检索结果-内蒙古大学图书馆

作者： Zheng, Xiao Liu, Houkai Wang, Xiujun Wu, Xuangou Yu, Feng School of Computer Science and Technology Anhui University of Technology Maanshan China Institute for Artificial Intelligence Hefei Comprehensive National Science Center Hefei China Anhui Engineering Laboratory for Intelligent Applications and Security of Industrial Internet Anhui University of Technology Anhui Maanshan China School of Computer Science and Engineering & School of Software Guangxi Normal University Guilin China Key Laboratory of Computer Network and Information Integration Southeast University Nanjing China

Today's internet applications generate massive temporal data anywhere and anytime. Although some disk-based temporal systems are currently available, they suffer from poor I/O performance, especially when applied in intelligent applications deployed in the cloud and edge environments. Therefore, how to process temporal operations with low latency and high throughput becomes a crucial problem for efficient data processing. This paper proposes Timo, a distributed in-memory temporal query and analytic model for big temporal data. Firstly, a space-efficient temporal index is proposed to support more efficiently query performance with less memory space than the state-of-art methods. Secondly, based on the temporal locality feature of temporal queries, Timo proposes a partitioner mechanism, which utilizes forward scan algorithm, to improve query throughput. Thirdly, some optimal strategies are proposed to improve the execution process of temporal query and analysis, which reduce intermediate result data size and improve the throughput much further. Lastly, we implement the Timo system on the Apache Spark platform, which extends Spark dataset API with temporal query and analysis API functions for users. Extensive experimental results show that the Timo system outperforms other Spark-based temporal systems in terms of both query latency and throughput. © 2021 John Wiley & Sons, Ltd.

关键词： Data handling

来源：评论

学校读者我要写书评

暂无评论

Genetic Algorithm-Based Feature Selection for Accurate Breast Cancer Classification

Genetic Algorithm-Based Feature Selection for Accurate Breas...

引用

2023 International Conference on IT and Industrial Technologies, ICIT 2023

作者： Shabbir, Attia Ali, Raja Hashim Shabbir, Muhammad Zeeshan Abideen, Zain Ul Khan, Talha Ali Ijaz, Ali Zeeshan Ali, Nisar Imad, Muhammad Bakar, Muhammad Abu Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany Department of Electrical Engineering Lahore Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain University of Regina Faculty of Electronic Systems Engineering Regina Canada

ISBN: (纸本)9798350315974

Breast cancer is a significant global healthcare challenge, particularly in developing and underdeveloped countries, with profound physical, emotional, and psychological consequences, including mortality. Timely diagnosis and accurate treatment are crucial in addressing this issue. We propose the utilization of a feature selection technique to identify the most relevant features from among all features for breast cancer diagnosis, and show that Genetic Algorithms are impressive for this task. The study compares the results of GA with no selection and an alternative method, Principle Component Analysis (PCA). Three machine learning models, all based on supervised learning with data split into training and test data, are employed for binary classification using the selected feature subset. The evaluation metrics employed encompass accuracy, precision, recall, and F1-score. Among the selected models, Random Forest demonstrates the most favorable outcomes, achieving an accuracy score of 0.96, precision score of 0.96, recall value of 0.98, and an F1-score of 0.97. These results underscore the effectiveness of GA in feature selection for breast cancer diagnosis. Consequently, the integration of Genetic Algorithms (GA) with Random Forest showcases the superior performance among the evaluated models. © 2023 IEEE.

