependent on many factors such as the company's business model, change in leadership, and investors' mood. It has been found to be insufficient to predict stock values just on the basis of historical data or textual information. Previous research in sentiment analysis have shown a strong correlation between the movement of stock prices and the publication of news stories. At different levels, a number of sentiment analysis research have been attempted utilizing methods. In this paper, we made a comparison of various Machine Learning methods on five datasets of financial news related to the company and domains in which the company. Encouraging results are obtained using 13 models i.e., Linear Regression, Ridge Regression, Lasso Regression, Random Forest, Naive Bayes, Logistic Regression, LSTM, ARIMA, Logistic Regression, Support Vector Machines, Decision Tree, Boosted Tree, and ensemble method which depict polarity of news articles being positive or negative and the accuracies are 93.90%, 92.31 %, 92.27%, 85.44%, 84.65%, 84.65%, 94.73%, 90.13%, 82%, 83%, 72%, 70%, 95.11 % respectively. © 2023 IEEE.

关键词： Financial markets

来源：评论

学校读者我要写书评

暂无评论

Computational Efficiency of Deep Learning-Based Super Resolution Methods for 5G-NR Channel Estimation

Computational Efficiency of Deep Learning-Based Super Resolu...

引用

2024 IEEE Latin-American Conference on Communications, LATINCOM 2024

作者： Goez, David Beyazit, Esra A. Slamnik-Krijestorac, Nina Marquez-Barja, Johann M. Gaviria, Natalia Latre, Steven Camelo, Miguel University of Antwerp - imec IDLab - Department of Computer Science Belgium Universidad de Antioquia Electronics Engineering Department Colombia University of Antwerp - imec IDLab - Faculty of Applied Engineering Belgium

ISBN: (纸本)9798331521110

The increasing demand for high-quality and efficient Channel Estimation (CE) in 5G New Radio (5G-NR) systems has prompted the exploration of advanced Deep Learning (DL) techniques. While traditional methods, such as Linear Interpolation (LI) and Least Squares (LS), provide reasonable accuracy and are practical for real-time physical layer processing, recent DL-based CE approaches have primarily focused on accuracy, often without evidence of real-time capabilities. In this paper, we present a comprehensive evaluation of DL-based Super-resolution (SR) methods for CE, comparing models like Super Resolution Convolutional Neural Network (SRCNN), ChannelNet, and Enhanced Deep Super-Resolution (EDSR) in both 1D and 2D convolutional architectures. We optimize these models using NVIDIA TensorRT to reduce computational complexity and latency. Our results show that the optimized 1D-EDSR model achieves the best performance with a Mean Squared Error (MSE) of 0.0126, outperforming all other models in terms of accuracy. However, the optimized 1D-EDSR model fails to meet real-time constraints due to additional computational overhead (0.6798 ms/sample). In contrast, the 1D-SRCNN model offers a balanced trade-off between MSE (0.01738) and inference time (0.0866ms/sample), achieving 40% higher accuracy than LS (0.0288) while maintaining the best energy efficiency (1.48 mJ/sample). © 2024 IEEE.

关键词： Mean square error

来源：评论

学校读者我要写书评

暂无评论

A MATHEMATICAL MODEL ILLUSTRATING THE INHIBITORY EFFECT OF THE MICRO RNA ON THE PROTEIN PRODUCTION

引用

UPB Scientific Bulletin, Series A: applied Mathematics and Physics 2023年第4期85卷 101-106页

作者： Olteanu, Mircea Ştefan, Radu Department of Mathematical Methods and Models Faculty of Applied Sciences University "Politehnica" of Bucharest Romania Department of Automatic Control and Systems Engineering Faculty of Automatic Control and Computer Science University "Politehnica" of Bucharest Romania

The present paper introduces a mathematical model for the cross-talking between microRNA and Protein. Studying the qualitative properties of the proposed model, we infer that the microRNA is an inhibitor for the Prote... 详细信息

关键词： Proteins

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：