检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

13,740 篇 会议
7,731 篇 期刊文献
256 册 图书

馆藏范围

21,727 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

13,528 篇 工学
- 10,494 篇 计算机科学与技术...
- 8,690 篇 软件工程
- 3,449 篇 信息与通信工程
- 2,242 篇 控制科学与工程
- 1,917 篇 生物工程
- 1,628 篇 电气工程
- 1,414 篇 生物医学工程（可授...
- 1,179 篇 电子科学与技术（可...
- 1,178 篇 光学工程
- 843 篇 网络空间安全
- 803 篇 机械工程
- 679 篇 安全科学与工程
- 638 篇 动力工程及工程热...
- 596 篇 交通运输工程
- 587 篇 仪器科学与技术
- 562 篇 化学工程与技术
6,875 篇 理学
- 3,496 篇 数学
- 2,094 篇 生物学
- 1,786 篇 物理学
- 1,212 篇 统计学（可授理学、...
- 746 篇 系统科学
- 669 篇 化学
4,031 篇 管理学
- 2,442 篇 管理科学与工程(可...
- 1,768 篇 图书情报与档案管...
- 1,070 篇 工商管理
1,569 篇 医学
- 1,228 篇 临床医学
- 1,121 篇 基础医学(可授医学...
- 819 篇 公共卫生与预防医...
- 655 篇 药学(可授医学、理...
850 篇 法学
- 703 篇 社会学
374 篇 经济学
335 篇 农学
229 篇 教育学
185 篇 文学
33 篇 军事学
28 篇 艺术学
5 篇 哲学
3 篇 历史学

主题

918 篇 deep learning
821 篇 accuracy
535 篇 computational mo...
499 篇 machine learning
448 篇 feature extracti...
441 篇 training
378 篇 real-time system...
376 篇 predictive model...
360 篇 internet of thin...
319 篇 convolutional ne...
312 篇 support vector m...
289 篇 computer science
273 篇 data models
267 篇 semantics
258 篇 optimization
230 篇 neural networks
218 篇 computer archite...
209 篇 reliability
209 篇 scalability
203 篇 image segmentati...

机构

261 篇 department of co...
170 篇 school of comput...
137 篇 school of comput...
136 篇 school of comput...
122 篇 department of co...
116 篇 school of comput...
112 篇 school of comput...
104 篇 college of compu...
102 篇 national enginee...
102 篇 department of co...
101 篇 school of comput...
101 篇 department of co...
100 篇 department of co...
100 篇 school of comput...
94 篇 north-west unive...
94 篇 institut univers...
93 篇 department of co...
88 篇 infn sezione di ...
88 篇 national centre ...
83 篇 tno technical sc...

作者

125 篇 niyato dusit
97 篇 ferrara g.
97 篇 ardid m.
96 篇 calvo d.
93 篇 chen a.
88 篇 gal t.
88 篇 bozza c.
88 篇 díaz a.f.
88 篇 boumaaza j.
86 篇 coleiro a.
86 篇 gozzini s.r.
85 篇 zheng wei-shi
82 篇 anjali t.
81 篇 dallier r.
73 篇 kalekin o.
65 篇 anguita m.
65 篇 boettcher m.
61 篇 huang f.
56 篇 kulikovskiy v.
54 篇 benoit d.m.

语言

19,244 篇 英文
2,366 篇 其他
158 篇 中文
2 篇 法文
1 篇 德文
1 篇 日文
1 篇 朝鲜文

检索条件"机构=MTech Computer Science and Engineering School of Computing Science and Engineering"

共 21727 条记录，以下是4691-4700 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：

AcuLearn - Precision Based Learning Analytics Platform Using Machine Learning Algorithms

AcuLearn - Precision Based Learning Analytics Platform Using...

引用

Emerging Techniques in Computational Intelligence (ICETCI), International Conference on

作者： Poluri Manaswini Sajjad Shaik Rejeti Kartik Sangita Khare Department of Computer Science and Engineering Amrita School Of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350387063

ISBN: (纸本)9798350387070

In today's world, the use of data analytics with precision has been extremely important and crucial with the changing times. The creation of virtual environments to facilitate communication and education among the students and teach-ers alike. It allows the creation of personalized education for students. Machine Learning algorithms, hence can be helpful to recommend students to what courses that can be taken and their likely improvement of the system. This study shows the creation of an website for creating better personalized education for students including course completion predictors and score predictors to help them make decisions proactively. The system shows the random forest regression for score prediction and random forest classifier for course completion to perform the best among all such algorithms.

