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

42,238 篇 会议
26,353 篇 期刊文献
692 册 图书

馆藏范围

69,283 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

42,506 篇 工学
- 31,532 篇 计算机科学与技术...
- 26,499 篇 软件工程
- 10,318 篇 信息与通信工程
- 6,156 篇 控制科学与工程
- 5,956 篇 生物工程
- 5,057 篇 生物医学工程（可授...
- 4,767 篇 电气工程
- 3,855 篇 电子科学与技术（可...
- 3,426 篇 光学工程
- 2,597 篇 机械工程
- 2,213 篇 仪器科学与技术
- 2,076 篇 化学工程与技术
- 1,980 篇 动力工程及工程热...
- 1,872 篇 网络空间安全
- 1,733 篇 安全科学与工程
- 1,572 篇 交通运输工程
24,799 篇 理学
- 12,424 篇 数学
- 7,094 篇 生物学
- 6,600 篇 物理学
- 4,811 篇 统计学（可授理学、...
- 2,447 篇 系统科学
- 2,421 篇 化学
13,147 篇 管理学
- 7,464 篇 管理科学与工程(可...
- 6,182 篇 图书情报与档案管...
- 3,774 篇 工商管理
5,549 篇 医学
- 4,535 篇 临床医学
- 4,052 篇 基础医学(可授医学...
- 2,464 篇 公共卫生与预防医...
- 2,317 篇 药学(可授医学、理...
2,473 篇 法学
- 2,095 篇 社会学
1,485 篇 经济学
1,165 篇 农学
985 篇 教育学
329 篇 文学
135 篇 军事学
106 篇 艺术学
12 篇 哲学
12 篇 历史学

主题

2,258 篇 accuracy
1,942 篇 deep learning
1,844 篇 machine learning
1,660 篇 computer science
1,604 篇 data mining
1,594 篇 data models
1,535 篇 training
1,485 篇 feature extracti...
1,307 篇 computational mo...
1,255 篇 predictive model...
1,134 篇 internet of thin...
997 篇 real-time system...
984 篇 support vector m...
927 篇 artificial intel...
906 篇 convolutional ne...
663 篇 optimization
645 篇 cloud computing
610 篇 security
602 篇 semantics
577 篇 wireless sensor ...

机构

975 篇 cavendish labora...
944 篇 department of ph...
883 篇 oliver lodge lab...
864 篇 physikalisches i...
590 篇 university of ch...
556 篇 fakultät physik ...
519 篇 department of ph...
511 篇 horia hulubei na...
503 篇 school of physic...
502 篇 imperial college...
497 篇 school of physic...
492 篇 nikhef national ...
491 篇 stfc rutherford ...
486 篇 iccub universita...
482 篇 h.h. wills physi...
478 篇 physik-institut ...
475 篇 school of physic...
472 篇 henryk niewodnic...
470 篇 school of physic...
464 篇 institute of par...

作者

250 篇 barter w.
238 篇 a. gomes
235 篇 beiter a.
223 篇 bellee v.
222 篇 s. chen
212 篇 c. alexa
211 篇 j. m. izen
211 篇 brundu d.
211 篇 bowcock t.j.v.
210 篇 g. bella
210 篇 j. strandberg
210 篇 d. calvet
210 篇 c. amelung
203 篇 blake t.
202 篇 boettcher t.
201 篇 amato s.
199 篇 betancourt c.
198 篇 borsato m.
196 篇 g. spigo
195 篇 m. gandelman

语言

65,288 篇 英文
3,460 篇 其他
551 篇 中文
31 篇 日文
11 篇 法文
9 篇 德文
6 篇 朝鲜文
5 篇 斯洛文尼亚文
4 篇 荷兰文
3 篇 西班牙文
1 篇 俄文

检索条件"机构=Data Science Program Computer Science Department School of Computer Science"

共 69283 条记录，以下是11-20 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：

Interpretable Machine Learning in Leveraging Customer Information for Success Direct Marketing Campaigns 10

Interpretable Machine Learning in Leveraging Customer Inform...

引用

10th International Conference on Smart Computing and Communication, ICSCC 2024

作者： Octavia Tjandra, Clarissa Shirley Cam, Grace Agung Santoso Gunawan, Alexander Eka Setiawan, Karli School of Computer Science Bina Nusantara University Data Science Program Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350363104

A bank's marketing campaign execution is very important. Undoubtedly, a well-crafted marketing plan will contribute to the bank's increased revenue. Marketing is crucial because it can be used to connect with consumers and inform them of the advantages of making a deposit with the bank, which will increase the number of deposits the bank receives. Thus, the goal of this research is to determine the best customer information that affects their decision on subscribing a term deposit and providing the best model for predicting the outcome of future marketing campaign. By determining the significance of each feature, this research constructed machine learning models using methods like Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF) and Logistic Regression (LR) as our research contribution. According to the research's findings, out of the three machine learning models, Random Forest (RF) has the highest accuracy scores. Additionally, the feature importance model reveals that a customer's decision to make a deposit or not is greatly influenced by factors such as the age of the customer, how much does the customer has in their bank account, did the bank have contacts with longer call durations, and did it has a positive outcomes on the previous marketing campaigns. The customers who are contacted more frequently and have subscribed before are more likely to subscribe in the future marketing campaign. © 2024 IEEE.

