检索结果-内蒙古大学图书馆

An intelligent fuzzy-induced recommender system for cloud-based cultural communities

International Journal of Web Based Communities 2019年第3期15卷 271-288页

作者： Ravi, Logesh Devarajan, Malathi Jeon, Gwanggil Bayat, Oguz Subramaniyaswamy, V. Department of Computer Science and Engineering Sri Ramachandra Faculty of Engineering and Technology Sri Ramachandra Institute of Higher Education and Research Chennai Tamilnadu India School of Computing SASTRA Deemed University Thanjavur India Department of Embedded Systems Engineering Incheon National University Incheon Korea Republic of School of Electronic Engineering Xidian University Xi'an China Department of Electrical Engineering Altinbas University Istanbul Turkey

The rapid development of communication technologies and web-based services generate a large amount of information. In recent years, recommender systems (RS) emerge as an effective mechanism to tackle the information overloading problems. By exploiting the cloud computing paradigm, RS discovers interesting new cultural items based on user preferences and interests. Recent investigations on RS reveal that employing social network data can yield enhanced personalised recommendations with better prediction accuracy. Since users tend to visit only conventional monuments, and many charming cultural items are hidden from them due to lack of awareness about the cultural sites. This article proposes a personalised recommendation model in the field of cultural heritage (CH) with the help of the cloud computing environment. The experimental results obtained demonstrate the improved performance of developed RS in the area of cultural heritage tourism services. Copyright © 2019 Inderscience Enterprises Ltd.

关键词： Recommender systems

来源：评论

学校读者我要写书评

暂无评论

Noise Estimation and Type Identification in Natural Scene and Medical Images using Deep Learning Approaches

引用

Contrast Media & Molecular Imaging 2023年第1期2023卷

作者： G. Kavitha Chetana Prakash Majid Alhomrani N. Pradeep Abdulhakeem S. Alamri Piyush Kumar Pareek Musah Alhassan Department of Computer Science and Engineering Bapuji Institute of Engineering and Technology Davangere 577004 Karnataka Indiabietdvg.edu Department of Computer Science and Engineering University BDT College of Engineering Davangere 577004 Karnataka *** Department of Clinical Laboratories Sciences The Faculty of Applied Medical Sciences Taif University Taif Saudi Arabiatu.edu.sa Centre of Biomedical Sciences Research (CBSR) Deanship of Scientific Research Taif University Taif Saudi Arabiatu.edu.sa Department of Computer Science and Engineering and Head of IPR Cell Nitte Meenakshi Institute of Technology Bengaluru Indianmit.ac.in University for Development Studies Tamale Ghanauds.edu.gh

The image enhancement for the natural images is the vast field where the quality of the images degrades based on the capturing and processing methods employed by the capturing devices. Based on noise type and estimation of noise, filter need to be adopted for enhancing the quality of the image. In the same manner, the medical field also needs some filtering mechanism to reduce the noise and detection of the disease based on the clarity of the image captured; in accordance with it, the preprocessing steps play a vital role to reduce the burden on the radiologist to make the decision on presence of disease. Based on the estimated noise and its type, the filters are selected to delete the unwanted signals from the image. Hence, identifying noise types and denoising play an important role in image analysis. The proposed framework addresses the noise estimation and filtering process to obtain the enhanced images. This paper estimates and detects the noise types, namely Gaussian, motion artifacts, Poisson, salt-andpepper, and speckle noises. Noise is estimated by using discrete wavelet transformation (DWT). This separates the image into quadruple sub-bands. Noise and HH sub-band are high-frequency components. HH sub-band also has vertical edges. These vertical edges are removed by performing Hadamard operation on downsampled Sobel edge-detected image and HH sub-band. Using HH sub-band after removing vertical edges is considered for estimating the noise. The Rician energy equation is used to estimate the noise. This is given as input for Artificial Neural Network to improve the estimated noise level. For identifying noise type, CNN is used. After removing vertical edges, the HH sub-band is given to the CNN model for classification. The classification accuracy results of identifying noise type are 100% on natural images and 96.3% on medical images.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A multi-hazard safety evaluation framework for a submerged bridge using machine learning model 13

A multi-hazard safety evaluation framework for a submerged b...

