检索结果-内蒙古大学图书馆

3rd Novel Intelligent and Leading Emerging sciences Conference, NILES 2021

作者： Mohammad, Karim Aly Kandil, Ahmed H. El-Bialy, Ahmed M. Ali Fawzi, Sahar Cairo university Systems and Biomedical Engineering Faculty of Engineering Giza Egypt Systems and Biomedical Engineering Faculty of Engineering El-Shorouk Academy Cairo Egypt Information Technology computer Science Nile university Giza Egypt

ISBN: (纸本)9781665421577

A consistency analysis is performed for one of the famous Tajweed rules in the Holy Quran - Madd rules. They are tested on records of one of the famous reference reciters - Sheikh El-Hosary-to find a consistent boundaries and to evaluate the performance of other new learners. A vowel detection algorithm is used to detect the duration of the detected Madd patterns. This algorithm was applied on a dataset of 105 minutes of Quranic records of Sheikh El-Hosary. The consistency result is found to be normally distributed with a mean of 0.379 second of one movement time of Madd and standard deviation of 0.058 second and minimum consistency of 84.6% for 68% of the detected Madd patterns. © 2021 IEEE.

关键词： Normal distribution

来源：评论

学校读者我要写书评

暂无评论

Deep learning model for efficient traffic forecasting in intelligent transportation systems

引用

Neural Computing and Applications 2024年 1-14页

作者： Khan, Shakir Alghayadh, Faisal Yousef Ahanger, Tariq Ahamed Soni, Mukesh Viriyasitavat, Wattana Berdieva, Uguloy Byeon, Haewon Riyadh Saudi Arabia University Centre for Research and Development Department of Computer Science and Engineering Chandigarh University Mohali140413 India Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia Department of Management Information Systems CoBA Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia Dr. D. Y. Patil Vidyapeeth Pune Pune India Dr. D. Y. Patil School of Science and Technology Tathawade Pune India Chulalongkorn Business School Faculty of Commerce and Accountancy Chulalongkorn University Bangkok Thailand The Department of Tax and Taxation Tashkent Institute of Finance Tashkent Uzbekistan Department of Digital Anti-Aging Healthcare Inje University Gimhae50834 Korea Republic of

In this paper, a novel intelligent transportation framework called graph convolutional neural network for distributed machine learning (GCN-DML) is presented. Its primary application is traffic forecasting in large-scale, dynamically-evolving traffic scenarios. One unique method in traffic forecasting, called GCN-DML, is that it allows user data to be processed locally on edge devices, removing the requirement to send all data to CS (CS). Designed specifically for edge computing, this distributed machine learning framework makes it easier to train neural networks on a variety of edge devices. In addition to protecting user privacy, this method reduces the difficulties related to centralised neural network training, minimises communication delays, lowers data transfer quantities, and improves overall data processing efficiency. GCN-LSTM, the composite graph convolutional networks used in this framework, combine edge-enhanced attention mechanisms with the feature transmission capabilities of graph convolutional neural networks to effectively handle the challenging task of traffic forecasting. Even as the number of cars in the network rises, they perform exceptionally well at quickly extracting and sending interaction information between vehicles, while preserving high prediction accuracy and low time complexity. The versatility of GCN-DML is extended to a range of traffic forecasting application scenarios, whereby edge devices can adjust the kind and scale of the neural network according to available compute and storage resources, all without sacrificing performance. The usefulness of GCN-DML in traffic forecasting is demonstrated by experimental evaluations on the NGSIM public dataset, where it outperforms other models with better computation time and prediction performance. It obtains noteworthy F1 scores of 0.9473, 0.9557, and 0.9391, respectively, for recall, precision, and precision. Even when there is an increase in the number of cars, the accuracy remains high,

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

The Impact of the Digital Transformation of Education on Literacy

The Impact of the Digital Transformation of Education on Lit...

引用

International IEEE Conference and Workshop: Óbuda on Electrical and Power engineering (CANDO-EPE)

作者： Imre Farkas Attila Kovari Mónika Rajcsanyi-Molnar Department of Computer Systems and Control Technology Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary Institute of Digital Technology Faculty of Computer Science Eszterházy Károly Catholic University Eger Hungary Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Obuda University Budapest Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemét Hungary Department of Economics and Management Institute of Social Sciences University of Dunaujvaros Dunaujvaros Hungary

ISBN: (数字)9798331531119

ISBN: (纸本)9798331531126

During the COVID19 epidemic, people of all ages from all walks of life around the world have become inevitably familiar with and almost dependent on the digital tools of the age and the opportunities they offer. A change had to take place very quickly that was unprecedented. This process has also affected education. During the time of the reading lock-in, there was a proliferation of digital curricular content. The second time around, online education came to the fore, thanks to less or well-functioning online platforms, and has since become part of everyday teaching. What is the effectiveness and usefulness of sharing knowledge in this way? Is literacy still needed at all? Can online and hybrid (a controlled mix of face-to-face and online) education replace the well-established contact and face-to-face education? The study show that the respondents believe that online education has had an impact on literacy and that it is perceived to have a positive impact. The results indicate that online education has a positive impact on the literacy levels of students, as it facilitates knowledge acquisition and enhances digital literacy skills.

