检索结果-内蒙古大学图书馆

11th International Conference on Information and Communication Technology, ICoICT 2023

作者： Hussain, Hussain Pahlevi, Rizka Reza Wardana, Aulia Arif Mugitama, Satria Akbar Telkom University School of Computing Bandung Indonesia Wroclaw University of Science and Technology Computer Science and System Engineering Poland

ISBN: (纸本)9798350321982

Managing camping tools is crucial to ensure the safety of campers and extend the life of the tools. Frequent Pattern-Growth (FP-Growth) Association Rule Algorithms have emerged as a powerful tool for managing assets effectively. This algorithm is used to identify frequent patterns in large datasets, making it an efficient method for discovering association rules to help organizations make informed decisions about their assets. In the context of asset management, FP-Growth Association Rule Algorithms can be used to identify patterns in asset usage, maintenance, and replacement. By analyzing historical data, the algorithm can identify which assets are most frequently used, which require the most maintenance, and which are most likely to fail. This paper proposes managing the assets of camping tools using FP-growth and radio frequency identification (RFID) cards for asset tags. The proposed management asset tools are built on an Android operating system smartphone. This research using 30%, 60%, and 80% minimum support. The experiment results show that the minimum support affects the time needed to build an association. A small minimum support requires a longer time, while a large minimum support requires a faster time. The confidence value is used to determine the relevance of the FP-Growth output results. The results of the tests show that the difference in minimum support affects the speed of building associations, and the confidence value is proportional to the minimum support used. This research can be used for other case studies, using equipment data other than equipment for outdoor activities. © 2023 IEEE.

关键词： Association rules

来源：评论

学校读者我要写书评

暂无评论

Image Enhancement via Associated Perturbation Removal and Texture Reconstruction Learning

引用

IEEE/CAA Journal of Automatica Sinica 2024年第11期11卷 2253-2269页

作者： Kui Jiang Ruoxi Wang Yi Xiao Junjun Jiang Xin Xu Tao Lu IEEE the School of Computer Science and Technology Harbin Institute of Technology Zhengzhou Research Institute Harbin Institute of Technology the School of Artificial Intelligence Jianghan University the School of Geodesy and Geomatics Wuhan University the Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology the School of Computer Science and Engineering and also with the Hubei Province Key Laboratory of Intelligent Robot Wuhan Institute of Technology

Degradation under challenging conditions such as rain, haze, and low light not only diminishes content visibility, but also results in additional degradation side effects, including detail occlusion and color distortion. However, current technologies have barely explored the correlation between perturbation removal and background restoration, consequently struggling to generate high-naturalness content in challenging scenarios. In this paper, we rethink the image enhancement task from the perspective of joint optimization: Perturbation removal and texture reconstruction. To this end, we advise an efficient yet effective image enhancement model, termed the perturbation-guided texture reconstruction network(PerTeRNet). It contains two subnetworks designed for the perturbation elimination and texture reconstruction tasks, respectively. To facilitate texture recovery,we develop a novel perturbation-guided texture enhancement module(PerTEM) to connect these two tasks, where informative background features are extracted from the input with the guidance of predicted perturbation priors. To alleviate the learning burden and computational cost, we suggest performing perturbation removal in a sub-space and exploiting super-resolution to infer high-frequency background details. Our PerTeRNet has demonstrated significant superiority over typical methods in both quantitative and qualitative measures, as evidenced by extensive experimental results on popular image enhancement and joint detection tasks. The source code is available at https://***/kuijiang94/PerTeRNet.

关键词： Association learning attention mechanism image enhancement perturbation modeling

来源：评论

学校读者我要写书评

暂无评论

RTGA: A Redundancy-free Accelerator for High-Performance Temporal Graph Neural Network Inference 24

RTGA: A Redundancy-free Accelerator for High-Performance Tem...

