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

161 篇 会议
157 篇 期刊文献

馆藏范围

318 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

143 篇 理学
- 58 篇 物理学
- 40 篇 数学
- 40 篇 生物学
- 16 篇 统计学（可授理学、...
- 11 篇 系统科学
- 10 篇 化学
142 篇 工学
- 106 篇 计算机科学与技术...
- 97 篇 软件工程
- 31 篇 信息与通信工程
- 23 篇 生物医学工程（可授...
- 20 篇 生物工程
- 12 篇 控制科学与工程
- 10 篇 光学工程
- 10 篇 电气工程
- 9 篇 化学工程与技术
- 8 篇 仪器科学与技术
- 5 篇 电子科学与技术（可...
- 5 篇 安全科学与工程
63 篇 管理学
- 35 篇 图书情报与档案管...
- 26 篇 管理科学与工程(可...
- 23 篇 工商管理
- 7 篇 公共管理
47 篇 医学
- 34 篇 临床医学
- 32 篇 基础医学(可授医学...
- 15 篇 药学(可授医学、理...
- 14 篇 公共卫生与预防医...
17 篇 农学
- 8 篇 作物学
7 篇 经济学
- 7 篇 应用经济学
7 篇 法学
- 5 篇 社会学
7 篇 教育学
- 6 篇 教育学
1 篇 文学

主题

7 篇 deep learning
7 篇 accuracy
5 篇 covid-19
5 篇 support vector m...
5 篇 education
5 篇 neural networks
5 篇 feature extracti...
5 篇 students
5 篇 sentiment analys...
5 篇 training
4 篇 real-time system...
4 篇 tumors
4 篇 adaptation model...
3 篇 internet of thin...
3 篇 computer science
3 篇 information syst...
3 篇 laparoscopy
3 篇 systematics
3 篇 decision trees
3 篇 semantics

机构

19 篇 department of co...
16 篇 department of in...
15 篇 department of el...
15 篇 faculty of commu...
14 篇 department of in...
12 篇 department of in...
10 篇 faculty of indus...
9 篇 graduate program...
8 篇 health managemen...
8 篇 department of in...
7 篇 informatics engi...
7 篇 department of ma...
7 篇 faculty of infor...
7 篇 department of ma...
7 篇 universitas kris...
6 篇 department of ph...
6 篇 royal stoke univ...
6 篇 department of bi...
6 篇 health systems a...
6 篇 university colle...

作者

13 篇 dahlan abdullah
10 篇 h hartono
9 篇 nurmawati
9 篇 akbar iskandar
8 篇 hartono h.
8 篇 kurniasih nuning
8 篇 iskandar akbar
8 篇 nuning kurniasih
8 篇 cut ita erliana
7 篇 erliana cut ita
7 篇 abdullah dahlan
6 篇 abdussakir
6 篇 maulana fairuz i...
6 篇 a a saleh
6 篇 s sriadhi
6 篇 sriadhi s.
6 篇 purnomo agung
6 篇 sriadhi
5 篇 wiest roland
5 篇 bakas spyridon

语言

316 篇 英文
2 篇 其他

检索条件"机构=Computer Science Program Faculty of Informatics"

共 318 条记录，以下是51-60 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：

Long Short Term Memory (LSTM) Improvement Accuracy Using FastText and Glove for Language Translation Batak - Indonesian

Long Short Term Memory (LSTM) Improvement Accuracy Using Fas...

引用

Technology Innovation and Its Applications (ICTIIA), International Conference on

作者： Atikah Rahayu Mandasari Erna Budhiarti Nababan Herman Mawengkang Master of Informatics Program Universitas Sumatera Utara Medan Indonesia Faculty Of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Faculty of Mathematics and Natural Sciences Universitas Sumatera Utara Medan Indonesia

ISBN: (数字)9798350351613

ISBN: (纸本)9798350351620

This research discusses the improvement of Long Short-Term Memory (LSTM) model accuracy in translating Batak to Indonesian by utilizing FastText and GloVe embeddings. Traditional LSTM models often struggle with the complexity and richness of underrepresented languages like Batak. By integrating FastText and GloVe, which provide word vectors with deeper semantic meanings, the translation quality can be significantly enhanced. Experimental results show that this approach offers a substantial performance improvement compared to conventional LSTM models, producing more accurate and contextual translations. These findings have the potential to be applied in broader multilingual translation systems and support the preservation and digital accessibility of minority languages.

