检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

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

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

1,195 篇 会议
36 篇 期刊文献

馆藏范围

1,231 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

642 篇 工学
- 551 篇 计算机科学与技术...
- 456 篇 软件工程
- 225 篇 信息与通信工程
- 126 篇 控制科学与工程
- 96 篇 电气工程
- 94 篇 生物工程
- 90 篇 生物医学工程（可授...
- 73 篇 电子科学与技术（可...
- 69 篇 网络空间安全
- 53 篇 光学工程
- 46 篇 安全科学与工程
- 34 篇 仪器科学与技术
- 32 篇 机械工程
- 30 篇 动力工程及工程热...
- 29 篇 化学工程与技术
- 27 篇 交通运输工程
368 篇 理学
- 226 篇 数学
- 114 篇 统计学（可授理学、...
- 102 篇 生物学
- 54 篇 物理学
- 33 篇 化学
- 29 篇 系统科学
242 篇 管理学
- 167 篇 管理科学与工程(可...
- 97 篇 图书情报与档案管...
- 55 篇 工商管理
85 篇 医学
- 80 篇 基础医学(可授医学...
- 79 篇 临床医学
- 43 篇 公共卫生与预防医...
- 43 篇 药学(可授医学、理...
39 篇 法学
- 36 篇 社会学
25 篇 经济学
21 篇 农学
7 篇 教育学
3 篇 军事学

主题

165 篇 feature extracti...
157 篇 accuracy
108 篇 convolutional ne...
79 篇 deep learning
54 篇 optimization
51 篇 support vector m...
50 篇 predictive model...
49 篇 internet of thin...
42 篇 computational mo...
35 篇 training
34 篇 long short term ...
33 篇 real-time system...
33 篇 machine learning
33 篇 data models
31 篇 classification a...
25 篇 security
25 篇 prediction algor...
25 篇 measurement
24 篇 visualization
24 篇 network security

机构

194 篇 department of co...
135 篇 department of el...
117 篇 the islamic univ...
98 篇 nitte meenakshi ...
87 篇 school of comput...
82 篇 the islamic univ...
58 篇 sr university sc...
53 篇 college of techn...
44 篇 school of comput...
37 篇 department of co...
36 篇 department of ec...
33 篇 nitte meenakshi ...
33 篇 nitte meenakshi ...
31 篇 nitte meenakshi ...
28 篇 department of ar...
28 篇 school of comput...
26 篇 east west instit...
26 篇 department of co...
26 篇 sr university sc...
25 篇 department of co...

作者

73 篇 pareek piyush ku...
73 篇 piyush kumar par...
24 篇 ramachandra a.c.
23 篇 muntather almusa...
23 篇 almusawi muntath...
21 篇 ramachandra a c
21 篇 parameshachari b...
20 篇 myasar mundher a...
20 篇 srinivas aluvala
20 篇 aluvala srinivas
19 篇 al-jawahry hassa...
18 篇 alabdeli haider
18 篇 hassan m. al-jaw...
18 篇 adnan myasar mun...
18 篇 haider alabdeli
16 篇 mohammed al-faro...
16 篇 nalini n.
15 篇 sundaram s. meen...
15 篇 alzubaidi laith ...
15 篇 mohammed i. habe...

语言

1,053 篇 英文
177 篇 其他
2 篇 中文

检索条件"机构=Computer Science and Engineering Nitte Meenakshi Intitute of Technology"

共 1231 条记录，以下是561-570 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

Deep Reinforcement Learning Based Resource Allocation Strategy in Cloud-Edge Computing System 2

Deep Reinforcement Learning Based Resource Allocation Strate...

引用

2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Adhoni, Zameer Ahmed Habelalmateen, Mohammed I Janardhana, D.R. Sattar, Khalid Nazim Abdul Audah, Lukman Gitam School of Technology Gitam University Department Computer Science Engineering Department Bengaluru Rural India The Islamic University Najaf Iraq Nitte Meenakshi Institute of Technology Dept of Cse Bengaluru India College of Science Majmaah University Department of Csi Al Majmaah Saudi Arabia Universiti Tun Hussein Onn Malaysia Electrical and Electronic Engineering Batu Pahat Johor Parit Raja Malaysia

