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

88,817 篇 会议
598 册 图书
350 篇 期刊文献
4 篇 学位论文
1 篇 科技报告

馆藏范围

89,769 篇 电子文献
3 种 纸本馆藏

日期分布

学科分类号

38,471 篇 工学
- 27,648 篇 计算机科学与技术...
- 22,647 篇 软件工程
- 9,289 篇 信息与通信工程
- 8,455 篇 控制科学与工程
- 6,054 篇 电气工程
- 4,317 篇 电子科学与技术（可...
- 3,754 篇 生物工程
- 3,703 篇 机械工程
- 2,738 篇 生物医学工程（可授...
- 2,595 篇 动力工程及工程热...
- 2,175 篇 仪器科学与技术
- 1,884 篇 光学工程
- 1,855 篇 交通运输工程
- 1,639 篇 安全科学与工程
- 1,379 篇 化学工程与技术
- 1,040 篇 网络空间安全
19,405 篇 理学
- 12,565 篇 数学
- 4,028 篇 生物学
- 3,745 篇 系统科学
- 3,694 篇 物理学
- 3,462 篇 统计学（可授理学、...
- 1,386 篇 化学
10,642 篇 管理学
- 7,045 篇 管理科学与工程(可...
- 4,273 篇 图书情报与档案管...
- 3,420 篇 工商管理
2,330 篇 医学
- 2,038 篇 临床医学
- 1,985 篇 基础医学(可授医学...
- 1,266 篇 药学(可授医学、理...
1,358 篇 法学
- 1,189 篇 社会学
1,203 篇 经济学
- 1,200 篇 应用经济学
825 篇 农学
328 篇 教育学
220 篇 文学
194 篇 军事学
48 篇 艺术学
1 篇 哲学

主题

6,015 篇 genetic algorith...
5,118 篇 heuristic algori...
4,452 篇 real-time system...
4,325 篇 accuracy
4,274 篇 algorithm design...
3,865 篇 clustering algor...
3,487 篇 machine learning...
3,412 篇 prediction algor...
3,165 篇 optimization
3,035 篇 feature extracti...
2,936 篇 signal processin...
2,801 篇 computational mo...
2,650 篇 training
2,548 篇 control systems
2,488 篇 classification a...
2,465 篇 predictive model...
2,314 篇 machine learning
2,259 篇 neural networks
2,195 篇 support vector m...
2,118 篇 hardware

机构

193 篇 chitkara univers...
103 篇 school of automa...
98 篇 school of electr...
80 篇 lovely professio...
74 篇 centre of resear...
71 篇 school of comput...
66 篇 school of comput...
47 篇 national mobile ...
46 篇 department of co...
40 篇 chitkara univers...
39 篇 department of co...
38 篇 department of la...
37 篇 school of comput...
35 篇 karnataka bangal...
33 篇 college of compu...
33 篇 school of electr...
32 篇 school of comput...
32 篇 school of inform...
32 篇 department of el...
31 篇 department of co...

作者

36 篇 lei wang
36 篇 jun wang
34 篇 jian wang
30 篇 hisao ishibuchi
29 篇 li yang
26 篇 hui wang
25 篇 yu wang
25 篇 khalgui mohamed
24 篇 li peng
23 篇 wang jing
21 篇 zhang tao
21 篇 jun zhang
20 篇 rui wang
20 篇 wang lei
19 篇 bin wang
18 篇 zhang lei
17 篇 j. relin francis...
17 篇 amandeep nagpal
16 篇 vikram kumar
16 篇 chu-sing yang

语言

87,142 篇 英文
2,190 篇 其他
509 篇 中文
7 篇 俄文
2 篇 德文
2 篇 法文
1 篇 西班牙文
1 篇 土耳其文
1 篇 乌克兰文

检索条件"任意字段=International Conference on Engineering of Reconfigurable Systems and Algorithms"

共 89770 条记录，以下是441-450 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：

Nearfield Sensing and Imaging with a Millimeter Wave reconfigurable Intelligent Surface

Nearfield Sensing and Imaging with a Millimeter Wave Reconfi...

