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

8,247 篇 期刊文献
7,688 篇 会议
213 册 图书

馆藏范围

16,148 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

9,433 篇 工学
- 4,851 篇 计算机科学与技术...
- 3,758 篇 软件工程
- 1,702 篇 电子科学与技术（可...
- 1,615 篇 电气工程
- 1,508 篇 信息与通信工程
- 1,221 篇 控制科学与工程
- 987 篇 材料科学与工程（可...
- 882 篇 化学工程与技术
- 868 篇 生物工程
- 780 篇 光学工程
- 673 篇 生物医学工程（可授...
- 653 篇 机械工程
- 596 篇 仪器科学与技术
- 566 篇 动力工程及工程热...
- 505 篇 核科学与技术
- 434 篇 力学（可授工学、理...
- 349 篇 冶金工程
7,918 篇 理学
- 4,259 篇 物理学
- 3,065 篇 数学
- 1,076 篇 生物学
- 1,060 篇 统计学（可授理学、...
- 1,010 篇 化学
- 561 篇 系统科学
- 323 篇 地球物理学
1,865 篇 管理学
- 1,211 篇 管理科学与工程(可...
- 704 篇 图书情报与档案管...
- 619 篇 工商管理
739 篇 医学
- 600 篇 临床医学
- 498 篇 基础医学(可授医学...
- 292 篇 药学(可授医学、理...
328 篇 法学
256 篇 经济学
191 篇 农学
185 篇 教育学
37 篇 文学
25 篇 艺术学
24 篇 军事学
4 篇 哲学
1 篇 历史学

主题

330 篇 deep learning
321 篇 computer science
259 篇 machine learning
206 篇 computational mo...
188 篇 accuracy
166 篇 feature extracti...
160 篇 optimization
153 篇 internet of thin...
134 篇 neural networks
133 篇 data mining
129 篇 artificial intel...
124 篇 predictive model...
124 篇 real-time system...
123 篇 support vector m...
122 篇 computational in...
116 篇 hadrons
104 篇 image segmentati...
100 篇 computer archite...
90 篇 educational inst...
87 篇 covid-19

机构

1,111 篇 cavendish labora...
1,045 篇 department of ph...
999 篇 physikalisches i...
994 篇 oliver lodge lab...
628 篇 fakultät physik ...
572 篇 department of ph...
570 篇 school of physic...
559 篇 horia hulubei na...
556 篇 iccub universita...
546 篇 school of physic...
533 篇 school of physic...
532 篇 h.h. wills physi...
531 篇 imperial college...
525 篇 nikhef national ...
523 篇 stfc rutherford ...
522 篇 university of ch...
516 篇 infn laboratori ...
504 篇 physik-institut ...
502 篇 infn sezione di ...
501 篇 infn sezione di ...

作者

292 篇 barter w.
264 篇 a. gomes
263 篇 beiter a.
254 篇 bellee v.
253 篇 c. alexa
252 篇 c. amelung
252 篇 blake t.
251 篇 j. m. izen
251 篇 g. bella
251 篇 j. strandberg
251 篇 d. calvet
249 篇 g. spigo
248 篇 brundu d.
246 篇 f. siegert
244 篇 bowcock t.j.v.
239 篇 s. chen
238 篇 betancourt c.
235 篇 borsato m.
231 篇 y. tayalati
230 篇 amato s.

语言

14,142 篇 英文
1,859 篇 其他
144 篇 中文
8 篇 日文
6 篇 德文
5 篇 法文
2 篇 西班牙文
2 篇 朝鲜文
1 篇 捷克文
1 篇 俄文
1 篇 斯洛文尼亚文

检索条件"机构=Computer Science Applied Computer Technology"

共 16148 条记录，以下是1521-1530 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：

Comparative Analysis of Docker Image Files Across Various Programming Environments 3rd

Comparative Analysis of Docker Image Files Across Various Pr...