关键词： Long short term memory Training Vectors Accuracy Machine translation Vocabulary Predictive models Numerical models Decoding Context modeling

来源：评论

学校读者我要写书评

暂无评论

Development and Validation of Customer Intention Factors for Live-Stream Shopping 7

Development and Validation of Customer Intention Factors for...

引用

7th International Conference on New Media Studies, CONMEDIA 2023

作者： Fernando, Erick Hidayat, Desman Pengaribuan, Christian Haposan Prabowo, Yulius Denny Universitas Multimedia Nusantara Faculty of Engineering and Informatics Department of Information System Tangerang Indonesia Bina Nusantara University Binus Entrepreneurship Center Management Department Jakarta11480 Indonesia Sampoerna University Management Study Program Faculty of Business Jakarta12780 Indonesia Bina Nusantara University Binus Online Learning Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350307504

Along with technological improvements, online shopping is currently developing quickly. Online shops started by selling electronics, clothing, food, and home appliances and continue to evolve, selling various things. Currently, digital marketing has a novel technique, namely live shopping, where sellers can present, promote and offer their products directly through live streaming on social media and e-commerce platforms. This research will focus on gaining insight into the factors influencing customers to watch and purchase through live streaming. This research is an early-stage study of exploring and searching for research models. The study used a literature review approach by reviewing similar previous research articles. Several variable components were used in this study. The findings of the factors include visibility capacity, meta-vocation capacity, purchase orientation capacity, social presence, price, perceived product quality, perceived enjoyment, purchase orientation, perceived usefulness, and intention to buy. These factors were tested and proved to be valid with Cronbach's Alpha value being more than 0.5 and reliable with the value of the loading factor a greater than 0.6. Criteria The factors of the findings for further analysis of the relationship between factors and other variables. © 2023 IEEE.

关键词： Domestic appliances

来源：评论

学校读者我要写书评

暂无评论

Digital Technology in Health Education Trend Mapping through Bibliometrics Analysis 4

Digital Technology in Health Education Trend Mapping through...

引用

4th International Conference on informatics, Multimedia, Cyber and Information System, ICIMCIS 2022

作者： Maulana, Fairuz Iqbal Lestari, Dian Widartha, Vandha Pradwiyasma Haidar, Nirwana Dirgantara, Wahyu School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Airlangga University Faculty of Medicine Surabaya60132 Indonesia Telkom University Information System of Industrial Engineering Faculty Bandung Indonesia University of Madura Informatics Engineering Study Program Pamekasan Indonesia University of Merdeka Malang Department of Electrical Engineering Malang Indonesia

ISBN: (纸本)9781665473279

This study examines the mapping of research data on digital technology in the field of health education using bibliometric analysis method. Data was collected by identifying keywords in the Scopus database and sorting them out to sort the studies from the last 10 years (2012-2021). Through this step, a total of 1482 documents were obtained (articles, journals, proceedings, books, and others). The data is then processed using the VOSViewer instrument to obtain a visualization of the mapping analysis. This study also analyzes the network types of authors and co-authors through the VOSViewer instrument. The result of this study indicates that the most document types published within ten years are Articles (66.5%), Review (22.9%), Conference Paper (4.2%), and others. The most studied subjects are Medicine (55.7%), Nursing (9.0%), Health Professions (8.2%), Social sciences (7.8%), Engineering (3.9%), computer science (2.6%), Environmental science (2.6%), Biochemistry-Genetics and Molecular Biology (2.2%), Psychology (1.6%), and Dentistry (1.3%). This study offers a written communication process and the nature and direction of developing descriptive means of counting and analyzing the various phases of communication as well as recognizing the authorship and direction of its symptoms in documents on the subject of digital technology in the health sector. © 2022 IEEE.

