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

3,076 篇 会议
793 篇 期刊文献

馆藏范围

3,869 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

1,798 篇 工学
- 1,565 篇 计算机科学与技术...
- 1,335 篇 软件工程
- 429 篇 信息与通信工程
- 222 篇 控制科学与工程
- 204 篇 生物工程
- 195 篇 电气工程
- 140 篇 生物医学工程（可授...
- 99 篇 网络空间安全
- 91 篇 安全科学与工程
- 88 篇 光学工程
- 76 篇 建筑学
- 74 篇 机械工程
- 73 篇 化学工程与技术
- 66 篇 环境科学与工程（可...
857 篇 理学
- 363 篇 数学
- 270 篇 物理学
- 236 篇 生物学
- 190 篇 统计学（可授理学、...
- 99 篇 系统科学
708 篇 管理学
- 430 篇 管理科学与工程(可...
- 379 篇 图书情报与档案管...
- 303 篇 工商管理
170 篇 医学
- 158 篇 临床医学
- 147 篇 基础医学(可授医学...
- 106 篇 公共卫生与预防医...
- 73 篇 药学(可授医学、理...
156 篇 法学
- 137 篇 社会学
142 篇 经济学
- 142 篇 应用经济学
92 篇 教育学
- 88 篇 教育学
71 篇 农学
- 63 篇 作物学
23 篇 文学
1 篇 军事学
1 篇 艺术学

主题

234 篇 accuracy
178 篇 deep learning
123 篇 training
113 篇 machine learning
113 篇 data models
109 篇 support vector m...
99 篇 predictive model...
95 篇 convolutional ne...
93 篇 sentiment analys...
89 篇 computational mo...
81 篇 augmented realit...
77 篇 social networkin...
72 篇 education
71 篇 testing
66 篇 artificial intel...
64 篇 technological in...
63 篇 students
62 篇 analytical model...
60 篇 random forests
56 篇 covid-19

机构

721 篇 computer science...
376 篇 computer science...
255 篇 school of comput...
191 篇 computer science...
166 篇 bina nusantara u...
101 篇 school of comput...
85 篇 bioinformatics a...
81 篇 bina nusantara u...
59 篇 computer science...
56 篇 information syst...
54 篇 school of comput...
54 篇 bioinformatics a...
53 篇 mathematics depa...
52 篇 computer science...
51 篇 computer science...
45 篇 mathematics depa...
45 篇 bioinformatics a...
42 篇 statistics depar...
42 篇 school of inform...
39 篇 statistics depar...

作者

130 篇 bens pardamean
119 篇 pardamean bens
107 篇 gaol ford lumban
91 篇 suhartono derwin
79 篇 derwin suhartono
76 篇 meyliana
76 篇 alexander agung ...
74 篇 warnars harco le...
72 篇 maulana fairuz i...
68 篇 suharjito
66 篇 heryadi yaya
65 篇 sasmoko
57 篇 harco leslie hen...
57 篇 ford lumban gaol
56 篇 anggreainy maria...
55 篇 andry chowanda
55 篇 gunawan alexande...
54 篇 prabowo harjanto
54 篇 fairuz iqbal mau...
54 篇 simeon yuda pras...

语言

3,584 篇 英文
282 篇 其他
5 篇 中文
1 篇 斯洛文尼亚文

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

共 3869 条记录，以下是491-500 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：

The Comparison of Random Forest and Artificial Neural Network for Customer Churn Prediction in Telecommunication 3

The Comparison of Random Forest and Artificial Neural Networ...

引用

3rd International Conference on Smart Cities, Automation and Intelligent Computing Systems, ICON-SONICS 2023

作者： Ramadhan, Aldandi Fitra Permai, Syarifah Diana Harefa, Jeklin Alexander Bina Nusantara University School of Computer Science Statistics Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781509062805

Customer churn is a significant issue that can affect a company's revenue, particularly in the telecommunication sector. Companies today are working hard to survive in this competitive market by implementing various types of strategies. The churn prediction system can be used as a strategy for long-term business success or failure. In this research, variable reduction was carried out using the chi-square test and RFE to select several influential features to be used as input in the model. The implementation of the Synthetic Minority Over-sampling Technique for Nominal and Continuous (SMOTE-NC) is also carried out to handle the imbalanced data. This research compares the Random Forest and Artificial Neural Network algorithms to obtain the best accuracy in predicting telecommunication customer churn. In addition, Grid Search is used to perform parameter tuning to obtain the best parameters from each method. Therefore, this research obtained 4 models that were compared. From this experiment, it can be inferred that the Random Forest model with some hyperparameter tuning works best with an AUC (Area Under Curve) value of 75% compared to the Artificial Neural Network with an AUC value of 65%. © 2023 IEEE.