引用

13th International Conference on Applications of Statistics and Probability in Civil engineering, ICASP 2019

作者： Liao, Kuo-Wei Hoang, Nhat-Duc Chien, Fu-Sheng Department of Bioenvironmental Systems Engineering National Taiwan University 1 Section 4 Roosevelt Road Taipei Taiwan Faculty of Civil Engineering Institute of Research and Development Duy Tan University Viet Nam Department of Civil and Construction Engineering Taiwan

This study proposes a submerged bridge safety evaluation process against seismic and flood hazards. Due to the uncertainties in the scours, seismic hazard, and structural performance for a given seismic excitation are inevitable, reliability analysis is adopted. A machine-learning based scour risk curve, which is established by the multivariate adaptive regression splines (MARS) incorporated with firefly algorithm (FA), is built to reflect the flood hazard. The seismic hazard is measured using a code-based probabilistic seismic hazard curve. A series of nonlinear time-history analyses are performed to determine the structural performance under different peak-ground-acceleration values. Displacement ductility is used to measure the bridge performance under attacks of both hazards. The influence of the immersed water depth on a bridge's performance is investigated. A case study, in which the nonlinear behaviors in concrete (including core and cover areas), steel bar and soil are included in a bridge model, is conducted to illustrate the proposed methodology and the structural performances with added mass are investigated to show the submerged water effect. According to the results obtained, highly variability of seismic performances is observed and it is important to include the immersed water depth to capture the seismic capacity of a bridge if the submerged bridge depth is great. © 13th International Conference on Applications of Statistics and Probability in Civil engineering, ICASP 2019. All rights reserved.

关键词： Scour

来源：评论

学校读者我要写书评

暂无评论

MONAI: An open-source framework for deep learning in healthcare

arXiv

引用

arXiv 2022年

作者： Cardoso, M. Jorge Li, Wenqi Brown, Richard Ma, Nic Kerfoot, Eric Wang, Yiheng Murrey, Benjamin Myronenko, Andriy Zhao, Can Yang, Dong Nath, Vishwesh He, Yufan Xu, Ziyue Hatamizadeh, Ali Zhu, Wentao Liu, Yun Zheng, Mingxin Tang, Yucheng Yang, Isaac Zephyr, Michael Hashemian, Behrooz Alle, Sachidanand Darestani, Mohammad Zalbagi Budd, Charlie Modat, Marc Vercauteren, Tom Wang, Guotai Li, Yiwen Hu, Yipeng Fu, Yunguan Gorman, Benjamin Johnson, Hans Genereaux, Brad Erdal, Barbaros S. Gupta, Vikash Diaz-Pinto, Andres Dourson, Andre Maier-Hein, Lena Jaeger, Paul F. Baumgartner, Michael Kalpathy-Cramer, Jayashree Flores, Mona Kirby, Justin Cooper, Lee A.D. Roth, Holger R. Xu, Daguang Bericat, David Floca, Ralf Zhou, S. Kevin Shuaib, Haris Farahani, Keyvan Maier-Hein, Klaus H. Aylward, Stephen Dogra, Prerna Ourselin, Sebastien Feng, Andrew School of Biomedical Engineering & Imaging Sciences King’s College London London United Kingdom NVIDIA Corporation Santa Clara and Bethesda United States School of Electrical and Computer Engineering Rice University Houston United States Department of Engineering Science University of Oxford Oxford United Kingdom Department of Medical Physics and Biomedical Engineering University College London London United Kingdom Department of BioAI InstaDeep Ltd London United Kingdom Department of Electrical & Computer Engineering University of Iowa Iowa City United States Mayo Clinic Jacksonville United States Mars Incorporated United States Div. Intelligent Medical Systems German Cancer Research Center Heidelberg Germany Division of Medical Image Computing German Cancer Research Center Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany School of Biomedical Engineering Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou China Department of Medical Physics Guy’s & St Thomas’ NHS Foundation Trust London United Kingdom National Cancer Institute Bethesda United States Kitware Inc. Clifton Park United States Department of Ophthalmology University of Colorado Aurora United States Frederick National Laboratory for Cancer Research Frederick United States Department of Pathology Northwestern University Chicago United States