关键词： Genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

The 2D Materials Roadmap

arXiv

引用

arXiv 2025年

作者： Ren, Wencai Bøggild, Peter Redwing, Joan Marie Novoselov, Kostya Sun, Luzhao Qi, Yue Jia, Kaicheng Liu, Zhongfan Burton, Oliver Alexander-Webber, Jack Hofmann, Stephan Cao, Yang Long, Yu Yang, Quan-Hong Li, Dan Choi, Soo Ho Kim, Ki Kang Lee, Young Hee Li, Mian Huang, Qing Gogotsi, Yury Clark, Nicholas Carl, Amy Gorbachev, Roman Olsen, Thomas Rosen, Johanna Thygesen, Kristian Sommer Efetov, Dmitri K. Jessen, Bjarke S. Yankowitz, Matthew Barrier, Julien Kumar, Roshan Krishna Koppens, Frank H.L. Deng, Hui Li, Xiaoqin Dai, Siyuan Basov, D.N. Wang, Xinran Das, Saptarshi Duan, Xiangfeng Yu, Zhihao Borsch, Markus Ferrari, Andrea C. Huber, Rupert Kira, Mackillo Xia, Fengnian Wang, Xiao Wu, Zhong-Shuai Feng, Xinliang Simon, Patrice Cheng, Hui-Ming Liu, Bilu Xie, Yi Jin, Wanqin Nair, Rahul Raveendran Xu, Yan Zhang, Qing Katiyar, Ajit K. Ahn, Jong-Hyun Aharonovich, Igor Hersam, Mark C. Roche, Stephan Hua, Qilin Shen, Guozhen Ren, Tianling Zhang, Hao-Bin Koo, Chong Min Koratkar, Nikhil Pellegrini, Vittorio Young, Robert J. Qu, Bill Lemme, Max Pollard, Andrew J. Shenyang National Laboratory for Materials Science Institute of Metal Research Chinese Academy of Sciences 72 Wenhua Road Shenyang110016 China Technical University of Denmark Denmark The Pennsylvania State University United States University of Manchester United Kingdom Institute for Functional Intelligent Materials National University of Singapore Singapore Beijing Graphene Institute China University of Cambridge United Kingdom Department of Chemical Engineering The University of Melbourne Victoria Australia Nanoyang Group Tianjin Key Laboratory of Advanced Carbon and Electrochemical Energy Storage School of Chemical Engineering and Technology Tianjin University Tianjin300072 China The Hong Kong University of Science and Technology Hong Kong Center for Integrated Nanostructure Physics Institute for Basic Science Suwon16419 Korea Republic of Sungkyunkwan University Suwon16419 Korea Republic of Zhejiang Key Laboratory of Data-Driven High-Safety Energy Materials and Applications Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences China Drexel University United States Linköping University Sweden Ludwig-Maximilians-Universität München Germany University of Washington United States ICFO The Institute of Photonic Sciences Castelldefels Barcelona08860 Spain University of Michigan United States University of Texas Austin United States Auburn University United States Columbia University United States State Key Laboratory of Catalysis Dalian Institute of Chemical Physics Chinese Academy of Sciences Dalian China University of California Los Angeles United States Suzhou Laboratory Suzhou China School of Integrated Circuit Science and Engineering Nanjing University of Posts and Telecommunications Nanjing210023 China Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI United States University of Regensburg Germany Department of Electrical and Computer Engineering Yale University New Have

Over the past two decades, 2D materials have rapidly evolved into a diverse and expanding family of material platforms. Many members of this materials class have demonstrated their potential to deliver transformative impact on fundamental research and technological applications across different fields. In this roadmap, we provide an overview of the key aspects of 2D material research and development, spanning synthesis, properties and commercial applications. We specifically present roadmaps for high impact 2D materials, including graphene and its derivatives, transition metal dichalcogenides, MXenes as well as their heterostructures and moiré systems. The discussions are organized into thematic sections covering emerging research areas (e.g., twisted electronics, moiré nano-optoelectronics, polaritronics, quantum photonics, and neuromorphic computing), breakthrough applications in key technologies (e.g., 2D transistors, energy storage, electrocatalysis, filtration and separation, thermal management, flexible electronics, sensing, electromagnetic interference shielding, and composites) and other important topics (computational discovery of novel materials, commercialization and standardization). This roadmap focuses on the current research landscape, future challenges and scientific and technological advances required to address, with the intent to provide useful references for promoting the development of 2D materials. © 2025, CC BY.

