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

2,590 篇 会议
929 篇 期刊文献
36 册 图书

馆藏范围

3,555 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

1,979 篇 工学
- 1,506 篇 计算机科学与技术...
- 1,239 篇 软件工程
- 553 篇 信息与通信工程
- 392 篇 控制科学与工程
- 259 篇 电气工程
- 209 篇 生物工程
- 202 篇 电子科学与技术（可...
- 196 篇 网络空间安全
- 181 篇 生物医学工程（可授...
- 151 篇 光学工程
- 149 篇 动力工程及工程热...
- 144 篇 安全科学与工程
- 125 篇 化学工程与技术
- 122 篇 机械工程
- 111 篇 交通运输工程
- 94 篇 仪器科学与技术
1,124 篇 理学
- 581 篇 数学
- 327 篇 物理学
- 252 篇 生物学
- 211 篇 统计学（可授理学、...
- 141 篇 化学
- 118 篇 系统科学
604 篇 管理学
- 367 篇 管理科学与工程(可...
- 266 篇 图书情报与档案管...
- 191 篇 工商管理
266 篇 医学
- 192 篇 临床医学
- 181 篇 基础医学(可授医学...
- 161 篇 公共卫生与预防医...
- 103 篇 药学(可授医学、理...
125 篇 法学
- 103 篇 社会学
67 篇 农学
62 篇 经济学
23 篇 教育学
15 篇 文学
8 篇 军事学
5 篇 艺术学

主题

225 篇 accuracy
147 篇 deep learning
130 篇 machine learning
126 篇 real-time system...
112 篇 computational mo...
109 篇 internet of thin...
109 篇 convolutional ne...
107 篇 feature extracti...
95 篇 predictive model...
84 篇 support vector m...
77 篇 training
70 篇 optimization
70 篇 technological in...
66 篇 cloud computing
66 篇 data models
65 篇 artificial intel...
60 篇 security
56 篇 machine learning...
53 篇 wireless sensor ...
48 篇 reliability

机构

115 篇 lovely professio...
107 篇 department of co...
90 篇 department of co...
57 篇 department of co...
52 篇 lloyd law colleg...
49 篇 gla university d...
47 篇 department of co...
42 篇 school of comput...
41 篇 lloyd institute ...
39 篇 school of comput...
39 篇 chitkara univers...
39 篇 beijing engineer...
35 篇 gla university d...
35 篇 institute of aer...
34 篇 department of co...
34 篇 graphic era deem...
33 篇 the islamic univ...
31 篇 graphic era deem...
30 篇 saveetha school ...
30 篇 lloyd institute ...

作者

47 篇 neeraj varshney
37 篇 rakesh kumar
37 篇 varshney neeraj
34 篇 singh ashutosh k...
29 篇 kumar rakesh
28 篇 singh kamred udh...
28 篇 saloni bansal
28 篇 huang heyan
27 篇 singh teekam
25 篇 bibhuti bhusan d...
25 篇 asha v.
24 篇 kumar ankit
24 篇 sudhansu shekhar...
23 篇 saxena deepika
22 篇 kumar gaurav
22 篇 patra sudhansu s...
20 篇 n. naga saranya
19 篇 shrivastava anur...
19 篇 bansal saloni
19 篇 anurag shrivasta...

语言

2,800 篇 英文
731 篇 其他
32 篇 中文

检索条件"机构=Institute for Computer Applications in Science and Engineering"

共 3555 条记录，以下是361-370 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：

Design and Analysis of High-Speed Communication Systems for 5G and Beyond 9

Design and Analysis of High-Speed Communication Systems for ...

引用

9th International Conference on science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Jegadeesan, R. Srinivas, Dava Deivakani, M. Joshi, Sunita Dinesh, P.M. Kumar, M. Jogendra Jyothishmathi Institute of Technology And Science Department of Computer Science And Engineering Telangana Karimnagar505481 India Psna College of Engineering And Technology Department of Electronics And Communication Engineering Tamil Nadu Dindigul624622 India Manav Rachna International Institute of Research And Studies Department of Faculty of Computer Applications Haryana Faridabad121004 India Sona College of Technology Department of Electronics And Communication Engineering Tamil Nadu Salem636005 India KoneruLakshmaiah Education Foundation Department of Computer Science And Engineering Andhra Pradesh Vaddeswaram522302 India

