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

6,815 篇 会议
15 篇 期刊文献
11 册 图书

馆藏范围

6,841 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

4,367 篇 工学
- 3,831 篇 计算机科学与技术...
- 3,203 篇 软件工程
- 885 篇 信息与通信工程
- 710 篇 生物工程
- 591 篇 控制科学与工程
- 562 篇 生物医学工程（可授...
- 500 篇 电气工程
- 304 篇 电子科学与技术（可...
- 290 篇 光学工程
- 285 篇 安全科学与工程
- 238 篇 动力工程及工程热...
- 223 篇 机械工程
- 216 篇 交通运输工程
- 210 篇 化学工程与技术
- 200 篇 仪器科学与技术
- 180 篇 网络空间安全
2,150 篇 理学
- 1,271 篇 数学
- 766 篇 生物学
- 656 篇 统计学（可授理学、...
- 399 篇 系统科学
- 331 篇 物理学
- 235 篇 化学
1,308 篇 管理学
- 717 篇 管理科学与工程(可...
- 648 篇 图书情报与档案管...
- 394 篇 工商管理
597 篇 医学
- 531 篇 临床医学
- 502 篇 基础医学(可授医学...
- 310 篇 药学(可授医学、理...
- 282 篇 公共卫生与预防医...
199 篇 法学
188 篇 农学
- 184 篇 作物学
172 篇 经济学
137 篇 教育学
11 篇 军事学
2 篇 文学

主题

998 篇 machine learning
672 篇 accuracy
630 篇 deep learning
392 篇 machine learning...
384 篇 support vector m...
360 篇 predictive model...
355 篇 data models
303 篇 training
300 篇 feature extracti...
246 篇 adversarial mach...
223 篇 computational mo...
207 篇 convolutional ne...
197 篇 data mining
181 篇 real-time system...
179 篇 prediction algor...
173 篇 forecasting
144 篇 decision trees
144 篇 random forests
132 篇 analytical model...
117 篇 classification a...

机构

131 篇 chitkara univers...
69 篇 maharishi school...
53 篇 centre of interd...
22 篇 department of co...
21 篇 uttaranchal inst...
21 篇 school of electr...
20 篇 department of el...
19 篇 maharishi school...
19 篇 department of ar...
18 篇 graphic era deem...
18 篇 computer science...
17 篇 college of techn...
17 篇 department of in...
16 篇 pune
15 篇 graphic era hill...
15 篇 computer science...
14 篇 chitkara univers...
14 篇 department of co...
14 篇 graphic era hill...
13 篇 vishwakarma inst...

作者

19 篇 gill kanwarparta...
17 篇 shiva mehta
17 篇 mehta shiva
16 篇 kanwarpartap sin...
14 篇 chauhan rahul
12 篇 vinay kukreja
11 篇 kukreja vinay
10 篇 singh jagendra
9 篇 rahul chauhan
8 篇 deepak upadhyay
8 篇 akhilendra prata...
8 篇 girija shankar s...
7 篇 deepak banerjee
7 篇 kumar manish
7 篇 babu tina
7 篇 anand vatsala
7 篇 sheifali gupta
7 篇 vatsala anand
7 篇 radha d.
6 篇 banerjee deepak

语言

5,563 篇 英文
1,275 篇 其他
91 篇 中文
3 篇 土耳其文

检索条件"任意字段=3rd International Conference on Machine Learning and Data Engineering, ICMLDE 2024"

共 6841 条记录，以下是4971-4980 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：

Enhancing RIS-aided D2D communication for IoT applications

Enhancing RIS-aided D2D communication for IoT applications

引用

Artificial Intelligence For Internet of Things (AIIoT), international conference on

作者： V Varun M Vishwaa R Shanjay V Noor Mohammed School of Electronics Engineering Vellore Institute of Technology Vellore India

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

Reconfigurable intelligent Surface (RIS) is a potential 5G/6G technology that provides energy efficient solutions to the increasing demand of communication network resources. The number of Internet of Things (IoT) nodes in a network is also on the rise as automation is being implemented in different fields. In this paper, the data rates for RIS-aided Device to Device (D2D) communication between two IoT nodes is maximized by defining a non-convex optimization problem with Quality of Service ($\mathrm{Q} 0 \mathrm{~S}$) constraints. Due to the non-convex nature of the problem, an alternate Reinforcement learning based approach is implemented instead of a traditional mathematical approach as it is computationally exhaustive. The results obtained on the implementation of the proposed approach gives enhanced performance in the communication link.