关键词： Machine learning algorithms Databases Scalability Education Linear regression Virtual environments Predictive models Prediction algorithms Real-time systems Random forests

来源：评论

学校读者我要写书评

暂无评论

Hierarchical Data Storage Model Representation in MongoDB for a Resume Ranking Application

Hierarchical Data Storage Model Representation in MongoDB fo...

引用

International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

作者： Sahil Kumar Gupta Raushan kumar Gupta Ishwor Acharya Mohit Kumar Rauniyar Divya KV Department of Computer Science and Engineering Amrita school of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505479

Introducing an innovative resume ranking website, representing Hierarchical Data Storage in MongoDB. By allowing candidates to fill out job-specific forms, the project assess their skills against customized criteria set by each company. Each skill criteria is stored in MongoDB in parent-child node formation making it easy for retrieval with minimum time complexity. Utilizing a point-based system, primary, secondary, and least priority skills are prioritized tailored to the requirements of each job opening. The goal is to make candidate selection, ensuring that the most qualified individuals rise to the top, facilitating efficient and effective hiring decisions for businesses.

关键词： Tree data structures Scalability Query processing Resumes Memory Companies Reliability Time complexity Information technology Indexing

来源：评论

学校读者我要写书评

暂无评论

SecureComment: Safeguarding Online Discussions with Intelligent Toxic Comment Filtering

SecureComment: Safeguarding Online Discussions with Intellig...

引用

Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Reddy Kowshik Rayani Samhitha Tekula Subhash Kovid Vattigunta Naveen Kumar Kovi Kalakunnath Namitha Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

Toxic comments, such as hate speech and abuse, are a widespread issue online, disrupting healthy conversation and user safety. Identifying and filtering these comments is crucial for ensuring respectful online communications. The primary challenge arises from the need to obtain accurate detection while addressing emerging tactics and balancing between false positives and negatives. This concern affects user interactions and platform credibility and raises legal and ethical risks. Therefore, this study examines the effectiveness of numerous machine-learning models and implements an ensemble method for analyzing and classifying toxic comments. We analyze the effectiveness of various algorithms using evaluation metrics like precision, recall, accuracy, and F1-score. Ensemble methods are utilized to combine the capabilities of several models, enhancing the classification of toxic comments. The study aspires to deliver insights into the comparative performance of various machine learning models in classifying toxic comments accurately and the advantages of ensemble methods in enhancing classification accuracy.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Enhancing Cloud Security through Log Aggregation and Real-Time Alerts

Enhancing Cloud Security through Log Aggregation and Real-Ti...

引用

International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： Gontla Venkat Sujan Hothur Ranga Neha Cheekati Mani Shankar C.R. Kavitha Department of Computer Science & Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

Cloud computing is widely adopted by the IT sector in many industry applications, and the immensity of the risk proportion inclines with its adoption. The number and irregularities in malicious cyber-attacks has multiplied gradually demanding a robust security solution. The project aims to cope with the growing number and complexity of cyber threats by resolving the immediate need for dependable cloud security solutions amid cloud computing’s rapid adoption. In the era of the networked and interconnectivity, promptly responding to the cyberthreats and having embracing data visualization in the cybersecurity program would make it possible for the business to prevent the cyberattacks, control the data flow, and avoid compliance violations, and safeguard the on-going company activities. The proposed approach implements industry standard applications such as Elasticsearch, AWS CloudWatch, and Kibana applications to act proactively by detecting and mitigating cyber threats. By processing and analyzing real-time log data in a cloud environment, they can minimize the effect of cyber-attacks and keep their cloud systems intact. The competence of the system to conduct effective forensic analysis and proactive threat hunting expands further with integration of ELK stack. This enhances security teams’ ability to go on par with changing threats and operate strong cyber defenses. The proposed methodology aims to deploy and implement a secure file upload system utilizing AWS services.

关键词： Technological innovation Web services Cloud computing security Scalability Real-time systems Encryption Monitoring Cyberattack Standards Secure storage

来源：评论

学校读者我要写书评

暂无评论

EBSDNET - Endanger Bird Species Detection Net using ResNet50

EBSDNET - Endanger Bird Species Detection Net using ResNet50

引用

Network, Multimedia and Information Technology (NMITCON) International Conference on

作者： R Divya Dharshan M. Diviya Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9798350372892

ISBN: (纸本)9798350372908

In India Numerous individuals enjoy bird watching, but identifying bird species often requires consulting reference books. However, many bird species are in the verge of extinction because of climatic change and environmental change. These Changes results in many problems like, forest degradation, urbanization and avian disease. It is a threat to biodiversity because the destruction of birds are happening in rapid phase so it must be safeguarded. The use of hidden camera in wildlife for monitoring these species is skyrocketed in todays world but manually examining and taking information from vast data gathered by hidden camera be tiresome and time consuming. So to overcome this we have proposed a ResNet-50 to detect and classify these endanger bird species using image processing. This model was trained using a dataset comprising 3520 images, each depicting one of 38 unique bird species. The images had a resolution of 224 by 224 pixels. The proposed model in this work attained an accuracy level of $98.64 \%$. Various performance measures also evaluated to test the efficacy of the proposed work. Comparison study with the existing work also reveals that the proposed methodology yields fine results.