ISBN: (纸本)9798350365092

Fifth Era Innovation (5G) is the late portable network in wireless communications. This article presents an assessment of the advancements in portable communications. The different issues that arise at each stage are reported in our guide to state-of-the-art wearable networks. The fifth era (5G) of portable networks offers everybody admittance to high-speed web at whatever point they need, any place they are. Since to its inventive qualities, such connecting individuals and controlling contraptions, machines, and items, 5G is somewhat unique. Various levels of execution and capacities presented by the 5G versatile innovation will make new client encounters and connection new organizations. It is pivotal to comprehend where the business can utilize5G's benefits. This work examines and models a microstrip fix antenna working at 29 GHz for impending 5G communication innovation. The result of this reproduction is better than past work that was finished. It can then be used as a significant source of strength for 5G wireless technologies. The ongoing logic diary demonstrates that the proposed antenna performs better than those currently in use. This antenna may therefore be adequate for his 5G wireless connection architecture. © 2024 IEEE.

关键词： 5G mobile communication systems

来源：评论

学校读者我要写书评

暂无评论

Lake Ice Infrared Image De-noising using Deep Learning Convolutional Neural Network 4

Lake Ice Infrared Image De-noising using Deep Learning Convo...

引用

4th International Conference on Emerging Research in Electronics, computer science and Technology, ICERECT 2022

作者： Bharathi, P.T. Kiran, K. Prashanth, G.K. Virupakshaiah, H.K. Karegowda, Asha Gowda Siddganga Institute of Technology Department of Computer Science & Engineering Tumakuru India Siddganga Institute of Technology Department of Master of Computer Applications Tumakuru India

ISBN: (纸本)9781665456357

Elimination of noise from the acquired images is one of the fundamental tasks so as to restore the high quality of images to increase the results of segmentation, classification, recognition, detection etc. In this research work, Infrared imaging is used to capture the images with various temperature ranges during day and night time. Infrared thermography is used as a nondestructive image acquisition sensor. So infrared imaging sensors are used to identify and classify different ice types formed on the rivers during winter season in western countries. However, you can observe blur and degradation in the acquired images through infrared imaging, as the climatic nature is involved in the process. These factors affect the quality of the image and also the quantitative analyses of the images. Especially, when there are deeper faults which are found in an under laying substance and high thermal conductivity materials can also be studied. Acquired infrared images are usually affected with noises. In this paper, de-noising of infrared images is carried out using convolution neural network model in deep learning. In this work Gaussian white noise is added to the infrared images with varying noise intensities from 1% to 10% and then the convolution neural network model is applied to de-noise the infrared images. In the qualitative analysis of the infrared images edge factor, uniform region, texture, smoothness, non-uniform region, structure of the region of interest is considered. For quantitative analysis, mean square error, peak-signal- noise-ratio and structure similarity index measure is used. The results of traditional methods of image de-noising are compared with convolution neural network based method. It is concluded from the experimental work that the convolution neural network based method proves to be better for removal of Gaussian white noise when compared with traditional methods. © 2022 IEEE.

关键词： Mean square error

来源：评论

学校读者我要写书评

暂无评论

Utilizing Random Forests for High-Accuracy Classification in Medical Diagnostics 7

Utilizing Random Forests for High-Accuracy Classification in...

引用

7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Maindola, Meenakshi Al-Fatlawy, Ramy Riad Kumar, Rakesh Boob, Nandini Shirish Sreeja, S.P. Sirisha, N. Srivastava, Abhinav Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India The Islamic university College of technical engineering Department of computers Techniques engineering Najaf Iraq Gla University Department of Computer Engineering & Applications Mathura India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamilnadu Chennai India New Horizon College of Engineering Master of Computer Application Bangalore India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Fs University Shikohabad Firozabad India

