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

分类表

所选分类

>> <<

限定检索结果

标题

标题
作者
主题词
出版物名称
出版社
机构
学科分类号
摘要
ISBN
ISSN
基金资助
索书号

作者

作者
标题
主题词
出版物名称
出版社
机构
学科分类号
摘要
ISBN
ISSN
基金资助
索书号

文献类型

16,099 篇 会议
2,501 篇 期刊文献
147 册 图书

馆藏范围

18,747 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

11,225 篇 工学
- 7,578 篇 计算机科学与技术...
- 6,334 篇 软件工程
- 3,208 篇 信息与通信工程
- 1,875 篇 控制科学与工程
- 1,712 篇 电子科学与技术（可...
- 1,701 篇 电气工程
- 1,166 篇 生物工程
- 1,029 篇 生物医学工程（可授...
- 874 篇 光学工程
- 754 篇 机械工程
- 698 篇 动力工程及工程热...
- 688 篇 仪器科学与技术
- 574 篇 化学工程与技术
- 473 篇 安全科学与工程
- 418 篇 交通运输工程
- 373 篇 材料科学与工程（可...
5,787 篇 理学
- 3,018 篇 数学
- 1,776 篇 物理学
- 1,276 篇 生物学
- 984 篇 统计学（可授理学、...
- 668 篇 系统科学
- 578 篇 化学
2,736 篇 管理学
- 1,684 篇 管理科学与工程(可...
- 1,205 篇 图书情报与档案管...
- 883 篇 工商管理
864 篇 医学
- 759 篇 临床医学
- 707 篇 基础医学(可授医学...
- 451 篇 药学(可授医学、理...
- 363 篇 公共卫生与预防医...
498 篇 法学
- 397 篇 社会学
326 篇 经济学
236 篇 农学
206 篇 教育学
78 篇 文学
44 篇 军事学
15 篇 艺术学
1 篇 历史学

主题

527 篇 accuracy
398 篇 feature extracti...
393 篇 deep learning
386 篇 computational mo...
288 篇 support vector m...
278 篇 training
275 篇 real-time system...
260 篇 internet of thin...
224 篇 machine learning
221 篇 optimization
213 篇 predictive model...
197 篇 computer science
195 篇 computer archite...
179 篇 neural networks
176 篇 wireless communi...
175 篇 reliability
171 篇 data mining
163 篇 convolutional ne...
161 篇 routing
160 篇 wireless sensor ...

机构

84 篇 institut univers...
82 篇 north-west unive...
82 篇 national centre ...
82 篇 tno technical sc...
79 篇 infn sezione di ...
79 篇 nikhef national ...
78 篇 university würzb...
78 篇 university of jo...
76 篇 infn sezione di ...
76 篇 université de st...
66 篇 università di ca...
65 篇 ncsr demokritos ...
64 篇 university moham...
54 篇 university moham...
53 篇 leiden universit...
51 篇 dept. of compute...
50 篇 school of automa...
43 篇 in2p3 lpc campus...
42 篇 infn lnf via enr...
41 篇 university of gr...

作者

85 篇 ferrara g.
85 篇 ardid m.
84 篇 calvo d.
84 篇 gal t.
84 篇 bozza c.
82 篇 kalekin o.
82 篇 díaz a.f.
82 篇 boumaaza j.
82 篇 coleiro a.
81 篇 chen a.
81 篇 gozzini s.r.
81 篇 dallier r.
78 篇 anguita m.
78 篇 enzenhöfer a.
76 篇 boettcher m.
75 篇 celli s.
73 篇 aly z.
72 篇 dornic d.
70 篇 huang f.
70 篇 carretero v.

语言

17,998 篇 英文
485 篇 其他
190 篇 中文
51 篇 日文
21 篇 朝鲜文
1 篇 阿拉伯文
1 篇 德文
1 篇 法文
1 篇 斯洛文尼亚文
1 篇 土耳其文

检索条件"机构=Dept. of Computer Engineering and Informatics and Computer Technology"

共 18747 条记录，以下是81-90 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：

相关度排序

相关度排序
时效性降序
时效性升序

Optical Character Recognition of Persian Epigraphs by Using Artificial Neural Networks in MATLAB

Optical Character Recognition of Persian Epigraphs by Using ...