关键词： Integrated optoelectronics

来源：评论

学校读者我要写书评

暂无评论

Boron-doped microporous nano carbon as cathode material for high-performance Li-S batteries

引用

Nano Research 2017年第2期10卷 426-436页

作者： Feng Wu Ji Qian Weiping Wu Yusheng Ye Zhiguo Sun Bin Xu Xiaoguang Yang Yuhong Xu Jiatao Zhang Renjie Chen Beijing Key Laboratory of Environmental Science and Engineering School of Material Science and Engineering Beijing Institute ofTechnology Beijing 10008 I China Collaborative Innovation Center of Electric Vehicles in 8eijing Beijing 100081 China School of Computer Science Mathematics and Engineering City University of London Northampton Square London EC 1 V OHB UK State Key Laboratory of Chemical Resource Engineering Beijing Key Laboratory of Electrochemical Process and Technology for Materials 8eijing University of Chemical Technology 8eijing 100029 China Research and Advanced Engineering Ford Motor Company M148121 USA Electrified Powertrain Engineering Ford Motor Research and Engineering (Nanjing) Co. Ltd. Nanjing 211100 China Beijing Key Laboratory of Construction-Tailorable Advanced Functional Materials and Green Applications School of MaterialScience and Engineering 8eijing Institute of Technology Beijing 100081 China

In this study, a boron-doped microporous carbon （BMC）/sulfur nanocomposite is synthesized and applied as a novel cathode material for advanced Li-S batteries. The cell with this cathode exhibits an ultrahigh cycling stability and rate capability. After activation, a capacity of 749.5 mAh/g was obtained on the 54t＂ cycle at a discharge current of 3.2 A/g. After 500 cycles, capacity of 561.8 mAh/g remained （74.96% retention）, with only a very small average capacity decay of 0.056%. The excellent reversibility and stability of the novel sulfur cathode can be attributed to the ability of the boron-doped microporous carbon host to both physically confine polysulfides and chemically bind these species on the host surface. Theoretical calculations confirm that boron-doped carbon is capable of significantly stronger interactions with the polysulfide species than undoped carbon, most likely as a result of the lower electronegativity of boron. We believe that this doping strategy can be extended to other metal-air batteries and fuel cells, and that it has promising potential for many different applications.

关键词： boron-doping microporous carbon binding energy Li-S batteries

来源：评论

学校读者我要写书评

暂无评论

EEiF: Efficient Isolated Forest with e Branches for Anomaly Detection 24

EEiF: Efficient Isolated Forest with e Branches for Anomaly ...

引用

24th IEEE International Conference on Data Mining, ICDM 2024

作者： Zhang, Yifan Xiang, Haolong Zhang, Xuyun Xu, Xiaolong Fan, Wei Zhang, Qin Qi, Lianyong School of Software Nanjing University of Information Science and Technology China Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services China School of Computing Macquarie University Australia University of Oxford Uffield Department of Women's & Reproductive Health United Kingdom College of Computer Science and Software Engineering Shenzhen University China Faculty of Science and Engineering China

ISBN: (纸本)9798331506681

Anomaly detection is a popular research topic in Artificial Intelligence and has been widely applied in network security, financial fraud detection, and industrial equipment failure detection. Isolation forest based methods are the base algorithms to detect anomalies in these scenarios for their simplicity and efficiency, which has been further exploited with multi-folk trees and learning mechanisms to realize the optimal isolation forest for high detection accuracy. However, the optimal isolation forest is time-consuming with the learning mechanisms, resulting in the task failing of time-constrained applications. Moreover, the original optimal isolation forest fails to construct the optimal tree structure restricted by the time complexity. To address the above challenges, we propose an efficient anomaly detection method called EEIF, which realizes the real e-folk structure of the optimal isolation forest in our practical algorithm design. Specifically, we design a distribution that perfectly matches the e-branch theory to construct the optimal isolation forest. Then, we design an FR clustering scheme to achieve fast training of the isolation forest with learning to hash and provide related proofs of accuracy and efficiency. Besides, a parallel algorithm is integrated into our method to reduce prediction time. Finally, extensive experiments are conducted on a large amount of real-world datasets and the results demonstrate that our method significantly improves efficiency while ensuring effectiveness, compared with the state-of-the-art methods. © 2024 IEEE.

