检索结果-内蒙古大学图书馆

arXiv 2022年

作者： Llopis, Hermes Sampedro Jeong, Cheol-Ho Engsig-Karup, Allan P. Acoustic Technology Group Department of Electrical and Photonics Engineering Technical University of Denmark Kongens Lyngby Denmark Rambøll Denmark Copenhagen Denmark Scientific Computing Section Department of Applied Mathematics and Computer Science Technical University of Denmark Kongens Lyngby Denmark

Quick simulations for iterative evaluations of multi-design variables and boundary conditions are essential to find the optimal acoustic conditions in building design. We propose to use the reduced basis method (RBM) for realistic room acoustic scenarios where the surfaces have inhomogeneous acoustic properties, which enables quick evaluations of changing absorption materials for different surfaces in room acoustic simulations. The RBM has shown its benefit to speed up room acoustic simulations by three orders of magnitude for uniform boundary conditions. This study investigates the RBM with two main focuses, 1) various source positions in diverse geometries, e.g., square, rectangular, L-shaped, and disproportionate room. 2) Inhomogeneous surface absorption in 2D and 3D by parameterizing numerous acoustic parameters of surfaces, e.g., the thickness of a porous material, cavity depth, switching between a frequency independent (e.g., hard surface) and frequency dependent boundary condition. Results of numerical experiments show speedups of more than two orders of magnitude compared to a high fidelity numerical solver in a 3D case where reverberation time varies within one just noticeable difference in all the frequency octave bands. © 2022, CC BY.

关键词： Boundary conditions

来源：评论

学校读者我要写书评

暂无评论

Blocking Delaunay Triangulations from the Exterior

arXiv

引用

arXiv 2022年

作者： Aichholzer, Oswin Hackl, Thomas Löffler, Maarten Pilz, Alexander Parada, Irene Scheucher, Manfred Vogtenhuber, Birgit Institute of Software Technology Graz University of Technology Austria Department of Information and Computing Sciences Utrecht University Netherlands Department of Applied Mathematics and Computer Science Technical University of Denmark Denmark Institut für Mathematik Technische Universität Berlin Germany

Given two distinct point sets P and Q in the plane, we say that Q blocks P if no two points of P are adjacent in any Delaunay triangulation of P ∪ Q. Aichholzer et al. (2013) showed that any set P of n points in general position can be blocked by (formula presented) points and that every set P of n points in convex position can be blocked by (formula presented) points. Moreover, they conjectured that, if P is in convex position, n blocking points are sufficient and necessary. The necessity was recently shown by Biniaz (2021) who proved that every point set in general position requires n blocking points. Here we investigate the variant, where blocking points can only lie outside of the convex hull of the given point set. We show that (formula presented) − O(1) such exterior-blocking points are sometimes necessary, even if the given point set is in convex position. As a consequence we obtain that, if the conjecture of Aichholzer et al. for the original setting was true, then minimal blocking sets of some point configurations P would have to contain points inside of the convex hull of P. Copyright © 2022, The Authors. All rights reserved.

关键词： Triangulation

来源：评论

学校读者我要写书评

暂无评论

Development, Implementation and Evaluation of Digital Games for Teenage Reproductive Health Education in Schools

SSRN

引用

SSRN 2022年

作者： Bada, Joseph Kizito Ayikoru, Maureen Asianzu, Elizabeth Bonface, Abima Miiro Kafuko, Maria Nakawoya, Fatuma Department of Computer Science and Engineering Makerere University Business School Kampala Uganda Depart of Applied Computing and Information Technology Makerere University Business School Kampala Uganda Department of Strategy International Business and Entrepreneurship Royal Holloway University of London United Kingdom

The aim of this study was to develop and to test the use of digital games for teenage reproductive health education in secondary schools in Uganda. The games were jointly designed by the researchers and teachers from the participating schools. These were tested and introduced for teaching and learning in 5 schools in north-western Uganda, followed by evaluation using a questionnaire survey after a period of seven weeks of classroom practice. The evaluation results indicate that social connection, dedication and game interface account for actual learning in the classroom with contribution of 33.5% from the adjusted r value in regression analysis. The data suggest that digital games are effective tools for teenage reproductive health education. The findings complement emerging research on harnessing the potential of digital games in solving global challenges in low-income countries. © 2022, The Authors. All rights reserved.

