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

5,448 篇 会议
1,033 篇 期刊文献
97 册 图书

馆藏范围

6,578 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

4,078 篇 工学
- 2,728 篇 计算机科学与技术...
- 2,300 篇 软件工程
- 952 篇 信息与通信工程
- 761 篇 控制科学与工程
- 586 篇 电气工程
- 552 篇 电子科学与技术（可...
- 351 篇 生物工程
- 274 篇 生物医学工程（可授...
- 273 篇 机械工程
- 249 篇 光学工程
- 238 篇 动力工程及工程热...
- 190 篇 仪器科学与技术
- 165 篇 化学工程与技术
- 144 篇 安全科学与工程
- 137 篇 交通运输工程
- 123 篇 建筑学
- 119 篇 材料科学与工程（可...
2,007 篇 理学
- 1,150 篇 数学
- 479 篇 物理学
- 385 篇 生物学
- 345 篇 统计学（可授理学、...
- 317 篇 系统科学
- 157 篇 化学
984 篇 管理学
- 631 篇 管理科学与工程(可...
- 410 篇 图书情报与档案管...
- 389 篇 工商管理
219 篇 医学
- 198 篇 临床医学
- 184 篇 基础医学(可授医学...
185 篇 法学
- 148 篇 社会学
138 篇 经济学
- 137 篇 应用经济学
111 篇 教育学
64 篇 农学
17 篇 文学
17 篇 军事学
6 篇 艺术学

主题

138 篇 accuracy
123 篇 computational mo...
114 篇 machine learning
110 篇 feature extracti...
106 篇 software
106 篇 training
93 篇 software enginee...
91 篇 optimization
85 篇 predictive model...
84 篇 artificial intel...
82 篇 deep learning
75 篇 real-time system...
72 篇 data models
68 篇 computer science
68 篇 computer archite...
62 篇 data mining
62 篇 mathematical mod...
61 篇 monitoring
60 篇 internet of thin...
58 篇 robustness

机构

56 篇 dept. of systems...
32 篇 dept. of systems...
23 篇 optical sciences...
20 篇 dept. of electri...
19 篇 faculty of compu...
19 篇 gik institute of...
19 篇 university of eu...
19 篇 dept. of technol...
16 篇 dept. of electri...
15 篇 dept. of softwar...
15 篇 dept. of compute...
14 篇 dept. of electri...
14 篇 dept. of compute...
14 篇 dept. of electri...
13 篇 dept. of systems...
12 篇 dept. of softwar...
12 篇 networking and i...
12 篇 ieee
12 篇 bcmaterials basq...
12 篇 dept. of compute...

作者

35 篇 wainer gabriel
21 篇 ali raja hashim
20 篇 tan mengxi
20 篇 moss david j.
19 篇 xu xingyuan
18 篇 gabriel wainer
18 篇 usama arshad
18 篇 ku wei-shinn
18 篇 arshad usama
16 篇 zain ul abideen
15 篇 yangquan chen
14 篇 baldwin timothy
13 篇 shabtai asaf
13 篇 ali zeeshan ijaz
13 篇 xi zhang
12 篇 reiner lisa
12 篇 hyunseung choo
12 篇 sakamoto makoto
12 篇 min dong
12 篇 nisar ali

语言

6,278 篇 英文
223 篇 其他
58 篇 中文
9 篇 朝鲜文
8 篇 日文
1 篇 德文
1 篇 俄文

检索条件"机构=Dept. of Computer Systems and Software Engineering"

共 6578 条记录，以下是781-790 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：

A New Rhythm in A1: Convolutional Neural Networks for Music Genre Classification 18

A New Rhythm in A1: Convolutional Neural Networks for Music ...

引用

18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Javed, Huzaifa Arshad, Usama Abideen, Zain Ul Ali, Raja Hashim BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany

ISBN: (纸本)9798350328172

Despite the significance of music genre classification in audio identification, it remains under-explored within AI research. This tool is crucial for personalized music recommendations and similar music detection. We have developed an efficient AI model that leverages Convolutional Neural Networks (CNNs), offering high-precision genre identification when integrated into a graphical user interface. The model effectively extracts audio features like Mel Frequency Cepstral Coefficients (MFCCs), zero-crossing rate, and tempo. Testing results reveal strong performance in genre prediction across diverse tracks, affirming the model's ability to discern unique characteristics of various music genres. This performance not only attests to the model's capability in discerning the unique characteristics inherent to different music genres but also suggests that it can effectively generalize to novel, unseen data. Our model lays the groundwork for future enhancements and demonstrates the potential of AI in transforming the music industry - from personalized music playlists to exploratory recommendation systems. The success of this model paves the way for more intricate applications of AI within music analysis. © 2023 IEEE.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