关键词： Mapping

来源：评论

学校读者我要写书评

暂无评论

A Study on Medicinal Plant Leaf Recognition Using Artificial Intelligence 3

A Study on Medicinal Plant Leaf Recognition Using Artificial...

引用

3rd International Conference on informatics, Multimedia, Cyber, and Information System, ICIMCIS 2021

作者： Ayumi, Vina Ermatita, Ermatita Abdiansah, Abdiansah Noprisson, Handrie Purba, Mariana Utami, Marissa Universitas Sriwijaya Doctoral Program in Engineering Palembang Indonesia Universitas Sriwijaya Faculty of Computer Science Palembang Indonesia Universitas Mercu Buana Faculty of Computer Science Jakarta Indonesia Program of Informatics Management Politeknik Anika Palembang Indonesia Muhammadiyah University of Bengkulu Faculty of Engineering Bengkulu Indonesia

ISBN: (纸本)9781665427333

Medicinal plant recognition manually takes a lot of time and money. Moreover, to reduce these resources, some researchers propose to implement artificial intelligence technology. This paper aims are to conduct a systematic literature review of medicinal plant leaf recognition published in the last two years (2019-2020) from IEEE, Springer and science Direct. We obtained 15 studies in the field of medicinal plant leaf recognition using artificial intelligence. The dataset used for medicinal plant leaf recognition is mostly used private dataset, however, there are public dataset named Leaf, Flavia, Swedish dataset. We also found robust method that can be used for medicinal plant leaf recognition is Multichannel Modified Local Gradient Pattern (MCMLGP) and Gray Level Co-Occurrence Matrix (GLCM) as feature extraction;and Convolutional Neural Network (CNN), Multi-Layer Perceptron trained with Backpropagation algorithm (MLP-BP), Support Vector Machine (SVM), and Transfer Learning (VGG19) as classifier. © 2021 IEEE.

关键词： Image processing

来源：评论

学校读者我要写书评

暂无评论

Dynamic programming Algorithm using Furniture Industry 0/1 Knapsack Problem 5

Dynamic Programming Algorithm using Furniture Industry 0/1 K...

引用

5th International Conference on Cybernetics and Intelligent Systems, ICORIS 2023

作者： Anditta, Mietha Amartya, Natashya Warnars, Laurens Spits Warnars, Harco Leslie Hendric Spits Ramadhan, Arief Siswanto, Teddy Mantoro, Teddy Noordin, Nurulhuda Bina Nusantara University School of Computer Science Computer Science Departement Jakarta11530 Indonesia Bina Nusantara University School of Information Systems Information Systems Departement Jakarta11530 Indonesia Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Departement Jakarta11530 Indonesia Telkom University Pjj Informatika Bandung40257 Indonesia Trisakti University Information Systems Department Jakarta11440 Indonesia Sampoerna University Faculty of Engineering and Technology Jakarta12760 Indonesia Universiti Teknologi Mara School of Computing Sciences College of Computing Informatics and Mathematics Selangor Malaysia

ISBN: (纸本)9798350369489

Dynamic programming is a fundamental algorithm that can be found in our daily lives easily. One of the dynamic programming algorithm implementations consists of solving the 0/1 knapsack problem. A 0/1 knapsack problem can be seen from industrial production cost. It is prevalent that a production cost has to be as efficient as possible, but the expectation is to get the proceeds of the products higher. Thus, the dynamic programming algorithm can be implemented to solve the diverse knapsack problem, one of which is the 0/1 knapsack problem, which would be the main focus of this paper. The implementation was implemented using C language. This paper was created as an early implementation algorithm using a Dynamic program algorithm applied to an Automatic Identification System (AIS) dataset. © 2023 IEEE.

关键词： Dynamic programming

来源：评论

学校读者我要写书评

暂无评论

Intrusion Detection using Deep Neural Network Algorithm on the Internet of Things 11

Intrusion Detection using Deep Neural Network Algorithm on t...