引用

2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Khan, Sumaiyya Bardekar, Aashish Sipna College of Engineering & Technology Dept. of Computer Science and Engineering Amravati India

ISBN: (纸本)9798331517953

Persian, also known as Farsi, was once a dominant language and cultural influence worldwide. Today, it is nearly extinct in the Indian subcontinent. However, for centuries, Farsi played a major role in high culture and administration across the subcontinent. Persian characters are complex, with various features such as ascenders, descenders, vertical lines, and curves. Automatic translation of Persian characters from inscriptions into English is currently unavailable. The process of deciphering and translating inscribed text in epigraphy can be time-consuming. This paper specifically focuses on identifying Persian epigraphs. First, the inscription image is cleaned up, and images containing Persian characters are obtained. Using preprocessing techniques, the images are then prepared for input to artificial neural networks. The artificial neural networks are trained to effectively recognize various patterns of Persian characters, as they can learn complex patterns and adapt to changes in images, resulting in high accuracy in Persian character recognition. Finally, the proposed method's performance is evaluated using accuracy, precision, and speed metrics. The results demonstrate that the method accurately and rapidly detects Persian characters in various images. This research has the potential to advance Persian character recognition and image processing, enabling broader applications such as converting image documents into text, character recognition in documents, and text translation with Persian image content. © 2024 IEEE.

关键词： MATLAB

来源：评论

学校读者我要写书评

暂无评论

Emotion Recognition from Video Frame Sequence using Face Mesh and Pre-Trained Models of Convolutional Neural Network 24

Emotion Recognition from Video Frame Sequence using Face Mes...

引用

24th International Seminar on Intelligent technology and Its Applications, ISITIA 2023

作者： Adi, Derry Pramono Yuniarno, Eko Mulyanto Wulandari, Diah Puspito Institut Teknologi Sepuluh Nopember Faculty of Intelligent Electrical and Informatics Technology Dept. of Electrical Engineering Surabaya Indonesia Institut Teknologi Sepuluh Nopember Faculty of Intelligent Electrical and Informatics Technology Dept. of Electrical Engineering Dept. of Computer Engineering Surabaya Indonesia Institut Teknologi Sepuluh Nopember Faculty of Intelligent Electrical and Informatics Technology Dept. of Computer Engineering Surabaya Indonesia

ISBN: (纸本)9798350313956

Emotions are a collection of subjective cognitive experiences and psychological and physiological characteristics that express a wide range of feelings, thoughts, and behaviors in human interaction. Emotions can be represented through several means, such as facial expressions, tone of voice, and behavior. Deep Learning (DL) research has focused on incorporating facial expressions. Images with facial expressions are commonly used as data input for the DL model. Unfortunately, most DL models in Facial Emotion Recognition (FER) use static images. This method does not take into consideration all conceivable facial expressions. The static image of facial expressions is insufficient for recognizing emotions, but a sequential image from a video is required. In this study, we extract MediaPipe's face mesh feature, the state-of-the-art multidimensional expression key points embedded in the video image sequence. Furthermore, we feed sequence image data into the pre-trained Convolutional Neural Network (CNN) model. The data we used is from The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) with the emotion classes of 'Anger,' 'Fearful,' 'Happy,' and 'Sad.' For this specific FER task, we found that the best pre-trained CNN model achieved 92.8% accuracy (using the VGG-19 model), with the fastest runtime of ∼2.3 seconds (achieved using the SqueezeNet model). © 2023 IEEE.

关键词： Emotion Recognition

来源：评论

学校读者我要写书评

暂无评论

Revolutionizing Road Safety for Advanced Vehicular Accident Prediction 2

Revolutionizing Road Safety for Advanced Vehicular Accident ...