关键词： Power engineering Epidemics Navigation Knowledge acquisition Digital transformation Education Information age Digital intelligence Cultural differences Problem-solving

来源：评论

学校读者我要写书评

暂无评论

The Emergence of Artificial Intelligence in Education and its Impact on Individual Literacy in Higher Education

The Emergence of Artificial Intelligence in Education and it...

引用

International IEEE Conference and Workshop: Óbuda on Electrical and Power engineering (CANDO-EPE)

作者： Imre Farkas Attila Kovari Mónika Rajcsanyi-Molnar Department of Computer Systems and Control Technology Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary Institute of Digital Technology Faculty of Computer Science Eszterházy Károly Catholic University Eger Hungary Institute of Electronics and Communication Systems Kandó Kalmán Faculty of Electrical Engineering Obuda University Budapest Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemét Hungary Department of Economics and Management Institute of Social Sciences University of Dunaujvaros Dunaujvaros Hungary

ISBN: (数字)9798331531119

ISBN: (纸本)9798331531126

In the last three years, a new partner has emerged for teachers and educators in the field of education. The mushrooming of applications based on large language models has greatly shaped the educational development fields of the present era. The use of various AI applications has become commonplace among students and teachers alike. Many questions are being raised by researchers in this field. What does the rapid development of AI applications bring to the field of pedagogy? Should the products of such applications be compared with human performance? The form presented in this thesis seeks to answer similar questions. What is the impact of frequent use of these applications on the literacy level of individuals? In which areas do students tend to use these applications? The results indicate that half of the students believe their literacy levels will decrease with the mass emergence of AI applications, while 36% feel it will not change. However, 88 % of students have already dealt with AI-based applications, with 64 % using them several times or more, suggesting a high level of integration into their educational processes.

关键词： Ethics Power engineering Large language models Education Collaboration Learning (artificial intelligence) Reliability Artificial intelligence Monitoring Guidelines

来源：评论

学校读者我要写书评

暂无评论

Adaptive Quantum and Graph-Attentive Security Framework for DoS Attack Mitigation in VANETs

Adaptive Quantum and Graph-Attentive Security Framework for ...

引用

Machine Learning and Autonomous systems (ICMLAS), International Conference on

作者： Asma Anjum P. S. V. Srinivasa Rao M. Sindhu Pooja Bhatt Priya M R. Ramya Maranan Department of Computer Science and Engineering HKBK College of Engineering Bengaluru Karnataka India Department of Computer Science and Engineering Joginpally B R Engineering College Hyderabad Telangana India Department of Computer Science and Business Systems R.M.K Engineering College Thiruvallur Tamil Nadu India Computer Science and Engineering Faculty of Engineering and Technology Parul institute of Engineering and Technology Parul University Post Limda Gujarat India Department of Computer Science NIE First Grade College Mysuru Karnataka India Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Vehicular Ad Hoc Networks (VANETs) are considered crucial for real-time vehicle-to-vehicle communication, which in turn enhances the efficiency of traffic and road safety. VANETs are very vulnerable to Denial-of-Service (DoS) attacks, which seriously disrupt communication and degrade network performance. This paper introduces a high-performance neural network-based detection and prevention mechanism that can effectively counter DoS attacks. The proposed method is initiated with the real-time monitoring of the network. Data collection and preprocessing through the Adaptive Self-Guided Loop Filter (ASGLF) are used to filter out noise from the data. The Fast Hybrid Vision Transformer (FHVT) conducts feature extraction for network traffic to effectively preserve the spatial and temporal relationships. Using the Reflecting Equivariant Quantum Neural Network (REQNN) integrated with Heterogeneous Edge-Enhanced Graph Attention Network (HEEGAT) ensures timely anomaly detection accuracy of attacks. Subtraction-Average-Based Optimizer (SAO) optimizes the detection parameters to effectively utilize resources. The experimental results have shown accuracy in the detection of DoS attacks as 99.9% with a PDR of 99.9% and also improved throughput efficiency by 99.8%. These results validate the effectiveness of the proposed model in securing VANETs from DoS attacks, offering uninterrupted network availability and improved vehicular communication security for intelligent transportation systems.