ISBN: (纸本)9798350350067

In recent years, medical diagnostics has increasingly relied on machine learning techniques to improve accuracy and efficiency. Among these, the Random Forest algorithm has emerged as a powerful tool for classification tasks due to its robustness and ability to handle complex datasets. This paper explores the application of Random Forests in medical diagnostics, specifically focusing on enhancing the accuracy of disease classification. By analyzing large, heterogeneous medical datasets, we demonstrate how Random Forests can outperform traditional classification algorithms, offering high precision in identifying and classifying diseases. Key features, such as decision tree ensemble methods and bootstrap aggregation, are utilized to minimize overfitting and improve model generalization. The research compares Random Forests to other machine learning models, such as Neural Networks and Support Vector Machines, and finds that Random Forests perform better in terms of dependability and classification accuracy. We further emphasise that the technique can scale to big datasets and that it can manage missing data. The findings show that Random Forests might be a useful tool for accurate and early diagnosis when incorporated into clinical procedures. The findings of this study highlight the promise of machine learning, and Random Forests in particular, to revolutionise healthcare diagnostics and enhance patient results. © 2024 IEEE.

关键词： Random forests

来源：评论

学校读者我要写书评

暂无评论

A Hybrid Edge-Cloud Computing Approach for Energy-Efficient Surveillance Using Deep Reinforcement Learning 3

A Hybrid Edge-Cloud Computing Approach for Energy-Efficient ...

引用

3rd International Conference on Technological Advancements in Computational sciences, ICTACS 2023

作者： Singh, Jagendra Dabas, Partibha Bhati, Sunita Kumar, Santosh Upreti, Kamal Shaik, Nazeer School of Computer Science Engineering and Technology Bennett University Greater Noida India Dr Akhilesh das Gupta Institute of Technology and Management Dept. of Computer Science & Engineering Delhi India Jecrc University Department of Computer Applications Jaipur India Raj Kumar Goel Institute of Technology Department of Information Technology Ghaziabad India Department of Computer Science Ghaziabad India SRIT-Autonomous Anantapur Department of Computer Science Engineering India

ISBN: (纸本)9798350342338

This paper explores the novel application of Deep Reinforcement Learning (DRL) in designing a more efficient, scalable, and distributed surveillance architecture, which addresses concerns such as data storage limitations, latency, event detection accuracy, and significant energy consumption in cloud data centers. The proposed architecture employs edge and cloud computing to optimize video data processing and energy usage. The study further investigates the energy consumption patterns of such a system in detail. The implementation leverages machine learning models to identify optimal policies based on system interactions. The proposed solution is tested over an extensive period, resulting in a system capable of reducing latency, enhancing event detection accuracy, and minimizing energy consumption. © 2023 IEEE.

关键词： Cloud Data Centers Deep Reinforcement Learning Edge Computing Energy Efficiency Smart Surveillance

来源：评论

学校读者我要写书评

暂无评论

Deep Learning-Based Image Analysis to Track Vehicles 2

Deep Learning-Based Image Analysis to Track Vehicles

引用

2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Prabakaran, P. Jayasudha, R. Sandhu, Mukta Gupta, Sandeep School of Computing Sciences Vels Institute of Science Technology and Advanced Studies Department of Computer Applications Chennai India Dr.N.G.P.Institute of Technology Department of Mathematics Coimbatore India Shri Vishwakarma Skill University Skill Department of Computer Science and Engineering Haryana Palwal India Department of Electrical Engineering Dehradun India

ISBN: (纸本)9798350371055

There are many ways to track the traffic conditions on roads. With the rise of AI-based image processing technology, there has been a surge in interest in developing traffic monitoring systems that rely on camera vision data. An improved road monitoring system may be derived from the data collected in this research by placing a camera at a junction. The technique detects and classifies vehicles using a deep-learning-based methodology (YOLOv4). By comparing the positions of the vehicle with the map HD, lane-by-lane trajectories of the vehicle may be approximated. The predicted vehicle trajectories are used to calculate each lane's wait lengths and traffic volumes, broken down by travel direction. This technique was evaluated using five distinct metrics: vehicle detection rates, trajectory prediction, type of vehicle categorization, traffic volume estimate, and queue length assessment. Thousands of samples were used to assess its performance. Based on the findings, it is acceptable to assume that the system can identify vehicles with a 98% success rate and an error rate of less than 25% regarding lane-by-lane trip volume estimation and vehicle type classification. Therefore, this study's approach reveals the viability of collecting comprehensive traffic information from a camera set up at a junction. Current traffic monitoring schemes have a reasonable likelihood of being developed by this study's significant contribution, which is a strategy that combines AI with HD map approaches. © 2024 IEEE.