引用

11th IEEE International Conference on Communication, Networks and Satellite, COMNETSAT 2022

作者： Ikhwan, Syariful Wibowo, Adi Warsito, Budi School of Postgraduate Studies Diponegoro University Doctoral Program of Information System Semarang Indonesia Institut Teknologi Telkom Purwokerto D3 Telecommunication Engineering Banyumas Indonesia Diponegoro University Computer Science Informatics Department of Science and Mathematics Faculty Semarang Indonesia Diponegoro University Statistics Department of Science and Mathematics Faculty Semarang Indonesia

ISBN: (纸本)9781665460309

The increasing use of IoT devices on future networks is very helpful for humans in their lives. However, the increase in devices connected to IoT networks also increases the potential for attacks against those networks. Vulnerabilities in Internet of Things (IoT) networks can be exposed at any time. Artificial intelligence can be used to protect the IoT network by being able to detect attacks on the network so that they can be prevented. In this study, network detection was carried out using the Deep Neural Network (DNN) algorithm. The test was carried out using the UNSW Bot-IoT dataset with a comparison of training data of 75% of the overall data. The results obtained show the ability of the algorithm to detect attacks on average with 99.999% accuracy. The validation loss and training loss look very small. In this study, there is a validation loss that still occurs in overfitting, but the difference is very small. © 2022 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Mapping the Literature of Artificial Intelligence in Medical Education: A Scientometric Analysis 7th

Mapping the Literature of Artificial Intelligence in Medical...

引用

7th International Conference on Inventive Communication and Computaional Technologies, ICICCT 2023

作者： Maulana, Fairuz Iqbal Zaın, Muhammad Yasır Lestari, Dian Purnomo, Agung Adi, Puput Dani Prasetyo Computer Science Department Bina Nusantara University Jakarta11480 Indonesia Informatics Engineering Study Program University of Madura Pamekasan Indonesia Faculty of Medicine Universitas Airlangga Surabaya60132 Indonesia Entrepreneurship Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta Jakarta11480 Indonesia Telecommunication Research Center National Research and Innovation Agency Jakarta11480 Indonesia

ISBN: (纸本)9789819951659

This study aims to investigate the use of artificial intelligence in health education in the last ten years from 2012 to 2022 using the Scopus database. Researchers use bibliometric analysis combined with the quantification method of Vosviewer and Rstudio software used for literature analysis. The results of the article that were flushed showed data such as the year of publication, journal, country, keywords, and authors, to the highest number of citations. The general keywords used by researchers are artificial intelligence, medical, and education. Researchers limit findings through keywords as in the last ten years and English only so that 1681 articles were obtained. According to the study's findings, McGill University affiliation had the greatest number of papers, with 69 pieces, and medicine had a proportion of 39.7% (n = 1067). This bibliometric study will be useful for other researchers to examine the development of research on artificial intelligence in health education in the last ten years. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Medical education

来源：评论

学校读者我要写书评

暂无评论

Challenges and Emerging Issues for Generative AI and Hyper Intelligence

Challenges and Emerging Issues for Generative AI and Hyper I...

引用

Cyber science and Technology Congress (CyberSciTech), IEEE

作者： Jianhua Ma Qun Jin Hui-Huang Hsu John Paul C. Vergara Antonio Guerrieri Claudio Miceli Ao Guo Faculty of Computer and Information Sciences Hosei University Japan Department of Human Informatics and Cognitive Sciences Waseda University Japan Department of Computer Science and Information Engineering Tamkang University Taiwan Department of Information Systems and Computer Science Ateneo de Manila University Philippines ICAR-CNR Italy Graduate Program in Systems Engineering and Computer Science Federal University of Rio de Janeiro Brazil Graduate School of Informatics Nagoya University Japan

ISBN: (数字)9798331532093

ISBN: (纸本)9798331532109

Generative Artificial Intelligence (GenAI) represents a significant milestone in the development of artificial intelligence, bringing sophisticated AI capabilities into daily life and work. As we approach the era of Hyper Intelligence (Hyper-I), a variety of critical challenges and emerging issues have come to light, ranging from computational complexity to ethical concerns. This paper explores the evolution of AI from the perspective of human learning, comparing machine and human intelligence, and identifying key considerations for the development of future AI systems. It also highlights the growing importance of regulating advanced AI models, such as Reinforcement Learning-based Long-Term Planning Agents, to ensure that Hyper-I remains under human control. Additionally, the paper discusses the computational complexity of transformer-based models, their applicability to intractable problems, and their role in cognitive building systems and resource-constrained environments through TinyML. By analyzing these pressing challenges, this work provides insights into the future of AI and the path toward responsible innovation in generative and hyper-intelligent systems.

