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561-570 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：

Enhancing Hate Speech Detection in Social Media Using IndoBERT Model: A Study of Sentiment Analysis during the 2024 Indonesia Presidential Election 5

Enhancing Hate Speech Detection in Social Media Using IndoBE...

引用

5th International Conference on computer and Applications, ICCA 2023

作者： Yulfa, Ramadhan Ihsani Setiawan, Benediktus Hengki Lourensius, Gerry Gilbert Purwandari, Kartika Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350303254

Legislative elections, presidential elections, and village head elections are all on the schedule for Indonesia in 2024. Law No. 7 of 2017 governs these elections, emphasizing the ideals of direct, public, free, confidential, honest, and fair elections. With the growth of social media, political personalities are employing these channels for campaign activity, resulting in a variety of user responses, including hate speech. Detecting hate speech is critical for preventing and mitigating the bad impact it may have on society. The purpose of this article is to investigate the usage of IndoBERT, a fine-tuned BERT model, to improve hate-speech identification during the 2024 Indonesia Presidential Election. The research focuses on hate speech in the context of the presidential election and the challenges raised by social media. In this paper, we utilized Twitter as a case study to develop effective hate-speech identification techniques. The method comprises fine-tuning IndoBERT using a dataset of Indonesian tweets on the election that have been preprocessed to reduce noise and classified as hate speech or non-hate speech using a pre-trained model. The accuracy, precision, recall, and F1-score of the model are used to assess its performance. The results reveal that the IndoBERT model detects hate speech in Indonesian Twitter data with high accuracy. A future study might look towards fixing typos in the dataset and examining expert recognition of hate speech scenarios. © 2023 IEEE.

关键词： Speech recognition

来源：评论

学校读者我要写书评

暂无评论

Sentiment analysis of the Indonesian community toward face-to-face learning during the Covid-19 pandemic 8

Sentiment analysis of the Indonesian community toward face-t...

引用

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

作者： Gozal, Andrew Giovanni Pranoto, Hady Hasani, Muhammad Fikri Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

This research aims to analyze how the opinions of the Indonesian people in the learning system in the middle of the covid-19 pandemic. The research method is carried out by performing the K-NN method to determine the accuracy level of the data used. The research data method is taken through public comments on Twitter social media using scrapping techniques with appropriate key word s. The data will then be processed through training and testing and classified using the K-NN method. After the data is classified, the accuracy, F1-Score, Recall, and Precision level will be tested using Confusion Matrix. The result showed that KNN performed well, with above 70% of the F1-score for each class. According to the confusion matrix, accuracy also showed promising results with 82%. Future research may include oversampling the class with fewer numbers. K-Fold cross-validation can also be used to see the general performance of the model. The same method may be used to find sentiment towards a political policy that is taken;whether a policy gets a good or bad response, if the response is bad, you can see the causal factors that cause the negative sentiment. In this way, you can find out the will of the public. © 2023 The Authors. Published by Elsevier B.V.

关键词： face to face learning k-nn sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

Distance Metric Analysis in Recommendation System Using Content-Based Filtering Method 6

Distance Metric Analysis in Recommendation System Using Cont...

引用

6th International Conference on Information and Communications Technology, ICOIACT 2023

作者： Christina Sanjaya, Febrian Cahyaningtyas, Valentina Tiara Edbert, Ivan Sebastian Suhartono, Derwin Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350315639

Despite the prevalence of music recommendations in the music industry, the rapid evolution of music and the increasing diversity of musical genres present a formidable challenge in discovering songs that match user's preferences. The content-based filtering method is a common recommendation system utilizing a music feature-based approach to identify user preferences. The K-Nearest Neighbor (KNN) algorithm is utilized to determine the similarity between songs. However, in order to enhance the quality of music recommendations, it is necessary to employ appropriate distance metrics and threshold values. The study collects song data and associated music features from Spotify to experimentally determine the best distance metric and feature selection threshold. The evaluation metrics employed in this study are root mean square error (RMSE) and mean absolute error (MAE). The results show that Cityblock Distance performs best with an average RMSE of 0.2257, and a threshold value of 0.05 achieves optimal performance with an average MAE of 0.124. These findings indicate that choosing suitable distance metrics and implementing feature selection can enhance the accuracy of music recommendation systems. However, limitations in dataset representation warrant further research with larger datasets and additional factors to improve generalization and user experience in music recommendations. © 2023 IEEE.

关键词： Recommender systems

来源：评论

学校读者我要写书评

暂无评论

Mall Customer Clustering Using Gaussian Mixture Model, K-Means, and BIRCH Algorithm 6

Mall Customer Clustering Using Gaussian Mixture Model, K-Mea...