ISBN: (纸本)9798350382952

A lot of real time processing as well as resource-intensive apps is what is needed more and thus, cloud-edge computing systems require compelling resource allocation schemes. This research focuses on the utility of Multiagent Learning framework with Deep Reinforcement Learning (MAL-DRL) which is used for solution deployment concerning resource allocation in such systems, such that the end user enjoys optimization while operators optimize resource utilization. In this work, the research focus on the simulation testing of the MAL-DRL algorithm against classical Random Allocation (RA) and singe agent DRL methods. This exhibition shows that MAL-DRL improves the average latency more (44% savings) and at the same time more resources are used (35% increase) compared both the alternatives with a combined reward measure score of 0.80. These results show that distributed decision-making and learning styles of MAL-DRL brings together faster resource allocation which can be interpreted as a reduction of users' delay experience and therefore lead to better performance of whole system. Although the article limits simulations in areas to be covered and complexity in training, it brings the prospective benefits of MAL-DRL in the management of cloud-edge resources on the spot. In the course of this undertake, the effect of ensuring communication goals, moving learning strategies, and security measures for the ultimate goal of boosting this method's applicability in real-world antics can be explored. © 2024 IEEE.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Escaping Strategy with Golden Jackal Optimization Based Clustering and Routing in Mobile Ad Hoc Network 2

Escaping Strategy with Golden Jackal Optimization Based Clus...

引用

2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Alkhayyat, Ahmad Kumar, Komuravelly Sudheer Hiremath, Murigendrayya M. Das, Abhijeet Madhura, G.K. College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq School of Computer Science & Artificial Intelligence Sr University Warangal India Dayananda Sagar College of Engineering Department of Medical Electronics Engineering Bengaluru India Department of Civil Engineering Odisha Bhubaneswar India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India

ISBN: (纸本)9798350373110

The Mobile Ad Hoc Network (MANET) is organized with various nodes which communicates wirelessly. Therefore, the communication in MANET suffered from different problems like low stability, high energy consumption and link failure. Additionally, the path failure happens in network because of unrestricted movement of node, that maximized energy usage, delay and minimized nodes lifetime. In this research, the Escaping Strategy with Golden Jackal Optimization (ES-GJO) algorithm is developed for cluster head selection and routing in the MANET network. The appropriate Cluster Head (CH) and routes are chosen using ES-GJO algorithm that boosts network lifetime and predicts link failure with less energy consumption. The ES-GJO algorithm is estimated with metrics of Packet Delivery Ratio (PDR), Delay and Energy consumption for 100 to 500 nodes. The proposed ES-GJO algorithm attained highest PDR of 99.87%, 99.83%, 99.65%, 98.76% and 97.38% for nodes 100, 200, 300, 400 and 500 which is efficient than Eagle Based Density Clustering (EBDC) and Optimal Route Selection Mobile Ad hoc Networks (ORSMAN) algorithms. © 2024 IEEE.

关键词： Mobile ad hoc networks

来源：评论

学校读者我要写书评

暂无评论

An Efficient Inter-Cluster Wireless Sensor Network Communication by using Priority Queues and Reliability Models

An Efficient Inter-Cluster Wireless Sensor Network Communica...

引用

2023 IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2023

作者： Devi, Renuka Patel, Suresh Shivaprasad Yadav, S.G. Shivakanth, Gandla Divya, G. H. K. E. Society's S. L. N. College of Engineering Department of Computer Science & Engineering Raichur India Ramaiah Institute of Technology Department of Electronics and Telecommunication Engineering Bangalore India K L University Vadeeshwaram Department of Computer Science & Engineering Vijayawada India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350398465

Even though Electromagnetic and Radio Interference, Reliability, Cyber Security, and Resource Depletion are still concerns with Wireless Sensor Networks (WSNs), even though WSNs have become increasingly widespread and are used in a wide variety of contexts. It is possible to detect and transmit a wide variety of data using a wide variety of sensors, including electric sensors, magnetic field sensors, infrared sensors, laser sensors, navigation sensors, and many others. The requirement for increased spatial and temporal precision in all applications has led to a decrease in the price of sensors as well as a reduction in their size and weight, respectively. Loss of packets and interference are extremely common in the real world because of the complexity and noise present in that environment. These sensors are extremely important for sensing and reporting data on a regular basis, in response to specific inquiries, or based on events that have occurred. In this investigation, the information that has been gathered by the leader nodes needs to be transmitted to the target node in the most effective manner possible through multiple hops. With the assistance of a number of different leader nodes located in their own distinct clusters, the data is moved to its ultimate location. Utilizing the appropriate strategy of data acknowledgments and queue thresholds is required in order to both ensure the data's integrity and reduce the amount of unneeded management overhead. Congestion limitations that are determined by wait levels contribute to helping assure dependability. © 2023 IEEE.