关键词： Neural networks

来源：评论

学校读者我要写书评

暂无评论

Heart Disease Risk Prediction using K-Nearest Neighbor: A Study of Euclidean and Cosine Distance Metrics 3

Heart Disease Risk Prediction using K-Nearest Neighbor: A St...

引用

3rd International Conference on Smart Cities, Automation and Intelligent Computing Systems, ICON-SONICS 2023

作者： Prasetyo, Simeon Yuda Kurniawan, Afdhal Sihotang, Erna Fransisca Angela Puspita, Rani Setiawan, Karli Eka Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Statistics Department Jakarta11480 Indonesia

ISBN: (纸本)9781509062805

Cardiovascular disease, with an annual fatality rate of 17.9 million individuals, poses a significant global health crisis. Swift and precise diagnosis is paramount, particularly in regions with limited access to advanced medical technology. This paper employs machine learning techniques to predict heart disease, emphasizing hyperparameter optimization for heightened accuracy. Utilizing a diverse dataset to address issues like duplicate data, we conduct a comparative analysis of Euclidean and Cosine distances within the K-Nearest Neighbor (KNN) algorithm, filling a research gap and providing a comprehensive evaluation of these distance metrics. Label Encoding is applied for categorical variables, and KNN is implemented with varying neighbor counts (1,3,5,7,9) using both Euclidean and Cosine distances. Evaluation metrics, including accuracy, precision, recall, and F1 score, gauge model performance. Our experiments reveal the highest accuracy of 90.761% when KNN is configured with 7 neighbors and the Cosine distance metric, surpassing the Euclidean distance. This underscores the criticality of careful distance metric selection in heart disease prediction models. The study offers insights into predictive modeling in healthcare, emphasizing the significance of distance metric choice. To streamline the abstract, omitting details related to future research and maintaining brevity is recommended. © 2023 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Implementing Welsh-Powell Algorithm on Coloring the Map of West Java 23

Implementing Welsh-Powell Algorithm on Coloring the Map of W...

引用

8th International Conference on Sustainable Information Engineering and Technology, SIET 2023

作者： Zaini, Dimas Dani Vincensius, Howsen Widjaja, Kevin Aurelian Nathanielle Untung Nurhasanah Handoyo, Alif Tri Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Statistics Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798400708503

Graph Theory is a very complex and difficult mathematical problem, with Graph Coloring as one of its most studied problem. Graph Coloring itself is an implementation of Graph Theory, where there can't be neighboring vertexes that share the same color. At the same time, the aim of Graph Coloring is to have the least color used (Chromatic Number) to color the vertexes in a graph, while there can't be any neighboring vertexes that have the same color. In this paper, we're experimenting on one of the Graph Coloring implementations, which is Map Coloring. We're experimenting on the usage of Welsh-Powell Algorithm, an algorithm deemed suitable for Scheduling, another implementation of Graph Coloring, and see if this algorithm is also suitable and usable for Map Coloring. By using the map of West Java as our dataset and converting it to a graph and then to a matrix, we obtained a result in the shape of a colored map. Our result proves that Welsh-Powell algorithm is indeed suitable and usable, providing a satisfactory output where it's uncomplicated to observe and the limits of areas (districts and cities) are clearly visible. In addition to that, Welsh-Powell Algorithm managed to perform better than the Greedy Algorithm from similar research, resulting a map with the same Chromatic Number as a map with lesser vertices colored by the Greedy Algorithm. © 2023 ACM.

关键词： Graph theory

来源：评论

学校读者我要写书评

暂无评论

Developing Interactive Learning Media Design for Futuristic Learning Activities 15

Developing Interactive Learning Media Design for Futuristic ...

引用

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

作者： Chandra, Matthew Anderson Dhanendra, Nyoman Wira Dhimas Arya Renato, Reynard Fredyan, Renaldy Pranoto, Hady School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350383829

In this digital literacy era, learning methods with interactive media are increasingly popular and are also used to improve the quality of education in various regions, especially in Indonesia. Many media can be used such as presentations, learning videos with interesting visuals and games with characters designed based on learning needs. In this research topic, we will figure out principles of what to use to create LMS while maximizing the interaction with students to support them in this modern era of education. Our research will be conducted using an experimental method in the form of a questionnaire involving teachers and students. The data we have collected will be processed into a reference to build a digital interactive media based on the study of Human computer Interaction. © 2023 IEEE.

关键词： Human computer interaction

来源：评论

学校读者我要写书评

暂无评论

Comparing Convolutional Neural Network Models with MobileNet-Based in Diabetic Retinopathy Classification

Comparing Convolutional Neural Network Models with MobileNet...