引用

2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Deepika, T. Yuvaraj, N. Sri Preethaa, K.R. KPR Institute of Engineering and Technology Dept. of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350327533

The detection and tracking of vehicle speed are crucial for ensuring the safety of civilians and preventing accidents. Speed tracking plays a pivotal role in traffic surveillance, where effective management and the well-being of citizens take precedence. The approach leverages computer vision-based models, specifically Multi-scale Detector for object detection, and a fusion of the DeepSORT and Centroid Sort algorithms for vehicle tracking from streaming video. To cater to the requirements of these algorithms, a custom dataset was developed within a controlled setting. Dataset augmentation was employed to prevent overfitting, thereby enhancing the dataset's diversity and the model's ability to generalize. The method demonstrates the capacity to accurately detect and track vehicles, maintaining performance even in high-traffic conditions. Cross-validation at every epoch during the training phase was implemented to increase the model's accuracy. In addition to the technical aspects, an LED display board was integrated to provide real-time data to oncoming vehicles at road curves. This display showcases information such as vehicle types, passage time, and the number of vehicles within the curvy road. The proposed Multi-Scale Detector model, a customized version of the You Only Look Once (YOLO) architecture, achieved a mean average precision (mAP) of 0.954 at a confidence interval of 0.5. This overall research provides an effective and accurate solution for optimizing vehicle tracking, enhancing transportation safety, and enabling a variety of practical applications in traffic management and monitoring. © 2024 IEEE.

关键词： DeepSORT Euclidean Distance and Vehicle Intelligence Multi-scale Detector Object Detection Speed Tacking

来源：评论

学校读者我要写书评

暂无评论

Advanced Forecasting and Analysis Techniques for Optimizing Website Performance: A Comparative Study of Time Series Models 2

Advanced Forecasting and Analysis Techniques for Optimizing ...

引用

2nd IEEE International Conference on IoT, Communication and Automation technology, ICICAT 2024

作者： Raikar, Gayathri Harshith, D. Guruprasad, N. Global Academy of Technology Dept. of Computer Science and Engineering Bangalore India Buddha Institute of Technology Computer Science and Engineering Gorakhpur India

ISBN: (纸本)9798350368109

Analyzing website performance is crucial for optimizing a site's functionality, enhancing user engagement, and aligning with business goals. This evaluation focuses on essential metrics such as average engagement time per session, engagement rate, and events per session, which significantly impact user experience and overall business success. Through effective performance analysis, organizations can improve user satisfaction, boost conversion rates, and enhance their profitability and reputation. This paper reviews different approaches for analyzing these key metrics, explores their effects on user behavior and business performance, and offers best practices for optimizing website performance. By employing detailed performance analysis, businesses can make informed decisions and realize their strategic goals in the digital environment. © 2024 IEEE.

关键词： Channel Performance Data Analysis Time Series Forecasting User Engagement Website Performance

来源：评论

学校读者我要写书评

暂无评论

Deep Feature Fusion Based Brain Tumor Classification From Imbalance MRI Dataset Using Weighted Loss Functions 2

Deep Feature Fusion Based Brain Tumor Classification From Im...

引用

2nd International Conference on Power, Electrical, Electronics and Industrial Applications, PEEIACON 2024

作者： Tasnim, Taieba Parvin, Farjana Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Rajshahi Bangladesh

ISBN: (纸本)9798331517984

Deep learning using MRI images to classify brain tumours presents a significant challenge due to imbalanced training datasets, where one tumor type may have far more samples than others. The overrepresented class may perform worse in classifiers as a result of this imbalance. In order to tackle this issue, the paper examines how feature learning in CNNs is affected by weighted loss functions to manage data imbalance. The article propose deep feature fusion, where particular CNN model features that were trained using three distinct loss functions are fused and classified using softmax classifier. This approach lead to notable improvements in brain tumor classification compared to a CNN trained with standard cross-entropy loss. Notably, the proposed methods substantially reduce classification errors between uneven class samples. The proposed model achieved an impressive 94.5% classification accuracy, accompanied by an average Fl-score of 94.6%, average precision of 94.6 %, and average recall of 95%. These findings underscore the effectiveness of our methodology in improving brain tumor classification accuracy, offering a promising avenue for more reliable diagnosis and treatment planning in clinical settings, © 2024 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

MeQANet: Deep Learning-based Framework for Classifying Medical Query Answering in Bengali

MeQANet: Deep Learning-based Framework for Classifying Medic...

