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

13,348 篇 会议
192 册 图书
163 篇 期刊文献

馆藏范围

13,702 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

9,154 篇 工学
- 7,629 篇 计算机科学与技术...
- 4,323 篇 软件工程
- 1,651 篇 信息与通信工程
- 1,540 篇 电气工程
- 1,252 篇 控制科学与工程
- 961 篇 生物工程
- 523 篇 机械工程
- 507 篇 生物医学工程（可授...
- 483 篇 电子科学与技术（可...
- 463 篇 动力工程及工程热...
- 331 篇 光学工程
- 329 篇 交通运输工程
- 309 篇 安全科学与工程
- 271 篇 材料科学与工程（可...
- 250 篇 化学工程与技术
- 222 篇 网络空间安全
3,551 篇 理学
- 2,236 篇 数学
- 1,057 篇 生物学
- 616 篇 物理学
- 593 篇 统计学（可授理学、...
- 474 篇 系统科学
- 241 篇 化学
1,886 篇 管理学
- 1,315 篇 管理科学与工程(可...
- 700 篇 图书情报与档案管...
- 548 篇 工商管理
601 篇 医学
- 498 篇 临床医学
- 407 篇 基础医学(可授医学...
- 223 篇 药学(可授医学、理...
307 篇 教育学
- 297 篇 教育学
252 篇 法学
- 229 篇 社会学
220 篇 经济学
137 篇 农学
37 篇 军事学
21 篇 文学
6 篇 艺术学

主题

1,332 篇 optimization
1,098 篇 machine learning
1,047 篇 deep learning
510 篇 accuracy
470 篇 reinforcement le...
326 篇 computational mo...
309 篇 machine learning...
297 篇 training
283 篇 support vector m...
281 篇 feature extracti...
262 篇 predictive model...
248 篇 neural networks
242 篇 real-time system...
216 篇 genetic algorith...
203 篇 adversarial mach...
162 篇 particle swarm o...
159 篇 convolutional ne...
152 篇 learning algorit...
142 篇 internet of thin...
131 篇 data mining

机构

46 篇 chitkara centre ...
36 篇 chitkara univers...
30 篇 chitkara univers...
29 篇 the islamic univ...
24 篇 department of co...
18 篇 centre of resear...
18 篇 al-farahidi univ...
17 篇 lloyd institute ...
17 篇 department of co...
15 篇 school of comput...
14 篇 school of comput...
14 篇 gla university d...
13 篇 vishwakarma inst...
12 篇 university of ch...
12 篇 college of manag...
11 篇 chitkara univers...
11 篇 school of comput...
10 篇 columbia univers...
10 篇 univ chinese aca...
10 篇 lovely professio...

作者

13 篇 lima jose
13 篇 pereira ana i.
11 篇 wang wei
9 篇 rakesh kumar
9 篇 wang hui
9 篇 de-shuang huang
9 篇 ye jieping
8 篇 yang tianbao
8 篇 neeraj varshney
8 篇 r. santhana kris...
7 篇 zhang tao
7 篇 wang lei
7 篇 zhang lei
7 篇 li li
7 篇 buzdalov maxim
7 篇 wang jian
7 篇 huang heng
7 篇 hu rong
7 篇 yang yang
7 篇 todorov venelin

语言

11,186 篇 英文
2,489 篇 其他
146 篇 中文
1 篇 阿拉伯文
1 篇 俄文
1 篇 土耳其文

检索条件"任意字段=4th International Conference on Optimization Learning Algorithms and Applications"

共 13703 条记录，以下是271-280 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：

Product process parameter prediction based on integrated ELM-ANFIS 4

Product process parameter prediction based on integrated ELM...

引用

4th international conference on Machine learning and Intelligent Systems Engineering (MLISE)

作者： Yan, Manting Han, Yu Zhang, Shuye Shenyang City Univ Sch Intelligence & Engn Shenyang Liaoning Peoples R China

ISBN: (纸本)9798350375084;9798350375077

Due to the complexity of process-based processing, it is difficult to accurately locate the quality factors of final products into specific production links and parameters. the prediction of parameters based on the collected data can improve the production efficiency and processing quality. Based on the integration of extreme learning machine and adaptive neural fuzzy inference system parameters prediction model is established, the results show that compared with the single machine learning method, the proposed algorithm model has higher prediction accuracy, at last, through actual production parameter test proved that the model has good prediction performance and generalization performance.

