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

20,415 篇 会议
13,181 篇 期刊文献
314 册 图书

馆藏范围

33,910 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

20,980 篇 工学
- 14,670 篇 计算机科学与技术...
- 12,071 篇 软件工程
- 4,961 篇 信息与通信工程
- 3,419 篇 控制科学与工程
- 2,781 篇 电气工程
- 2,696 篇 生物工程
- 2,191 篇 电子科学与技术（可...
- 2,059 篇 生物医学工程（可授...
- 1,789 篇 光学工程
- 1,381 篇 机械工程
- 1,257 篇 化学工程与技术
- 1,213 篇 动力工程及工程热...
- 1,111 篇 网络空间安全
- 1,055 篇 仪器科学与技术
- 997 篇 安全科学与工程
- 936 篇 材料科学与工程（可...
12,052 篇 理学
- 5,890 篇 数学
- 3,915 篇 物理学
- 3,006 篇 生物学
- 2,010 篇 统计学（可授理学、...
- 1,400 篇 化学
- 1,247 篇 系统科学
5,714 篇 管理学
- 3,524 篇 管理科学与工程(可...
- 2,450 篇 图书情报与档案管...
- 1,528 篇 工商管理
2,257 篇 医学
- 1,792 篇 临床医学
- 1,596 篇 基础医学(可授医学...
- 1,097 篇 公共卫生与预防医...
- 956 篇 药学(可授医学、理...
1,186 篇 法学
- 978 篇 社会学
612 篇 经济学
521 篇 农学
383 篇 教育学
216 篇 文学
65 篇 军事学
43 篇 艺术学
7 篇 哲学
4 篇 历史学

主题

1,194 篇 accuracy
1,165 篇 deep learning
757 篇 machine learning
744 篇 computational mo...
706 篇 training
695 篇 feature extracti...
544 篇 real-time system...
534 篇 predictive model...
483 篇 internet of thin...
468 篇 convolutional ne...
447 篇 support vector m...
430 篇 data models
344 篇 semantics
294 篇 computer science
293 篇 reliability
283 篇 artificial intel...
280 篇 optimization
268 篇 scalability
267 篇 neural networks
261 篇 computer archite...

机构

235 篇 school of comput...
204 篇 department of co...
166 篇 university of ch...
157 篇 key laboratory o...
128 篇 university of sc...
122 篇 school of comput...
122 篇 department of co...
118 篇 school of comput...
116 篇 institute of com...
114 篇 school of comput...
113 篇 school of comput...
112 篇 department of co...
108 篇 department of co...
105 篇 school of comput...
103 篇 college of compu...
103 篇 school of comput...
101 篇 school of comput...
96 篇 north-west unive...
91 篇 tianfu cosmic ra...
91 篇 national enginee...

作者

176 篇 niyato dusit
105 篇 bastieri d.
99 篇 ferrara g.
99 篇 ardid m.
98 篇 chen a.
97 篇 calvo d.
86 篇 gal t.
86 篇 bozza c.
82 篇 zheng wei-shi
82 篇 anjali t.
74 篇 fang j.
74 篇 feng c.f.
72 篇 li xin
69 篇 li cheng
68 篇 jiang k.
67 篇 kuleshov d.
65 篇 díaz a.f.
63 篇 liu d.
62 篇 chen m.j.
62 篇 kalekin o.

语言

25,476 篇 英文
8,209 篇 其他
326 篇 中文
7 篇 法文
6 篇 德文
6 篇 日文
2 篇 斯洛文尼亚文
1 篇 捷克文
1 篇 西班牙文
1 篇 朝鲜文
1 篇 俄文

检索条件"机构=School of Computing Science and Engineering School Of Computing Science & Engineering"

共 33910 条记录，以下是781-790 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 Multimedia Security through Advanced Image Authentication and Encryption 9

Enhancing Multimedia Security through Advanced Image Authent...