引用

2023 International Workshop on Artificial Intelligence and Image Processing, IWAIIP 2023

作者： Prasetyo, Simeon Yuda Wulandari, Ajeng Izdihar, Zahra Nabila Nabiilah, Ghinaa Zain Saputri, Hanis Amalia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350382914

Diabetic Retinopathy (DR), a severe complication of diabetes, can lead to blindness. Early detection and classification of DR stages are essential, given the global rise in Diabetes Mellitus cases. This study explores the application of Convolutional Neural Network Models with the base MobileNet to automate the early detection and severity classification of Diabetic Retinopathy. Using the Diabetic Retinopathy Arranged Dataset, the models are developed through transfer learning with fine-tuning. MobileNetV2, especially during fine-tuning, exhibits the highest accuracy. In the fine-tuning phase, it achieves an accuracy of 72.5% in the testing dataset, highlighting the efficacy of this process. The findings emphasize the potential of MobileNetV2 for early Diabetic Retinopathy detection and severity classification. As the prevalence of diabetes continues to rise globally, automated systems like this play a vital role in reducing the risk of permanent vision loss. Future research can explore alternative CNN architectures and optimization through ensemble methods to further enhance performance. This study provides a valuable contribution to addressing the urgent healthcare challenges posed by Diabetic Retinopathy in the context of the growing diabetes epidemic. © 2023 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Virality Prediction on Twitter Using Combined CNN and BERT Models 4

Virality Prediction on Twitter Using Combined CNN and BERT M...

引用

4th International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2024

作者： Benedecas, Hilarius Ryan Auxilio Girsang, Abba Suganda Bina Nusantara University Binus Graduate Program Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798331506490

Nowadays, social media serves as a platform for sharing and receiving information. One indicator of the importance of information is its virality. Virality is an interesting topic in the world of Natural Language Processing (NLP), including the prediction of virality and identifying features that influence it. Research in this area has utilized Bidirectional Encoder Representations from Transformers (BERT) models and yielded promising results. Previous studies aimed to predict virality based on specific features. This research aims to predict virality by combining the abilities of Convolutional Neural Network (CNN) and BERT models. The BERT model transforms textual data into numerical and adds new features, while the CNN model excels in numerical classification. Data for this study is sourced from Twitter and in Indonesian language. The proposed model demonstrates improvements compared to the model from previous research. Specifically, it shows a 7.1% increase in precision, a 9.9% increase in recall, a 3.8% increase in accuracy, and an 8.1% increase in F1-Score compared to the previous model. These findings contribute to future research on similar topics, particularly in leveraging multiple models for prediction tasks. Overall, the study provides insights into effectively utilizing the strengths of various models and combining them in prediction tasks. © 2024 IEEE.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

Development of 3D Scanner Application with Stereo Camera for 3D Object Reconstruction 8

Development of 3D Scanner Application with Stereo Camera for...

引用

8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Lukita, Ridan Pranoto, Hady Faisal Mathematics Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Visual perception in RGBD (Red-Green-Blue and Depth) camera technology allows the camera to provide color image and depth structure information simultaneously from the surface of the scanned object. Based on this technology, data from an RGBD camera can be processed to reconstruct the scanned object in a three-dimensional (3D) model. However, devices that are prebuilt to perform this task with good accuracy are expensive and are only intended for 3D scanning tasks. On the other hand, some multi-purpose depth cameras can be used for 3D scanning but require some tweaks to ensure a good and accurate reconstructed model. A method has been proposed to optimize the camera and reconstruction system to compile RGBD information from recorded close-up objects and process them into a full-scale 3D digital model. This process involves developing the appropriate recording method along with settings and adjustments for scanning and model reconstruction of close-range objects. As a result, information from multi-purpose RGBD camera can be reconstructed in three-dimensional form that accurately represents real-life objects. The reconstructed model can be utilized for applications that rely on real-life objects as their main basis. The proposed method enables the use of inexpensive multi-purpose RGBD cameras for 3D scanning. © 2023 The Authors. Published by Elsevier B.V.

关键词： 3D scan reconstruction RGBD

来源：评论

学校读者我要写书评

暂无评论

A Systematic Literature Review: Diagnosing Pneumonia Through X-Ray Images Using Deep Learning Approach

A Systematic Literature Review: Diagnosing Pneumonia Through...