引用

3rd International Semantic Intelligence Conference, ISIC 2023

作者： Singh, Kamred Udham Kumar, Ankit Kumar, Gaurav Singh, Teekam Choudhury, Tanupriya Kotecha, Ketan School of Computing Graphic Era Hill University Dehradun India Department of Computer Engineering & Applications GLA University UP Mathura India Department of Computer Science and Engineering Graphic Era Deemed to Be University Dehradun India Bidholi Uttarakhand Dehradun248007 India Symbiosis Centre for Applied Artificial Intelligence Symbiosis Institute of Technology Symbiosis International University Pune411045 India

ISBN: (纸本)9789819773558

Docker technology plays an essential part in improving the process of software development while also efficiently solving the issues related to deployment and hosting. Docker is able to work independently of the underlying hardware infrastructure since it uses virtualisation as its mode of operation. Instead, it makes use of its own collection of code in order to carry out the implementations of applications. The concept of enclosing the whole of an application's environment together with its source code is fundamental to Docker's approach. Docker successfully avoids the problems that are often associated with having a variety of working environments by using this method. This method, on the other hand, might result in a larger file size since it includes the whole environment in its representation. Within the Docker environment, these data files are referred to as "Images," and the considerable size of these images may be affected by a wide variety of variables. In the course of this study, a number of tests were carried out, each of which included the use of Docker to run the same lines of code on a variety of different platforms. The major aim was to improve the feasibility of conducting a thorough comparison study. Software engineers were given the ability to evaluate and compare results by exploiting Docker's capabilities, which finally enabled them to arrive at well-informed conclusions about the most appropriate course of action. The software development community is able to get useful insights into the benefits and considerations of using Docker on multiple platforms as a result of this research, which paves the way for a journey that is more streamlined in terms of both development and deployment. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Software design

来源：评论

学校读者我要写书评

暂无评论

Recognizing and Understanding Sport Activities based on Wearable Sensor Signals using Deep Residual Network 4

Recognizing and Understanding Sport Activities based on Wear...

引用

4th Research, Invention, and Innovation Congress: Innovative Electricals and Electronics, RI2C 2023

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Jitpattanakul, Anuchit Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand

ISBN: (纸本)9798350330618

The prevalence of intelligent wearable devices has significantly increased, offering advantages to individuals of all age groups. In the field of human activity recognition (HAR) research, wearable sensor data plays a crucial role in classifying various human actions. Sensor-based assessments are commonly employed in hazard analysis and risk assessments. However, traditional machine learning (ML) algorithms have faced challenges when applied to sports activities due to their unpredictable nature. These challenges have prompted the need for manual feature extraction, limiting the efficacy of ML methodologies in data categorization. To address this, our study introduces ResNet, a deep residual network, for effectively categorizing sports activities using wearable sensor data. To evaluate our proposed approach, we utilized a publicly available benchmark dataset consisting of sensor data collected from the dominant wrist, neck, and thigh of each study participant. The experimental results demonstrated the exceptional performance of the ResNet model, achieving an impressive accuracy rate of 99.83% in recognizing sport activities. Additionally, we compared the efficacy of our suggested model with five fundamental deep learning models, namely CNN, LSTM, BiLSTM, GRU, and BiGRU. The comparative analysis revealed that the ResNet model outperformed the other deep learning models, demonstrating superior performance in sport activity recognition. ©2023 IEEE.

关键词： Wearable sensors

来源：评论

学校读者我要写书评

暂无评论

Improving Inertial Sensor-based Human Activity Recognition using Ensemble Deep Learning 8

Improving Inertial Sensor-based Human Activity Recognition u...