关键词： Automation Reinforcement learning Quality of service Reconfigurable intelligent surfaces Energy efficiency Convex functions Device-to-device communication

来源：评论

学校读者我要写书评

暂无评论

Design and Smart Audit System Using PSO Algorithm

Design and Smart Audit System Using PSO Algorithm

引用

data Analytics, Computing and Artificial Intelligence (ICDACAI), international conference on

作者： Yiqiang Wu Tianbo Sun Da Chang State Grid Shanghai Electric Power Company Shanghai China Beijing Xintong Accenture Information Technology Co. Ltd. Beijing China

ISBN: (数字)9798331519032

ISBN: (纸本)9798331519049

Internal audit is an important function of enterprises, and its effective operation is directly related to the healthy development of enterprises. In the network environment, the process of enterprise informatisation is accelerating, and the internal audit of enterprises can face more and more complex multi-source data. In such an environment, the traditional audit mode can no longer meet the needs of the new situation. Using a variety of deep learning methods such as web crawler, visual analysis, recurrent neural network natural language processing, convolutional neural network image processing, etc., it can establish an intelligent auditing system, break the information barrier and data barrier of management, assets, business and other aspects, quickly and accurately locate and describe the problems found by auditing, do the most basic work in the auditing business, improve the efficiency of business processing, and reduce the number of Auditor's mechanical man-hours, which is an inevitable trend. In the field of auditing, traditional methods face challenges in handling big data and complex calculations. This study designed an intelligent audit system using the Particle Swarm Optimization (PSO) algorithm, which can automate data analysis and risk assessment. By combining audit knowledge and machine learning techniques, it not only improve the efficiency of auditing, but also enhance the accuracy of decision-making. Research has shown that PSO optimization methods are more accurate in identifying abnormal projects. This new audit system has important practical and theoretical value in improving audit quality and efficiency.

关键词： Visualization machine learning algorithms Accuracy data analysis Recurrent neural networks Big data Risk management Particle swarm optimization Faces Business

来源：评论

学校读者我要写书评

暂无评论

Multifunctional Pre-processing Approach for Diabetes Mellitus Prediction Using Ensemble learning

Multifunctional Pre-processing Approach for Diabetes Mellitu...

引用

Artificial Intelligence For Internet of Things (AIIoT), international conference on

作者： S Divya M Sudha School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

Diabetes mellitus when untreated can result in a number of health issues. It is a metabolic disease marked by abnormal blood glucose levels. Early detection of diabetes improves a person’s long-term health by halting its progression. Making better medical decisions can be greatly aided through the implementation into practice automated techniques that can precisely identify pre- and diabetic situations. In this paper Synthetic Minority Oversampling Technique and Adaptive Oversampling Technique is used to address class imbalance problem and Chi-square test, Recursive feature elimination methods are involved in detecting and extracting relevant components from unprocessed data. These techniques are applied to the framework and compared using different ensemble learning classifiers such as XGBoost, AdaBoost, LightGBM and CATBoost. The result shows us CATBoost with ADASYN and RFE provides us with better accuracy of 0.96%.

关键词： Accuracy Organizations Medical services Feature extraction Prediction algorithms Diabetes Planning

来源：评论

学校读者我要写书评

暂无评论

Deep learning-Driven Diagnosis A CNN-SVM Hybrid Approach for Automated Detection of Strawberry Leaf Diseases

Deep Learning-Driven Diagnosis A CNN-SVM Hybrid Approach for...