引用

61st ACM/IEEE Design Automation Conference, DAC 2024

作者： Yu, Hui Zhang, Yu Tan, Andong Lu, Chenze Zhao, Jin Liao, Xiaofei Jin, Hai Liu, Haikun National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China

ISBN: (纸本)9798400706011

Temporal Graph Neural Network (TGNN) has attracted much research attention because it can capture the dynamic nature of complex networks. However, existing solutions suffer from redundant computation overhead and excessive off-chip communications for TGNN inference because they often rely on redundant graph sampling and repeatedly fetching the features and vertex memory. This paper proposes a redundancy-free accelerator, RTGA, for high-performance TGNN inference. Specifically, RTGA proposes a redundancy-aware execution approach with temporal tree into a novel accelerator design to effectively eliminate unnecessary data processing for fewer redundant computations and off-chip communications and also designs a temporal-aware data caching method to improve data locality for TGNN. We have implemented and evaluated RTGA on a Xilinx Alveo U280 FPGA card. Compared with cutting-edge software solutions (i.e., TGN and TGL) and hardware solutions (i.e., BlockGNN and FlowGNN), RTGA improves the performance of TGNN inference by an average of 473.2x, 87.4x, 8.2x, and 6.9x and saves energy by 542.8x, 102.2x, 9.4x, and 8.3x, respectively. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Spatio-temporal data

来源：评论

学校读者我要写书评

暂无评论

User Interface Rendering Time in Android Applications: Revealing the Effects of Design Patterns 23

User Interface Rendering Time in Android Applications: Revea...

引用

8th International Conference on Sustainable Information engineering and Technology, SIET 2023

作者： Paramadani, Rizky Bagus Akbar, Muhammad Aminul Pinandito, Aryo Informatics Engineering Departement Faculty of Computer Science Universitas Brawijaya Malang Indonesia Information System Department Universitas Brawijaya Faculty of Computer Science Indonesia

ISBN: (纸本)9798400708503

The Google Play platform boasts a total of 2,597,819 applications. A pivotal gauge of an application's success rests on its practical utility in daily life, coupled with its demonstrated strong performance metrics. There are several performance metrics to evaluate the performance of an application, such as RAM usage, CPU utilization, and their impact on battery life. Additionally, it is crucial to consider the User Interface (UI) factor, which serves as the primary connection between users and applications. Beyond its role as a bridge between users and applications, the UI can also significantly affect the performance of an application, as rendering a UI often takes a considerable amount of time. Various approaches can be employed to enhance the rendering time of a UI, one of which involves the use of design patterns like Model-View-Controller (MVC) and Model-View-ViewModel (MVVM), which effectively separate the business logic from the UI layer. This research study compares the performance of these two design patterns in the context of UI rendering time. The findings of this study confirm that MVVM outperforms MVC significantly in rendering UI components more rapidly. This superiority is attributed to MVVM's inherent ability to isolate business logic, ultimately reducing rendering delays. Furthermore, MVVM's prominent data binding feature further strengthens its efficiency in terms of UI rendering time. © 2023 ACM.

关键词： User interfaces

来源：评论

学校读者我要写书评

暂无评论

Securing the IoT Cyber Environment: Enhancing Intrusion Anomaly Detection With Vision Transformers

引用

IEEE Access 2024年 12卷 82443-82468页

作者： Sana, Laraib Nazir, Muhammad Mohsin Yang, Jing Hussain, Lal Chen, Yen-Lin Ku, Chin Soon Alatiyyah, Mohammed Alateyah, Sulaiman Abdullah Por, Lip Yee Lahore College for Women University Department of Computer Science Punjab Lahore54000 Pakistan Universiti Malaya Department of Computer System and Technology Kuala Lumpur50603 Malaysia The University of Azad Jammu and Kashmir Department of Computer Science and IT Neelum Campus Azad Kashmir13230 Pakistan King Abdullah Campus The University of Azad Jammu and Kashmir Department of Computer Science and IT Azad Kashmir Muzaffarabad13100 Pakistan National Taipei University of Technology Department of Computer Science and Information Engineering Taipei106344 Taiwan Universiti Tunku Abdul Rahman Department of Computer Science Kampar31900 Malaysia Prince Sattam Bin Abdulaziz University College of Sciences and Humanities-Aflaj Department of Computer Science Al-Kharj16278 Saudi Arabia Qassim University College of Computer Department of Computer Engineering Buraydah52571 Saudi Arabia

