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

22,801 篇 会议
3,747 篇 期刊文献
200 册 图书
4 篇 科技报告

馆藏范围

26,752 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

16,774 篇 工学
- 13,661 篇 计算机科学与技术...
- 11,869 篇 软件工程
- 4,380 篇 信息与通信工程
- 2,072 篇 控制科学与工程
- 1,859 篇 生物工程
- 1,504 篇 生物医学工程（可授...
- 1,281 篇 电气工程
- 1,170 篇 电子科学与技术（可...
- 957 篇 光学工程
- 766 篇 仪器科学与技术
- 764 篇 机械工程
- 710 篇 动力工程及工程热...
- 613 篇 安全科学与工程
- 574 篇 化学工程与技术
- 551 篇 交通运输工程
- 536 篇 建筑学
8,611 篇 理学
- 5,316 篇 数学
- 2,089 篇 生物学
- 1,997 篇 统计学（可授理学、...
- 1,622 篇 物理学
- 991 篇 系统科学
- 647 篇 化学
5,155 篇 管理学
- 2,997 篇 图书情报与档案管...
- 2,482 篇 管理科学与工程(可...
- 1,434 篇 工商管理
1,263 篇 医学
- 1,103 篇 临床医学
- 1,067 篇 基础医学(可授医学...
- 655 篇 药学(可授医学、理...
- 592 篇 公共卫生与预防医...
914 篇 法学
- 765 篇 社会学
534 篇 经济学
412 篇 农学
283 篇 教育学
106 篇 文学
58 篇 军事学
28 篇 艺术学
2 篇 哲学
1 篇 历史学

主题

847 篇 data mining
845 篇 accuracy
701 篇 data models
688 篇 computational mo...
652 篇 training
607 篇 feature extracti...
579 篇 computer science
557 篇 deep learning
485 篇 predictive model...
483 篇 machine learning
427 篇 support vector m...
343 篇 internet of thin...
315 篇 real-time system...
298 篇 computer archite...
250 篇 neural networks
244 篇 reliability
239 篇 big data
237 篇 convolutional ne...
204 篇 cloud computing
200 篇 scalability

机构

49 篇 institut univers...
49 篇 université de st...
48 篇 north-west unive...
48 篇 national centre ...
47 篇 infn sezione di ...
46 篇 tno technical sc...
44 篇 infn sezione di ...
42 篇 nikhef national ...
38 篇 university würzb...
37 篇 università di ca...
36 篇 dept. of compute...
31 篇 university of jo...
30 篇 leiden universit...
29 篇 school of automa...
29 篇 university moham...
28 篇 dept. of compute...
27 篇 dept. of compute...
27 篇 dept. of informa...
26 篇 dept. of informa...
26 篇 department of ph...

作者

56 篇 ferrara g.
56 篇 ardid m.
56 篇 kalekin o.
54 篇 boumaaza j.
53 篇 díaz a.f.
53 篇 coleiro a.
52 篇 calvo d.
49 篇 huang f.
48 篇 chen a.
47 篇 gal t.
47 篇 bozza c.
42 篇 gozzini s.r.
41 篇 dallier r.
41 篇 anguita m.
37 篇 kulikovskiy v.
37 篇 enzenhöfer a.
35 篇 celli s.
32 篇 ahmad jalal
32 篇 boettcher m.
31 篇 dornic d.

语言

25,900 篇 英文
640 篇 其他
157 篇 中文
31 篇 朝鲜文
24 篇 日文
2 篇 德文
1 篇 西班牙文
1 篇 葡萄牙文
1 篇 斯洛文尼亚文

检索条件"机构=Dept. of Statistics and Data Science Dept. of Computer Science"

共 26752 条记录，以下是131-140 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：

Multiclass Brain Tumor Detection using Deep Transfer Learning

Multiclass Brain Tumor Detection using Deep Transfer Learnin...

引用

2024 MIT Art, Design and Technology School of Computing International Conference, MITADTSoCiCon 2024

作者： Deshmukh, Aishwarya V. Bendre, M.R. Prawara Rural Engineering College Dept. of Data Science Engineering Loni India Prawara Rural Engineering College Dept. of Computer Engineering Loni India

ISBN: (纸本)9798350362879

Brain tumors, identified as abnormal growth of cell inside the brain, necessitate accurate categorization for tailored treatment plans. This classification aids in determining the severity of the condition and guiding therapeutic decisions. Iimaging technologies in medical like Magnetic Resonance Imaging (MRI) are instrumental in identifying brain pathologies without emitting ionizing radiation. Deep learning, a subset of AI, enhances brain tumor detection from MRI scans, improving prediction rates. Digital image processing is vital for medical image analysis and diagnosis. Brain tumors manifest in various forms, categorized as benign or malignant based on their tissue characteristics. Convolutional Neural Networks (CNNs), particularly VGG-16, Inception-v3, and ResNet-50 are prominent in brain tumor analysis, segmentation, and classification. These models are compared for their efficacy in identifying and forecasting brain tumor cells. Common brain tumor types include glioma, meningioma, and pituitary tumors, each requiring specific treatment approaches. © 2024 IEEE.