引用

Applied Intelligence and Computing (AIC), IEEE World conference on

作者： Ravi Ranjan Kumar Rahul Chauhan Shiv Ashish Dhondiyal Akira Singh Chitkara University Institute of Engineering and Technology Chitkara University Punjab India CSE Department Graphic Era Hill University Dehradun Uttarakhand India

ISBN: (数字)9798350384598

ISBN: (纸本)9798350384604

This hybrid approach is the combination of a Convolutional Neural Network and Support Vector machine used in the automatic detection and classification of diseases in strawberry leaves. The architecture used, comprising three convolutional layers, three max-pampling layers, and one fully connected layer with ReLU for feature extraction, identifies the discriminative features from strawberry leaf images extracted using CNN and then feeds them into the SVM classifier for disease classification. The technique of transfer learning is followed in order to achieve the best model performance based on limited data. The efficiency of the model is measured based on accuracy, precision, recall, and F1-score. The encouraging results given by the hybrid model CNN-SVM provide an effective framework for the automatic diagnosis of a lot of the diseases of the strawberry leaf, thus helping in the contribution to the best practices of managing crops and optimizing yields in strawberry cultivation.

关键词： Support vector machines Accuracy Transfer learning Crops Feature extraction data models Convolutional neural networks Reliability Medical diagnosis Diseases

来源：评论

学校读者我要写书评

暂无评论

Actionable Intelligence for Sales Call Recordings

Actionable Intelligence for Sales Call Recordings

引用

Delhi Section conference (DELCON), IEEE

作者： Palanati Neha Gudditi Chetan Srinaini Garimella Malla Sreejagadeesh Beebi Naseeba School of Computer Science and Engineering VIT-AP University Vijyawada India

ISBN: (数字)9798331518592

ISBN: (纸本)9798331518608

This research highlights the challenges associated with assessing large volumes of recorded discussions, including issues with data privacy and legal compliance. This research main objective is to increase customer happiness and sales performance by using data-driven insights from sales call records. The dataset consists of sales call recordings transcribed into text, containing customer interactions, questions, objections, and competitor mentions. This work utilizes natural language processing (NLP) techniques such as Named Entity Recognition (NER), sentiment analysis (using VADER), and keyword extraction. The results revealed key insights, such as customer pain points, sentiment trends, competitor mentions, and common objections. The negative statements of text is around 2.7%, neutral statements of text reflects 69.7%, positive statements of text reflects 27.6% and compound statements of text reflects 0.9999%.

关键词： Sentiment analysis Analytical models machine learning algorithms Pain Organizations Named entity recognition Market research Natural language processing Real-time systems Recording

来源：评论

学校读者我要写书评

暂无评论

machine learning-Driven Demand Prediction for Off-Grid PV-Integrated EV Charging Stations

Machine Learning-Driven Demand Prediction for Off-Grid PV-In...

引用

Robotics, Automation, Artificial-intelligence and Internet-of-Things (RAAICON), 2019 IEEE international conference on

作者： Sowrov Komar Shib Durjoy Roy Dipto Md Tanvir Rahman Md Mostafizur Rahman Riadul Islam Abu Shufian Dept. of Electrical & Electronic Engg American International University-Bangladesh Dhaka Bangladesh Dept. of Management Information System Lamar University Texas United States Dept. of Electrical & Electronic Engg Lamar University Texas United States

ISBN: (数字)9798331534400

ISBN: (纸本)9798331534417

The rapid growth of electric vehicles (EVs) has led to an increasing demand for charging infrastructure, particularly in off-grid locations. Developing off-grid EV charging stations powered by photovoltaic (PV) systems is crucial to support this expansion. However, the variability of solar power presents challenges in maintaining efficient and reliable charging services. This study focuses on developing off-grid EV charging stations and optimizing load demand prediction using machine learning models to enhance the operational efficiency of these charging stations. Various algorithms, including Random Forest, Decision Tree, SVM, Logistic Regression, and Naïve Bayes, were employed to model and predict load demand based on historical data and real-time inputs. The results indicate that the Random Forest and Decision Tree models achieved the highest accuracy, at 93% and 92%, respectively. The effectiveness of machine learning in dynamically adjusting power distribution according to predicted demand is highlighted, reducing reliance on traditional energy grids and supporting the sustainable development of EV charging solutions in remote areas. Integrating these predictive models allows for better management of energy resources, ensuring a consistent and cost-effective power supply and facilitating the broader adoption of renewable energy in EV infrastructure.

