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

371 篇 会议
58 篇 期刊文献

馆藏范围

429 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

194 篇 工学
- 171 篇 计算机科学与技术...
- 156 篇 软件工程
- 35 篇 信息与通信工程
- 24 篇 控制科学与工程
- 21 篇 生物工程
- 19 篇 生物医学工程（可授...
- 15 篇 机械工程
- 13 篇 安全科学与工程
- 11 篇 光学工程
- 11 篇 网络空间安全
- 10 篇 交通运输工程
- 9 篇 电气工程
- 7 篇 仪器科学与技术
- 7 篇 化学工程与技术
88 篇 管理学
- 53 篇 管理科学与工程(可...
- 45 篇 图书情报与档案管...
- 41 篇 工商管理
81 篇 理学
- 26 篇 数学
- 26 篇 生物学
- 23 篇 物理学
- 13 篇 统计学（可授理学、...
- 9 篇 系统科学
- 6 篇 化学
19 篇 法学
- 15 篇 社会学
18 篇 经济学
- 18 篇 应用经济学
16 篇 医学
- 15 篇 基础医学(可授医学...
- 15 篇 临床医学
- 10 篇 公共卫生与预防医...
- 8 篇 药学(可授医学、理...
11 篇 教育学
- 11 篇 教育学
5 篇 农学
1 篇 文学
1 篇 军事学

主题

22 篇 computer science
17 篇 sentiment analys...
16 篇 training
14 篇 support vector m...
13 篇 social networkin...
11 篇 information syst...
10 篇 education
9 篇 covid-19
9 篇 systematics
9 篇 computational mo...
9 篇 machine learning
9 篇 blockchain
9 篇 accuracy
9 篇 data models
8 篇 tools
8 篇 reliability
8 篇 companies
8 篇 face recognition
8 篇 mobile applicati...
8 篇 predictive model...

机构

35 篇 computer science...
30 篇 computer science...
29 篇 computer science...
25 篇 binus graduate p...
23 篇 binus graduate p...
17 篇 bina nusantara u...
16 篇 computer science...
16 篇 advanced institu...
16 篇 school of comput...
14 篇 computer science...
14 篇 school of inform...
14 篇 information syst...
13 篇 computer science...
13 篇 school of inform...
11 篇 computer science...
9 篇 computer science...
8 篇 computer science...
8 篇 information syst...
8 篇 school of comput...
7 篇 computer science...

作者

61 篇 gaol ford lumban
45 篇 harco leslie hen...
44 篇 warnars harco le...
44 篇 meyliana
42 篇 yaya heryadi
41 篇 ford lumban gaol
39 篇 heryadi yaya
32 篇 lukas
30 篇 prabowo harjanto
25 篇 harjanto prabowo
19 篇 haryono soeparno
19 篇 maria susan angg...
19 篇 yulyani arifin
18 篇 matsuo tokuro
17 篇 agung trisetyars...
17 篇 wayan suparta
16 篇 trisetyarso agun...
15 篇 suparta wayan
15 篇 soewito benfano
15 篇 abdurachman edi

语言

424 篇 英文
3 篇 中文
2 篇 其他

检索条件"机构=Doctor of Computer Science Program"

共 429 条记录，以下是21-30 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：

Predicting Stock Market Movements Using Long Short-Term Memory (LSTM) 4

Predicting Stock Market Movements Using Long Short-Term Memo...

引用

4th International Conference on Artificial Intelligence and Data sciences, AiDAS 2023

作者： Fauzan, Achmad Susananggreainy, Maria Nathaniel, Nicholas Kurniawan, Afdhal School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350318432

This paper explores using artificial intelligence (AI) to predict stock market movements and build optimal portfolios. The research methodology involves using LSTM networks to predict stock performance. The study aims to combine AI with human expertise to develop an intelligent trading system. The findings emphasize the importance of selecting appropriate AI approaches for accurate predictions and optimal portfolio management. The results of this study state that with LSTM, we can predict stock prices that are very close to their real prices. The average LSTM method in predicting stock prices is about 97.2938%. Average error obtained when using LSTM was 2.3223%. © 2023 IEEE.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

The Impact of Emotion Recognition Models Towards Believability Factor of Chatbots 15

The Impact of Emotion Recognition Models Towards Believabili...