关键词： Tumors

来源：评论

学校读者我要写书评

暂无评论

Advancing Medical Imaging: A Comparative Exploration of Generative Adversarial Networks for Chest X-ray Synthesis

Advancing Medical Imaging: A Comparative Exploration of Gene...

引用

2024 IEEE International Conference on computer Vision and Machine Intelligence, CVMI 2024

作者： Keerthi, R. Kiran, Kuval Kiran, S. Likitha, P. Pes University Dept. of Computer Science Bangalore India

ISBN: (纸本)9798350376876

Chest X-ray imaging is essential for diagnosing and treatment planning of individuals with diseases like COVID-19, Tuberculosis, and Pneumonia. However, creating efficient machine-learning algorithms for early detection is made difficult by a lack of labeled medical images. The study investigates the possibility of using Generative Adversarial Networks (GANs) to create synthetic chest X-ray images for various lung diseases in order to address this issue. This study compares three distinct GAN architectures: Progressive Growing of Generative Adversarial Network (ProGAN), Deep Convolutional Generative Adversarial Network (DCGAN), and Auxiliary Classifier Generative Adversarial Network (ACGAN). Acknowledging constraints within existing GAN architectures, the study employs a hybrid methodology. To improve training stability and image quality, this method combines the progressive generation of ProGAN with the Gradient Penalty loss of Wasserstein GAN (WGAN-GP). An InceptionV3 classifier was used to assess the quality of the generated images. On the original dataset, this classifier recorded an accuracy of 91%. The classifier achieved 72.08 %, 68.62 %, and 45.50% accuracy on images generated by ProGAN, DCGAN, and ACGAN, respectively. The study demonstrates GANs potential to assist in alleviating data scarcity, hence improving the precision and effectiveness of machine learning algorithm training. © 2024 IEEE.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Dynamic Adoptive Gaussian Mixture Model for Multi-Object Detection Over Natural Scenes 5

Dynamic Adoptive Gaussian Mixture Model for Multi-Object Det...

引用

5th International Conference on Advancements in Computational sciences, ICACS 2024

作者： Ahmed, Muhammad Waqas Jalal, Ahmad Air University Dept. of Computer Science Islamabad Pakistan

ISBN: (纸本)9798350394764

Recent strides in visionary technologies have profoundly impacted the domain of multi-object recognition, serving as a linchpin in transformative applications such as augmented reality integration, robotic navigation, and autonomous driving. This research introduces an innovative paradigm for multi-object detection, marked by substantial advancements in accuracy and efficiency. The proposed methodology combines Dynamic Adoptive Gaussian Mixture Model (DAGMM) with advanced feature extraction and fusion techniques. with cutting-edge feature extraction and fusion techniques. Specifically, we leverage the capabilities of HOG, Akaze and Brisk feature extraction methods to capture intricate object descriptors with a rich data profile. Subsequently, we introduce a nuanced approach involving weighted variance thresholds for feature fusion, enhancing the discriminative prowess of the extracted features. Finally, for classification, we incorporate Convolutional Neural Networks (CNN). We meticulously assessed the performance of our model with two demanding datasets: UIUC sports and Caltech-101. Our DAGMM showcased exceptional proficiency, delivering impressive segmentation accuracy rates of 86.9 % and 8 1. 1 % over UIUC sports and Caltech-101, respectively, when evaluated against rigorously self-annotated ground truth. Furthermore, our approach achieved a substantial 87.2 % object classification accuracy, validating its effectiveness. © 2024 IEEE.

关键词： Object recognition

来源：评论

学校读者我要写书评

暂无评论

data Driven Approach to Leaf Recognition: Logistic Regression for Smart Agriculture 5

Data Driven Approach to Leaf Recognition: Logistic Regressio...