关键词： Regression analysis

来源：评论

学校读者我要写书评

暂无评论

A PCA-based fuzzy tensor evaluation model for multiple-criteria group decision making

引用

applied Soft computing 2023年 132卷

作者： Singh, Meenu Pant, Millie Kong, Lingping Alijani, Zahra Snášel, Václav Department of Applied Mathematics and Scientific Computing Indian Institute of Technology Roorkee Uttarakhand247667 India Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Roorkee Uttarakhand247667 India Faculty of Electrical Engineering and Computer Science VŠB-TU Ostrava Czech Republic Institute for Research and Applications of Fuzzy Modeling University of Ostrava 30. dubna 22 701 03 Ostrava Czech Republic

Multiple-criteria group decision-making (MCGDM) problems mainly consist of multiple factors and multiple Decision Makers (DMs) or Users, for which dimension extension is necessary when considering all the entries of DMs together. Tensor, a generalized form of a matrix, displays a multi-way array item, which is the most suitable and practical way to represent high-dimensional data without losing any information. In this paper, we first reduce the dimension through Principal Component Analysis (PCA), which helps consider the most-informative criteria. Then, we reintroduced the tensor as a fuzzy-form tensor for the MCGDM problem because of user information uncertainty. We choose Interval-valued Neutrosophic Fuzzy numbers (IVNFNs) as the basis for the tensor form because of their ability to distinguish between truth, indeterminacy, and falsity in the data. Lastly, a Generalized Interval-valued Neutrosophic Fuzzy Weighted Geometric (GIVNFWG) operator is defined. Moreover, a generalized framework for fuzzy-form tensors for high-dimensional MCGDM problems is proposed. The feasibility and efficiency of this proposed process is illustrated for a real-world MCGDM problem of ranking the most efficient Third Party Reverse Logistics Partners (3PRLPs), i.e., recycled fiber-based paper mills for the packaging industry. The obtained results are according to the experts and are validated using sensitivity analysis. This analysis facilitates in assessing the impact on the overall ranking performance of 3PRLPs by considering various combinations of environment and technological sub-criteria. © 2022 Elsevier B.V.

关键词： Principal component analysis

来源：评论

学校读者我要写书评

暂无评论

AI-based big data analytics model for medical applications

引用

Measurement: Sensors 2022年 24卷

作者： Lokesh, S. Chakraborty, Sudeshna Pulugu, Revathy Mittal, Sonam Pulugu, Dileep Muruganantham, R. Department of Computer Science and Engineering PSG Institute of Technology and Applied Research Coimbatore641062 India School of Computing Science and Engineering Galgotias University Uttar Pradesh Greater Noida203201 India Department of Computer Science and Engineering Narsimha Reddy Engineering College Kompally Telangana State Hyderabad500100 India Department of Information Technology B K Birla Institute of Engineering and Technology Rajasthan Pilani333031 India Department of Computer Science and Engineering Malla Reddy College of Engineering and Technology Kompally Telangana State Hyderabad500100 India Department of Information Technology TKR College of Engineering and Technology Meerpet Telangana Hyderabad500097 India

Today, people's daily lives depended on their mobile phones, facilitating many beneficial tasks. Mobile computing and cloud computing combine cloud-based mobile computing to improve their features and overcoming their disadvantages such as memory space, processor, and longer battery life. By combining the four "V's" of volume, variety, velocity and veracity, Big Data analytics tools allow the utility to be extracted through knowledge. This paper explores the development of interconnected healthcare systems and the contribution of mobile cloud applications and big data analysis. With the implementation of cloud computing in the healthcare system, inspiration and development of interconnected health - software and care services are given. Big Data services for the healthcare sector on a cloudlet-based mobile cloud computing platform. Big data analysis methods, equipment, and solutions are examined. We have concluded the development of interconnected medical systems using Big Data and mobile cloud computing. © 2022 The Authors

关键词： Mobile computing

来源：评论

学校读者我要写书评

暂无评论

Multi-Source Fusion-Based Model with Customized Loss Function for Stock Selection

SSRN

引用

SSRN 2022年

作者： Snášel, Václav Velasquez Silva, Juan Domingo Pant, Millie Georgiou, Dimitrios Kong, Lingping Faculty of Electrical Engineering and Computer Science VŠB-TU Ostrava Czech Republic Departament of Industrial Engineering University of Chile Santiago Chile Chile Department of Applied Mathematics and Scientific Computing Indian Institute of Technology Uttarakhand Roorkee247667 India Faculty of Electrical Engineering and Computer Science National Technical University of Athens Greece