关键词： Renewable energy sources machine learning algorithms Accuracy Biological system modeling Predictive models Electric vehicle charging Decision trees Reliability Random forests Load modeling

来源：评论

学校读者我要写书评

暂无评论

Leveraging Transfer learning from SkimLit to Improve Sentence Classification in NSF Grants and Awards Abstracts

Leveraging Transfer Learning from SkimLit to Improve Sentenc...

引用

Computing and machine Intelligence (ICMI), international conference on

作者： Tianyi Li Chengcheng Li CECH University of Cincinnati Cincinnati USA

ISBN: (数字)9798350372977

ISBN: (纸本)9798350372984

Transfer learning has emerged as a pivotal technique in natural language processing, enabling the reuse of knowledge learned from one domain to enhance performance in another. In this study, we explore the application of transfer learning to the domain of research abstracts, specifically focusing on the task of sentence classification. Leveraging the PubMed 200k RCT dataset introduced by Franck Dernoncourt and Ji Young Lee, this study investigates the potential for transferring knowledge from machine learning models generated from this dataset to improve the classification of manually collected abstracts from NSF's grants and awards submissions. Our research demonstrates that transfer learning from the PubMed 200k RCT dataset model enhances the performance of a machine learning model when applied to the new dataset of NSF abstracts. The model trained on the combined knowledge from both datasets outperforms a model trained solely on the NSF dataset. The accuracy in classification improved by 28.36% for a transfer learning model with one re-trained layer and improved by 51.07% for a model with two re-trained layers. The time-to-run for the transfer models demonstrated significant improvement as well, while there were only minimal improvements in loss. The observed improvements in classification accuracy and predictive capability demonstrate the effectiveness of knowledge transfer between abstracts of different domains. Through this research, we provide valuable insights into the benefits of transfer learning for sentence classification tasks. Our findings not only validate the utility of pre-existing domain-specific datasets but also highlight the potential for broader application of transfer learning techniques in medical research and document classification. This study contributes to the growing field aimed at utilizing the power of transfer learning in text analysis and classification.

关键词： Adaptation models Accuracy Computational modeling Transfer learning Text categorization data models Natural language processing

来源：评论

学校读者我要写书评

暂无评论

Recognition of Mild Cognitive Impairment in the Elderly Based on machine learning 3

Recognition of Mild Cognitive Impairment in the Elderly Base...

引用

3rd IEEE international conference on Communications, Information System and Computer engineering, CISCE 2021

作者： Guo, Shuhan Liu, Yanru Wang, Pengyun Zhang, Sen Han, Buxin Li, Juan Xiao, Wendong Center on Aging Psychology Cas Key Laboratory of Mental Health Institute of Psychology Beijing China University of Chinese Academy of Sciences Department of Psychology Beijing China School of Automation Electrical Engineering University of Science and Technology Beijing Beijing100083 China

ISBN: (纸本)9780738112152

As the prodromal stage of Alzheimer's disease, effective recognition of mild cognitive impairment can reduce the prevalence of Alzheimer's disease. At present, most of the research on the recognition of mild cognitive impairment is carried out through biomarkers and neuroimaging, which is not conducive to large-scale analysis and research. Based on neuropsychological evaluation and life habits questionnaires, this article applies machine learning methods to the recognition of mild cognitive impairment and conducts experimental research. A questionnaire survey of the elderly was conducted to obtain raw data, including demographic variables, daily habits and neuropsychological data. Feature selection is carried out through filter screening method and the influence of factors such as lifestyle, physical health and learning ability on the morbidity of mild cognitive impairment is analyzed. The classifier mainly uses three methods: artificial neural network, support vector machine and random forest. The experimental results show that the random forest classification effect is the best and the accuracy rate is as high as 92%. Artificial neural network has strong generalization ability with 91% accuracy rate. Support vector machine has inferior effect. © 2021 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Assessment of Developmental Dysgraphia Utilising a Display Tablet 21st

Assessment of Developmental Dysgraphia Utilising a Display...