引用

15th International Congress on Advanced Applied Informatics Winter, IIAI-AAI-Winter 2023

作者： Sutoyo, Rhio Warnars, Harco Leslie Hendric Spits Muhamad Isa, Sani Budiharto, Widodo Bina Nusantara University Binus Graduate Program - Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350383829

Emotionally-aware chatbots are chatbots that are equipped with emotional intelligence. Based on the literature, using emotional elements in chatbots can improve user engagement and believability factors. This study attempts to make a novel contribution by empirically evaluating the impact of emotion recognition models on the believability factor of chatbots. This study examines the impact of the emotions model and avatar on chatbot interactions through three implementations. Thirty-one participants volunteered to evaluate emotionally aware chatbots. The participants evaluated the interaction with the chatbot using the Godspeed Questionnaire Series (GQS). The questionnaire results are utilized to measure the effect of the emotions model on the chatbot's believability factor. The evaluation results of the experiment show that implementing the emotion recognition model on the chatbot increases its believability factors. On average, the believability measures in interaction type B (with an emotion model) are enhanced 1.71 times compared to interaction type A (a basic model). Furthermore, the believability measures in interaction type C (with an emotion model and an avatar) are enhanced 2 times more than in interaction type A (a basic model). The believability factor is also heightened by integrating a chatbot avatar into the interaction system. Using avatars in chatbots increases the believability variables of the system by 1.17 times if compared with not using avatars. © 2023 IEEE.

关键词： Emotion Recognition

来源：评论

学校读者我要写书评

暂无评论

An Analysis of Hate Speech Detection Techniques 4

An Analysis of Hate Speech Detection Techniques

引用

4th International Conference on Artificial Intelligence and Data sciences, AiDAS 2023

作者： Krisdianto, Ricky Apriani, Ivana Halim, Peter Miracle Anggreainy, Maria Susan Kurniawan, Afdhal School of Computer Science Bina Nusantara University Computer Science Departement Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Doctor of Computer Science Computer Science Departement Jakarta11480 Indonesia

ISBN: (纸本)9798350318432

Hate speech is any act of provoking or insulting another person or group based on their ethnicity, religion, race, gender, sexual orientation, physical ability, or other characteristics. This can be done in a variety of ways. Because of this, a study was conducted that focused on ascertaining the most effective technique for identifying hate speech. It was discovered that LSTM, a form of recurrent neural network (RNN) that is particularly good at modeling sequential data, is the most successful technique to apply. Many natural language processing (NLP) activities may be performed with it, including hate speech detection, which makes LSTM have advantages over other techniques, both in terms of long-term memory mechanisms. LSTM, also able to handle long and complex text sequences, capable of recognizing more complex patterns, able to understand and consider the context in the text, to recognize the meaning of a word or phrase in a broader context. Then, based on the dataset obtained from the Kaggle website with a total of 24783 data, then the dataset is divided into training data and validation data, with a ratio of training data to validation data. of 80%: 20%. Then, the testing step results in an accuracy value of 87.10%. © 2023 IEEE.

关键词： Speech recognition

来源：评论

学校读者我要写书评

暂无评论

Determining the Main Symptoms of Lung Cancer with Machine Learning Methods 10

Determining the Main Symptoms of Lung Cancer with Machine Le...

引用

10th International Conference on ICT for Smart Society, ICISS 2023

作者： Meylia, Belinda Christina Immanuel, David Gabriel Faustine, Regina Dharma, Surya Anggreany, Maria Susan Parmonangan, Ivan Halim School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program Doctor of Computer Science Computer Science Department Jakarta11530 Indonesia

ISBN: (纸本)9798350339543

The ability to identify cancer at an early stage is critical to increase the likelihood of effective treatment or stopping the progression of the disease in the body. Lung cancer is one of the most common deadly diseases and quickly kills the patient. The number of deaths caused by lung cancer surpasses those of colon, rectal, breast, and prostate cancers combined. Unfortunately, only two percent of patients with advanced lung cancer survive for five years or more. However, the survival rates are better, with 49 % of the patients surviving for five years or more, if the disease is detected early. On the other hand, in this modern era, machine learning has become one of the most reliable tools in the world for healthcare, as machine learning can learn from the data obtained and process the data that will later be used to help complete certain tasks. Therefore, in this research, machine learning or especially classification algorithms such as K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest, Logistic Regression, and XGBoost are used to identify three main symptoms that serve as markers for early detection of lung cancer. Determining the three main symptoms is done by combining the results of the feature importance score on each model using the rank-averaging method. The result is an average ranking of each feature based on its combined importance with an accuracy of 93.5 %. © 2023 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Diagnosing Diabetes Using Soft Voting Ensemble Machine Learning Model 10

Diagnosing Diabetes Using Soft Voting Ensemble Machine Learn...