关键词： Artificial Intelligence HD Map Traffic Monitoring System Vehicle Type Classification YOLOv4

来源：评论

学校读者我要写书评

暂无评论

A CNN-Based Framework for Detection and Recognition of Objects Using Transfer Learning: A Case Study on Flower Recognition 1

A CNN-Based Framework for Detection and Recognition of Objec...

引用

2023 1st DMIHER International Conference on Artificial Intelligence in Education and Industry 4.0, IDICAIEI 2023

作者： Tiwari, Shivam Sharma, Vikrant Rathore, Yogesh Singh Gandhi, Parul Srivastava, Durgesh Sharda University Department of Computer Science & Engineering Greater Noida India Graphic Era Hill University Computer Science and Engineering Dehradun India Sharda University Computer Science and Application UP Greater Noida India Manav Rachna International Institute of Research and Studies Department of Computer Applications Haryana Faridabad India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India

ISBN: (纸本)9798350338423

The vast variety of floral species present on Earth poses a considerable challenge in memorizing their individual names. Acquiring knowledge about specific flower types inhabiting diverse environments like forests, mountains, and dense regions becomes crucial for their recognition and differentiation. Consequently, the development of a technique for accurately determining flower categories becomes imperative. The task of distinguishing and categorizing a particular blossom amidst the extensive array of floral species presents a formidable task. Hence, the automation of flower species identification has been a subject of extensive research interest. This research domain is characterized by the utilization of features extracted from floral images in conjunction with specialized algorithms aimed at classifying flower species. A prominent aspect of these endeavors is the employment of three widely recognized flower image datasets. The present study constitutes a comprehensive survey of various methodologies proposed by diverse researchers with the objective of accomplishing precise identification of flower species. © 2023 IEEE.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Relevance Feature Discovery in Text Mining Using NLP 3

Relevance Feature Discovery in Text Mining Using NLP

引用

3rd International Conference for Emerging Technology, INCET 2022

作者： Naik, Darshana A Mythreyan, S Seema, S Ramaiah Institute of Technology Department of Computer Science and Engineering Bengaluru India Ramaiah Institute of Technology Department of Master of Computer Applications Bengaluru India

ISBN: (纸本)9781665494991

The volume of text that is created and formed each day is dramatically increased and increasing. This colossal sum or volume of profoundly unstructured text cannot be basically and effortlessly handled and seen by computer systems. Therefore, we can get an useful patterns from the text using an efficient and effective techniques and algorithms. Text mining is a strategy for dissecting text records to extricate valuable data and knowledge from the text document, analysis is done using Text mining. Mining contains a few exercises that assists you with deriving data from unstructured text information. Before you'll apply diverse text mining procedures, you might want to begin with text preprocessing, which is that the act of cleaning and rebuilding text information into a usable organization. This training could likewise be a center part of Natural Language handling (NLP) and it generally includes the utilization of strategies like language identification, tokenization, filtering, lemmatization and stemming to design information suitably for examination. At the point when text preprocessing is finished, after that you will apply text mining calculations to get experiences from the information. However, ensuring the excellent of chose highlights from message is a test because of the monstrous measure of insignificant (boisterous) data in message report is a major test, to guarantee the norm of found significance highlights in the message archives for portraying client inclinations because of huge scope terms and information designs. © 2022 IEEE.

关键词： Data mining

来源：评论

学校读者我要写书评

暂无评论

Smart City Waste Management System using IOT 6

Smart City Waste Management System using IOT

引用

6th International Conference on Information Systems and computer Networks, ISCON 2023

作者： Sathyamoorthy, Malathy Vanitha, C.N. Praveen Raja, S. Sharma, Ajeet Kumar Sharma, Bhisham Chowdhury, Subrata Kongu Engineering College Erode India GLA University Department of Computer Engineering and Applications Mathura India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Sreenivasa Institute of Technology and Management Studies Department of Computer Science and Engineering Andra Pradesh Chittoor India

ISBN: (纸本)9798350346961

Dustbins are used for disposing the waste. When it gets overflowed, people used to throw the waste outside the dustbin until the garbage collector clears the dustbin. It makes the place surrounded by mosquitoes, bugs etc., and mainly affecting the environment and creates several unhygienic problems. To overcome this problem, we have proposed a system called 'Smart City Waste Management System using IOT'' to manage the waste. The sensors are placed in the top of normal garbage bins to measure the amount of trash and find the formation of any toxic gases. These measures make the sensors send the notifications in form of messages to waste management board. So that they can identify the location and garbage collector reach the place as soon as possible and clean the place. The proposed system is developed using Arduino, ultrasonic sensor, temperature sensor, gas sensor and RFID tag. It offers several benefits to the users such as Protect from bacteria and germs- motion sensor trash can protect you from harmful bacteria and germs and gives hygiene surroundings. Because of sensor technology, there is no need to touch the trash can for opening or closing.. © 2023 IEEE.