引用

9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Chakradhar, Kotha Tharun, K. Thulasi Rajagopal, Shinu M. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science And Engineering Bengaluru India

ISBN: (纸本)9798350394474

With this flood of multimedia data, protecting it is an absolute necessity. This paper describes a complete method for securing images that combines encryption and watermarking. A technique of the sort suggested further prolongs picture encryption, by interweaving a Rubik's idea with advanced encryptions like those based on Triple DES and AES. As further, a brand new strong watermarking algorithm is displayed. In this way, a high degree of protection for the copyright and genuineness is hoped to be achieved. The proposed method has encouraging results ensuring harvested photographs 'accessibility, confidentiality and integrity. © 2024 IEEE.

关键词： Cryptography

来源：评论

学校读者我要写书评

暂无评论

Data-Driven Clustering and Insights for Rural Development in India 5

Data-Driven Clustering and Insights for Rural Development in...

引用

5th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Sha, Akhbar Madhan, S. Karthikeya, Moturi Megha, R. Swain, Dhruvjyoti Gopakumar, G. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

This study aims to reveal the complex socioeconomic structure of Indian villages by utilizing a dataset obtained from several government sources, which includes a wide range of 150 distinct variables. This study intends to uncover the underlying structure of these groups by using powerful data analytics and clustering approaches and further group Indian villages into meaningful clusters by finding socioeconomic differences and geographical details in India's rural landscape. The final research findings suggest a four-cluster strategy as the most insightful solution for providing a more detailed understanding of India's rural regions' diversity and its specific requirements. By describing the diversity of these clusters, this research study improves the framework for data-driven policy formation and development interventions that address the specific requirements of each group. Finally, this study highlights not only the potential of data-driven techniques in rural development, but also the importance of targeted customized solutions in solving India's rural development challenges. © 2024 The Authors. Published by Elsevier B.V.

关键词： Data Analytics

来源：评论

学校读者我要写书评

暂无评论

Enhancing Credit Card Fraud Detection with Deep Learning and Graph Neural Networks 15

Enhancing Credit Card Fraud Detection with Deep Learning and...

引用

15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Sherwin Akshay, J.G. Vinusha, T. Sharon Bianca, R. Sarath Krishna, C.K. Radhika, G. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350370249

Financial fraud poses a major threat to financial service institutions and clients, facilitating anomaly detection capabilities. This paper dives into deep learning models which can be implemented to detect and distinguish fraudulent transactions from normal transactions and flag anomalies. Further, conversion of tabular data to graphs have been implemented for keen understanding and improved pattern analysis of fraudulent transactions, challenges include evolving attack strategies, class imbalance and model interpretability. The proposed methods utilize a synergistic combination of ensemble learning techniques and unsupervised deep neural networks, like Auto-Encoders and Recurrent Neural Networks. In the graphical representation, this study focuses on implementing Louvain Modularity, Girvan-Newman and Label Propagation Algorithm for effectiveness in community detection. The outcomes exhibit consistently reliable results on credit card transaction datasets. A comprehensive comparative analysis follows to compare the proposed approach to traditional fraud detection methods, demonstrating its superiority in accurately identifying fraudulent transactions, adapting to emerging threats, handling high-dimensional data, and mitigating class imbalances with respect to data privacy and security. © 2024 IEEE.

关键词： Auto-Encoders CatBoost GAN GNN GRU RNN Unsupervised learning XG-Boost

来源：评论

学校读者我要写书评

暂无评论

Prognostic Modeling of Diabetes Mellitus in Women: A comparitive analysis of Machine Learning algorithms integrating Clinical and Genetic Data

Prognostic Modeling of Diabetes Mellitus in Women: A compari...

引用

2024 IEEE International Conference on Emerging Technologies in Computer science for Interdisciplinary Applications, ICETCS 2024

作者： Prashanth, M. Venkat Jose, Nikhil Tom Verma, Konda Hemesh Sreevidya, B. Rajesh, M. Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350372502

Diabetes is considered as one of the most commonly observed medical condition among human beings. Millions of people worldwide are affected by a chronic condition that goes by the name of diabetes. There are 3 sorts of diabetes and they comprise of Type 1, Type 2 and Gestational diabetes. Early prediction of diabetes helps to avoid complications like diabetic retinopathy which leads to blindness. There is a 13% higher risk of death among people with diabetes, especially in women, cardiovascular disease caused by diabetes attributes to 50% higher risk of death, coronary heart disease caused by diabetes contributes to a 58% higher risk of death. The proposed research work aims in predicting diabetes from the symptoms a patient exhibit. The proposed system is a healthcare assisting system which is accessible to patients as well as medical practitioners. The system is developed as a web application using the features of Django python framework along with suitable database management system. The system is powered with gradient boosting machine learning algorithm for predictions. By using the web application along with a blood test to find out the insulin levels and glucose levels in blood, any user can predict whether he/she would be affected by diabetes in future and can avoid the complications caused by diabetes. © 2024 IEEE.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