引用

14th International Conference on Education Technology and computers, ICETC 2022

作者： Segarra-Faggioni, Veronica Ratté, Sylvie De Jong, Frank Dept. Software and IT Engineering École de Technologie Supérieure Montreal Canada Dept. Computer Science and Electronics Universidad Tecnica Particular de Loja Loja Ecuador Dept. Learning and Innovation Aeres Wageningen Applied University Wageningen Netherlands

ISBN: (纸本)9781450397766

Knowledge Building as a pedagogy supports collaborative work between students to improve ideas. The result of knowledge-building discourse in student communities is the development of academic artifacts. Student reports are academic artifacts that can result from a knowledge-building process during their learning. However, the automatic analysis of student reports is quite challenging due to the documents' length and written language. Given this context, our goal is to analyze the semantic similarity between student reports and curriculum literature using the information coverage measure based on the Skip-gram word2vec model. Word2vec is a well-known set of techniques that provides a way to create a representation of words to predict the nearby words between every word and its context to capture the internal semantic and syntactic information. In this work, through the experimental comparison between the vocabulary list, the best similarity value is 0.90 using word2vec. The results show that each student report was aligned with each document in the curriculum literature using the information coverage measure based on word2vec. © 2022 ACM.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

An Enhanced Genetic Algorithm Framework for Efficient Solutions to Capacitated Vehicle Routing Problems 18

An Enhanced Genetic Algorithm Framework for Efficient Soluti...

引用

18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Arshad, Usama Ellahie, Hamza Hasan Abideen, Zain Ul Ali, Raja Hashim BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany

ISBN: (纸本)9798350328172

Capacitated Vehicle Routing Problems (CVRPs), a widely acknowledged NP-hard issue, pertains to the optimal routing of a limited-capacity vehicle fleet to fulfill customer demand, aiming for the least possible travel distance or cost. Despite the presence of numerous heuristic and exact approaches, the combinatorial characteristic of CVRP renders it challenging, especially for large-scale instances. This research provides an in-dept. exploration of utilizing Genetic Algorithms (GAs) to address Capacitated Vehicle Routing Problems (CVRPs), a recognized and intricate optimization issue in the realm of logistics and supply chain management. Our paper concentrates on the innovative usage of GAs, a category of stochastic search methodologies inspired by natural selection and genetics, to grapple with CVRP. We put forth a fresh framework grounded in GA that infuses unique crossover and mutation operations tailor-made for CVRP. Our comprehensive computational trials on benchmark datasets suggest that our GA-centric method is proficient in deriving high-standard solutions within acceptable computational durations, surpassing multiple contemporary techniques concerning solution quality and resilience. Our results also underscore the scalability of our proposed approach, marking it as a viable choice for tackling extensive, real-world CVRPs. This paper enriches the current knowledge bank by demonstrating the prowess of GAs in deciphering complicated combinatorial optimization issues, thus offering a novel viewpoint for future advancements in crafting more robust and efficient CVRP resolutions. © 2023 IEEE.

关键词： Genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

Pneumonia Classification using deep learning: A comparative study 8

Pneumonia Classification using deep learning: A comparative ...

引用

8th IEEE International Conference on Image and Signal Processing and their Applications, ISPA 2024

作者： Tiar, Mohamed Lakhdar Terki, Nadjiba Dominguez-Jimenez, Juan Jose University of Mohamed Khider Biskra IL3CUB Laboratory Algeria University of Mohamed Khider Biskra Electrical Engineering Department Algeria University of Cadiz Escuela Superior de Ingenieria Cadiz Dept. Computer Languages and Systems Spain

ISBN: (纸本)9798350309249

Pneumonia is an infectious disease of the lungs, caused by viruses, bacteria or fungi. Pneumonia is distinguished by acute inflammation of the lung tissue, causing the consolidation of the terminal bronchioles and alveoli. According to the WHO (the World Health Organization), this disease causes about 4 million deaths. Among the methods of diagnosing pneumonia uses a chest X-ray. This is widely used to visualize pulmonary abnormalities. This work aims at the detection and characterization of pneumonia by the development of computer-assisted diagnosis systems (DAOC). We use deep learning algorithms and chest X-ray images from a pneumonia database to examine the classification of pneumonia. To improve the accuracy, we compare several deep learning models. This research contributes to meeting the growing demand for medical personnel by addressing the global incidence of chest disorders. © 2024 IEEE.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

Enhancing Performance in Hybrid Satellite-Terrestrial Networks: A Novel Joint NOMA-NC Approach

Enhancing Performance in Hybrid Satellite-Terrestrial Networ...