引用

2025 United States National Committee of URSI National Radio Science Meeting, USNC-URSI NRSM 2025

作者： Lu, Daniel Shekhawat, Aditya Aladsani, Mohammed Trichopoulos, Georgios C. School of Electrical Computer and Energy Engineering Arizona State University TempeAZ United States

ISBN: (纸本)9781946815200

Millimeter waves (mmWaves) provide new opportunities for wireless communications and sensing due to the ample bandwidth, narrow beams, high aperture gains and spatial resolution, and high information capacity. MmWaves can penetrate several non-conductive materials which allow the respective imaging systems to detect concealed objects in security screening or carry out remote sensing of biomarkers (e.g. breathing, heartbeat, etc). Additionally, mmWave imaging has been proposed for non-line-of-sight (NLoS) simultaneous localization and mapping (G. Torcolacci et al., "RIS-Empowered Near-Field Imaging in NLOS Scenarios"). reconfigurable intelligent surfaces (RISs) present a practical topology that allows the implementation of electrically large apertures with electronically controlled beam focusing. Along with their low-profile, conformal, high-gain, and low power consumption, RISs become attractive for high spatial resolution mmWave imaging/sensing applications. They are electronic periodic structures of tunable radiating elements that can be programmed to manipulate electromagnetic (EM) wavefronts with large degrees of freedom (DoF). Traditionally, RISs have been used in far field applications such as wireless communications. Nevertheless, when the scene of interest (SoI) is in the near field, a proper set of phase masks needs to be designed that will allow image reconstruction with the optimum spatial resolution. © 2025 USNC-URSI.

关键词： Image reconstruction

来源：评论

学校读者我要写书评

暂无评论

Machine Learning algorithms for Multiclass Fruit Classification: A Comparative Analysis

Machine Learning Algorithms for Multiclass Fruit Classificat...

引用

international conference on Computing and Machine Learning, CML 2024

作者： Haq, Zeeshan Ali Jaffery, Zainul Abdin Mehfuz, Shabana Department of Computer Science and Engineering SEST Jamia Hamdard New Delhi India Department of Electrical Engineering Jamia Millia Islamia Jamia Nagar New Delhi India

ISBN: (纸本)9789819778386

Classification of fruits into different classes based on their species, cultivars, shape and other aspects has been performed for a very long time. However, these processes had been largely manual in nature and were inhabited with various concerns including laborious processes, time consuming, contamination due to human handling and others. There is a need for the development of an automated system for classification of fruits. Combined strength of image processing and machine learning tools are a boon towards development of such systems. In this paper, a dataset of images of these fruits are developed containing a total of 9600 images for three different types of fruits including apples, bananas, and oranges. Different machine learning algorithms including Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbour (KNN) Random Forest (RF) and Convolutional Neural Network (CNN) are employed for classification of these fruits into their respective classes. After simulation of these models, DT and RF are observed to provide highest classification accuracy of 98.47 and 98.63% respectively. Two convolutional layer based CNNs achieved the accuracy of 97.33%. Therefore, it is recommended to use DT or RF based classifiers for segregating different fruits into their respective classes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Credit Card Fraud Detection Using Random Forest and K-Nearest Neighbors (KNN) algorithms 5th

Credit Card Fraud Detection Using Random Forest and K-Neares...

引用

5th international conference on Computing, Communications, and Cyber Security, IC4S 2023

作者： Alhabib, Abdulaziz Abdulrhman Alasiri, Abdulaziz Fae Alharbi, Mazen Bunayan Ahmad, Sultan Eljialy, A.E.M. Department of Information Systems College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University P.O.Box. 151 Alkharj11942 Saudi Arabia Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University P.O.Box. 151 Alkharj11942 Saudi Arabia Chandigarh University Punjab Mohali Gharuan140413 India