关键词： Visualization Accuracy Biological system modeling Wildlife Urban areas Transfer learning Birds Cameras Monitoring Residual neural networks

来源：评论

学校读者我要写书评

暂无评论

Fake News Detection for COVID-19 Using PySpark Framework MLlib

Fake News Detection for COVID-19 Using PySpark Framework MLl...

引用

Communication & computing (WCONF), World Conference on

作者： Lalith Abhiram Dasari Jetty Sowmith Naveen Krishna Mamidi Manju Venugopalan Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350395327

ISBN: (纸本)9798350395334

Besides causing a very severe health crisis for the whole world, the COVID-19 epidemic has speeded the spread of fake news and false information. Solving false news problems is important in these time-sensitive conditions to provide correct information and guarantee people's safety. This work aims to use PySpark, a distributed data processing platform, to create a prediction model for identifying COVID-19 fake news. The experiments are conducted on a dataset sourced from Kaggle and a prediction model is created using PySpark's machine learning libraries, such as MLlib and SparkML, to classify news articles as real or false. Different classification methods, such as logistic regression, decision trees, and random forests, are researched and improved for the best performance. The accuracy, precision, recall, and F1 score of the model are assessed using relevant measures, confirming its efficacy in detecting bogus news. The best results were achieved by the logistic regression classifier which achieved accuracy and F-measure of 0.9.

关键词： COVID-19 Logistic regression Epidemics Accuracy Predictive models Data processing Data models Safety Fake news Random forests

来源：评论

学校读者我要写书评

暂无评论

An Empirical Analysis on ARIMA and Regression Models for Time Series Forecasting on Bitcoin Dataset

An Empirical Analysis on ARIMA and Regression Models for Tim...

引用

Information Technology, Electronics and Intelligent Communication Systems (ICITEICS), IEEE International Conference on

作者： K Ganesh M Anbazhagan Shreyas Visweshwaran Department of Computer Science and Engineering Amrita School of Computing Coimbatore Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350382693

ISBN: (纸本)9798350382709

n the rapidly evolving world of cryptocurrency markets, the precise forecasting of Bitcoin's value against the US Dollar acquires paramount importance, catering to the interests of diverse stakeholders including investors, regulatory agencies, and academia. This study ventures into a comprehensive assessment of various time series forecasting methodologies, including but not limited to Random Forest Regression, ARIMA, Linear Regression, and XGBoost. Notably, our investigation unveils a pivotal revelation: the foundational models like Linear Regression and Random Forest Regression, traditionally con-sidered less complex, not only contend but also surpass the forecast accuracy of ARIMA models in the realm of Bitcoin. This paper aims to demystify the underpinnings of this superior performance, especially in mitigating the inherent volatility and unpredictability characteristic of Bitcoin. Our findings herald a transformative perspective in financial time series forecasting, potentially reshaping investment strategies and predictive analytics in the digital currency landscape.n the rapidly evolving world of cryptocurrency markets, the precise forecasting of Bitcoin's value against the US Dollar acquires paramount importance, catering to the interests of diverse stakeholders including investors, regulatory agencies, and academia. This study ventures into a comprehensive assessment of various time series forecasting methodologies, including but not limited to Random Forest Regression, ARIMA, Linear Regression, and XGBoost. Notably, our investigation unveils a pivotal revelation: the foundational models like Linear Regression and Random Forest Regression, traditionally considered less complex, not only contend but also surpass the forecast accuracy of ARIMA models in the realm of Bitcoin. This paper aims to demystify the underpinnings of this superior performance, especially in mitigating the inherent volatility and unpredictability characteristic of Bitcoin. Our findings

关键词： Analytical models Accuracy Linear regression Time series analysis Bitcoin Predictive models Forecasting

来源：评论

学校读者我要写书评

暂无评论

Music Recommendation System based on Big Data using Pyspark

Music Recommendation System based on Big Data using Pyspark

引用

International Conference for Convergence of Technology (I2CT)