引用

6th International Conference on Information and Communications Technology, ICOIACT 2023

作者： Sugiharto, Nathanael Deciano Elbert, Darian Arnold, Juan Edbert, Ivan Sebastian Suhartono, Derwin Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350315639

Nowadays, online shopping has become the norm of customers around the world, therefore businesses need ways to identify their customer demographics, one of the way available is by using machine learning clustering algorithms to help in clustering customer demographic. This research paper aims to study machine learning algorithms which can be used to cluster customers based on the features which define them, those being: Gender, Age, Annual Income, Spending Score (1-100). The study employs clustering algorithms such as GMM (Gaussian Mixture Model), K-Means Clustering, and BIRCH. The metric Davies Bouldin Score is used to measure the quality of clusters produced by these 3 different algorithms. The findings show that K-Means clustering is the best clustering algorithm to be used in clustering the customers because in this case, the dataset which was used is more suitable to be clustered in this algorithm, however the other algorithms also perform good when the dataset suits them, overall, the findings can be used to better understand the business' customer demographic. The implications of this study extend to various sectors of marketing, including e-commerce, retail, digital marketing, where marketing strategies are employed to gain a competitive edge. © 2023 IEEE.

关键词： K-means clustering

来源：评论

学校读者我要写书评

暂无评论

Guidance in Designing A Smart Campus: A Systematic Literature Review 8

Guidance in Designing A Smart Campus: A Systematic Literatur...

引用

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

作者： Al Akbar, Hadad Faturrahman, Muhammad Reza Sidharta, Sidharta Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

With the latest development in information technology, smart campuses have become a topic that attracts attention in the world of education, especially in higher education. The smart campus has the potential to change the way of managing, accessing facilities, and teaching and learning process. The smart campus can collaborate various latest technologies such as edge computing, blockchain, deep learning, virtual reality, and other trend recent technologies. Therefore, this research is a guideline for developing an effective and efficient smart campus system with the latest information and communication technology. This paper explains smart campus technology, features, architecture that have been implemented in real life. Thirty-two papers will be analyzed using the Systematic Literature Review (SLR) method. Hopefully, this guideline can be a valuable reference for smart campus designers, researchers, and practitioners interested in developing innovative and efficient smart campus systems. © 2023 The Authors. Published by Elsevier B.V.

关键词： Architecture Guideline IoT Technology Smart Campus Systematic Literature Review

来源：评论

学校读者我要写书评

暂无评论

Tree Counting with Deep Learning Algorithm and Carbon Evaluation using Pleiades Satellite Imagery Data in Kulon Progo, Yogyakarta, Indonesia

Tree Counting with Deep Learning Algorithm and Carbon Evalua...

引用

2023 International Conference on Informatics, Multimedia, Cyber and Information Systems, ICIMCIS 2023

作者： Wijaya, David Irwansyah, Edy Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350307306

Trees are important living creatures on earth where their existence is very important for the health of the Earth's atmosphere, for the exchange of oxygen and carbon dioxide, etc. That's why the number of existing trees must be maintained as much as possible. In this case, there are many ways to maintain the existence of the tree. To maintain the existence of trees, there are several ways to maintain them such as counting or mapping the trees that exist in the area. Since it's too hard to count the trees by hand, luckily some algorithms can be used to count the trees themselves, which can help to count or map the trees, known as deep forest algorithms. The deep forest algorithm is a package in python that can be used to train, predict, and evaluate the individual tree crowns from aerial RGB imagery using a deep learning network for object detection. The reason for using the algorithm itself is that, as of 2019, there are not many tree counting experiments using this algorithm, only 14 experiments based on the official website. Not only tree counting as the goal of this experiment, evaluating carbon for every detected tree itself can also help so that carbon can be maintained and not disposed of as a hazardous substance. The main purpose of conserving the carbon itself is to protect the environment from sustainable development. As for the result of this experiment, the trees detected by deep learning deep forest algorithms successfully detected 613 trees from a total of 788 trees, making it an accuracy of 77.97% in the Kulon Progo area. As the carbon evaluation from counting all parts of an oil palm tree, know that for single biomass of oil palm tree is 354,8641 kg/ tree and it makes a total of 613 detected trees by Deep Forest Algorithms is 217531.6933 kg carbon in the study area. © 2023 IEEE.

关键词： Carbon dioxide

来源：评论

学校读者我要写书评

暂无评论

An Evaluation of the Effectiveness of OpenAI's ChatGPT for Automated Python program Bug Fixing using QuixBugs

An Evaluation of the Effectiveness of OpenAI's ChatGPT for A...