引用

IEEE Conference on Global Communications (GLOBECOM)

作者： Michail Karavolos Nektarios Moraitis Lefteris Tsipi Demosthenes Vouyioukas Dept. of Information and Communication Systems Engineering University of the Aegean Samos Greece School of Electrical and Computer Engineering National Technical University of Athens Zografou Greece Dept. of Digital Systems University of Piraeus Piraeus Greece

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

An appealing alternative towards dealing with mobility and coverage challenges in future wireless networks, are hybrid satellite-terrestrial networks (HSTNs). The combination of network coding (NC) and non-orthogonal multiple access (NOMA) techniques, have recently gained popularity due to their performance gains in improving the quality of a wireless transmission and exploiting the accessible spectrum. This paper introduces a joint NOMA-NC approach, integrated to an HSTN comprising a satellite, a terrestrial base station (BS), and multiple ground-based mobile terminals (MTs). The suggested method facilitates a cluster of end-users to be concurrently served through NOMA from a satellite using optimal power allocation, whereas the BS utilizes random linear NC to augment the MTs’ reception in the event of errors. The simulation results confirm the effectiveness of the proposed methodology in improving system sum throughput, demonstrating substantial enhancements compared with both OMA with NC, and standalone NOMA and OMA approaches. The resilience of the suggested method is proved, improving the sum throughput even at low power transmissions and in the worst case channel conditions.

关键词： NOMA Satellites Wireless networks Simulation Network coding Space-air-ground integrated networks Throughput Hybrid power systems Resource management Resilience

来源：评论

学校读者我要写书评

暂无评论

Hybrid Vision Transformer Model for Accurate Prostate Cancer Classification in MRI Images

Hybrid Vision Transformer Model for Accurate Prostate Cancer...

引用

International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Jesika Debnath Arafath Bin Mohiuddin Al Shahriar Uddin Khondakar Pranta Anamul Sakib Amira Hossain Md. Moniruzzaman Shanto Shamim Forhad Md. Redwan Ahmed Rezaul Haque Shafiur Rahman Dept. of Computer Science Westcliff University Irvine CA USA Department of Collage of Engineering and Technology Westcliff University CA USA Dept. of Computer Science Wright State University Dayton USA Department of Business Administration International American University CA USA Dept. of Computer Science and Engineering East West University Dhaka Bangladesh Dept. of Electrical and Electronics Engineering American International University Bangladesh Dept. of Software Engineering Daffodil International University Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Prostate cancer is a widespread disease in men worldwide, posing challenges in early detection due to its diverse nature and the limitations of current diagnostic techniques. In this study, we have developed a new method that combines CNNs with Vision Transformers (ViTs) to improve the classification of prostate cancer using MRI images. To address data imbalance and enhance model robustness, we used dual synthetic oversampling techniques and data augmentation methods. Our preprocessing pipeline reduced noise, preserved edges, and improved local contrast without amplifying noise. For feature extraction, we utilized the GrayLevel Co-occurrence Matrix (GLCM) and shape-based features to represent complex MRI data better. Among the evaluated architectures, ConvNeXt showed the best performance. Using SMOTE, it achieved an F1-score of 97.21% and a MCC of 95.32%. With ADASYN, performance improved further, reaching an F1-score of 98.82% and an MCC of 97.86%. Additionally, we created a web-based application for realtime analysis of prostate MRI scans, which can aid in clinical decision-making. These results demonstrate the potential of our approach to improve the accuracy of prostate cancer diagnosis and provide an interpretable model for clinical use.

关键词： computer vision Accuracy Magnetic resonance imaging Noise Pipelines Transformers Feature extraction Robustness Prostate cancer Resilience

来源：评论

学校读者我要写书评

暂无评论

Deciphering Faces: Enhancing Emotion Detection with Machine Learning Techniques 18

Deciphering Faces: Enhancing Emotion Detection with Machine ...

引用

18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Ishaq, Muhammad Haseeb Mustafa, Rafay Arshad, Usama Abideen, Zain Ul Ali, Raja Hashim Habib, Abdullah Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany

ISBN: (纸本)9798350328172

Facial emotion detection holds significant relevance across various domains, from psychology and marketing to education and security. Despite its importance, prevalent techniques often grapple with issues like low precision, susceptibility to lighting changes, obstructions, and distinct facial characteristics. Addressing these challenges, our research embarked on devising a robust and precise facial emotion detector harnessing the potential of machine learning, focusing on convolutional neural networks (CNN). Comprehensive testing revealed that our model surpasses existing state-of-the-art techniques, showcasing superior performance on benchmark datasets. The salience of our research is underscored by its profound implications for myriad real-world applications hinging on accurate facial emotion recognition. We present an enhanced model, distinguished not just by its accuracy but also its robustness, making it apt for diverse scenarios from insightful marketing initiatives and nuanced medical diagnoses to enriched educational experiences. Through this endeavor, we have accentuated the transformative capacity of machine learning in refining and redefining facial emotion detection methodologies. © 2023 IEEE.