ISBN: (纸本)9789819773701

In this paper, we explored the application of both the Random Forest and K-Nearest Neighbors (KNN) algorithms for fraud detection. Fraudulent activities pose significant threats to various industries, making their detection crucial. We reviewed related works on fraud detection, highlighting the effectiveness of both the Random Forest and KNN methods. We proposed a system utilizing a combination of Random Forest and KNN models for credit card fraud detection, detailing its architecture and methodology. The system preprocesses and splits data, trains the models, and evaluates their performance on a testing set. We analyzed the results, demonstrating the accuracy, robustness, and scalability of the proposed system. Additionally, we presented data on the most common hours for fraudulent transactions and the top jobs associated with them. Overall, this study emphasizes the efficacy of utilizing both Random Forest and KNN algorithms in mitigating the risks associated with fraud and offers a valuable tool for safeguarding against financial malfeasance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Random forests

来源：评论

学校读者我要写书评

暂无评论

Optimization-Driven Vendor Selection with Tabu Search and Neural Networks 14

Optimization-Driven Vendor Selection with Tabu Search and Ne...

引用

14th IEEE international conference on Communication systems and Network Technologies, CSNT 2025

作者： Gupta, Santosh Kr. Srivastava, Anubhava Kumar, Vivek Department of Computer Science and Engineering SSET Sharda University Greater Noida India Department of Computer Science and Engineering NIET Greater Noida India

ISBN: (纸本)9798331531935

Vendor selection in supply chain management is a complex but extremely important process that requires the evaluation of multiple factors such as cost, quality, delivery time, and responsiveness. This study presents a new hybrid methodology that makes the vendor selection process more efficient by combining tabu search optimization and neural network analysis. The proposed model operates in two stages. In the first stage, tabu search algorithms systematically analyze the solution space and identify optimal vendor combinations based on predefined criteria. In the second stage, these selected combinations are evaluated through a neural network. This trained network has the ability to recognize patterns in performance indicators such as cost efficiency, delivery time, and operational wastage. Tests conducted on simulated data clearly demonstrated the superior performance of this hybrid model compared to traditional single-method approaches. The model significantly improved key parameters such as Mean Absolute Error and R2 score. Moreover, it also proved to be adaptable to changing conditions. This framework not only addresses challenges such as scalability and integration of qualitative-quantitative factors in vendor selection, but also highlights the effectiveness of combining optimization algorithms and machine learning techniques for complex supply chain decisions. This model not only serves as a practical decision-support tool for procurement managers but also lays a strong foundation for the development of hybrid AI techniques in the field of operational optimization. © 2025 IEEE.

关键词： Tabu search

来源：评论

学校读者我要写书评

暂无评论

Improved Gated Recurrent Unit Based Network Traffic Anomaly Detection 3

Improved Gated Recurrent Unit Based Network Traffic Anomaly ...

引用

3rd international conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Jasim, Layth Hussein Nagarathna, P. Sunitha, Yada Lekha, V. Padmavathy, R. College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Computer Science Bengaluru India Cvr College of Engineering Department of Computer Science and Engineering Hyderabad India Dhanalakshmi Srinivasan College of Engineering Technology Department of Computer Science and Engineering Mamallapuram India New Prince Shri Bhavani College of Engineering and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798331508456

The detection of unpredicted events or rare patterns in traffic data plays an important role in intelligence traffic management, that offers alerts of incidents. To address the problem of less accuracy in industrial network traffic anomaly detection, in this manuscript developed the Improved Gated Recurrent Unit (GRU) for multi-type anomaly detection. The pre-processing included label encoding and non-string numerical process. The Synthetic Minority Over-Sampling Technique with Edited Nearest Neighbors (SMOTE-ENN) technique is utilized for balancing the data by oversampling and under sampling. The improved GRU is assigned for extracting linear, non-linear and temporal features from network traffic data. These features are given to classifier developed based on Softmax for obtaining high performance detection on traffic attacks. The efficiency is evaluated based on UNSW-B15 dataset with the detection accuracy of 0.97 for DoS attacks, 0.96 for Fuzzers attacks and 0.94 for Worms attacks while compared to existing algorithms like conventional GRU for network traffic anomaly detection. © 2025 IEEE.

关键词： Telecommunication traffic

来源：评论

学校读者我要写书评

暂无评论

Generation of Routes Using Genetic algorithms to Optimize the Domestic Solid Waste Collection 8th

Generation of Routes Using Genetic Algorithms to Optimize th...