引用

5th International Conference on Advancements in Computational sciences, ICACS 2024

作者： Abbasi, Altamash Ahmad Jalal, Ahmad Air University Dept. of Computer Science Islamabad Pakistan

ISBN: (纸本)9798350394764

Artificial Intelligence has been involved in the restructuring of farming practices. Compared to conventional methods that involved using pesticides to remove weeds, smart and precision agriculture has increased productivity and produced healthy crops. Plant leaves with varying behaviors should be separated as one of the measures in preserving yield quality. We suggest using logistic regression to implement a four-step AI-based process to classify different leaves. Pre-processing, segmentation and feature extraction, have been used for images classification. Various grapevine and swedish leaves have been used in the work by applying the suggested technique to two different datasets. We used image of size $256\times 256$. During pre-processing, the median filter was used, k-means was used for segmentation, Kaze and Blob were selected for feature extraction. Logistic regression was used for classification. Our findings demonstrate that the suggested strategy beats alternative approaches, yielding an accuracy of 88% on the swedish leave dataset and 83% on the datasets pertaining to grapevine leaves. © 2024 IEEE.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

MFFCNN: Multi-Feature Fusion-based Deep Learning Architecture for Typewritten Word Recognition System 4

MFFCNN: Multi-Feature Fusion-based Deep Learning Architectur...

引用

4th International Conference on Innovative Practices in Technology and Management, ICIPTM 2024

作者： Kaur, Gurvir Kumar, Ajit Punjabi University Dept. of Computer Science Patiala India

ISBN: (纸本)9798350307757

For the preservation of cultural legacy, to provide accessibility, to support language learning, and to facilitate effective data processing, accurate word recognition in Gurmukhi, particularly for typewritten text, is of utmost importance. The precise identification of diacritics, dealing with problems like broken or fading letters, and adjusting differences in ink density are a few difficulties this recognition method faces. For Gurmukhi language writings to be seamlessly digitized, translated, and distributed and, in turn, for more people to have access to and comprehend this rich linguistic legacy, these challenges must be overcome. This work contributed to the same and proposed a multi-feature fusion-based deep learning architecture (MFFCNN) that extracts both handcrafted and CNN-based features, then fuses all the features and uses them further for classification and recognition. The dataset of Gurmukhi typewritten content is captured using a mobile camera from different dissertation files, books, novels, etc., available in typewritten. The dataset is pre-processed, and segmentation is performed before MFFCNN for word recognition and extraction. The performance of the proposed method is computed based on the accuracy, and a comparison is made with the CNN-based model. The results of the proposed method presented the effectiveness of multi-feature fusion with deep learning architecture specifically for text recognition. © 2024 IEEE.

关键词： Extraction

来源：评论

学校读者我要写书评

暂无评论

Human Action Recognition Based on Embedded HMM 5

Human Action Recognition Based on Embedded HMM

引用

5th International Conference on Advancements in Computational sciences, ICACS 2024

作者： Bukht, Tanvir Fatima Naik Jalal, Ahmad Air University Dept. of Computer Science Islamabad Pakistan

ISBN: (纸本)9798350394764

One of the most active subfields of computer vision and pattern recognition, human action recognition seeks to identify specific actions in still photos. The wide range of possible uses in this area has attracted a lot of interest from researchers in fields like human-computer interaction, video analysis, and surveillance. Researchers have developed a variety of algorithms and methods to accurately identify and classify the various actions that individuals performed while they were captured in photographs. An embedded HMM is going to be utilised in the process of developing an action recognition technique that will be presented in this article. It is recommended that the HSV colour transformation be carried out first in order to enhance the clarity of the video frames. The MOT and Vibe methods are used to get the silhouette. Feature extraction is accomplished through the utilisation of Texton Map and ORB. The fuzzy optimisation process is then utilised for the purpose of feature discrimination. After that, the features are input into an embedded HMM, where they are categorised into specific human actions based on the final qualities that they possess. The UT interaction data that was used in the experiment was recognised 93.8%. © 2024 IEEE.

关键词： computer vision

来源：评论

学校读者我要写书评

暂无评论

Neuro-Insights: Unveiling the Power of Deep Learning for Brain Tumor Detection in Neuroimaging

Neuro-Insights: Unveiling the Power of Deep Learning for Bra...

引用

2024 IEEE International Conference on Computing, Semiconductor, Mechatronics, Intelligent Systems and Communications, COSMIC 2024

作者： Keshava Reddy, V. Dhanush Reddy, Kotte Sai Jayanth, Bobbili Sai Guru Prasad, M.S. Joshi, Aditi Vatsyayan, Shradha Dept. of Computer Science and Engg. Dehradun India