关键词： Extreme learning machine Parameter prediction Adaptive neuro-fuzzy inference system Ensemble learning

来源：评论

学校读者我要写书评

暂无评论

Improved Alzheimer's Disease Early Prediction using Machine learning Method : A Comprehensive Diagnostic Approach with Random Forest Classifier 4

Improved Alzheimer's Disease Early Prediction using Machine ...

引用

4th international conference on Soft Computing for Security applications, ICSCSA 2024

作者： Munawar, Madiha Monika Singh, T. Kishor Kumar Reddy, C. Hanafiah, Marlia Mohd Stanley College of Engineering and Technology for Women Department of Computer Science and Engineering Hyderabad India Universiti Kebangsaan Malaysia Faculty of Science and Technology Malaysia

ISBN: (纸本)9798331515720

Alzheimer's Disease (AD) often affects the elder persons and is the prevalent kind of Dementia. AD has a huge expense, especially when it comes to the treatment. AD is a main reason the older generations die whereas when Alzheimer Disease is detected early there will be minimal deaths and accurate diagnosing likely occurs in most cases. Using anonymized Oasis longitudinal MRI data, the study trains several machine learning algorithms to identify AD patients. Analyzing the test result includes utilizing different learning strategies or algorithms e.g., Ensemble Random Forest (RF), Hierarchical Decision Tree (DT), Logistic Regression (LR), Convolutional Neural Network (CNN), Deep Neural Network binary classifier (DNN), soft voting classifier (SVC), transfer learning approaches (TLA), Support Vector Machine (SVM) and boosting AdaBoost in reference of their Accuracy, Error Rate, AUC (area under the curve), Time Taken and Recall for each method. the obtained results concluded that the proposed Random Forest classifier achieves higher accuracy (92.84%), Error Rate (7.16%), AUC (95%) (Area Under Curve) and Recall (89%). the Random Forest classifier also achieves higher Time Taken that is 483.654 sec. © 2024 IEEE.

关键词： Alzheimer's Disease Decision Trees Deep learning Dementia learning Approaches Machine learning Random Forest

来源：评论

学校读者我要写书评

暂无评论

Cloud-Driven AI: Enhancing Machine learning Performance through Distributed Computing 4

Cloud-Driven AI: Enhancing Machine Learning Performance Thro...

引用

4th IEEE international conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Dixit, Kunal Himani Bhardwaj, Parul Sachdeva, Ritu Manav Rachna International Institute of Research & Studies School of Computer Applications Faridabad India

ISBN: (纸本)9798331518981

the exponential data volume and complexity of Machine learning (ML) algorithms has resulted in an increase in computational limitations, which affects artificial intelligence [AI]. this research seeks to establish whether cloud-based distributed computing can effectively address these limitations and improve the performance of ML. Our approach is a new architecture that deploys the computing load over a cloud framework in order to distribute and process in parallel some of the computational majorities of learning algorithms. To further enhance the application's efficiency of processing the circuit data, a combination of adaptive resource allocation techniques, data partitioning strategies, and a new synchronization protocol has been proposed and implemented. this framework was assessed with deep neural networks, random forests, gradient-boosting machines to different datasets, and complex tasks. Focusing on the obtained results, it becomes possible to state that the proposed methods yield up to 87% improvement in the training speed and 5-12% increase in the accuracy of the models compared to the traditional implementations in single nodes. Moreover, our approach proved its capability of scalability did not degrade the performance and achieved near-linear speedup throughout the distributed nodes of up to 1000. these results propose that cloud-based AI can significantly reduce time to on-ramp for training numerous models on larger datasets. In light of this, distributed computing in the cloud offers a way to meet the computational demands of today's AI applications while opening the door to new advancements in ML throughout different domains. © 2024 IEEE.

关键词： Distributed cloud

来源：评论

学校读者我要写书评

暂无评论

Research on Power Safety Monitoring Action Recognition Algorithm through Neural Network and Deep learning 4

Research on Power Safety Monitoring Action Recognition Algor...

引用

4th IEEE international conference on Power, Electronics and Computer applications, ICPECA 2024

作者： Bai, Xingtao Wang, Ningguo Li, Yongliang Luo, Hairong Gao, Bo State Grid Ningxia Electric Power Co. LTD. Electric Power Research Institute Ningxia Yinchuan750001 China State Grid Ningxia Power Co. Ltd Ningxia Yinchuan750001 China