引用

Joint 8th International Conference on Digital Arts, Media and technology and 6th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications Engineering, ECTI DAMT and NCON 2023

作者： Rojanavasu, Pornthep Jantawong, Ponnipa Jitpattanakul, Anuchit Mekruksavanich, Sakorn School of Information and Communication Technology Department of Computer Engineering University of Phayao Phayao Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Faculty of Applied Science King Mongkut's University of Technology North Bangkok Department of Mathematics Bangkok Thailand

ISBN: (纸本)9798350396546

Sensor-based human activity recognition (S-HAR) is a famous study focusing on detecting human physiological actions by interpreting various sensors, especially one-dimensional time series information. Typically, S-HAR machine learning methods were developed using handcrafted characteristics. Unfortunately, this is a complicated process that involves feature engineering and a high level of domain knowledge. Due to the development of deep neural networks, classification techniques could efficiently handle relevant characteristics from raw sensor data, resulting in enhanced classification outcomes. In this study, we describe a unique method for S-HAR based on ensemble deep learning with sensor nodes connected to the waist, chest, leg, and arm. Implementing and training three deep learning networks is performed using a publically available dataset, including wearable sensors from eight human actions. The findings demonstrate that the proposed Ens-ResNeXt model provides the maximum accuracy and F1-score, which is superior to existing techniques. © 2023 IEEE.

关键词： Learning systems

来源：评论

学校读者我要写书评

暂无评论

Human Activity Recognition and Payload Classification for Low-Back Exoskeletons Using Deep Residual Network 4

Human Activity Recognition and Payload Classification for Lo...

引用

4th Research, Invention, and Innovation Congress: Innovative Electricals and Electronics, RI2C 2023

ISBN: (纸本)9798350330618

Exoskeletons created by robotics currently hold significant potential for improving the well-being and quality of life for manufacturing laborers. However, the effectiveness of existing solutions is often hindered by inadequate control systems. The limited adoption of exoskeletons in occupational settings can be attributed to their inability to adapt to users and tasks, which is a result of the constantly changing circumstances in which they are utilized. To address this issue, wearable sensors capable of being carried on the human body can employ learning-based methodologies to anticipate and classify user motion characteristics by training on extensive datasets. This study introduces a deep learning approach that utilizes inertial sensors to equip manufacturing exoskeletons with the ability to recognize human movements and adjust for payload compensation. Inertial measurement equipment can be conveniently integrated into manufacturing exoskeletons through either wearable or embeddable means. A deep residual network was employed for human action identification and categorization of lifted object weights up to 15 kg. The study achieved an average F1-score of 93.91% for activity identification and 94.35% for payload classification using the provided model. The model was trained and tested on a sample of 12 young, healthy volunteers. ©2023 IEEE.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

Attention Based Hybrid Deep Learning Network for Locomotive Mode Recognition in Natural Environments Using Wearable Sensors 4

Attention Based Hybrid Deep Learning Network for Locomotive ...

引用

4th Research, Invention, and Innovation Congress: Innovative Electricals and Electronics, RI2C 2023

ISBN: (纸本)9798350330618

The field of pervasive computing encompasses the significant domain of study known as WS-LMR, which focuses on identifying locomotive modes through wearable sensors. WS-LMR aims to automatically evaluate and interpret individual locomotive actions along with contextual information using wearable sensor data. This technology finds applications in medical surveillance systems and various systems involving interactions between individuals and intelligent wearable devices. However, existing WS-LMR algorithms are often evaluated using data collected under controlled conditions, limiting their effectiveness in uncontrolled real-world environments. This scholarly article addresses the primary objective of identifying human locomotion activities in real-world contexts. To enhance the efficacy of the WS-LMR system, our study proposes the utilization of an attention-based hybrid deep learning network that combines convolutional neural network and long-short term memory networks, referred to as the Att-CNN-LSTM model. We evaluated the performance of the proposed network using the publicly available Bath Natural Environment HAR dataset, a benchmark LMR dataset. Both model training and testing were conducted using a 5-fold cross-validation approach. The Att-CNN-LSTM model achieved the highest accuracy of 94.25% and an F1-score of 91.47% based on the results obtained from multiple experiments. ©2023 IEEE.

关键词： Benchmarking

来源：评论

学校读者我要写书评

暂无评论

Position-aware Human Activity Recognition with Smartphone Sensors based on Deep Learning Approaches 46

Position-aware Human Activity Recognition with Smartphone Se...