Selecting outstanding tech stocks for investment is challenging for any market investor. This challenge comes from the complexity caused by the dynamic and volatile nature of public policies and macroeconomic factors. Moreover, this task appears complex as an investor needs to decide from a diverse set of stocks, where performance ranking can conflict with another for different criteria, e.g., the contradiction between pure returns and risk performance. The paper proposes a novel model for stock evaluation and selection problems based on multi-source data fusion and decision-level fusion by multi-criteria decision-making (MCDM) techniques. The narrow criteria performance of a single source usually restricts a candidate's overall evaluation, especially on stock selection. Therefore, the proposed multi-source acquisition is promising to enhance reliability with reasonable data fusion, Furthermore, proper raw data processing and information integration by data fusion techniques are essential to obtain more accurate and concise data for decision-making support. Most important, the proposed multi-MCDM decision-fusion procedure can enhance the strength of a model to overcome the weakness of a single-performed model. Besides, we propose a customized loss function that improves the accuracy of time-series data prediction and increases the data's credibility. Meanwhile, the experiment shows that the proposed method reduces the prediction log error by 28\% in a best case on the experimental real-situation data and decreases the time-series data matching cost by 20\% between the prediction and observation. Finally, we conducted the real-world stock data selection by the proposed fusion-based model. The proposed model locates five potential preferred stocks, and the results are practical and effective, which are further justified through a detailed ablation study. Meanwhile, the time-series forecasting data got a lower error prediction than the baseline loss. © 2022, The Author

关键词： Data fusion

来源：评论

学校读者我要写书评

暂无评论

Patient Clustering to Improve Process Mining for Disease Trajectory Analysis Using Indonesia Health Insurance Dataset

Patient Clustering to Improve Process Mining for Disease Tra...

引用

International Conference on Artificial Intelligence and Big Data (ICAIBD)

作者： Angelina Prima Kurniati Gede Agung Ary Wisudiawan Guntur Prabawa Kusuma Siti Saadah Nurul Aida Osman Zulhelmy Wan Nur Syahidah Binti Wan Firdaus Hafidz School of Computing Telkom University Bandung Indonesia School of Applied Science Telkom University Bandung Indonesia Department of Computer & Information Sciences Universiti Teknologi Petronas Perak Malaysia Faculty of Ecnomoy and Business Universitas Islam Riau Riau Indonesia Yusoff Centre for Mathematical Sciences Universiti Malaysia Pahang Pahang Malaysia Health Policy and Management Universitas Gadjah Mada Yogyakarta Indonesia

ISBN: (数字)9798350385106

ISBN: (纸本)9798350385113

Process mining is a process-oriented data analytics approach that has been applied in many domains, including healthcare. Healthcare process mining aims to improve health care services through treatment enhancement and disease traj ectory analysis, among others. One of the common challenges in process mining for healthcare is the high variability in the healthcare data. This paper proposes clustering as a method to improve process mining for disease trajectory analysis. The proposed method is applied in a case study coming from a real dataset of Indonesia health insurance, consisting of records of patient treatments and diagnoses of Indonesia health insurance participants. Clustering was done to group patient profiles into clusters based on their similarities. This approach comes from a prior assumption that similar patients might have similar sequent of treatments. This research contributes to two directions: a technical contribution to improve process mining projects in handling high variability data using categorization and a research contribution to promote Indonesia health insurance dataset as a real-world healthcare dataset.

关键词： Process mining Data analysis Clustering methods Insurance Medical treatment Medical services Robustness

来源：评论

学校读者我要写书评

暂无评论

A novel transfer learning-based model for diagnosing malaria from parasitized and uninfected red blood cell images

引用

Decision Analytics Journal 2023年 9卷

作者： Qadri, Azam Mehmood Raza, Ali Eid, Fatma Abualigah, Laith Institute of Computer Science Khwaja Fareed University of Engineering and Information Technology Rahim Yar Khan 64200 Pakistan Technology Management Stony Brook University 11794 NY United States Department of Applied Artificial Intelligence College of Computing and Informatics Sungkyunkwan University Seoul 03063 South Korea Computer Science Department Al al-Bayt University Mafraq 25113 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos 13-5053 Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan MEU Research Unit Middle East University Amman 11831 Jordan Applied Science Research Center Applied science private university Amman 11931 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang 11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya 27500 Malaysia

Malaria represents a potentially fatal communicable illness triggered by the Plasmodium parasite. This disease is transmitted to humans through the bites of Anopheles mosquitoes that carry the infection. This disease has significant and devastating consequences on the health systems of fragile countries, particularly in sub-Saharan Africa. Malaria affects red blood cells by invading and replicating within them, destroying them, and releasing toxic byproducts into the bloodstream. The parasite's ability to stick and modify the surface of red blood cells can cause them to become sticky, obstructing blood flow in vital organs such as the brain and spleen. Therefore, efficient approaches for the early detection of malaria are critical to saving patients’ lives. The main aim of this study is to develop an efficient model for early malaria diagnosis. We used malaria images based on parasitized and uninfected red blood cells for the study experiments. We applied neural network-based approaches such as Neural Search Architecture Network (NASNet) and compared its performance with machine learning techniques. Moreover, we proposed a novel NNR (NASNet Random forest) method for feature engineering. The proposed NNR approach first extracts spatial features from input malaria images, then class prediction probability features are extracted from these spatial features. The feature set obtained from the data extraction trains machine learning models. Our comprehensive experiments show that the support vector machine outperformed state-of-the-art models, achieving a high-performance score of 99% and having an inference time near 0.025 s. We validated the performance using k-fold cross-validation and optimized the hyperparameters through tuning. Our proposed research has improved the early diagnosis of malaria and can assist medical specialists in reducing the mortality rate. © 2023 The Author(s)