关键词： Accuracy Image edge detection Prevention and mitigation Neural networks Vehicular ad hoc networks Denial-of-service attack Feature extraction Transformers Throughput Real-time systems

来源：评论

学校读者我要写书评

暂无评论

A Hybrid Particle Swarm Optimization to Forecast Implied Volatility Risk

引用

computers, Materials & Continua 2022年第11期73卷 4291-4309页

作者： Kais Tissaoui Sahbi Boubaker Waleed Saud Alghassab Taha Zaghdoudi Jamel Azibi University of Ha’il Applied CollegeHail CitySaudi Arabia University of Tunis El Manar Faculty of Economic Sciences and Management of Tunisthe International Finance GroupTunisTunisia Department of Computer and Network Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah21959Saudi Arabia Research Unit on Study of Systems and Renewable Energy National College of Engineering of MonastirUniversity of MonastirMonastirTunisia LAREQUAD&FSEGT University of Tunis El ManarTunisTunisia University of Jendouba Law FacultyManagement and Economic Sciences of JendoubaTunisia

The application of optimization methods to prediction issues is a continually exploring *** line with this,this paper investigates the connectedness between the infected cases of COVID-19 and US fear index from a forecasting *** complex characteristics of implied volatility risk index such as non-linearity structure,time-varying and nonstationarity motivate us to apply a nonlinear polynomial Hammerstein model with known structure and unknown *** use the Hybrid Particle Swarm Optimization(HPSO)tool to identify the model parameters of nonlinear polynomial Hammerstein *** indicate that,following a nonlinear polynomial behaviour cascaded to an autoregressive with exogenous input(ARX)behaviour,the fear index in US financial market is significantly affected by COVID-19-infected cases in the US,COVID-19-infected cases in the world and COVID-19-infected cases in China,*** performance indicators provided by the developed models show that COVID-19-infected cases in the US are particularly powerful in predicting the Cboe volatility index compared to COVID-19-infected cases in the world and China(MAPE(2.1013%);R2(91.78%)and RMSE(0.6363 percentage points)).The proposed approaches have also shown good convergence characteristics and accurate fits of the data.

关键词： Forecasting Cboe’s volatility index COVID-19 pandemic nonlinear polynomial hammerstein model hybrid particle swarm optimization

来源：评论

学校读者我要写书评

暂无评论

Iterative Refinement Quantum Amplitude Estimation

Iterative Refinement Quantum Amplitude Estimation

引用

IEEE International Symposium on Embedded Multicore Socs (MCSoC)

作者： Yoshiyuki Saito Ningyi Xie Jungpil Shin Xinwei Lee Dongsheng Cai Nobuyoshi Asai Graduate School of Computer Science and Engineering University of Aizu Fukushima Japan Graduate School of Systems and Information Engineering University of Tsukuba Ibaraki Japan School of Computer Science and Engineering University of Aizu Fukushima Japan Faculty of Engineering Information and Systems University of Tsukuba Ibaraki Japan

Quantum Amplitude Estimation (QAE) is an important quantum algorithm that has the potential to quadratically speed up Monte Carlo based *** this paper, we present a variant of the QAE without Phase Estimation Algorithm called Iterative Refinement QAE (IRQAE). IRQAE can refine the current estimation to a more accurate estimation iteratively, hence it can provide an estimation with arbitrary required accuracy ∊. The key idea of IRQAE is to use a rotation gate to create a quantum state for samplings with the current estimation. Using this idea, we show that IRQAE can provide a highly accurate estimation with lower classical computational complexity and with the same quantum computational complexity compared to state-of-the-art QAEs without phase estimation using numerical experiments. We prove that the computational complexity of IRQAE of the quantum part is O(1/∊) and the classical one is O(1/∊). The quantum cost gives a quadratic advantage over that of the classical Monte Carlo simulation.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Enhanced prairie dog optimization with Levy flight and dynamic opposition-based learning for global optimization and engineering design problems

引用

Neural Computing and Applications 2024年第19期36卷 11137-11170页

作者： Biswas, Saptadeep Shaikh, Azharuddin Ezugwu, Absalom El-Shamir Greeff, Japie Mirjalili, Seyedali Bera, Uttam Kumar Abualigah, Laith Department of Mathematics National Institute of Technology Agartala Tripura Agartala India Unit for Data Science and Computing North-West University 11 Hoffman Street Potchefstroom2520 South Africa Faculty of Natural and Agricultural Sciences School of Computer Science and Information Systems North-West University Vanderbijlpark South Africa Centre for Artificial Intelligence Research and Optimization Torrens University Australia Fortitude Valley BrisbaneQLD4006 Australia Research Center University of Tabuk Tabuk71491 Saudi Arabia Computer Science Department Al al-Bayt University Mafraq25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Applied Science Research Center Applied Science Private University Amman11931 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia College of Engineering Yuan Ze University Taoyuan32003 Taiwan MEU Research Unit Middle East University Amman11831 Jordan