ISBN: (纸本)9798350359589

this paper studied the methods for improving the accuracy and efficiency of the action of power employees by deeply studying the application of neural network algorithm in power safety supervision video. Firstly, this paper summarizes the research status of power safety monitoring system and related action recognition algorithms. For the power industry, timely and accurate identification of workers' movements is essential for accident prevention and management. In this context, various motion recognition algorithms are emerging, among which neural network algorithm has attracted much attention due to its excellent performance in image processing and pattern recognition. through deep learning, neural networks can automatically learn key features from a large number of video data, providing a more reliable means for human action recognition. Secondly, this paper introduces in detail the proposed method of power safety monitoring video personnel action recognition based on neural network algorithm. through the optimization of neural network structure and the careful selection of training data, we construct an efficient and accurate action recognition model. the model can quickly and accurately identify the actions of different personnel at work by monitoring the video of electric power operation, including common operation actions and special actions in emergency situations. Our method can more comprehensively understand the various actions of personnel in the electric power environment. through the analysis of a large number of experimental results, we verify the effectiveness and robustness of the proposed algorithm. Compared with traditional methods, the motion recognition algorithm based on neural network has achieved significant improvement in accuracy and response speed. this proves the practical application prospect of this method in the field of power safety supervision. © 2024 IEEE.

关键词： motion recognition neural networks power safety

来源：评论

学校读者我要写书评

暂无评论

Detecting Fake News on Social Networks: A Naive Bayes Approach to Enhance Information Trustworthiness 4

Detecting Fake News on Social Networks: A Naive Bayes Approa...

引用

4th international conference on Sustainable Expert Systems, ICSES 2024

作者： Abirami, S. Amuthaguka, D. Kalasalingam Academy of Research and Education Department of Computer Applications Krishnankovil India

ISBN: (纸本)9798331540364

In modern life, the influence of social network takes the first place. the news from a social network may be wrong, in the manner it's the necessary to check the trustworthiness of a news is very important. this article proceeds a novel approach to find a fake news on social media by implementing the machine learning algorithm. the classification of real or fake news using Naïve Bayes classifier and TF-IDF, NLP were used for tokenization, count vectorizer, and so on. Two different datasets (fake & real) were used here. Various types of machine learning algorithms such as logistic regression, SVM, Decision tree and random forest are implemented in the same datasets. While comparing the results Naïve Bayes give 95% of accuracy for the dataset. Various preprocessing techniques were employed here to achieve the results. It is efficient solution to find or classify, a news is real or fake on social media. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Performance optimization and Model Research of Machine learning in Speech Recognition System 6

Performance Optimization and Model Research of Machine Learn...

引用

6th international conference on Artificial Intelligence and Computer applications

作者： Shang, Wenwen Xian Fanyi Univ Xian 710105 Shaanxi Peoples R China

ISBN: (纸本)9798350377255;9798350377262

this paper proposes an improved method based on machine learning, which combines the deep neural network (DNN) architecture and speech enhancement technology to significantly improve the recognition accuracy and robustness of the system. In traditional speech recognition models, complex noise environments often lead to inaccurate feature extraction and poor model training effects, thereby reducing the practical application value of the system. To solve the above problems, this paper introduces adaptive noise reduction algorithms and time-frequency domain enhancement technologies in speech preprocessing to optimize the quality of input signals. In addition, through the improved DNN model structure, the ability to model the spatiotemporal features of speech signals is enhanced. At the same time, the dynamic learning rate strategy and gradient optimization algorithm are adopted to improve the convergence efficiency and generalization performance of the model. Experiments verify the superior performance of the improved model in multi-noise scenarios. Compared with traditional methods, the speech recognition accuracy of the improved model is improved by 12.5%, indicating that it has stronger robustness under noise interference. the processing delay of the system is reduced by an average of 15.3%, which greatly improves the real-time performance.

关键词： Speech recognition machine learning performance optimization model research speech enhancement human-computer interaction

来源：评论

学校读者我要写书评

暂无评论

Behavioral Changes Based Trust Computation in Mutli-Agent Predictive Maintenance applications 4

Behavioral Changes Based Trust Computation in Mutli-Agent Pr...

引用

4th international conference on Advanced Research in Computing, ICARC 2024

作者： Premarathne, Uthpala Subodhani Rajasingham, Shalini The Open University of Sri Lanka Department of Electrical and Computer Engineering Nawala Sri Lanka The Open University of Sri Lanka Center for Education Technology and Media Nawala Sri Lanka

ISBN: (纸本)9798350384864

Predictive maintenance practices rely on reliable scheduling and allocation of resources to sustain seamless operations. Advent of agent technology has increased the avenues to manage efficient schedules in distributed industry settings. this paper proposes a multi-agent architecture with behavior based trust computations and reinforcement learning to effectively manage multiple schedule assignments. To formalize the schedule maintenance problem, bigraph was used to model the centralized resource allocations among multiple schedules. Reinforcement learning algorithms was used to compute and update trust values based on the agent behaviors. Results reveal that the cooperation among agents helps to maintain a variation in assignments to less than 40%. © 2024 IEEE.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Solving the Influence Maximization Problem Via a Deep-Reinforcement-learning-Guided Evolutionary Approach 6

Solving the Influence Maximization Problem Via a Deep-Reinfo...