引用

8th international conference on Intelligent Technologies, ICIT 2023

作者： Vega-Huerta, Hugo Marcos, Jordy Mayhuay Acosta, Marx Campos Monrroy, Xavier Villanueva Maquen-Niño, Gisella Luisa Elena Puelles-Bulnes, María Gil-Calvo, Rubén School of Systems Engineering Universidad Nacional Mayor de San Marcos Lima Peru School of Computing and Informatics Engineering Universidad Nacional Pedro Ruiz Gallo Lambayeque Peru

ISBN: (纸本)9789819738588

The district of San Juan de Lurigancho (SJL) in Lima—Peru, is one of the districts crowed in the capital and has one of the highest amounts solid domestic wastes. To fix this problem, a model was developed to map the distance and calculate the best route to collect the domes-tic solid waste in the whole San Carlos neighborhood in SJL district. Our model was developed using genetic algorithms in JavaScript with Node JS to calculate the shortest path. For the data visualization was used the React JS for the frontend and Google Maps API for the display of an interactive map. The results obtained allowed were able to find an optimal route that joins two opposite points within the map of the San Carlos neighborhood, which is made up of 5 nodes and measures 514 m away. The model developed could generate populations of 10 to 100 individuals with the map nodes through selection, crossing and mutation processes, obtaining an optimal route that facilitates the domestic waste collection processes with the shortest distance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Collection Genetic algorithm Minimum route Pathfinding Solid waste

来源：评论

学校读者我要写书评

暂无评论

Enhanced Performance of Electric Vehicle Powertrains Using Hybrid Neural Network Models 3

Enhanced Performance of Electric Vehicle Powertrains Using H...

引用

3rd international conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Prasanna, K. Reddy Baby Vaishnavi, G. Vaishnavi, K. Venkata Sai, K. Annamacharya Institute of Technology & Sciences Department of Electrical and Electronics Engineering Tirupati India

ISBN: (纸本)9798331508456

As oil reserves decline, fossil fuel transportation's political, economic, and environmental issues worsen. EVs, a new green technology, might reduce oil use, increase sustainable road transport, and reduce greenhouse gas emissions. Fault categorization is essential for accelerating electric vehicle power train installation by improving efficiency and reliability. To solve fault classification problems, significant data preparation was used to prepare the dataset for model training. These algorithms removed missing values, found duplicates, and transformed data. Cross-validation was used to train and evaluate different neural network (NN) algorithms, including BiRNN, CNN-GRU, GRU, BiGRU, and BiCRNN, to assess performance. BiCRNN has the highest accuracy of the models we examined, 97.95%. This confirms its EV power train fault detection superiority. The model's impressive performance suggests it may be used to ensure electric vehicle efficiency and reliability. This paper emphasizes the value of advanced neural network models, particularly BiCRNN, in EV power train fault classification. The findings emphasize the importance of adopting cutting-edge machine learning approaches to promote greener transport systems to address important energy and environmental challenges. © 2025 IEEE.

关键词： Petroleum transportation

来源：评论

学校读者我要写书评

暂无评论

An Empirical Comparison of Machine and Deep Learning algorithms for Predicting Maternal Health Risk 5th

An Empirical Comparison of Machine and Deep Learning Algorit...

引用

5th EAI international conference on Cognitive Computing and Cyber Physical systems, IC4S 2024

作者： N, Srinivasa Gupta Valarmathi, B. Kavitha, A. Gopini, Sai Bhuvan Seela, Hari Kumar Department of Manufacturing Engineering School of Mechanical Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Department of Mathematics School of Advanced of Sciences Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9783031770777