引用

10th International Conference on ICT for Smart Society, ICISS 2023

作者： Kusuma, Christian Chandra Chandra, Nathalia Utomo, Galang Nurbudi Parimartha, Putu Agus Hidayat, Jessica Anggreainy, Maria Susan Parmonangan, Ivan Halim School of Computer Science Computer Science Department Bina Nusantara University Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program Doctor of Computer Science Computer Science Department Jakarta11530 Indonesia

ISBN: (纸本)9798350339543

Diabetes is a metabolic disorder caused by a dysfunction in insulin secretion which leads to an increase in blood sugar levels in the body. The disease has dangerous risks for those who suffer from it, as it can lead to various complications and even death. Because of the dangerous risks associated with diabetes, early detection of the disease is important. Machine learning can help the process of diagnosing diabetes quickly and accurately because of its ability to learn and process data independently. The goal of this research is to develop a diabetes detection system using ensemble learning with a soft voting classifier. In predicting diabetes, this research uses 14 types of algorithms with the help of the PyCaret library, including extra trees classifier, random forest, light gradient boosting, machine, gradient boosting classifier, decision tree, adaboost classifier, logistic regression, linear discriminant analysis, k-neighbors classifier, quadratic discriminant analysis, naive bayes, support vector machine-linear kernel, dummy classifier, and ridge classifier. From the evaluation process, the five best algorithms are chosen with an accuracy level of 91.3%, 91.3%, 89.5%, 92.2% and 85.3 % These five algorithms are then blended using ensemble learning with a soft voting classifier. The accuracy produced by the soft voting classifier method is higher than using a single classifier, at 97.3%. © 2023 IEEE.

关键词： Learning systems

来源：评论

学校读者我要写书评

暂无评论

Analysis of the Utilization of Human Motion Tracking in the Security Sector: Examining the Accuracy Level and Key Factors in Operation 6

Analysis of the Utilization of Human Motion Tracking in the ...

引用

6th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2023

作者： Indradjaja, Nicholas Chen, Michael Masada, Hossey Anggreainy, Maria Susan Kurniawan, Afdhal School of Computer Science Computer Science Department Bina Nusantara University Jakarta11480 Indonesia BINUS Graduiate Program Doctor of Computer Science Computer Science Department Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798350358346

The use of surveillance cameras (CCTV) is very limited and has many weaknesses. Surveillance cameras can only record events that are happening without being detected, so supervisors still need protection. From these limitations, it is necessary to use Human Motion Tracking on surveillance cameras that can help detect objects caught on camera. This paper analyzes each Human Motion Tracking method in terms of the level of accuracy of the supporting or inhibiting factors, as well as the estimated costs required. This paper will compare each of the methods found to conclude that the Human Motion Tracking method has the highest detection accuracy, equipped with adequate key factors, and accompanied by a balanced cost. In conclusion, this study recommends the Kalman Filter method as the most optimal choice for human Motion Tracking on surveillance cameras, offering a balanced combination of accuracy and efficiency. The implementation of this method is expected to contribute significantly to enhancing security measures and creating a safer environment by effectively monitoring potential security issues. © 2023 IEEE.

关键词： Cameras

来源：评论

学校读者我要写书评

暂无评论

Authorship Attribution in Bahasa Indonesia Using Twitter Dataset on Political Topic 4

Authorship Attribution in Bahasa Indonesia Using Twitter Dat...

引用

4th International Conference on Artificial Intelligence and Data sciences, AiDAS 2023

作者： Muliono, Yohan Gaol, Ford Lumban Chowanda, Andry Budiharto, Widodo Bina Nusantara University Binus Graduate Program-Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350318432

Fake news so called made-up news intended to cause misinformation is identified as Hoax. In Indonesia, hoaxes could not be ignored, fairly low literacy rate is the cause of hoaxes could spread quickly in Indonesia, to make it even worse, there are so many people would believe in hoaxes without any intention to make sure of the integrity of the news. even the media in Indonesia are competing with one another to provide content with provocative names in the hopes of increasing their rating and reaching as many people as possible. In the course of this research, a method for identifying authors on the basis of their writings will be developed to reduce the spread of hoaxes and encourage people to re-evaluate their decision to create harmful content and hoax news in the future. In this investigation, a transformer-based methodology focusing on IndoBERTbase, IndoBERTbase-uncased, and IndoBERTtweet will be employed in conjunction with the self-collected Indonesian tweet data that will be crawled from Indonesian Social Actor Focused on politicians in Twitter and will be combined with another topic in the future study. This research has shown a promising 98,93% in Training Accuracy using IndoBERTtweet. © 2023 IEEE.