引用

6th international conference on Data-Driven optimization of Complex Systems (DOCS)

作者： Huang, Jing Wang, Shuai Xia, Chenglin Yang, Yuan Wang, Weilu Sun Yat Sen Univ Sch Intelligent Syst Engn Shenzhen Campus Shenzhen Peoples R China

ISBN: (纸本)9798350377859;9798350377842

the influence maximization (IM) problem aims to identify a set of seeds with the maximal influential ability within the network, where numerous applications can be detected such as market marketing and idea dissemination. In the early stages, the IM problem has primarily been addressed using heuristic-based and population- based algorithms. Due to the NP-hard nature of this problem, these approaches are unable to meet the demands of large-scale networks. Previous work incorporates deep learning into this problem and algorithms have been developed based on Deep Q-Network (DQN). However, the performance in handling the seed determination task tends to be inferior to heuristic algorithms. For achieving competitive results, this paper proposes a seed selection method based on the reinforcement learning to be embedded in an evolutionary diagram, namely RLEA-IM, to tackle the IM problem. through experiments conducted on both synthetic and real-world networks, RLEA-IM demonstrates a superior performance over existing DQN-based algorithms. It is capable of generating seed sets with better influential ability.

关键词： Complex networks the influence maximization deep reinforcement learning graph embedding evolutionary optimization

来源：评论

学校读者我要写书评

暂无评论

learning Disability Detection and Classification Analysis Between Two Classification algorithms 4

Learning Disability Detection and Classification Analysis Be...

引用

4th international conference on Expert Clouds and applications, ICOECA 2024

作者： Darwin Roach, D. Pearly Princess, J. Vijula, V. Kumar, J. Raj Shibu, Anit Mary Karunya Institute of Technology and Sciences Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350385793

this research study presents the analysis of learning disability detection and classification using two classification algorithms. learning disabilities are neurodevelopmental disorders that affect the ability to acquire and process information. Detecting and classifying these disabilities accurately is crucial for providing appropriate interventions and support to affected individuals. this research analyzes two categorization algorithms: Random Forest (RF) and Support Vector Machine (SVM). Although RF is an ensemble learning method that incorporates several decision trees to produce predictions, SVM is a supervised machine learning algorithm that employs hyperplanes to split data points into distinct groups. this analysis compares the two algorithms' performance in terms of F1-score, recall, accuracy, and precision. A dataset consisting of features related to cognitive abilities, academic performance, and behavioral characteristics of individuals with and without learning disabilities is used for the analysis. the results show that both SVM and RF achieve high accuracy in learning disability detection and classification. However, SVM performs better than RF because it has greater precision and recall. this indicates that SVM is better at correctly identifying individuals with learning disabilities and minimizing false positives and false negatives. these algorithms can assist in early identification and intervention, enabling targeted support for individuals with learning disabilities. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

the Application of Machine learning in Android Malware Detection 4

The Application of Machine Learning in Android Malware Detec...

引用

4th international conference on Neural Networks, Information and Communication Engineering, NNICE 2024

作者： Xie, Wei Zhang, Xueying Rdfz Chaoyang Branch School Beijing China

ISBN: (纸本)9798350394375

the android system had been a popular system among mobile phone users, there are lots of applications based on android system, which contain malware and good software. Malware is very harmful to the computer because it can embed rubbish advertisements, applications and viruses in order to disrupt the functioning of your mobile phone or steal your personal information. Network information security has gained its significance on the internet which is related to our safety directly. the traditional method to detect malware is to use anti-virus software to check the mobile phone. However, with the rapid uploading and increase in the number of software, some malware cannot be detected well and also the anti-virus software may embed some advertisements or download bundle software (usually hard to uninstall) automatically by itself. With development, machine learning 's calculation efficiency has been improved significantly and it has been used in many regions. In this paper, we select several typical machine-learning algorithms to build the malware detection model. Considering the unbalanced data, we made an unweighted and weighted model simultaneously. As a result, the weighted random forest model has the best effect and its accuracy is 98.94% © 2024 IEEE.

关键词： Machine learning

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 << < 24 25 26 27 28 29 30 31 32 33 > >>

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：