关键词： bank marketing classification direct marketing machine learning predictive analysis

来源：评论

学校读者我要写书评

暂无评论

Image-Based Natural Disaster Classification with Deep Learning: A Comparative Analysis

Image-Based Natural Disaster Classification with Deep Learni...

引用

2024 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 2024

作者： Alamsyah, Samuel Eka Djamhari, Simen Ferdinand Sudjono, Salomo Hendrian Gunawan, Alexander A.S. Setiawan, Karli Eka Data Science Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798350353464

Natural disasters, including earthquakes, cyclones, floods, and wildfires, cause significant environmental damage and have emerged as a major global issue. These events can result in loss of life and disrupt communities and economy. Effective disaster management relies on timely and accurate information. With social media becoming a primary source of real-time information during such events, many images of these disasters are shared, though some may not be accurately labeled. Therefore, the purpose of this study is classifying natural disaster images, mainly cyclone, wildfire, flood, earthquake from social media images. This paper is to conduct a comparative analysis over 4 models namely VGG16, VGG19, EfficientNetB0, and ResNet-50. This paper uses a dataset that contains 4428 human-annotated images including cyclones, earthquakes, floods, and wildfires. Results concluded that ResNet-50 and EfficientNetB0 give the best result with both got over 96% average accuracy. ResNet-50 delivers more stable and more convergent results compared to EfficientNetB0, but it delivers slightly less classification accuracy. VGG16 and VGG19 are both outclassed in terms of accuracy, but better in training time, with VGG19 having a more convergent result. ©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

Regression-Based Exploration of Factors Influencing Movie Profits

Regression-Based Exploration of Factors Influencing Movie Pr...

引用

2024 International of Seminar on Application for Technology of Information and Communication, iSemantic 2024

作者： Veronica, Lindawaty Wibowo, Tifara Beata Gunawan, Alexander Agung Santoso Setiawan, Karli Eka School of Computer Science Bina Nusantara University Data Science Program Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350366648

Accurate box office prediction is crucial for managing financial risks in film production. The internet has transformed consumer behavior, affecting marketing strategies. Critical online reviews, more than early revenue, predict a film's profit, while establishing film critics as the predictors. Hence, this study investigates the intricate relationship between ratings and movies profit. While also finding the best model to predict those variables. This research was conducted through a comprehensive process encompassing data collection and processing, feature selection, prediction modeling, and a detailed discussion of the conducted analysis. Several manual interventions were applied to the data to support further discoveries and analysis in this research. These interventions were carried out based on a solid foundation to enhance the depth of exploration and understanding within the study. The findings reveal that the random forest regression emerged as the most effective in this study, achieving the Mean Squared Error (MSE) of 768.40. Leveraging a dataset of 500 movies from ***, incorporating critics' ratings from ***. Through this research, industry professionals not only enhance their understanding but also identify factors that can be considered to increase a movie's revenue. © 2024 IEEE.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

Enhancing Tourism Industry Insights: LeveragingLDA and KMeans for Topic Extraction in Indonesian OTAs' Reviews 12

Enhancing Tourism Industry Insights: LeveragingLDA and KMean...

引用

12th International Conference on Information and Communication Technology, ICoICT 2024

作者： Castilani, Laurentia Alyssa Winata, Natasha Hartanti Nugraha, Nathania Christy Gunawan, Alexander Agung Santoso Setiawan, Karli Eka Wulandari, Lili Ayu School of Computer Science Bina Nusantara University Data Science Program Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350363432

Indonesia's tourism sector, boosted by its captivating landscapes, has seen a rise in the popularity of Online Travel Agencies (OTAs) like Traveloka, ***, and Agoda. To effectively assist tourists, OTAs are anticipated to comprehend tourists' needs and interests, with insights gathered through scraping data from the Google Play Store. Through topic extraction from customers' reviews, this research aims to optimize the tourist experience and offer actionable insights for the tourism industry's marketing strategies. Furthermore, it endeavors to eliminate the reliance on brute force approaches in determining the candidate number of topics. To achieve the research goal, this research employs topic modeling, specifically Latent Dirichlet Allocation (LDA) enhanced through the K- means elbow method. The evaluation of the optimal topic number utilizes a coherence score, while human judgement serves as a quantitative metric for overall performance, with a remarkable validity rate across three platforms: Agoda (91%), *** (88%), and Traveloka (94%). This highlights the approach's effectiveness in accurately identifying and categorizing topics. In conclusion, the model accurately discerned essential topics, revealing that the majority of the reviews on these OTAs focus on the transaction and refund systems on each platform. Furthermore, this model offers promising recommendations to enhance the understanding and response to customers' reviews, facilitating faster and more effective responses. These insights are invaluable for continuous development and improvement strategies within the dynamic changes in the tourism industry across these influential platforms. © 2024 IEEE.