关键词： Curricula

来源：评论

学校读者我要写书评

暂无评论

Systematic Literature Review on IT Strategic Workspaces: Approach to Develop an Ideal Environment 9

Systematic Literature Review on IT Strategic Workspaces: App...

引用

9th IEEE International Conference on Computing, Engineering and Design, ICCED 2023

作者： Putra, Alden Ardiwinata Fusanto, Princessa Fortunata Valonika, Nebrisca Anggreainy, Maria Susan Kurniawan, Afdhal Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia BINUS Graduate Program Doctor of Computer Science Computer Science Department Bina Nusantara University Jakarta11530 Indonesia

ISBN: (纸本)9798350370126

Technology and information will always develop dynamically;this statement demands programmers to always be creative and keep up with the times. Despite this, their work ethic is always the same and tends to stagnate. programmers' perspectives on their workplace: this perspective is rarely seen as important and the opportunity for a comprehensive study is still widely open. This study aims to conduct a systematic literacy study to ascertain that the workplace has a crucial relationship with a programmer's performance. The focus of the study includes a discussion of how programmers relate to their workplace in a tech company, benchmarking between various office layouts, and criteria that can be derived from the literature review regarding a degree of whether such a workplace is good or bad. The proper base facts are obtained. Nevertheless, the two entities are proven to be interrelated to each other. Such correlations are derived into tangible metrics and parameters (e.g., human cognitive, cultural, physical, behavioral, etc.). Relating issues regarding these topics are also presented. © 2023 IEEE.

关键词： Software engineering

来源：评论

学校读者我要写书评

暂无评论

The Model of Personalized Machine Learning to Enhance Students’ Achievements 6th

The Model of Personalized Machine Learning to Enhance Studen...

引用

6th International Conference on Intelligent Sustainable Systems, ICISS 2023

作者： Rusdi, Mikail Rifqi Enrico, T. S. Gregorio Salomo, Austin Ordell Gaol, Ford Lumban Matsuo, Tokuro Bina Nusantara University Undergraduate Program-Computer Science Jakarta Indonesia Computer Science Department Binus University BINUS Graduate Program – Doctor of Computer Science Jakarta11480 Indonesia Advanced Institute of Industry Technology Tokyo Japan

ISBN: (纸本)9789819917259

Global technological improvements are accelerating and influencing numerous industries, including education. Using machine learning, technological innovations can be utilized in the education industry. Machine learning is a subset of artificial intelligence (AI) that enables computers or teaching machines to make intel-ligent decisions by learning from all past data. The framework for machine learning involves acquiring and retaining a large quantity of data and translating it into orga-nized knowledge. AI advancements enable educators to acquire a deeper knowledge of their students’ learning progression. Using Personalized Machine Learning can increase the efficiency of student learning by analyzing their performance in real time and modifying teaching techniques and the curriculum based on this information. It helps to have a tailored approach that adapts to the individual in order to provide a better education. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Rice Commodity Supply Chain Issues in Indonesia: Exploring Blockchain Technology as an Alternative Solution 5

Rice Commodity Supply Chain Issues in Indonesia: Exploring B...

引用

5th Technology Innovation Management and Engineering science International Conference, TIMES-iCON 2024

作者： Taufiq, Rohmat Warnars, Harco Leslie Hendric Spits Soeparno, Haryono Oktavia, Tanty Computer Science Department - BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Information System Management Department BINUS Graduate Program Master of IS Management Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798350362602

—Agriculture has long been recognized as pivotal to economic development, offering avenues to alleviate poverty, foster prosperity, and sustainably meet the food demands of a burgeoning global population projected to reach 9.7 billion by 2050. However, persistent inefficiencies within agricultural supply chains, particularly in sectors like rice production, pose formidable challenges. Addressing these inefficiencies is imperative to avert a surge in hunger and malnutrition. Failure to do so could condemn 815 million people to hunger and leave 3 out of 10 individuals malnourished. Thus, investigating solutions to enhance the resilience of rice supply chains is paramount. This research endeavors to identify and analyze challenges inherent in tracing the rice supply chain, offering a conceptual model proposal as a potential remedy. Employing a methodology centered on scholarly discourse and extensive literature review, our study reveals various impediments facing rice commodity supply chains in Indonesia. Furthermore, this study leverages blockchain technology, specifically the Sawtooth Hyperledger platform, to streamline the tracing process for rice commodities and rationalize the supply chain by reducing intermediary actors. From the proposed model, there is great hope that it can provide implications for the rice supply chain to run more efficiently. ©2024 IEEE.

关键词： Supply chains

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43页 << < 1 2 3 4 5 6 7 8 9 10 > >>

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：