关键词： Radio interference

来源：评论

学校读者我要写书评

暂无评论

Advanced Modulation Technique for Power Electronic Converters in Grid-Tied Inverters

Advanced Modulation Technique for Power Electronic Converter...

引用

2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Jyothsna, Kalakonda Sundar, Y Shyam Sathyavani, B. Roy, Upendra B. P. Sapna Kumari, C. Vaageshwari College of Engineering Department of Electronics and Communication Engineering Karimnagar India Cmr Engineering College Department of Computer Science and Engineering Hyderabad India Sr University Department of Electrical and Electronics Engineering Warangal India Engineering Channabasaveshwara Institute of Technology Department of Electronics and Communication Tumkur India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350313413

In stationary reference frames for both single phase and three phase ac systems, proportional and resonant (PR) current controllers have been employed. Transforming PR controllers, which are suitable for controlling DC signals, produced the initial structure of the PR controller. In terms of ac signal, the PR controllers should theoretically attain zero stable error. For low frequency signal control, PR controllers often operate satisfactorily with a comparatively large cutoff frequency. However, the performance decreases and the transient become more dependent on system characteristics as the regulated as frequency and the cutoff frequency get closer. This behaviour is noticeable when controlling high frequency signals, like when dead time or grid harmonics compensation is involved. Thus, a modified PR controller (MPRC) with better performance of transient for controlling signals of high frequency is implemented in this study. Simulation is used to demonstrate the effectiveness of the implemented controller. © 2023 IEEE.

关键词： and Transient response Current controllers cutoff frequency Harmonics suppression PR controller

来源：评论

学校读者我要写书评

暂无评论

Hybrid Salp Swarm and Particle Swarm Optimization based Secure Aware Energy-Efficient Routing in Wireless Sensor Network

Hybrid Salp Swarm and Particle Swarm Optimization based Secu...

引用

2023 International Conference on Integrated Intelligence and Communication Systems, ICIICS 2023

作者： Vanitha, M. Yamsani, Nagendar Mohammed, Ibraheem Hatem Madhavan, Sowmya Al-Attabi, Kassem Malla Reddy Engineering College for Women Hyderabad India SR University School of Computer Science and Artificial Intelligence Warangal India Al Rafidain University College Department of Computer Communications Engineering Bagdad Iraq Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India The Islamic University Najaf Iraq

ISBN: (纸本)9798350315455

The Wireless Sensor Network (WSN) is an interconnected of Sensor Nodes (SN) which are combined with one other in network through wireless. The main objective of the WSN design is to maximize the network lifetime. The network lifetime can be extended and energy consumption can be balanced properly through the utilization of clustering and routing. However, the WSN's restricted battery supply means that energy efficiency is observed as a complex task in a network. In this paper, Hybrid of Salp Swarm Algorithm and Particle Swarm Optimization (HSSPSO) is proposed to maximize the efficiency of energy in WSN. The secure aware Cluster Head (CH) and route path from the network are selected by utilizing HSSPSO. The proposed HSSPSO minimizes the usage of energy nodes when maximizing the data transmission in WSN. The performance of HSSPSO is evaluated using throughput, packet delivery ratio, delay and energy efficiency by various nodes of 100, 200, 300 and 400. The HSSPSO attained a high throughput of 580kbps and packet delivery ratio of 99.4% with less delay of 12.3ms and energy consumption of 321.8J when compared to existing methods like Enhanced Trust-Based Secure Route Protocol (ETBSRP) and Enhanced Energy Efficient-Secure Routing (EEE-SR) protocol. © 2023 IEEE.

关键词： Cluster Head Data Transmission Particle Swarm Optimization Salp Swarm Algorithm Sensor Nodes Wireless Sensor Network

来源：评论

学校读者我要写书评

暂无评论

Enhancing Quality-of-Service in LoRa Low-Power Wide-Area Networks via Multi-Hop Optimized Radio Resource Management 2

Enhancing Quality-of-Service in LoRa Low-Power Wide-Area Net...