关键词： Sales

来源：评论

学校读者我要写书评

暂无评论

Machine Learning for Imbalanced data in Telecom Churn Classification

Machine Learning for Imbalanced Data in Telecom Churn Classi...

引用

2024 International Conference on Information Technology Research and Innovation, ICITRI 2024

作者： Dewi, Olivia Intan Permata Santiko, Vina Nathania Safa, Snow White Putri Gunawan, Alexander Agung Santoso Setiawan, Karli Eka Bina Nusantara University School of Computer Science Data Science Program Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350376210

In the highly competitive telecommunications industry, customer churn poses a significant threat to revenue and long-term sustainability. To proactively address this challenge, machine learning (ML) techniques offer promising solutions for predicting and mitigating customer churn. This study applies ML algorithms to churn prediction, focusing on the challenges of imbalanced data. Using a publicly available Telecom dataset from Kaggle, the data was meticulously prepared through a multi-stage process, including Random Under-Sampling (RUS) to address class imbalance. The study employed a range of ML algorithms, evaluating their performance using key metrics such as recall, precision, F1score, and accuracy. The findings reveal valuable insights into how RUS can enhance model performance by improving the prediction of the minority class. XGBoost outperformed Logistic Regression and Random Forest in accurately identifying positive outcomes, suggesting its effectiveness in recognizing true instances of customer churn. The confusion matrix supports this observation, showing that the recall value produced by XGBoost slightly exceeded those for Logistic Regression and Random Forest. © 2024 IEEE.

关键词： Logistic regression

来源：评论

学校读者我要写书评

暂无评论

HIV Inhibitory Activity Classification with Morgan Fingerprint and Integrated Graph-Based Deep Learning 12

HIV Inhibitory Activity Classification with Morgan Fingerpri...

引用

12th International Conference on Information and Communication Technology, ICoICT 2024

作者： Verna Siswanto, Jocelyn Angelina Sunarya, Caroline Calista, Agnes Agung Santoso Gunawan, Alexander Eka Setiawan, Karli School of Computer Science Bina Nusantara University Data Science Program Computer Science Department Jakarta11480 Indonesia School of Computer Science Computer Science Department Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798350363432

This research offers a new perspective on predicting the activity of the HIV virus from the Drug Therapeutics program (DTP) Antiviral Screen by using the molecular data represented in SMILES notation. The topic has significance as it focuses on a major global health issue using modern computational approaches and has the potential to uncover new antiviral drug candidates, which could eventually save lives and improve public health outcomes. The study addresses the data imbalance between two classes, active and inactive, and employs the Morgan Fingerprint method for feature extraction, along with the Graph Convolutional Network (GCN) and Graph Attention Network (GAT) as the baseline architectures and the fusion of GCN's and GAT's main features as the proposed architecture. The random oversampling technique is applied to alleviate dataset imbalances. However, even though it improved the training process, the performance of the model flopped when the test set was fed into the model. Combining the main features in GCN and GAT, the proposed model was able to do the classification task more accurately. The attention mechanism from GAT allows the model to focus more on the parts that are more relevant and ignore the irrelevant ones. It managed to outperform the baseline models. Despite a high overall accuracy of 94%, the fusion model exhibits significant disparities in precision, recall, and f1-score metrics, potentially due to class imbalance. Random oversampling led to improved training but compromised model performance on the test set. © 2024 IEEE.

关键词： Diseases

来源：评论

学校读者我要写书评

暂无评论

Enhancing Online Learning Experience: An Ensemble Approach for Classifying Student Adaptivity Level 4

Enhancing Online Learning Experience: An Ensemble Approach f...