引用

46th International Conference on Telecommunications and Signal Processing, TSP 2023

作者： Mekruksavanich, Sakorn Jitpattanakul, Anuchit Technology University of Phayao School of Information and Communication Department of Computer Engineering Phayao Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9798350303964

Numerous research studies on human activity recognition (HAR) have utilized smartphone sensors, such as accelerometers, gyroscopes, and magnetometers. The accelerometer, in particular, has been considerably explored as the primary sensor in HAR research. Recently, researchers have integrated different smartphone sensors to enhance recognition interpretation. Nevertheless, it is still challenging to determine the optimal usage of these sensors, either individually or in combination, for better identification effectiveness in various motion situations. In this article, we explore how different motion sensors behave during the activity recognition process, using various deep learning (DL) approaches to classify physical actions based on smartphone sensor data. We evaluate the DL models using a publicly benchmarked dataset of data collected from ten participants performing eight movements while carrying smartphones in different situations. Our experimental results reveal that, except for the magnetometer, each sensor can play a leading role in HAR, depending on the recognized activity, body position, data features employed, and classification approach. Additionally, we find that the combination of sensors only improves overall recognition interpretation when their individual performances are low. © 2023 IEEE.

关键词： Smartphones

来源：评论

学校读者我要写书评

暂无评论

Optimizing HCV Diagnosis and Hospital Resource Management Using Machine Learning Techniques 3

Optimizing HCV Diagnosis and Hospital Resource Management Us...

引用

3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Bisht, Shilpi Bisht, Neeraj Pandey, Bishwajeet Patil, Vedant Srivastava, Sonalika Srivastava, Anshul Birla Institute of Applied Sciences Department of Mathematics Bhimtal India Birla Institute of Applied Sciences Department of Computer Science Bhimtal India Astana It University Department of Information Technology Kazakhstan Amity International Business School Amity University Noida India King George Medical University Department of Oral Medicine and Radiology Lucknow India

ISBN: (纸本)9798331518592

The aim of this research effort is to improve hospital profitability and management by using cutting-edge machine learning algorithms to accurately diagnose the Hepatitis C virus (HCV). Given that over 80% of individuals have no symptoms and that HCV can induce potentially fatal liver malignancies, this virus poses a serious threat to public health. Efficient hospital administration and resource optimization depend on timely and reliable HCV prediction. The study we conducted makes use of the 'HCV' dataset from the UCI Machine Learning Repository, which contains laboratory results from 615 cases involving HCV patients and blood donors, some of which have missing values. The dataset includes 13 attributes altogether and includes 533 instances of blood donors, 7 suspected cases, and 75 instances of HCV patients. We used Linear Regression to fill in the missing data to keep our predictive models accurate and reliable. We selected to use Analysis of Variance (ANOV A) for feature selection since it is a powerful tool for evaluating the correlations between features, which allowed us to increase model efficiency and decrease computation time. The decision tree algorithm, which is resilient in HCV identification, attained the greatest accuracy of 98.78% among the many machine learning techniques used, both before and after applying ANOV A. This paper presents novel strategies that can greatly increase diagnostic efficiency and accuracy in hospital settings. Examples of these strategies include the use of ANOV A for feature selection and Linear Regression for managing missing data. We tried to improve patient outcomes and maximize hospital resource use by utilizing machine learning for improved HCV prediction, which ultimately drives profitability. To determine which machine learning algorithm is the most successful, future research and real-world applications in comparable medical situations can benefit greatly from a comparative examination of several algorithms. © 2024 I

关键词： Efficiency

来源：评论

学校读者我要写书评

暂无评论

Study the Improved Efficiency of the Lead-Free CsSnI3 Perovskite Solar Cells with Variation of Absorber Layer in SCAPS-1D 3

Study the Improved Efficiency of the Lead-Free CsSnI3 Perovs...