关键词： Hyper-intelligent systems Ethics Technological innovation Generative AI Computational modeling Tiny machine learning Pressing Transformers Regulation Computational complexity

来源：评论

学校读者我要写书评

暂无评论

Performance Evaluation of Oversampling Methods on Deep Learning-Based Skin Cancer Classification

Performance Evaluation of Oversampling Methods on Deep Learn...

引用

International Seminar on Application for Technology of Information and Communication (ISemantic)

作者： Catur Supriyanto Abu Salam Junta Zeniarja Adi Wijaya Bachelor Program in Informatics Engineering Faculty of Computer Science Universitas Dian Nuswantoro Semarang Indonesia Department of Health Information Management Universitas Indonesia Maju Jakarta Indonesia

The field of dermatology faces considerable challenges when it comes to early detection of skin cancer. Our study focused on using different datasets, including original data, augmented data, and SMOTE oversampled data, to identify skin cancer. Our dataset consisted of images of skin lesions from the MNIST Skin Cancer dataset (HAM 10000), including samples of both cancerous and benign cases in the dataset. We employed data augmentation to expand the dataset’s size and increase the diversity of skin lesion features. Furthermore, to tackle class imbalance in the dataset, we applied the SMOTE oversampling technique to generate synthetic samples for the under-represented group. With the original, augmented, and SMOTE oversampled datasets, we trained a Convolutional Neural Network (CNN) model. The performance of the model was evaluated using accuracy, recall, precision, and F1-score. The comparison between the results obtained from the original data, augmented data, and SMOTE oversampling data clearly revealed distinctions in performance. Our findings clearly demonstrate that employing data augmentation and SMOTE oversampling can significantly enhance the efficacy of skin cancer detection.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Implementation of Face Patterns and Smile Recognition for Intelligent Class Attendance Systems

Implementation of Face Patterns and Smile Recognition for In...

引用

International Conference of computer and informatics Engineering (IC2IE)

作者： Miftakhurrokhmat Teddy Suparyanto Gregorius Natanael Elwirehardja Shofiyati Nur Karimah Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Department of Informatics Faculty of Computer Science Universitas Amikom Yogyakarta Yogyakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia

Students “attendance in class is one important success parameter in face-to-face learning processes. Conventional attendance systems, such as paper-based attendance sheets or identity card systems, require a long time in the manual recapitulation process. Without additional verifications, even computer vision-based methods are prone to fraudulent practices by the students instead of gaining their excitement and attention in a class. To stimulate students” attention in a class, this work designs an intelligent class attendance system, in which facial pattern and smile recognition are implemented by using the latter as an additional task-based verification to reduce the risks of fake attendance. For the face recognition module, this pilot study used FaceNet as a feature extractor combined with SVM for classification, whereas the Haar cascade algorithm is used for recognizing smiles. This face recognition pipeline was implemented as a service installed on minicomputers or Internet of Things (IoT) devices in each classroom and connected to an IP camera. Every recorded attendance will be sent as a notification to a mobile application for students that requires their active participation to confirm it with a smiling self-photo. The proposed pipeline obtained 92.86% accuracy on the test data, and 66.67% accuracy when evaluated in a real-life simulation setting through the implemented system. The lower accuracy in the simulation indicated that further improvements are indispensable, especially since the model obtained 28.57% False Negative Rate. Future studies will need to acquire more data and experiment with more efficient methods of attendance verification.

关键词：

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共32页 << < 2 3 4 5 6 7 8 9 10 11 > >>

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：