关键词： Anomaly Detection Efficient FR clustering Parallel Algorithm

来源：评论

学校读者我要写书评

暂无评论

Identifying COVID-19 through X-ray and CT scan images using Machine Learning 18

Identifying COVID-19 through X-ray and CT scan images using ...

引用

18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Shehzadi, Maham Arshad, Usama Abideen, Zainul Ali, Raja Hashim Khan, Ammad Aslam Ijaz, Ali Zeeshan BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany Virtual University of Pakistan Dept. of Biological Sciences Rawalpindi Pakistan National University of Computer & Emerging Sciences Dept. of Computer Science Islamabad Pakistan

ISBN: (纸本)9798350328172

In the current global scenario, marked by the COVID-19 pandemic, it has become imperative for healthcare systems around the globe to swiftly and accurately diagnose the disease. This is where cutting-edge approaches such as machine learning (ML) come into play, specifically, ML-based identification of COVID-19 from chest X-ray and CT-Scan imagery. Our research collective is devotedly working towards devising a robust, accurate ML model capable of deciphering these images to identify COVID-19 effectively. This involves the application of a variety of AI algorithms and vast data resources. Our work is grounded on a large, annotated image dataset that aids in training and refining the model. Our findings indicate that ML methodologies can discern COVID-19 from chest X-ray and CT scan images with a mean accuracy of 90%. Such a tool holds promise in aiding medical practitioners in prompt and precise patient diagnosis, thereby improving patient prognosis. The most notable contribution of our study lies in the creation of an ML model that can accurately detect COVID-19 in chest X-ray and CT-Scan images, potentially aiding in early disease detection and management. © 2023 IEEE.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

An auto code generator for stencil on SW26010 21

An auto code generator for stencil on SW26010

引用

21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Zhu, Xiaomin Zeng, Yunhui Wei, Yanjie Feng, Shengzhong Liu, Weiguo Balaji, Pavan Joint Engineering Research Center Health Big Data Intelligent Analysis Technology Center for High Performance Computing Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Shandong Computer Science Center National SuperComputer Center in Jinan Jinan China Sclool of Software Shandong University Jinan China Argonne National Laboratory United States

ISBN: (纸本)9781728120584

Stencil is a basic building block widely used in many HPC areas and applications. It generally dominates the time cost and is critical to the overall performance. Given that heterogeneous many-core is frequently adopted to build super computers, porting and optimizing stencil codes on modern accelerator-based architectures is important. Porting is non-trivial, and optimization is more difficult as it requires better understanding of the underlying architecture. As a result, auto tuning targeting on accelerators such as GPU becomes a hot research topic. In this paper, we focus on tuning stencil automatically on SW26010, which is a heterogeneous many-core CPU equipped in Sunway TaihuLight Supercomputer, and mainly work on 2D stencil variants code in ROMS (Regional Ocean Modeling System). The code generator can generate codes running on slave cores for 50+ different FORTRAN stencil-alike loops in ROMS in seconds, and optimization methods are integrated into the generator as well. The performance is better than the directive based method (OpenACC) and the hand-drafted code by a junior programmer, and it is comparable compared with the code written by a senior programmer with several years of programming experience. © 2019 IEEE.

关键词： Supercomputers

来源：评论

学校读者我要写书评

暂无评论

A Multiple Features Distance Preserving (MFDP) Model for Saliency Detection

A Multiple Features Distance Preserving (MFDP) Model for Sal...