引用

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

作者： Wuisang, Marchel Christhoper Kurniawan, Marcel Wira Santosa, Komang Andika Agung Santoso Gunawan, Alexander Saputra, Karen Etania Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350339215

In recent years, the use of Artificial Intelligence (AI) has become increasingly common in various fields, including in software development. One such field is where AI can automatically detect and fix bugs in code. GPT-3.5 is a state-of-the-art language model developed by OpenAI that has been trained on a massive amount of text data to generate natural language responses to a wide range of prompts. One of the main challenges in software development is bug fixing, which can be a time-consuming and complicated process. QuixBugs is a framework for evaluating automatic program repair techniques, which can be used to test the effectiveness of GPT-3.5 and similar bug-fixing tools. This paper evaluates the effectiveness of GPT-3.5 in automatically fixing bugs in Python code using QuixBugs. Through testing with 40 different Python bugs, We discovered that GPT-3.5 was able to accurately fix 30 out of 40 bugs cases from QuixBugs benchmark. Compared with other tools like standard program repair and Codex, ChatGPT outperformed them significantly. These findings highlight the potential of ChatGPT as a powerful tool for enhancing code quality and reducing the burden of manual bug fixing. © 2023 IEEE.

关键词： Python

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 6

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

引用

6th International Conference on computer and Informatics Engineering, IC2IE 2023

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

ISBN: (纸本)9798350345162

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. © 2023 IEEE.

关键词： class attendance convolutional neural network deep learning face recognition smile

来源：评论

学校读者我要写书评

暂无评论

Tackling Clickbait with Machine Learning: A Comparative Study of binary Classification Models for YouTube Title 8

Tackling Clickbait with Machine Learning: A Comparative Stud...

引用

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

作者： Winarto, Tora Sangputra Yopie Wijaya, Kevin Faqih, Muhammad Abdullah Prasetyo, Simeon Yuda Muliono, Yohan Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Clickbait is a form of internet content whose function is to attract attention and entice users to click on a link. Its main goal generally is to generate more advertisement revenue for the creator. Clickbait is featured heavily on all kinds of social media, especially YouTube where there is a strong financial incentive to do so. Clickbait content constitutes 47.56% of content from mainstream broadcast media and US companies spent an average of 9.8% of their advertising budget on clickbait contents. Clickbait classification is the first and most important step in resolving the proliferation of clickbait content. Contributing to this, we aimto detect YouTube clickbait videos by building several binary classification machine learning models trained on an open-sourceddataset of 31.987 English YouTube video titles from GitHubGist to differentiate between clickbait or non-clickbait YouTube *** machine learning models are based on Näıve-Bayes, Support Vector Machine (SVM), and Long Short-Term Memory (LSTM)Network, with the final objective to compare each model's resulting effectiveness. The best-performing resulting model from thisstudy is a kernel TF-IDF SVM model scoring 98.53% on accuracy, precision, recall, and f1-score which outperforms the past experiments that is using the same models. © 2023 The Authors. Published by Elsevier B.V.

关键词： clickbait LSTM machine learning Na¨ıve-Bayes SVM

来源：评论

学校读者我要写书评

暂无评论

Gestive: Evaluation of Multi-Class Classification Methods for Gesture Recognition to Improve Presentation Experience 8

Gestive: Evaluation of Multi-Class Classification Methods fo...

引用

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

作者： Setiawan, Christiella Abinosy Utomo, Galang Nurbudi Pangesa, Vianny Wijayanti, Vierdaria Basuki, Vieri Ferdian Putra Hermanto, Yuliani Hasana, Silviya Permonangan, Ivan Halim Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Gesture recognition is able to transform how we interact with technology, including during presentations. We aim to classify slide-control gestures and create a model that allows users to add their personalized gestures to to control presentation commands. To avoid wasting time on capturing and training the model, we aim to construct a model with a short training time and high accuracy. We assessed the efficiency of various multi-class classification methods for gesture recognition in presentation settings. We compared K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree, Linear Discriminant Analysis (LDA), and Random Forest. The results demonstrated that KNN offers the shortest training time while maintaining high accuracy. The findings of this study provide a basis for future research on gesture recognition technology and its implementation to improve presentation experience. © 2023 The Authors. Published by Elsevier B.V.

关键词： decision tree gesture recognition k-nearest neighbor linear discriminant analysis multi-class classification random forest support vector machine training time

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 << < 53 54 55 56 57 58 59 60 61 62 > >>

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：