Artificial Intelligence (AI) is having a tremendous impact across most areas of science. Applications of AI in healthcare have the potential to improve our ability to detect, diagnose, prognose, and intervene on human disease. For AI models to be used clinically, they need to be made safe, reproducible and robust, and the underlying software framework must be aware of the particularities (e.g. geometry, physiology, physics) of medical data being processed. This work introduces MONAI, a freely available, community-supported, and consortium-led PyTorch-based framework for deep learning in healthcare. MONAI extends PyTorch to support medical data, with a particular focus on imaging, and provide purpose-specific AI model architectures, transformations and utilities that streamline the development and deployment of medical AI models. MONAI follows best practices for software-development, providing an easy-to-use, robust, well-documented, and well-tested software framework. MONAI preserves the simple, additive, and compositional approach of its underlying PyTorch libraries. MONAI is being used by and receiving contributions from research, clinical and industrial teams from around the world, who are pursuing applications spanning nearly every aspect of healthcare. © 2022, CC BY.

关键词： Clinical research

来源：评论

学校读者我要写书评

暂无评论

An ensemble machine learning model for the prediction of danger zones: Towards a global counter-terrorism

引用

Soft Computing Letters 2021年 3卷

作者： Olabanjo, Olusola A. Aribisala, Benjamin S. Mazzara, Manuel Wusu, Ashiribo S. Department of Computer Science Lagos State University Lagos Nigeria Department of Mathematics Morgan State University MD United States Brain Research Imaging Centre University of Edinburgh Edinburgh United Kingdom Institute of Software Development and Engineering Innopolis University Innopolis Russia Department of Mathematics Lagos State University Lagos Nigeria

Terrorism can be described as the use of violence against persons or properties to intimidate or coerce a government or its citizens to some certain political or social objectives. It is a global problem which has led to loss of lives and properties and known to have negative impacts on tourism and global economy. Terrorism has also been associated with high level of insecurity and most nations of the world are interested in any research efforts that can reduce its menace. Most of the research efforts on terrorism have focused on measures to fight terrorism or how to reduce the activities of terrorists but there are limited efforts on terrorism prediction. The aim of this work is to develop an ensemble machine learning model which combines Support Vector Machine and K-Nearest Neighbor for prediction of continents susceptible to terrorism. Data was obtained from Global Terrorism Database and data preprocessing included data cleaning and dimensionality reduction. Two feature selection techniques, Chi-squared, Information Gain and a hybrid of both were applied to the dataset before modeling. Ensemble machine learning models were then constructed and applied on the selected features. Chi-squared, Information Gain and the hybrid-based features produced an accuracy of 94.17%, 97.34% and 97.81% respectively at predicting danger zones with respective sensitivity scores of 82.3%, 88.7% and 92.2% and specificity scores of 98%, 90.5% and 99.67% respectively. These imply that the hybrid-based selected features produced the best results among the feature selection techniques at predicting terrorism locations. Our results show that ensemble machine learning model can accurately predict terrorism locations. © 2021 The Author(s)

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Combined effects of host genetics and diet on human gut microbiota and incident disease in a single population cohort (vol 54, pg 134, 2022)

引用

NATURE GENETICS 2024年第3期56卷 554-554页

作者： Qin, Youwen Havulinna, Aki S. Liu, Yang Jousilahti, Pekka Ritchie, Scott C. Tokolyi, Alex Sanders, Jon G. Valsta, Liisa Brozynska, Marta Zhu, Qiyun Tripathi, Anupriya Vazquez-Baeza, Yoshiki Loomba, Rohit Cheng, Susan Jain, Mohit Niiranen, Teemu Lahti, Leo Knight, Rob Salomaa, Veikko Inouye, Michael Meric, Guillaume Cambridge Baker Systems Genomics Initiative Baker Heart and Diabetes Institute Melbourne Victoria Australia School of BioSciences The University of Melbourne Melbourne Victoria Australia Department of Clinical Pathology Melbourne Medical School The University of Melbourne Melbourne Victoria Australia Cambridge Baker Systems Genomics Initiative Department of Public Health and Primary Care University of Cambridge Cambridge UK British Heart Foundation Centre of Research Excellence University of Cambridge Cambridge UK British Heart Foundation Cardiovascular Epidemiology Unit Department of Public Health and Primary Care University of Cambridge Cambridge UK Health Data Research UK Cambridge Wellcome Genome Campus & University of Cambridge Cambridge UK The Alan Turing Institute London UK Department of Infectious Diseases Central Clinical School Monash University Melbourne Victoria Australia Department of Public Health and Welfare Finnish Institute for Health and Welfare Helsinki Finland Institute for Molecular Medicine Finland FIMM-HiLIFE Helsinki Finland Department of Medicine Turku University Hospital and University of Turku Turku Finland Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK Department of Ecology and Evolutionary Biology Cornell University Ithaca NY USA Cornell Institute for Host-Microbe Interaction and Disease Cornell University Ithaca NY USA Department of Pediatrics School of Medicine University of California San Diego La Jolla CA USA Division of Biological Sciences University of California San Diego La Jolla CA USA Center for Microbiome Innovation University of California San Diego La Jolla CA USA Department of Computer Science & Engineering Jacobs School of Engineering University of California San Diego La Jolla CA USA NAFLD Research Center Department of Medicine University of California San Diego La Jolla CA USA Smidt Heart Institute Cedars-Sinai Medical Center Los Angeles CA USA Department of Computing Univer