Maternal health is among the greatest challenges in the world, especially in rural areas as there lack medical practitioners, they do not have easily accessible publics clinics and transport is difficult. Therefore, high rates of maternal as well as infant morbidity and mortalities are recorded. This research utilizes Artificial Intelligence (AI) with machine learning algorithms to forecast and address maternal health hazards right at their onset stage. The current research utilizes the concept of AI along with many Machine Learning (ML) methods like the Ensemble Learning Model (ELM), Random Forest (RF), K-Nearest Neighbour (KNN), Decision-Tree (DT), XG-Boost (XGB), Cat Boost (CB), and Gradient Boosting (GB), along with Synthetic Minority Over-sampling Technique (SMOTE) algorithm used for dealing with the problem class imbalance within the data set. SMOTE algorithm is utilized for the dataset balancing process. The handling system involves refining data preprocessing with the help of feature engineering and robust data cleaning which makes sure that anomalies do not erode the reliability of the predictive model. The existing methods [1] used RF (90%), DT (87%), XGB (85%), CB (86%), and GB (81%) algorithms and were compared with the accuracies of the proposed models like Logistic Regression (LR), Ensemble Learning Bagging (ELB), Ensemble Learning Stacking (ELS), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). The existing methods used only imbalance dataset. The accuracies of the proposed models with using SMOTE algorithm (balanced dataset) are LR (61.33%), KNN (81%), ELB (92.33%), ELS (90.66%) CNN (40.67%), RNN (59.67%), LSTM (54%), GRU (56%) respectively. Among these methods, ELB achieved 92.33% of accuracy with using SMOTE algorithm using imbalanced dataset. Whereas the accuracies of the proposed models without using SMOTE algorithm (imbalanced dataset) are LR (66.09%), KNN (68.47%)

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Novel systems Based on Artificial Intelligence and Numerical algorithms for Predicting Laboratory Results: A Comparative Study of Original Automatic Prediction Model with Advances in the Field 18th

Novel Systems Based on Artificial Intelligence and Numeric...

引用

18th international conference on Interdisciplinarity in engineering, INTER-ENG 2024

作者： Pawuś, Dawid Porazko, Tomasz Paszkiel, Szczepan Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Prószkowska Street 76 Opole45–758 Poland Department of Internal Medicine and Nephrology Institute of Medical Sciences University of Opole Oleska 48 Street Opole45–052 Poland Department of Nephrology with Dialysis Unit Opole University Hospital Witosa 26 Street 46–020Opole Poland

ISBN: (纸本)9783031816840

This study develops and evaluates a predictive model for estimating glomerular filtration rate using advanced machine learning algorithms. We trained and tested seven models, including K-nearest neighbors, support vector machine, gradient boosting, AdaBoost, XGBoost, LightGBM, and linear support vector regression, with a focus on clinical parameters like creatinine, urea, sodium, potassium, age, and gender. Gradient boosting and XGBoost demonstrated superior performance, achieving over 96% accuracy and mean absolute percentage errors around 2.5%. Additionally, we reviewed recent advancements in the field and compared our findings with existing research. The comparative analysis highlights the effectiveness of our proposed models in accurately predicting laboratory test results. This underscores the potential of machine learning to enhance diagnostic accuracy and improve patient outcomes in nephrology. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

Comparison in Detection of Shilling Attackers Using KNN and SVM

Comparison in Detection of Shilling Attackers Using KNN and ...

引用

international conference on systems, Control and Automation, ICSCA 2023

作者： Gambhir, Sarika Dhawan, Sanjeev Singh, Kulvinder Department of Computer Engineering University Institute of Engineering and Technology Kurukshetra University Haryana Kurukshetra136119 India

ISBN: (纸本)9789819773831

Recommender systems based on collaborative filtering are vulnerable to shilling assaults owing to its openness. Shiller’s introduce fictitious profiles into the database of the system with the goal of modifying the recommendations to their advantage. To make product recommendations more reliable and robust, it is essential to find potential attackers concealed in recommender systems. Several shilling attack detection techniques have been proposed thus far, and they all have advantages and disadvantages that are complementary to different attacks. We present a thorough experimental evaluation of detection of attackers using K-nearest neighbour and Support Vector machine in this paper. Amazon electronic dataset is used to simulate several attack models. In the meantime, for investigation attackers are produced in our dataset using average-filling, random-filling and bandwagon attack models. For detection of shilling attack two models i.e., K Nearest neighbour and Support Vector Machine are also compared. Precision, recall, F1-score and accuracy are used to analysis the performance of these two algorithms. The result of our experiments shows that SVM is more accurate than KNN in detection of shilling attackers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Wiener filtering

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500页 << < 41 42 43 44 45 46 47 48 49 50 > >>

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：