关键词： Deep learning Machine learning Malaria diagnosis Neural search architecture network Red blood cells Transfer learning

来源：评论

学校读者我要写书评

暂无评论

A Scalable Power Splitter using High Sheet Polysilicon Resistor Suitable for RFIC Application

A Scalable Power Splitter using High Sheet Polysilicon Resis...

引用

2022 International Conference on Intelligent Technology, System and Service for Internet of Everything, ITSS-IoE 2022

作者： Uddin, Jasim Shakib, M.N. Islam, Syed Zahurul Hasan, Md. Mahadi Ullah, Habib Rahman, Md Arafatur Cardiff Metropolitan University Cardiff School of Technologies Department of Applied Computing and Engineering Wales CardiffCF5 2YB United Kingdom Green University of Bangladesh Faculty of Science and Engineering Dhaka1207 Bangladesh Universiti Tun Hussein Onn Malaysia Power Integration System Faculty of Electrical and Electronic Engineering Johor Batu Pahat Parit Raja86400 Malaysia Asian University of Bangladesh Department of Computer Science and Engineering Dhaka1341 Bangladesh University of Malaya Faculty of Engineering Department of Electrical Engineering Kuala Lumpur50603 Malaysia University of Wolverhampton Faculty of Science and Engineering WV1 1LY United Kingdom

ISBN: (数字)9781665459983

ISBN: (纸本)9781665459983

Integrated circuits employing CMOS RF technology are increasingly common and widely deployed. There are currently a wide range of materials available for use in the process that are optimised for RF noise, matching characteristics, and improved resistivity. In this paper, the highly resistive passive resistor component is used in the power splitter circuits, where a high sheet polysilicon resistor is the best choice. The high sheet polysilicon resistor and its extracted parasitic capacitance are shown to be the best match among the various resistors mentioned in the literature. Power splitter circuits use these resistor characteristics to get the best possible results, such as low return and insertion losses and good isolation. In order to get the desired resistor values for the power splitter's individual components, twelve parallel high sheet poly resistors are employed. A total of six electronic componentsâtwo metal finger capacitors and four lumped element spiral inductorsâare employed. Power splitter and probe pad components from the layout are on display. The correctness of the layout is confirmed by simulations run before to and after layout. The design schematic, pre-layout and post layout simulation results confirms that high sheet polysilicon resistors are the best selected potential candidate for RFIC application. © 2022 IEEE.

关键词： Resistors

来源：评论

学校读者我要写书评

暂无评论

Quantum machine learning on near-term quantum devices: Current state of supervised and unsupervised techniques for real-world applications

引用

Physical Review applied 2024年第6期21卷 067001-067001页

作者： Yaswitha Gujju Atsushi Matsuo Rudy Raymond Department of Computer Science University of Tokyo 4-6-1 Shirokanedai Tokyo Minato-ku 108-8639 Japan IBM Quantum IBM Research - Tokyo 19-21 Nihonbashi Hakozaki-cho Chuo-ku Tokyo 103-8510 Japan Global Technology and Applied Research J.P. Morgan Chase & Co. New York United States Quantum Computing Center Keio University 3-14-1 Hiyoshi Kohoku-ku Kanagawa Yokohama 223-8522 Japan

The past decade has witnessed significant advancements in quantum hardware, encompassing improvements in speed, qubit quantity, and quantum volume—a metric defining the maximum size of a quantum circuit effectively implementable on near-term quantum devices. This progress has led to a surge in quantum machine learning (QML) applications on real hardware, aiming to achieve quantum advantage over classical approaches. This survey focuses on selected supervised and unsupervised learning applications executed on quantum hardware, specifically tailored for real-world scenarios. The exploration includes a thorough analysis of current QML implementation limitations on quantum hardware, covering techniques like encoding, ansatz structure, error mitigation, and gradient methods to address these challenges. Furthermore, the survey evaluates the performance of QML implementations in comparison to classical counterparts. In conclusion, we discuss existing bottlenecks related to applying QML on real quantum devices and propose potential solutions to overcome these challenges in the future.

关键词： Physics & society Quantum algorithms & computation Quantum information processing Machine learning

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：