引用

Proceedings of the Digital Image Computing: Technqiues and applications (DICTA)

作者： Dongyan Guo Jian Zhang Min Xu Xiangjian He Minxian Li Chunxia Zhao School of Computer Science and Engineering Nanjing University of Science and Technology China Advanced Analytics Institute & School of Software University of Technology Sydney Australia Center for Innovation in IT Services and Applications (iNEXT) University of Technology Sydney Australia

ISBN: (纸本)9781479954100

Playing a vital role, saliency has been widely applied for various image analysis tasks, such as content-aware image retargeting, image retrieval and object detection. It is generally accepted that saliency detection can benefit from the integration of multiple visual features. However, most of the existing literatures fuse multiple features at saliency map level without considering cross-feature information, i.e. generate a saliency map based on several maps computed from an individual feature. In this paper, we propose a Multiple Feature Distance Preserving (MFDP) model to seamlessly integrate multiple visual features through an alternative optimization process. Our method outperforms the state-of-the-arts methods on saliency detection. Saliency detected by our method is further cooperated with seam carving algorithm and significantly improves the performance on image retargeting.

关键词： Feature extraction Visualization Mathematical model Image color analysis Equations Educational institutions Computational modeling

来源：评论

学校读者我要写书评

暂无评论

Multiscale Bilateral Attention Fusion Network for Pansharpening

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5828-5843页

作者： Guo, Zhongyuan Li, Jiawei Lei, Jia Liu, Jinyuan Zhou, Shihua Wang, Bin Kasabov, Nikola K. Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian116622 China University of the Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Dalian University of Technology School of Mechanical Engineering Dalian116024 China Auckland University of Technology Knowledge Engineering and Discovery Research Institute Auckland1061 New Zealand University of Ulster Intelligent Systems Research Center LondonderryBT48 7JL United Kingdom Bulgarian Academy of Sciences IICT Sofia1000 Bulgaria

High-resolution multispectral (HRMS) images combine spatial and spectral information originating from panchromatic (PAN) and reduced-resolution multispectral (LRMS) images. Pansharpening performs well and is widely used to obtain HRMS images. However, most pansharpening approaches determine the ratio of PAN and LRMS images through direct interpolation, which may introduce artifacts and distort the color of the fused results. To address this issue, an unsupervised progressive pansharpening framework, MSBANet, is proposed, which adopts a multistage fusion strategy. Each stage contains an attention interactive extraction module (AIEM) and a multiscale bilateral fusion module (MBFM). The AIEM extracts spatial and spectral features from input images and captures the correlations between features. The MBFM can efficiently integrate information from the AIEM and improve MSBANet context awareness. We design a hybrid loss function that enhances the ability of the fusion network to store spectral and texture details. In qualitative and quantitative experimental studies on four datasets, MSBANet outperformed state-of-the-art pansharpening techniques. The code will be released. © 2024 IEEE.

关键词： Data mining

来源：评论

学校读者我要写书评

暂无评论

Towards Automatic Content Transfer between Web Pages 6

Towards Automatic Content Transfer between Web Pages

引用

6th International Conference on Digital Home, ICDH 2016

作者： Long, Yonghao Chen, Xiangping Rao, Yongsheng Shi, Xiaohong School of Data and Computer Science National Engineering Research Center of Digital Life Sun Yat-san University Guangzhou China Institute of Advanced Technology Sun Yat-san University Guangdong Province Engineering Technology Research Center for Mathematical Educational Software Guangzhou China School of Computer Science and Educational Software Guangzhou University School of Data and Computer Science Sun Yat-sen University Guangzhou China School of Data and Computer Science Sun Yat-san University Guangzhou China Research Institute Sun Yat-sen University in Shenzhen Shenzhen China

ISBN: (纸本)9781509044009

The Internet provides numerous web pages for learning and reusing. But reusing these pages requires manually modification and integration of the source codes, which is inefficient and complex. In this paper, we propose a method to do the content transfer between web pages. This method is based on the machine-learning, and it can use any web to be the templates. With transferring the contents from source to the reference, a web page is synthesized with the source's contents and reference's styles. The result can help inspiring the designers and reducing the developing cost. © 2016 IEEE.

关键词： Websites

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：