引用

4th International Conference of science and Information Technology in Smart Administration, ICSINTESA 2024

作者： Mutiara, Belinda Florencia Ariyanto, Gabrielle Felicia Gunawan, Alexander Agung Santoso Setiawan, Karli Eka Bina Nusantara University Data Science Program Computer Science Department School of Computer Science Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350376111

The study investigates the increasing demand of online learning as a means of addressing education issues in the context of the COVID-19 epidemic. Online learning requires several adaptations for teaching methods, learning methodologies, and devices needs. The flexibility of both teachers and students is essential to these adaptations. This research uses dataset collected from a survey about student adaptability level in online education. Carrying out the preprocessing is a challenge as the data used in this research has imbalanced value on the target category. Based on this problem, the aim of this research was created, namely to categorize students' adaptivity levels in online learning and also focuses on finding out approaches that can provide solutions to overcome imbalanced values in the dataset. The model uses ensemble methods - Bagging, Boosting, and Voting with machine learning algorithms. There are two models that stand out, with Soft Voting obtaining the best performance with 9 0% accuracy. © 2024 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Assessing IT Job Candidates Through Interpretable Machine Learning Model 10

Assessing IT Job Candidates Through Interpretable Machine Le...

引用

10th International Conference on Smart Computing and Communication, ICSCC 2024

作者： Sukardi, Andrew Saputra, Jayagatha Hariman, Sebastian Gunawan, Alexander Agung Santoso Setiawan, Karli Eka School of Computer Science Bina Nusantara University Data Science Program Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350363104

The escalating demand for skilled IT professionals underscores the increasing significance of the recruitment process. Traditional methods often fall short in identifying individuals poised for success in the dynamic technology industry. This paper aims to address the challenges in recruiting proficient applicants by proposing an interpretable machine learning model for assessing IT job candidates. The study conducts experiments comparing various machine learning algorithms, including Logistic Regression, Random Forest, and Decision Tree, using data from the 'IT job applicant' public dataset. Evaluation methods such as the confusion matrix, accuracy, recall, and precision are employed to assess the model's performance. The findings reveal that the decision tree exhibits an accuracy exceeding 99.8%, establishing it as the top-performing model. The key contributing factor to this accuracy is identified as the 'computerSkills' feature. Additionally, within the decision tree, *** and Microsoft SQL Server are highlighted as influential factors positively impacting individual decision interpretation. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Predicting Company Default Risks: A Machine Learning Perspective on Importance Features

Predicting Company Default Risks: A Machine Learning Perspec...

引用

2024 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 2024

作者： Hanestan, Fanes Nathaniel, Fritz Kusuma, Raiyen Dewi Santoso Gunawan, Alexander Agung Setiawan, Karli Eka Data Science Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798350353464

The 2008 Global Financial Crisis was a devastating event for financial market investors worldwide, as it came unexpectedly and affected every financial market in the world, causing millions of people to suffer massive loss of value of their pension funds and assets. The trigger cause of this Global Financial Crisis was the default of Lehman Brothers, one of the biggest companies in the United States. This paper is intended to prevent this event from happening again by predicting the most important variables causing company default. Company management and investors could take preventive measures when they see signs of these variables in their company. This experiment uses XGBoost, Support Vector Machine, and Random Forest machine learning model, with data retrieved from the Taiwan Economic Journal from 1999 to 2009. The experiment suggests the top 5 most important variables regarding company default are Continuous interest rate (after tax), Persistent EPS in the Last Four Seasons, Total debt/Total net worth, Interest Expense Ratio, and Net Value per Share, with an importance score of 0.447, 0.104, 0.038, 0.033, and 0.032, respectively. These findings show that features related to a company’s profitability and liabilities have a strong impact on a company’s risk of defaulting. ©2024 IEEE.

关键词： Decentralized finance

来源：评论

学校读者我要写书评

暂无评论

Evaluating Public Opinion on the 2024 Indonesian Presidential Election Candidate: An IndoBERT Approach to Twitter Sentiment Analysis 10

Evaluating Public Opinion on the 2024 Indonesian Presidentia...

引用

10th International Conference on Smart Computing and Communication, ICSCC 2024

作者： Tanaja, Rafael Nicholas Widjaya, Andrew Johnny Gunawan, Alexander Agung Santoso Setiawan, Karli Eka School of Computer Science Bina Nusantara University Data Science Program Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350363104

As social media significantly shapes societal norms and ethical paradigms, understanding real-time public sentiment provides valuable insights for political parties to evaluate their candidates, key issues, and campaign strategies. This study aims to gauge each presidential candidate's public sentiment from August 1, 2023, until October 31, 2023, using Twitter data. In this study, the authors would like to conduct an IndoBERT with a Fully Connected Layer to predict the sentiment analysis of Indonesian public sentiment toward the 2024 upcoming presidential election. The paper concluded that the IndoBERT model achieved an accuracy of 84.7%. In conclusion, the findings of this study lay the groundwork for more extensive analyses, enriching our comprehension of the intricate dynamics of public opinion in the political sphere. Furthermore, Sentiment Analysis is dynamic and will change as significant events occur. As we look to the future, integrating cutting-edge language models like IndoBERT with real-time social media data emerges as a transformative force in reshaping political analysis. © 2024 IEEE.

关键词： Tweets

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 << < 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：