引用

2023 International Seminar on Application for Technology of Information and Communication, iSemantic 2023

作者： Pratama, Nobert Moses Gunawan, Alexander Agung Santoso Bina Nusantara University School of Computer Science Mathematics Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350339215

Pneumonia is an infectious disease affecting human lungs, it majorly affects the elderly and may be life-threatening in some cases. The World Health Organization or WHO reported that in India, one in three deaths is caused due to pneumonia. Early diagnosis of Pneumonia will save many human lives. Due to the success of deep learning algorithms in analyzing images, there are so many algorithms for pneumonia detection now. This paper aims to analyze and evaluate the accuracy of the deep learning algorithm and its application on detecting or diagnosing pneumonia from chest x-ray images. This paper used a systematic literature review approach on several papers found on Google Scholar using a keyword-based search mechanism related to pneumonia detection and deep learning. The authors compare each method from the papers and sort it with accuracy as the parameter. The method with the highest accuracy is the AlexNet model with the accuracy of 99.62%. Automation of pneumonia detection using a deep learning model is possible, however a doctor's supervision is still necessary to prevent a case of misdiagnose. © 2023 IEEE.

关键词： Learning algorithms

来源：评论

学校读者我要写书评

暂无评论

A Systematic Literature Review: Forecasting Stock Price Using Machine Learning Approach

A Systematic Literature Review: Forecasting Stock Price Usin...

引用

2023 International Conference on Data science and Its Applications, ICoDSA 2023

作者： Lumoring, Niclauss Chandra, Daniel Gunawan, Alexander Agung Santoso Bina Nusantara University School of Computer Science Mathematic Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350305197

With the increasing popularity of stock trading, individuals, and financial entities such as investment companies, hedge funds, and retail investors are actively participating in the stock market to generate profits. Numerous strategies, ranging from traditional methods employing fundamental and technical analysis to modern approaches utilizing cutting-edge technology, have been developed and implemented. However, determining the optimal method remains challenging. Designing an effective strategy in the complex and dynamic stock market environment poses significant difficulties. Therefore, this study aims to provide an overview of machine learning applications in the stock market and identify the most frequently used machine learning models or methods for market prediction. Furthermore, the study aims to identify the strategy that achieves the highest accuracy in predicting stock prices. Based on a systematic literature review encompassing quantitative and qualitative analyses, it is found that Support Vector Machine (SVM) emerges as the most popular machine learning technique for predicting stock prices. However, Long Short-Term Memory (LSTM) stands out as the machine learning technique that demonstrates the highest level of accuracy, achieving an impressive accuracy rate of 99.58%. These findings highlight the effectiveness of LSTM in forecasting stock values with exceptional precision. The results of this study contribute to a comprehensive understanding of machine learning in the stock market domain and offer valuable insights for developing effective trading strategies. Traders and investors can leverage LSTM and SVM techniques to enhance their stock market predictions and make informed investment decisions. © 2023 IEEE.

关键词： Financial markets

来源：评论

学校读者我要写书评

暂无评论

VGG-Powered Convolutional Neural Networks in Diabetic Retinopathy Classification: A Comparative Investigation 3

VGG-Powered Convolutional Neural Networks in Diabetic Retino...

引用

3rd International Conference on Smart Cities, Automation and Intelligent Computing Systems, ICON-SONICS 2023

作者： Prasetyo, Simeon Yuda Alam, Islam Nur Rachmawati, Ika Dyah Agustia Purwanto, Eko Setyo Ngarianto, Heri Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781509062805

Diabetic Retinopathy (DR) is a severe and debilitating complication associated with diabetes, often leading to visual impairment in affected adults. Its primary cause is the adverse impact of prolonged hyperglycemia on the retinal vasculature. DR is categorized into Non-Proliferative Diabetic Retinopathy (NPDR) and Proliferative Diabetic Retinopathy (PDR), with early detection and accurate classification being vital, especially given the rising global incidence of Diabetes Mellitus (DM). Effective DR diagnosis involves the identification of retinal lesions and their severity classification. As diabetes cases increase worldwide, there's a growing need for efficient diagnostic methods. Automated systems, employing deep learning techniques show promise for DR detection and classification. However, manual grading of DR remains time-consuming and expensive. computer-aided diagnosis (CAD) systems offer a more accessible and efficient alternative. In this paper, we introduce a novel approach using Convolutional Neural Network (CNN) models based on the VGG architecture with transfer learning to address the pressing healthcare challenges posed by DR. Notably, our approach achieves an accuracy rate of 73.01% during transfer learning, which surges to 75.83% after fine-tuning, particularly when classifying Diabetic Retinopathy in our test dataset. VGG16, demonstrates the effectiveness of fine-tuning with a notable increase in accuracy. Future research opportunities include exploring alternative CNN model architectures like ResNet, Inception, and ConvNeXt, as well as optimizing performance through ensemble methods for further enhancements. © 2023 IEEE.

关键词： Classification (of information)

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387页 << < 46 47 48 49 50 51 52 53 54 55 > >>

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：