引用

The 21st international Graphonomics Society conference, IGS 2023

作者： Mekyska, Jiri Galaz, Zoltan Safarova, Katarina Zvoncak, Vojtech Cunek, Lukas Urbanek, Tomas Havigerova, Jana Marie Bednarova, Jirina Mucha, Ján Gavenciak, Michal Smekal, Zdenek Faundez-Zanuy, Marcos Department of Telecommunications Faculty of Electrical Engineering and Communication Brno University of Technology Brno Czech Republic Department of Research Methodology Institute of Psychology The Czech Academy of Sciences Brno Czech Republic Tecnocampus Universitat Pompeu Fabra Barcelona Mataro Spain

ISBN: (纸本)9783031454608

Even though the computerised assessment of developmental dysgraphia (DD) based on online handwriting processing has increasing popularity, most of the solutions are based on a setup, where a child writes on a paper fixed to a digitizing tablet that is connected to a computer. Although this approach enables the standard way of writing using an inking pen, it is difficult to be administered by children themselves. The main goal of this study is thus to explore, whether the quantitative analysis of online handwriting recorded via a display/screen tablet could sufficiently support the assessment of DD as well. For the purpose of this study, we enrolled 144 children (attending the 3rd and 4th class of a primary school), whose handwriting proficiency was assessed by a special education counsellor, and who assessed themselves by the Handwriting Proficiency Screening Questionnaires for Children (HPSQ–C). Using machine learning models based on a gradient-boosting algorithm, we were able to support the DD diagnosis with up to 83.6% accuracy. The HPSQ–C total score was estimated with a minimum error equal to 10.34%. Children with DD spent significantly higher time in-air, they had a higher number of pen elevations, a bigger height of on-surface strokes, a lower in-air tempo, and a higher variation in the angular velocity. Although this study shows a promising impact of DD assessment via display tablets, it also accents the fact that modelling of subjective scores is challenging and a complex and data-driven quantification of DD manifestations is needed. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： machine learning

来源：评论

学校读者我要写书评

暂无评论

Optimizing Electric Vehicle Charging Station Placement in Urban Areas: A data-Driven Approach

Optimizing Electric Vehicle Charging Station Placement in Ur...

引用

Advance Computing and Innovative Technologies in engineering (ICACITE), international conference on

作者： Mridul Shukla Deepak Kumar Singh Ashwani Yadav Abhishek Kumar Singh Nadia Adel Jumaa Aymen Mohammed Galgotias College of Engineering and Technology Collage of Engineering National University of Science and Te Medical Laboratory Techniques Department Al-Farahidi Universit

This paper presents a new method for determining the best locations for electric vehicle charging stations in cities. The proposed optimization model uses data analysis and machine learning techniques to predict the demand for charging stations based on various factors, including driving patterns, population density, and the distribution of commercial and residential areas. An optimization algorithm then identifies the optimal placement of charging stations that can meet the predicted demand while minimizing infrastructure costs. Simulation studies demonstrate that the proposed model provides a more efficient and cost-effective deployment of charging stations when compared to existing approaches. This practical and innovative solution can inform decision-making processes for charging station deployment, reduce the cost and complexity of charging infrastructure installation. The proposed data-driven approach can provide valuable insights for policymakers, helping to guide infrastructure deployment and reduce costs. It also advances the state of the art in electrical and electronics engineering, introducing a novel and practical method to optimize EV charging infrastructure deployment in urban areas

关键词：

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 << < 491 492 493 494 495 496 497 498 499 500 > >>

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：