This study proposes a new prairie dog optimization algorithm version called EPDO. This new version aims to address the issues of premature convergence and slow convergence that were observed in the original PDO algorithm. To improve performance, several modifications are introduced in EPDO. First, a dynamic opposite learning strategy is employed to increase the diversity of the population and prevent premature convergence. This strategy helps the algorithm avoid falling into local optima and promotes global optimization. Additionally, the Lévy dynamic random walk technique is utilized in EPDO. This modified Lévy flight with random walk reduces the algorithm’s running time for the test function’s ideal value, accelerating its convergence. The proposed approach is evaluated using 33 benchmark problems from CEC 2017 and compared against seven other comparative techniques: GWO, MFO, ALO, WOA, DA, SCA, and RSA. Numerical results demonstrate that EPDO produces good outcomes and performs well in solving benchmark problems. To further validate the results and assess reliability, the authors employ average rank tests, the measurement of alternatives, and ranking according to the compromise solution (MARCOS) method, as well as a convergence report of EPDO and other algorithms. Furthermore, the effectiveness of the EPDO algorithm is demonstrated by applying it to five design problems. The results indicate that EPDO achieves impressive outcomes and proves its capability to address practical issues. The algorithm performs well in solving benchmark and practical design problems, as supported by the numerical results and validation methods used in the study. © The Author(s) 2024.

关键词： Global optimization

来源：评论

学校读者我要写书评

暂无评论

Access Control Based on Self-Sovereign Identity 16

Access Control Based on Self-Sovereign Identity

引用

16th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2024

作者： Nita, Stefania Loredana Mihailescu, Marius Iulian Marascu, Valentina Rogobete, Marius Faculty of Information Systems and Cyber Security Military Technical Academy 'Ferdinand i' Department of Computers and Cybersecurity Bucharest Romania 'Spiru Haret' University Scientific Research Center in Mathematics and Informatics Faculty of Engineering and Computer Science Bucharest Romania National Institute for Laser Plasma and Radiation Physics Low Temperature Plasma Department Bucharest Romania Telematics Harman International Bucharest Romania

ISBN: (纸本)9798350371154

In the rapidly growing interest in digital identity management, there is an important shift toward adopting verifiable credentials, often based on blockchain technology. This transition highlights a growing interest in the concept of Self-Sovereign Identity (SSI), a paradigm that empowers individuals with control over their own digital identities through the use of verifiable credentials. This paper introduces a novel access control framework that leverages the principles of SelfSovereign Identity to significantly enhance the security of system access based on the users' credentials. By integrating the decentralized and user-centric nature of SSI, our proposed framework aims to address existing vulnerabilities in traditional access control systems. It provides a robust, scalable, and controlled mechanism that ensures higher levels of trust and security in digital interactions. This approach aligns with the broader digital identity trends favoring transparency, user autonomy, and trust. © 2024 IEEE.

关键词： Electronic document identification systems

来源：评论

学校读者我要写书评

暂无评论

Fuzzy Control of a Hydrodynamic Process

Fuzzy Control of a Hydrodynamic Process

引用

International Symposium on INFOTEH-JAHORINA (INFOTEH)

作者： Igor Krčmar Petar Marić Velibor Đalić Aleksandar Rakić Bojan Derajić Faculty of Electrical Engineering University of Banja Luka Banja Luka BiH The Department of Signals and Systems School of Electrical Engineering University of Belgrade Belgrade Serbia Faculty of Electrical Engineering and Computer Science Technical University of Berlin Berlin Germany

Hydrodynamic processes, part of many industrial and urban infrastructure systems, usually lack accurate models, hence introducing ambiguity in a controller design procedure. Operators and control engineers provide significant knowledge about successful control of hydrodynamic systems. Fuzzy controller, suitable to exploit and incorporate available knowledge, is designed for control of a benchmark coupled tank system. Successful fuzzy controller must cope with slow dynamics of the process, dead zone of an actuator, and unipolar control signal, when employed in reference tracking tasks. The designed fuzzy controller implements fuzzy inference system with 15 rules, two inputs and one output, as well as three adjustable gain parameters. The proposed controller is successful in reference tracking tasks and disturbance suppression. Experiments, organised as reference tracking tasks and implemented as computer simulations, support the analysis.

关键词：

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：