引用

3rd IEEE International Conference of Electron Devices Society Kolkata Chapter, EDKCON 2024

作者： Barik, Arijit Kumar Das, Himadri Sekhar Banerjee, Sudipta Maity, Heranmoy Mishra, Santanu Chatterjee, Mousam Bhattachrya, Bidrohi Jana, Asim Kumar Roymahpatra, Gourisankar Ramakrishna Mission Vidyamandira Electronics and communication Engineering Howrah India Haldia Institute of Technology Electronics and communication Engineering Haldia India Computer Science and Enginerring Pune India Haldia Institute of Technology Department of Applied Science Haldia India Electronics and Communication Engineering B. P. Poddar Institute of Management and Technology Kolkata India Electrical and Electronics Engineering Pailan college of Management and Technology Kolkata India

ISBN: (纸本)9798350374643

In this research work, a novel orderly study was conducted to explore the different parameters effect of thickness of the protection tier on adeptness of the FTO/TiO2/CsSnI3/Cu2O/Au Perovskite solar cell by SCAPS-1D. To realize the optimized power conversion efficiency (PCE), acceptor density of the Perovskite absorption layer is (NA=2×1019cm-3) with a 40nm TiO2layer, 300 nm for the Perovskite absorption layer (CsSnI3) and 10 nm Cu2o as HTL, with Au as electrode. After optimization process, efficiency reached from 13.60% to 17.17%. A result indicates the enhancement performance of the Perovskite solar cells (PSC). © 2024 IEEE.

关键词： Perovskite

来源：评论

学校读者我要写书评

暂无评论

Deep Learning Approaches for Epileptic Seizures Recognition based on EEG Signal 46

Deep Learning Approaches for Epileptic Seizures Recognition ...

引用

46th International Conference on Telecommunications and Signal Processing, TSP 2023

作者： Mekruksavanich, Sakorn Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9798350303964

Recognition system of human actions is a popular research field in healthcare which is essential in identifying abnormal patient activities to estimate their psychological state. Epileptic seizures are a common neurological disorder affecting millions of individuals globally. Early and accurate diagnosis of epilepsy can lead to up to 70% of seizure-free patients. There is a requirement for smart and automated HAR systems to aid clinicians in accurately diagnosing neurological disorders. This study examines the latest deep learning networks for time-series classification to detect epileptic seizures using EEG signal data. To enhance recognition performance, a hybrid residual network called Inception-ResNet is presented for the binary classification of epileptic activities. Through thorough analysis, the Inception-ResNet model was observed to provide the best results, with an average accuracy and F1-score of 99.61%. © 2023 IEEE.

关键词： Electroencephalography

来源：评论

学校读者我要写书评

暂无评论

A Sustainable Approach for Determining Compromise Ranking Based on Intuitonistic Fuzzy Score Functions

A Sustainable Approach for Determining Compromise Ranking B...

引用

Special Sessions in the Advances in Information Systems and Technologies Track, AIST 2022 and 17th Conference on Information Systems Management, ISM 2022 held as part of the Federated Conference on computer science and Information Systems, FedCSIS 2022

作者： Paradowski, Bartosz Kizielewicz, Bartlomiej Więckowski, Jakub Salabun, Wojciech Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology Szczecin Poland National Institute of Telecommunications Szachowa 1 Warsaw04-894 Poland

ISBN: (纸本)9783031295690

Many real-world decision-making problems require some degree of uncertainty to be taken into account. For purpose of representing such problems, intuitionistic fuzzy sets are used, however, most well-known multi-criteria decision-making methods operate in a crisp environment. In this paper, we present an assessment of score functions that are used to convert fuzzy numbers into crisp ones. Five score functions were selected to assess their usefulness and effectiveness. Those functions were used to transform the theoretical fuzzy decision matrix problems into a crisp environment for evaluating alternatives using the Measurement Alternatives and Ranking according to COmpromise Solution (MARCOS) method. In addition, the compromise between score functions is presented and compared with the other results. The research showed that score functions are useful tools when dealing with problems in an uncertain environment and might prove helpful for decision-makers. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Decision making

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 << < 149 150 151 152 153 154 155 156 157 158 > >>

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：