The ever-expanding Internet of Things (IoT) landscape presents a double-edged sword. While it fosters interconnectedness, the vast amount of data generated by IoT devices creates a larger attack surface for cybercriminals. Intrusions in these environments can have severe consequences. To combat this growing threat, robust intrusion detection systems (IDS) are crucial. The data comprised by this attack is multivariate, highly complex, non-stationary, and nonlinear. To extract the complex patterns from this complex data, we require the most robust, optimized tools. Machine learning (ML) and deep learning (DL) have emerged as powerful tools for IDSs, offering high accuracy in detecting and preventing security breaches. This research delves into anomaly detection, a technique that identifies deviations from normal system behavior, potentially indicating attacks. Given the complexity of anomaly data, we explore methods to improve detection performance. This research investigates the design and evaluation of a novel IDS. We leverage and optimize supervised ML methods like tree-based Support Vector Machines (SVM), ensemble methods, and neural networks (NN) alongside the cutting-edge DL approach of long short-term memory (LSTM) and vision transformers (ViT). We optimized the hyperparameters of these algorithms using a robust Bayesian optimization approach. The implemented ML models achieved impressive training accuracy, with Random Forest and Ensemble Bagged Tree surpassing 99.90% of accuracy, an AUC of 1.00, an F1-score, and a balanced Matthews Correlation Coefficient (MCC) of 99.78%. While the initial deep learning LSTM model yielded an accuracy of 99.97%, the proposed ViT architecture significantly boosted performance with 100% of all metrics, along with a validation accuracy of 78.70% and perfect training accuracy. This study demonstrates the power of our new methods for detecting and stopping attacks on Internet of Things (IoT) networks. This improved detection offers

关键词： Intrusion detection

来源：评论

学校读者我要写书评

暂无评论

Fine classification of rice paddy using multitemporal compact polarimetric SAR C band data based on machine learning methods

引用

Frontiers of Earth science 2024年第1期18卷 30-43页

作者： Xianyu GUO Junjun YIN Kun LI Jian YANG Huimin ZOU Fukun YANG School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China Department of Electronic Engineering Tsinghua UniversityBeijing 100084China College of Global Change and Earth System Science Beijing Normal UniversityBeijing 100875China

Rice is an important food crop for human *** distinguishing different varieties and sowing methods of rice on a large scale can provide more accurate information for rice growth monitoring,yield estimation,and phenological monitoring,which has significance for the development of modern *** polarimetric(CP)synthetic aperture radar(SAR)provides multichannel information and shows great potential for rice monitoring and ***,the use of machine learning methods to build classification models is a controversial *** this paper,the advantages of CP SAR data,the powerful learning ability of machine learning,and the important factors of the rice growth cycle were taken into account to achieve high-precision and fine classification of rice ***,CP SAR data were simulated by using the seven temporal RADARSAT-2 C-band data ***,20-two CP SAR parameters were extracted from each of the seven temporal CP SAR data *** addition,we fully considered the change degree of CP SAR parameters on a time scale(ΔCP_(DoY)).Six machine learning methods were employed to carry out the fine classification of rice *** results show that the classification methods of machine learning based on multitemporal CP SAR data can obtain better results in the fine classification of rice paddies by considering the parameters ofΔCP_(DoY).The overall accuracy is greater than 95.05%,and the Kappa coefficient is greater than *** them,the random forest(RF)and support vector machine(SVM)achieve the best results,with an overall accuracy reaching 97.32%and 97.37%,respectively,and Kappa coefficient values reaching 0.965 and 0.966,*** the two types of rice paddies,the average accuracy of the transplant hybrid(T-H)rice paddy is greater than 90.64%,and the highest accuracy is 95.95%.The average accuracy of direct-sown japonica(D-J)rice paddy is greater than 92.57%,and the highest accuracy is 96.13%.

关键词： compact polarimetric(CP)SAR rice paddy machine learning fine classification multitemporal

来源：评论

学校读者我要写书评

暂无评论

Lung Cancer Risk Prediction Features Influence Model Based on Machine Learning Techniques 38

Lung Cancer Risk Prediction Features Influence Model Based o...