引用

2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Jasim Alzubaidi, Laith H. Sunil, G. Praveena, Hirald Dwaraka Sathishkumar, N. Manjula, B.M. The Islamic University Najaf Iraq Sr University School of Computer Science and Artificial Intelligence Warangal India School of Engineering Mohan Babu University Erstwhile Sree Vidya Nikethan Engineering College Department of Ece Andhra Pradesh Tirupati India Kpr Institute of Engineering and Technology Department of Electronics and Communication Engineering Coimbatore India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350317558

In transport systems in particular, LoRa wireless communication has become an essential technology for intelligent critical infrastructures. Optimizing transmission policy parameters, including carrier frequency, bandwidth, coding rate, and spreading factor, is crucial for achieving excellent Quality of Service (QoS) for LoRa systems;however, current methods have not fully optimized all LoRa parameters. In order to efficiently share information, the proposed Multiobjective Q Learning based LoRa (MQ-LoRa) uses a hybrid topology consisting of a cluster root rotating tree, wherein IoT nodes adhere to a cluster topology inspired by cellular tissues, while gateways follow a tree topology. For cluster construction, a membrane-inspired clustering algorithm is created, and influence scores are used to determine which header node is best. The study presents Data QoS Ranking for IoT nodes, which uses the new LoRa frame structure field QoS Ranking (QRank) to classify data into priority and non-priority categories. The Soft Actor Critic (SAC) fast deep reinforcement learning model is used for optimal transmission policy enforcement, taking into account signal-to-interference-plus-noise ratio, QRank, and other environmental characteristics. After that, the transmission policy is optimized taking carrier frequency, bandwidth, coding rate, and spreading factor into account. Furthermore, a multi-hop routing method with concurrent optimization that uses shuffling shepherd and mayfly optimization rates routes according to fitness standards. A multi-weighted sum approach is used to establish a weighted duty cycle in LoRa IoT networks to minimize resource inefficiency and loss of information. Using the NS3.26 LoRaWAN module, performance evaluation measures energy consumption, latency, throughput, packet receipt ratio, and packet rejection ratio, among other parameters. Comparison with popular LoRa techniques shows that the suggested MQ-LoRa performs better energy consumption of 56 mJ t

关键词： LoRA Mayfly Quality of Service Rank Shuffled Shepherd Optimization Soft Actor Critic

来源：评论

学校读者我要写书评

暂无评论

CNN-LSTM Based on Crow Search Optimization Algorithm for Product Review Classification

CNN-LSTM Based on Crow Search Optimization Algorithm for Pro...

引用

2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Yamsani, Nagendar Jothi, B. Srinivasan, N. Dineshkumar, R. Ramachandra, A.C. Sr University School of Computer Science and Artificial Intelligence Warangal India Srm Institute of Science and Technology Kattankulathur Department of Computational Intelligence Chennai India School of Computing Sathyabama Institute of Science and Technology Department of Cse Chennai India Saveetha School of Engineering Sriperumbudur Department of Ece Thandalam India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350313413

Product reviews means an everyone's opinion or views towards particular products or services that delivered to a specific company. In modern global, reviews of online product play a vital part in the estimation process of product in a company and their customers. The extracted features are selected by using the Chi-Squared technique and for the classification of product review proposed a model by utilizing the combined deep learning techniques like Convolutional Neural Network (CNN) and Long- Short Term Memory (LSTM) and it optimized by Crow Search Algorithm (CSA). CNN is used for capturing the local information and LSTM neural network is used for the long-distance dependencies. By utilizing the benefits of CNN and LSTM and it is optimized by CSA for the effective classification of product review. The proposed model performance is estimated by using the performance metrics like Accuracy, Recall, Specificity, Sensitivity and F1-score are 98.35%, 93.20%, 97.57%, 94.82% and 95.78% respectively. The proposed model is compared with other existing model in terms of performance metrics which shows the better performance results. © 2023 IEEE.

关键词： Chi-Squared Convolutional Neural Network Crow Search Algorithm Long-Short Term Memory and Product Review

来源：评论

学校读者我要写书评

暂无评论

Traffic Congestion Prediction in Smart Cities using Multilevel-Gated Recurrent Unit

Traffic Congestion Prediction in Smart Cities using Multilev...

引用

2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Sravani, Busani Shreyas, A.V. Abbas, Haider Mohammed Chanti, Yerrolla Punitha, S. Malla Reddy Engineering College for Women Hyderabad India Nitte Meenakshi Institute of Technology Department of Civil Engineering Bengaluru India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Sr University School of Computer Science & Artificial Intelligence Warangal India Vinayaka Mission's Kirupananda Variyar Engineering College Department of Mathematics Salem India