作者： Nikunj Jain Nabin Kumar Sah Jawed Hawari Sangita Khare Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

In the evolving landscape of digital music consumption, personalized song recommendation systems play a pivotal role in enhancing user engagement and satisfaction. This article presents an in-depth exploration and analysis of an innovative song recommendation framework designed to address the challenges of delivering tailored music experiences. Utilizing advanced machine learning techniques, especially K-Means Clustering Algorithm and data analytics, the proposed system aims to offer song suggestions according to user preferences. The proposed system introduces a hybrid song recommendation system that combines the two distinct approaches i.e. Content and Collaborative based filtering. Various algorithms were implemented for Content based filtering whereas Hadoop and Pyspark was used for implementation of Collaborative filtering. Wide research and Evaluations based on various metrics were considered. The research also draws the strengths and drawbacks or limitations of Content based Recommendation over Collaborative based Recommendation System. The results highlight the system’s capability to overcome limitations eventually enhancing user experience by offering a better experience.

关键词： Measurement Machine learning algorithms Data analysis Filtering Collaboration Clustering algorithms Machine learning

来源：评论

学校读者我要写书评

暂无评论

Advancing Personality Detection through Word Embedments and Deep Learning: An Examination Using the MBTI Dataset

Advancing Personality Detection through Word Embedments and ...

引用

IEEE Recent Advances in Intelligent Computational Systems (RAICS)

作者： Aditya G Shanmukha R.S Shamyuktha S Karan Deepa Gupta Suja Palaniswamy Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350381689

ISBN: (纸本)9798350381696

Personality detection and classification hold significant potential for enhancing decision-making processes and optimizing outcomes across various sectors. This interdisciplinary field finds applicative relevance in numerous domains such as business, healthcare, education, and personal development. In an effort to contribute to this burgeoning area of research, this paper introduces a comprehensive model specifically designed for personality detection. The model is rigorously tested on the Myers-Briggs Type Indicator (MBTI) dataset, a widely recognized framework in personality typology. The core of this proposed model lies in its innovative integration of four advanced word embeddings: Keras, Word2Vec, Glove, and FastText. These embeddings are coupled with cutting-edge deep learning architectures, including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, Bidirectional LSTM (BI-LSTM), a hybrid of CNN and LSTM, and Gated Recurrent Units (GRU). This amalgamation results in a suite of 18 distinct models, each offering unique insights into personality classification. The implementation of these models is meticulously undertaken, and the outcomes are documented in detail. The results of this research which is 82% of accuracy, not only demonstrate the efficacy of combining various word embeddings with deep learning techniques but also pave the way for future explorations in the field of personality detection and classification.

关键词： Deep learning Accuracy Education Decision making Medical services Logic gates Convolutional neural networks Business

来源：评论

学校读者我要写书评

暂无评论

Chatbot to chat with medical books using Retrieval-Augmented Generation Model

Chatbot to chat with medical books using Retrieval-Augmented...

引用

North Karnataka Subsection Flagship International Conference (NKCon), IEEE

作者： Sai Deepa Bhavani Peri S Santhanalakshmi Radha Radha Department of Computer Science & Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350364569

ISBN: (纸本)9798350364576

This research paper presents a novel AI tool aimed at revolutionizing medical education by addressing the specific needs of medical students, fostering effective learning, and managing stress. The implementation leverages cutting-edge technology known as Large Language Models (LLMs), with a focus on utilizing ChatGPT4, Streamlit, and Trulens. The digital assistant serves as a friendly and accurate resource, responding promptly to complex medical queries, thereby optimizing study time. The study meticulously examines the balance between technical innovation and practical benefits, emphasizing the project’s goal of not only creating a sophisticated tool but also providing tangible assistance to medical *** report outlines the successful development and deployment of the AI-powered study buddy, showcasing its potential to significantly improve learning environments, particularly in demanding fields such as medicine. Notably, the project empha-sizes the importance of groundedness in its chatbot, achieving an impressive accuracy rate of approximately 84% for a single textbook. The integration of Streamlit, ChatGPT4, and Trulens contributes to creating a seamless and user-friendly experience for medical *** research underscores the broader implications of AI in education, demonstrating how advancements in technology can positively impact learning outcomes. By reducing the time spent on information retrieval, the AI study buddy empowers medical students to concentrate more on core learning objectives, ultimately facilitating their journey to becoming proficient and successful medical practitioners.

关键词： Technological innovation Accuracy Large language models Education Chatbots Information retrieval Stress

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 << < 466 467 468 469 470 471 472 473 474 475 > >>

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：