引用

38th IEEE Region 10 Conference, TENCON 2023

作者： Mondal, Subhash Maity, Ranjan Rai, Chirag Pramanik, Souptik Nag, Amitava Central Institute of Technology Kokrajhar Computer Science and Engineering Kokrajhar India Meghnad Saha Institute of Technology Computer Science and Bussiness System Kolkata India

ISBN: (纸本)9798350302196

Currently, lung cancer is a very common form of cancer. This is because many people are chain smokers nowadays, and many are affected due to their work hazards. The pollution level in modern cities is also a major cause of this type of cancer. This model is built to predict the chances of the occurrence of lung cancer in an individual with the help of certain conditions. The acquired dataset used in this study contains multiple features, but not all are necessary for predicting the risk of lung cancer. Hence, an embedding feature importance model Light Gradient Boosting Machine (LGBM) is used to find the impact of every feature, and the model had trained using the features with maximum influence. The dataset has been divided into two parts for training and testing the model. The models achieve a k-fold mean accuracy of 97.63% and above with all the features and more than 93% on the reduced features for all the deployed models. The models are developed based on a resource-constrained device perspective over the reduced features low resource dataset and use an algorithm to measure the execution time taken for every model to run and complete its prediction after fitting with the respective classifiers. The model developed on medical data should have maximum accuracy and is necessary for time efficiency that reflects on all the deployed models with stability and efficacy, indicating the robustness and non-overfitted model. © 2023 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Dynamic Services Migration Based on Dueling Deep Q-Network in MEC 15

Dynamic Services Migration Based on Dueling Deep Q-Network i...

引用

15th International Conference on Communication Software and Networks, ICCSN 2023

作者： Zhang, Xinyang Bu, Chao School of Computer Science and Engineering Tianjin University of Technology Key Laboratory of Computer Vision and System of Ministry of Education Tianjin China

ISBN: (纸本)9798350329360

As an extended computing paradigm of cloud computing, Mobile Edge Computing (MEC) facilitates real-time service responses by deploying resources near network edges. However, services should frequently move among multiple edge computing servers because of the mobility of most users, which accordingly leads to increased network operation costs and influences service quality. In this paper, we formulate the service migration problem as a Markov Decision Process (MDP) and introduce the dueling Deep Q-Network (DQN) to solve the problem, so as to reduce the network operating cost without lowering the service quality. We also propose a trajectory prediction approach to further optimize the service migration. Simulation experimental results demonstrate that the proposed mechanism can achieve a lower network operation cost without reducing the service quality. © 2023 IEEE.

关键词： Markov processes

来源：评论

学校读者我要写书评

暂无评论

Stackelberg Game-Driven Computational Offloading in Edge Computing Scenarios 15

Stackelberg Game-Driven Computational Offloading in Edge Com...

引用

15th International Conference on Communication Software and Networks, ICCSN 2023

作者： Zhang, Lan Bu, Chao School of Computer Science and Engineering Tianjin University of Technology Key Laboratory of Computer Vision and System of Ministry of Education Tianjin China

ISBN: (纸本)9798350329360

With the number of users that use mobile devices for frequent transactions increasing rapidly, it is a great challenge to guarantee the credibility of transactions. Blockchain is regarded as a practical technology for such demand, however, the limited computing capacity of each user's device becomes a bottleneck. In this paper, the edge computing pattern is introduced to support complex computing for mobile devices of users by renting computing resource from computing service providers. By considering demands of both the user and the service provider, we propose a two-level game approach based on the Stackelberg Game for multiple users and multiple service providers on computing resources renting and pricing. The simulation results show that the proposed mechanism is feasible and effective. © 2023 IEEE.

关键词： Computation offloading

来源：评论

学校读者我要写书评

暂无评论

Deep learning based three-dimensional computer holography from stereo/single images

Deep learning based three-dimensional computer holography fr...

引用

2024 Conference on Lasers and Electro-Optics/Pacific Rim, CLEO-PR 2024

作者： Chang, Chenliang Dai, Bo Zhuang, Songlin Zhang, Dawei School of Optical-Electrical and Computer Engineering Engineering Research Center of Optical Instrument and System Ministry of Education Shanghai Key Laboratory of Modern Optics System University of Shanghai for Science and Technology Shanghai200093 China

In the field of computer holography, generating three-dimensional (3D) computer-generated hologram (CGH) in real-time speed has been a significant challenge due to the huge amount of data in 3D object wave. Deep learning has been proposed to compute CGH very fast using convolutional neural network (CNN). However, the input of the such CNN still depends on the acquisition of 3D information like RGB-D image. Here we present novel deep learning based computer holography technique which can rapidly generate CGH using CNN directly from stereo/single images rather than from RGB-D data. The method can simplify the CGH generation by using images as input, therefore avoiding the cumbersome 3D depth measuring process. © 2024 The Author(s)

关键词： Stereocenters

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：