关键词： Bacteria

来源：评论

学校读者我要写书评

暂无评论

Cloud Top Management Role in Reducing Mobile Broadband Transmission Hazards and Offering Safety 3

Cloud Top Management Role in Reducing Mobile Broadband Trans...

引用

3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Manjunath, C.R. Sharma, Sachin Sharma, Aditi Sunil Kumar, M. Kelkar, Sandeep Vishal Deshmukh, Sheetal Department of Computer Science and Engineering Bangalore India Dev Bhoomi Uttarakhand University Department of Computer Science and Engineering Uttarakhand Dehradun India Parul University PIT Department of Computer Science and Engineering Gujarat Vadodara India Mohan Babu University Erstwhile SreeVidyanikethan Engineering College Department of Computer Science and Engineering AP Tirupathi India Prin L.N. Welingkar Institute of Management Development and Research India Yashwantrao Mohite Institute of Management Department of Computer Applications Karad India

ISBN: (纸本)9798350399264

In both the academic and industrial worlds, cloud technology is becoming a widely used research area. Both providers of cloud services and consumers profit from cloud computation. The literature has mostly focused on the security issues related to the cloud. This thorough literature study's objective is to assess the state of knowledge on dangers, difficulties, and privacy for public cloud. This studies from different countries at the areas of study that some well rising institutions are focusing between on 2010. and 2020. We chose 81 papers after carefully examining several attempts to solve the stated research issues. The study's results revealed seven serious security threats to internet data centres. The results revealed that a few of the extensively covered subjects with in chosen works included data modification and leaking. Network discontinuities, storing, and information inside the realm of public cloud were also recognised potential threats. The findings of this Study also shown that CSPs and internet users alike still have difficulties when reassessing client data. The cryptocurrency was identified in our research report as an innovation activitythat would help to allay security worries. The SLR findings reveal a few things that should be done in efforts to provide categorization, legitimacy, and information availability in the present. © 2023 IEEE.

关键词： Industrial research

来源：评论

学校读者我要写书评

暂无评论

The Outlook of Food Industry: Effectual Traceability Practice in Supply Chain Management using Internet of Things 14

The Outlook of Food Industry: Effectual Traceability Practic...

引用

14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Gurumoorthi, E. Balamurugan, S. Maruthamuthu, R. Kumar, N. Naveen Department of IT CMR College of Engineering and Technology Telangana India School of Computers Madanapalle Institute of Technology and Science Andhra Pradesh India Dept of Computer Applications Madanapalle Institute of Technology and Science Andhra Pradesh India

Traceability can be used to improve processes, increase productivity, and ensure the quality and safety of perishable foods. But it's frequently seen as unnecessary red tape and a financial bur-den. Small and medium-sized businesses frequently exhibit this. Since they make up the majority of the food industry, the adoption of traceability systems is relatively sluggish and primarily done to meet legal requirements. In this paper, we propose effective food traceability techniques using the Internet of things and derive a solution for data communication. The improved Naïve Bayes Classifier is developed for food traceability using the enhanced prediction data. The purpose of the food traceability is used to maintain the computerization, reduced cost and reasonable system complexity. The most important parameter for this system is the food transportation from the manufacturer to the consumer. The Internet of Things is developed to connect the manufacturer to the consumer with an efficient transportation system. An economical solution is achieved using the IoT based food traceability. The Naïve Bayes Classifier is simulated and the investigational results proved that the proposed algorithm is more efficient for food traceability techniques compared to the SVM and KNN methods. © Grenze Scientific Society, 2023.

关键词： Internet of things

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356页 << < 33 34 35 36 37 38 39 40 41 42 > >>

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：