关键词： Emotion Recognition

来源：评论

学校读者我要写书评

暂无评论

Machine-Learning-Based Path Loss Prediction for In-Cabin Wireless Networks

Machine-Learning-Based Path Loss Prediction for In-Cabin Wir...

引用

Machine Learning for Communication and Networking (ICMLCN), IEEE International Conference on

作者： Nektarios Moraitis Lefteris Tsipi Demosthenes Vouyioukas School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Dept. of Information and Communication Systems Engineering University of the Aegean Samos Greece Dept. of Digital Systems University of Piraeus Piraeus Greece

ISBN: (数字)9798350343199

ISBN: (纸本)9798350343205

This article examines the performance of machine learning (ML) techniques for accurate path loss predictions inside aircrafts. An artificial neural network (ANN) and support vector regression (SVR) methods are introduced and assessed. Both algorithms are trained, validated and tested with measurements collected inside a long-range Airbus aircraft at three different frequencies. According to the statistical analysis both methods adapt very well to the measured data, delivering low root-mean-square errors, being 1.5 and 1.9 dB for ANN and SVR models, respectively. In any case, the ANN method exhibits better performance in predicting accurately path loss. Finally, the results verified that the appropriateness of the applied ML techniques is much better than that of conventional empirical models, thus forecasting path loss with remarkable efficacy.

关键词： Support vector machines Accuracy Statistical analysis Atmospheric modeling Wireless networks Artificial neural networks Machine learning

来源：评论

学校读者我要写书评

暂无评论

Misogynistic Content Detection in Roman Urdu Tweets Based on Transformer Models

Misogynistic Content Detection in Roman Urdu Tweets Based on...

引用

IEEE International Conference on Multi Topic

作者： Waseem Ullah Khan Salman Ahmed Safdar Nawaz Khan Marwat Yousaf Khan Aftab Khan Shafi Ullah Kamran Dept. of Computer Systems Engg University of Engineering & Technology Peshawar Pakistan Dept. of Electrical Engg University of Engineering & Technology Peshawar Pakistan Dept. of Computer Science Allama Iqbal Open University Islamabad Pakistan Zong CMPak Islamabad Pakistan

ISBN: (数字)9798331507213

ISBN: (纸本)9798331507220

Facebook, Instagram and Twitter serve as influential social media platforms for individuals where they express and share their thoughts, skills, knowledge and talents with a broad audience. However, these platforms are also used to disseminate offensive content including trolling and content that targets a person’s gender, religion, or race. When women become the targets of such content, it is often manifests as misogyny. In recent years, the growing prevalence of racial and verbal abuse directed at women on social media has drawn considerable attention to the issue of online misogyny and women based offending, by making the automatic detection of such offensive content an urgent priority. Moreover, many researchers have addressed misogyny detection in high resource languages i.e., English, Italian, Arabic, Hindi, and more, tackling this issue in low resource language like Roman Urdu presents a significant challenge. In this paper, a framework is proposed for detection of misogynistic content from Roman Urdu tweets. This paper leverages well known transformer models such as BERT, RoBERTa, GPT-2, and XLNet to train and evaluate misogyny detection performance. The research findings reveal that RoBERTa surpasses the other models in misogyny detection, by achieving the highest accuracy, precision, F1-score and recall i.e., 90.87%, 90%, 89%, and 91%, respectively.

关键词： Accuracy Social networking (online) Blogs Linguistics Transformers Web sites Reliability Multimedia communication

来源：评论

学校读者我要写书评

暂无评论

Faster Response Chemical Reaction Using Machine Learning

Faster Response Chemical Reaction Using Machine Learning

引用

2023 International Conference on Disruptive Technologies, ICDT 2023

作者： Srivastava, Bharvee Sivakumar, C. Kumar, Prashant Maguluri, Lakshmana Phaneendra Naini Prayagraj India Dept. of Computer Science and Systems Engineering Andhra Pradesh Tirupati India Lovely Professional University Department of Chemical Engineering Punjab India Koneru Lakshmaiah Education Foundation Department of Cse Guntur AP India

ISBN: (纸本)9798350323887

The majority of advancements are still generally attributed to trial-and-error experimentation, which takes a long time and a lot of money and can be a significant portion of research in physics, chemistry, and materials science. This type of experimentation can be a significant portion of the process. Despite the fact that a large percentage of research is devoted to the identification and characterization of novel chemicals that have the potential to be of use to society. In this paper, the chemical reactions are quickened using machine learning modelling. The paper uses supervised machine learning approach to fasten the process of chemical reaction by learning it in a supervised manner. The research provides details of how the process of chemical reactions taking place in conventional and using machine learning models. The simulation is conducted in python to test if the chemical reactions are produced with faster convergence time than the existing methods. © 2023 IEEE.

关键词： Chemical reactions

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 << < 75 76 77 78 79 80 81 82 83 84 > >>

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：