MBTI Based Personality Prediction using Social Media Data 15

MBTI Based Personality Prediction using Social Media Data

引用

15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Prasanna Kumar, R. Bharathi Mohan, G. Rithani, M. Pallavi, Gundala Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350370249

Existing methods for assessing an individual's personality are very cumbersome due to those tests being very monotonous and erroneous due to a self-report bias. Whereas data-driven, automatic personality deduction raises privacy concern due to them needing external personal information. In our model, a person's personality can be classified based on the 16 categories of Myers-Briggs personality types. With the availability of a huge amount of data on human behavior and personality traits, a machine learning model can be trained, and it can predict the personality trait of a person. A MBTI dataset is used to analyze and train the model. Upon analyzing the dataset, we found out that the classes are not balanced. To overcome this problem, we used binary classification. Next, we used the NLTK library to pre-process the data. We selected four machine learning models and trained our data in those models. Furthermore, we compared the results of four different machine learning models and chose the most accurate model. This research can be used to personalize advertisements, coaching for students and companies can use this research to attract specific types of audience. © 2024 IEEE.

关键词： Logistic regression Myers-Briggs personality types Naïve Bayes NLTK Personality prediction Random Forest SVM

来源：评论

学校读者我要写书评

暂无评论

Traffic Rules Violation Detection System using YOLO v8 Model 10

Traffic Rules Violation Detection System using YOLO v8 Model

引用

10th International Conference on Electrical Energy Systems, ICEES 2024

作者： Prathilothamai, M. Sujan, K. Ganapathi, G. Nandagopal, B. Ramkumar, Varnika Amrita Vishwa Vidyapeetham Amrita School of Computing Coimbatore Department of Computer Science and Engineering India

ISBN: (纸本)9798350353778

Addressing violations of traffic laws is critical for ensuring public safety, encouraging responsible driving behavior, lowering economic costs, and minimizing transportation's environmental impact. It is imperative to implement punitive measures for traffic violations. An AI model can play a crucial role in monitoring and issuing fines to those who breach traffic regulations through the alerts. High resolution cameras and sensors captures vehicular movements, while the algorithms analyze the footage to detect infractions. Hence, a YOLO v8 Convolutional Neural Networks model is proposed for the traffic rules violation detection and management. The YOLO v8 model is trained to identify major five specific traffic violations starting with Wrong Way Violation, Red Light Crossing, Over Speeding, Triple Riding, and Helmet Less Driving. The model operates in real-time, providing immediate identification of violations, thereby contributing to the enhancement of enforcement system and road safety measures. A unique feature of this project is the integration of an automated alert system. This automated approach decreases the need for manual monitoring, improves the accuracy of violation detection, and provides a robust mechanism for monitoring roads. The system notifies the respective person through email alert when a violation is detected, enabling them to take immediate action. This study highlights how artificial intelligence can be used to improve road safety and traffic management. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Enhancing Early Detection and Classification of Alzheimer's Disease Stages Using Deep Learning and LIME for Interpretability 3

Enhancing Early Detection and Classification of Alzheimer's ...

引用

3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Prasad, P Syam Mokshit, P. Reddy, N Sumanth Singh, Rimjhim Padam Bengaluru Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering India

ISBN: (纸本)9798331528201

The issue of transparency in diagnostic methods has been a crucial challenge in Alzheimer's Disease detection. This paper focuses on analyzing state-of-art deep learning algorithms namely, customized convolutional network, InceptionV3, DenseNet121, AlexNet, VGG16, and ResNet50 for detecting Alzheimer's disease. The work also focuses on analyzing the transparency and decision-making capabilities of these models by using the Explainable AI tool, Local Interpretable Model-agnostic Explanations, for clinicians and patients. The learnings and decision outputs obtained from these convolutional models are related and explained using LIME, where ResNet and DenseNet lead to efficient solutions with appropriate feature extraction and importance provided in LIME visualization. This explication of the model's decision-making process affected by LIME can improve diagnostic accuracy and foster trust that can offer a great chance for the medical experts to offer better care for patients, who are suffering from Alzheimer's Disease. © 2024 IEEE.