来源：评论

学校读者我要写书评

暂无评论

A systematic review of artificial intelligence applications in education: Emerging trends and challenges

引用

Decision Analytics Journal 2025年 15卷

作者： Matos, Tomás Santos, Walter Zdravevski, Eftim Coelho, Paulo Jorge Pires, Ivan Miguel Madeira, Filipe Escola Superior de Gestão e Tecnologia Polytechnic University of Santarém 2001-904 Portugal Escola Superior de Tecnologia e Gestão de Águeda Universidade de Aveiro Águeda Portugal Faculty of Computer Science and Engineering University Ss Cyril and Methodius Skopje 1000 North Macedonia School of Technology and Management Polytechnic University of Leiria Leiria Portugal Institute for Systems Engineering and Computers at Coimbra (INESC Coimbra) Coimbra Portugal Instituto de Telecomunicações Escola Superior de Tecnologia e Gestão de Águeda Universidade de Aveiro Águeda Portugal Digital Literacy and Social Inclusion Research Hub from CIAC Polytechnic University of Santarém 2001-904 Portugal

The academic world is becoming increasingly interested in the applications of Artificial Intelligence technology in education. A systematic review examines AI applications in education, focusing on their effectiveness, challenges, and implications. A comprehensive analysis of studies published between 2011 and 2024 encompassed 45 research articles from major databases, such as PubMed Central, IEEE Xplore, Elsevier, Springer, MDPI, ACM, and PMC. The findings highlight the predominant use of generative AI tools like ChatGPT (30%), followed by other advanced technologies, such as GPT-4, machine learning, and virtual reality. research across global regions, particularly in Canada (18%), the United States (12%), and China (8%), highlights the multifaceted applications of AI in enhancing personalized learning, fostering critical thinking, and supporting professional education. Tools such as ChatGPT have demonstrated strong performance in theoretical knowledge delivery and medical education, while augmented and virtual reality excels in practical skill development. Despite these advances, challenges such as data privacy concerns, algorithmic bias, and the need for specialized educator training remain critical. © 2025 The Author(s)

关键词： Adaptive learning Artificial intelligence ChatGPT Educational technology Machine learning Systematic review

来源：评论

学校读者我要写书评

暂无评论

New Approach to Improving the Quality for Images Processed with Fractal Compression

New Approach to Improving the Quality for Images Processed w...

引用

Digital Signal Processing and its Applications (DSPA), International Conference on

作者： Alexander V. Dvorkovich Nikolay B. Novinsky Do Ngoc Diep Multimedia Systems and Technology Lab Moscow Institute of Physics and Technology National Research University Moscow Russian Federation Phystech-School of Radio Engineering and Computer Science Moscow Institute of Physics and Technology National Research University Moscow Russian Federation

ISBN: (数字)9781728175287

ISBN: (纸本)9781728175294

A new method for improvement of images compressed using fractal compression method is under investigation. The experiments had show that the method is principally functional. The improvement of restored images (in terms of PSNR) is relatively small but the method is computationally simple. The effect of its usage is waiting to be greater for large images and complex images.