ISBN: (纸本)9798350360660

Traffic Congestion (TC) is a main issue in smart cities because of increase in urbanization, as a result in financial losses. However, existing prediction algorithms failed to predict TC effectively due to dynamic changes of road network. To overcome this problem, a Multilevel-Gated Recurrent Unit (MGRU) is proposed for accurate prediction of traffic flow and congestion avoidance in smart cities. The main advantage of proposed M-GRU model is that it reduces errors while prediction by multilevel layers in softmax that passes information to adapt dynamic changes of road network. The data acquired based on weather conditions, number of vehicles improved the prediction process which utilized a few factors for prediction than previous prediction methods. Then the traffic density is estimated to predict TC on the road. Finally, the TC is predicted accurately by the proposed M-GRU model. The experimental results of the proposed method attained better results accuracy of 0.887, Mean Absolute Error (MAE) is 82.34 which is less than compared to existing method such as Convolutional Neural Network and Bidirectional-Long Short-Term Memory (Conv-Bi-LSTM). © 2024 IEEE.

关键词： Traffic congestion

来源：评论

学校读者我要写书评

暂无评论

A Hand Gesture Recognition Based on SqueezeNet and Octave Convolution Feature Extraction Approach

A Hand Gesture Recognition Based on SqueezeNet and Octave Co...

引用

2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Priyanka, K. Rajesh, K.V. Hassan, Muntather Muhsin Jagadish, Sripelli Obaid, Mohammed Kadhim Nitte Meenakshi Institute of Technology Dept. of Ise Bengaluru India Department of Cse Secunderabad India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Sr University School of Computer Science & Artificial Intelligence Warangal India Ahl Al Bayt University College of Arts Karbala Iraq

ISBN: (纸本)9798350360660

In recent days, sign recognition plays an important role in overcoming the communication barriers between hearing-impaired community and the normal society where sign language is essential. This paper proposes an efficient feature extraction and feature mapping technique by employing SqueezeNet fusion Octave Convolution method (SqueezeNet fusion OctConv). Initially, the input image data is preprocessed to transform the data to a common scale and avoid imbalance of model. The SqueezeNet model with fire module is employed for efficient parameter reduction and significant feature extraction. The Octave Convolutional (OctConv) technique is applied to refine the extracted features that optimizes the feature utilization, handles spatial components and reduces redundancy which enhances the feature representation. The integration of fully connected layers with spatial attention mechanism enables the model to learn complex relationships and assign weights based on relevance ensuring precise classification. The proposed method is evaluated on NUS-II dataset and achieved high accuracy of 99.25% which surpassed the existing sign recognition models such as Intensive Feature Extrication Deep Network, Densely Connected Res2Net. © 2024 IEEE.

关键词： Mapping

来源：评论

学校读者我要写书评

暂无评论

Signal Processing Techniques for Classifying Heartbeat Patterns in ECG Signals for Cardiac Disease Detection Using Graph Convolutional Networks 4

Signal Processing Techniques for Classifying Heartbeat Patte...

引用

4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Alzubaidi, Laith H. Priyadharshini, C. Vasanthakumar, G.U. Samala, Srinivas Ananthi, S. College of Technical Engineering The Islamic University of Al Diwaniyah Department of Computers Techniques Engineering Al Diwaniyah Iraq Bharathiyar Institute of Engineering for Women Department of Electronics and Communication Engineering Salem India Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bengaluru India Sr University Department of Electronics and Communication Engineering Warangal India New Prince Shri Bhavani College of Engineering and Technology Department of Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9798350352931

In recent days, the information about electrical activity is obtained in the form of Electro-Cardo-Diograms (ECG) which is become a typical way of monitoring heart rhythm and function of patients. In existing, the Convolutional Neural Networks (CNN) is utilized for the effective detection of Cardiac Disease (CD). However, the CNN model may not extract the spatial features from the signals effectively. To overcome this issue, Graph Convolutional Network (GCN) is proposed to extract the spatial features from signal effectively. Initially, the data is taken from Massachusetts Institute of technology and Beth Israel Hospital (MIT-BIH) dataset. Then, min-max normalization is utilized to preprocess the data and the high frequency noise is removed with Butterworth Frequency Filter (BFF). After that, the preprocessed data is further processed into classification with proposed GCN model. In the classification, the convolutional layers are utilized to extract the spatial features by using graph-based structure. Finally, the fully connected layer with softmax activation function is utilized to classify the CD into normal or abnormal labels. From the results, the proposed GCN model established better results than existing CNN including precision (99.75%), recall (98.60%), F1-score (98.20%) and Area Under Curve (99.70%) respectively. © 2024 IEEE.

关键词： Butterworth filters

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共124页 << < 53 54 55 56 57 58 59 60 61 62 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

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：