ISBN: (纸本)9798331517892

The accurate identification and segmentation of brain tumors using the medical imaging of the data is crucial for patient treatment planning. This study will look into advanced deep learning techniques, including Residual Networks (RNs), Transfer Learning (TL), Generative Adversarial Networks (GANs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Ensemble Learning (EL), and Hybrid Deep Learning (HDL), to achieve their precise and accurate results. Comprehensive evaluation using metrics like F1-score, accuracy, validation loss, precision, recall, and specificity score has been performed on a dataset of 7023 MRI images. The findings highlight the superior performances of RNs, CNNs, and RNNs, with F1 scores of 97.75%, 98.25%, and 98.75%, respectively, along with high accuracies and precisions. GANs demonstrate excellent recall, while TL, EL, and HDL exhibit nuanced trade-offs. TL enhances its performance with limited data, GANs aid data synthesis, and EL combines models for their robust predictions. However, the innovative HDL method integrates with diverse models to capture complementary features, leading to precise brain tumor classification. This synergistic blend of deep learning approaches the representation of a significant advancement towards the intelligent diagnostic system for early and accurate brain tumor detection in the medical imaging field. © 2024 IEEE.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Blockchain and Deep Learning for Agricultural Advancement 9

Blockchain and Deep Learning for Agricultural Advancement

引用

9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Rashmi, K.R. Vinod, Rhesha Susil Sepuri, Sai Pranathi Sethuraman, Santosh Annapurna, D. Pes University Dept. of Computer Science Bengaluru India

ISBN: (纸本)9798350394474

The agriculture sector plays a significant role as it not only serves in meeting a substantial portion of worldwide food requirements but is also servers as one of the key contributors to the country's economy. However, some issues including economic inequality, unpredictability of weather patterns, and crop diseases seek for a solution which offers enhanced crop traceability and insurance automation to address food safety problems and financial difficulty. This study creates a prototype of a crop management system merging blockchain and deep learning technologies, assessing its practical aspects. Blockchain enhances data storage, traceability, and transparency, while deep learning boosts monitoring, disease detection, and crop recommendation. The proposed work demonstrates the system's advantages and hurdles thereby contributing to a more efficient and transparent crop management paradigm which aims to revolutionize the crop management system by adopting sustainable agricultural practices on a globally. © 2024 IEEE.

关键词： Smart contract

来源：评论

学校读者我要写书评

暂无评论

Dynamic Real-Time Scheduling on Distributed Hierarchical Fog Networks 31

Dynamic Real-Time Scheduling on Distributed Hierarchical Fog...

引用

31st IEEE International Conference on High Performance Computing, data, and Analytics Workshops, HiPCW 2024

作者： Sharma, Amit Auluck, Nitin Dept. of Computer Science & Engineering IIT Ropar India

ISBN: (纸本)9798331509118

We present a novel approach for efficient task scheduling on hierarchical fog nodes, catering to real-time (RT) and non-real-time (NRT) tasks with varying sizes and deadline constraints. Leveraging machine learning (ML) techniques, our proposed solution autonomously allocates tasks across fog nodes, dynamically adapting to changing workload patterns and system conditions. Our novel methodology integrates supervised learning algorithms to predict workload patterns and resource availability, enabling intelligent decision-making in task assignment. Our findings contribute to advancing the state-of-the-art in fog computing by offering a scalable and adaptive solution for dynamic task scheduling in hierarchical heterogeneous environments. © 2024 IEEE

关键词： Fog computing

来源：评论

学校读者我要写书评

暂无评论

Computational Approach for Halegannada to Hosagannada Poem Translation 4

Computational Approach for Halegannada to Hosagannada Poem T...

引用

4th International Conference on Intelligent Technologies, CONIT 2024

作者： Harsha, A.C. Baig, Toukheer Gurav, Laxmikant Bhujang Bhat, Mahesh Shripad Narayan, Surabhi Pes University Dept. of Computer Science Bangalore India

ISBN: (纸本)9798350349900

Halegannada, or Old Kannada, occupies a pivotal place in Karnataka's linguistic and cultural tapestry and cultural heritage, embodying a significant epoch in the evolutionary timeline of the Kannada language, with a history spanning over 2000 years. This research pioneers the translation of Halegannada poetry into contemporary Hosagannada, employing cutting-edge Natural Language Processing (NLP) models. Indic languages exhibit a notable scarcity of substantial parallel and monolingual corpora in comparison to English and other European languages. Dravidian languages requires more annotated data compared to European or Indo-European languages. Within the realm of Indic languages, several Dravidian languages are categorized as low-resource languages and Halegannada is one among them. To overcome this limitation, strategies such as Back Translation, Unsupervised Machine Translation, Neural Machine Translation, Unsupervised Neural Machine Translation (UNMT), and Transfer Learning are employed. To overcome the lack of pre processed Halegannada data, a comprehensive dataset comprising 20,000 meanings extracted from Pampa's architectural work is created. This paper proposes a Halegannada word splitting model based on LSTM and a computational approach for Halegannada to Hosagannada poem translation. The word splitting model has an accuracy of77.92%. © 2024 IEEE.

关键词： Neural machine translation

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 << < 10 11 12 13 14 15 16 17 18 19 > >>

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：