关键词： Bridges Image coding Fractals Image restoration Standards Physics Distortion

来源：评论

学校读者我要写书评

暂无评论

Smoothed Least-Laxity-First Algorithm for EV Charging

arXiv

引用

arXiv 2021年

作者： Chen, Niangjun Kurniawan, Christian Nakahira, Yorie Chen, Lijun Low, Steven H. the Department of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore the Institute for High Performance Computing Agency for Science Technology and Research Singapore138632 Singapore the Department of Electrical and Computer Engineering Carnegie Mellon University PA15213 United States the Department of Computer Science University of Colorado BoulderCO80309 United States the Department of Computing and Mathematical Sciences California Institute of Technology CA91125 United States

Adaptive charging can charge electric vehicles (EVs) at scale cost effectively, despite the uncertainty in EV arrivals. We formulate adaptive EV charging as a feasibility problem that meets all EVs’ energy demands before their deadlines while satisfying constraints in charging rate and total charging power. We propose an online algorithm, smoothed least-laxity-first (sLLF), that decides the current charging rates without the knowledge of future arrivals and demands. We characterize the performance of the sLLF algorithm analytically and numerically. Numerical experiments with real-world data show that it has a significantly higher rate of feasible EV charging than several other existing EV charging algorithms. Resource augmentation framework is employed to assess the feasibility condition of the algorithm. The assessment shows that the sLLF algorithm achieves perfect feasibility with only a 0.07 increase in resources. © 2021, CC BY.

关键词： Scheduling

来源：评论

学校读者我要写书评

暂无评论

Electromagnetic Microwave Absorption Performances of Aminated Carbon Fibers

Electromagnetic Microwave Absorption Performances of Aminate...

引用

Ukrainian Microwave Week (UkrMW), IEEE

作者： Liudmyla M. Grishchenko Vladyslav A. Moiseienko Volodymyr Yu. Malyshev Igor P. Matushko Vasyl V. Kuryliuk Anna V. Vakaliuk Oleksandr V. Mischanchuk Olga Yu. Boldyrieva Vladyslav V. Lisnyak Vitaliy E. Diyuk Faculty of Radiophysics Electronics and Computer Systems Taras Shevchenko National University of Kyiv Kyiv Ukraine Independent Research & Development Laboratory “200k Electronics” Kyiv Ukraine Institute of High Technologies Taras Shevchenko National University of Kyiv Kyiv Ukraine Department of physical chemistry Chemical Faculty Taras Shevchenko National University of Kyiv Kyiv Ukraine Faculty of Physics Taras Shevchenko National University of Kyiv Kyiv Ukraine Chuiko Institute of Surface Chemistry the NAS of Ukraine Kyiv Ukraine Department of analytical chemistry Chemical Faculty Taras Shevchenko National University of Kyiv Kyiv Ukraine

ISBN: (纸本)9798350331530

Here we reported the effect of the amination of the brominated polyacrylonitrile carbon fibers (PAN CFs) on the efficiency of absorption of electromagnetic radiation studied for the X- and Ka-bands. The fiber modification was carried out by bromination with bromine liquid followed by amination with one of the amines: ethylenediamine (En), sulfolanylethylenediamine (SuEn), diethylamine (Et2N), monoethanolamine (MEA), and piperazine (Py). The amination caused the replacement of attached bromine groups with amino groups. The thermal desorption properties of the prepared PAN CFs were investigated using TGA and TPD MS methods. Thermal desorption of the products of amino groups decomposition from the surface of PAN CFs ranges from 140 to 415 °C with maxima at 240–258 °C depending on the modifying amine and the surface concentration of the amino groups. For the X-band, the type of amine used is not of significant importance, and the value of the absorption for electromagnetic radiation does not exceed −18 dB. In the Ka range, the brominated PAN CFs modified with Py, En, and SuEn reduce the electromagnetic radiation by about −19 dB, −23 dB, and −28 dB, correspondingly. From all the above, it is clear that the brominated PAN CFs modified with amines have a perspective for use as a protective material against the harmful effects of electromagnetic radiation on biological tissues in the Ka-band frequency range. By choice of amine modifier, the absorption magnitude can be regulated when forming a surface protective-and-absorptive layer.

关键词： Bromine Radiation effects Liquids Absorption Surface waves Electromagnetic scattering Thermal decomposition

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：