关键词： Diagnosis

来源：评论

学校读者我要写书评

暂无评论

Smart Online Examination Cheat detection using Gaze Analysis 3

Smart Online Examination Cheat detection using Gaze Analysis

引用

3rd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2024

作者： Padmavathi, A. Shome, Tandra Debarati Kumar, D. Prasanth Reddy, K. Nithish Kumar Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350364569

Online exams have become popular and are adapted by many companies for initial shortlisting of the students, but the test-takers are manipulating the cheating detector software, which can detect only objects like smartphones and multiple persons in a frame based on head movement by moving only their eyes without moving the head. In this paper, we address this challenge by providing a simple method to detect and prevent such misleading behaviors: eye tracking technology. We developed an application that helps to get the user's screen boundaries by using eye coordinates, and this application is not limited only to people who have proper eyes;it also works for squinted eyes, which is the limitation of the present technology that tries to detect cheating. © 2024 IEEE.

关键词： Tweets

来源：评论

学校读者我要写书评

暂无评论

IoT-Driven Intelligent Trash Bin (IntelliBIN) Enabled with Edge Segregation for Malls 3

IoT-Driven Intelligent Trash Bin (IntelliBIN) Enabled with E...

引用

3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Reddy, D HarshaVardhan Gupta, T Subhash Reddy, C Gowthami Vishnu, S. Vishnuvarthan, R. Darshan, B Sree Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350372120

Urban waste management, especially from commercial sources like shopping malls, presents a significant challenge. A large amount of packaging waste generated by retail outlets and food courts complicates the waste management process. The current method of segregating waste at centralized facilities leads to time-consuming processes, increased transportation costs, and increased labor demands. In response, this work proposes an innovative solution: an IoT-Driven Intelligent Trash Bin with Edge Segregation for shopping malls. By deploying a deep learning model on a Raspberry Pi board, waste segregation is optimized at the edge, reducing latency and improving efficiency. The system includes an intelligent bin named IntelliBIN, featuring four compartments, and utilizes the proposed deep learning model to analyze and segregate the wastes into paper, metal, food, and others via a servo mechanism. In addition, the bin is equipped with ultrasonic sensors to monitor the filled levels of each compartments and a Wi-Fi radio device to enable real-time data transmission and monitoring. The statistical data of waste disposal is presented, and automated notifications are sent to the mall management to take appropriate action. The system assures effective waste segregation and management with the proposed model by achieving a training accuracy of 94%, and it outperforms some of the existing works such as VGG 19 (92%), ResNet (93%), and Inception Net (87%). © 2024 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Parser for TINY Language 8

Parser for TINY Language

引用

8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Priya, Ch. Durga Raju, Ch. Vikram Daniel, A. Issac Patel, G. Rishik Belwal, Meena Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer science and Engineering Bangalore India

ISBN: (纸本)9798331505462

This study provides a novel method to generate a compiler, which translates a program using TINY language. TINY language is a simple programming language which is designed to understand the basic concepts of compiler. A compiler is a type of program which is designed to convert the code written in high-level language into machine language that is understandable by the computer. The procedure for building a parser requires various sub-tasks, there are a list of operations that include parsing or splitting the code into tokens, checking's the code's syntax using grammar rules, understanding, interpreting and optimizing the provided code and generating the execution-ready code by the performance of last assembling. In order to face the challenges of compiler construction, which includes rule-based complex grammar, error handling, and fast tokenization, the python programming language is implemented for developing pyLexer(tokenization) and pyParsing (syntactic analysis) libraries. Lexer is used for matching regular expressions to return corresponding tokens(lexemes), while parser is supposed to check whether the given syntax is according to the grammar rules or not through recursion. Web application is created for this using Streamlit, which provides users with many useful features like text rendering capability, error highlighting and code suggestions. © 2024 IEEE.

关键词： Program compilers

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 << < 75 76 77 78 79 80 81 82 83 84 > >>

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：