引用

2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Tasnia, Fairooz Hoque, Mohammed Moshiul Chittagong University of Engineering and Technology Dept. of Computer Science and Engineering Chattagram Bangladesh

ISBN: (纸本)9798331529765

The automatic classification of medical QA pairs is crucial for improving healthcare efficiency, allocating specialized care, optimizing resource distribution, and facilitating robust data analysis. Bengali, the 7th most widely spoken language globally, has a growing volume of health-related queries. However, unlike English, there is no benchmark large-scale medical QA dataset, pre-trained language model, or human baseline score for medical QA in Bengali. This paper introduces a deep learning-based framework called MeQANet that leverages CNN and BiLSTM techniques for classifying medical QA texts in Bengali. It also presents a novel medical QA corpus in Bengali (BnMedQA), consisting of approximately 4500 medical QA pairs across eight domains: Orthopedic, ENT, Gastroenterology and Hepatology, Gynecology, Pediatrics, General Medicine, Dermatology, and Cardiology. Various techniques (including LR, SVM, RF, MNB, AdaBoost (AB), CNN, BiL-STM, and CNN+BiLSTM) are employed to perform the classification tasks. Experimental results indicate that the CNN+BiLSTM approach achieved the highest F1-score of 0.85 among all employed techniques. © 2024 IEEE.

关键词： Query languages

来源：评论

学校读者我要写书评

暂无评论

Mental State Report From Social Media Activity

Mental State Report From Social Media Activity

引用

2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Ajit, Akshay Mathew, Crispin Denny, David Johns Devananth, H. Dhanya, P.M. Rajagiri School of Engineering and Technology Dept. of Computer Science and Engineering Ernakulam India

ISBN: (纸本)9798331540685

This paper is about making user experience with social media better, by developing the complete analysis tool that would parse textual data from different platforms like Instagram, Facebook or Reddit. By developing a custom browser extension, the user-generated content is captured in real-time and can also be processed subsequently. The project comprises two key components: Emotion Detection and Hate Speech Detection. The Emotion Detection system uses sophisticated Natural Language Processing NLP techniques, such as BERT models to identify users' emotional state through their social media posts. This information is then used to develop a daily mental state report by which users are able to gain insights into their emotional health over time. Further, the Hate Speech Detection system leverages machine learning algorithms to detect and mark words that may be harmful or offensive in user- generated content. This feature enhances positive and inclusive Internet environment. The project goes beyond the analysis;it includes a pragmatic component suggesting specific tasks for users depending on their mental state reports. These recommendations are aimed at promoting positive interaction and enhancing the user's overall well-being. This project offers a comprehensive insight into users' emotional experiences on social media using state-of-the-art NLP and machine learning approaches. The combination of task recommendations and text summarization increases interaction among users, creating a pleasant atmosphere where people can enjoy using social media. The browser extension is a multipurpose tool that does not merely analyze but provides some improvements to the user's mental health and online activity. © 2024 IEEE.

关键词： Emotion Recognition

来源：评论

学校读者我要写书评

暂无评论

Addressing the Long-Tailed Data Distribution in Bone Marrow Cell Identification through Class Balance Deep Classification Model 2

Addressing the Long-Tailed Data Distribution in Bone Marrow ...

引用

2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Ghosh, Rakesh Shankar Shill, Pintu Chandra Khulna University of Engineering & Technology Dept. of Computer Science and Engineering Khulna Bangladesh

ISBN: (纸本)9798350317558

The accurate classification of bone marrow cells has long been a critical component of diagnosing blood diseases. However, traditional methods rely on subjective human interpretation and lack standardized quantitative criteria. To address this challenge, this research proposes a Class Balance Deep Classification Model (CBDCM) designed to classify bone marrow cells using EfficientNet to handle the long-tailed data distribution problem. In order to validate the propose model by applying it to proper dataset and analyzing the experimental outcomes of the propose method. Notably, CBDCM attains impressive precision, sensitivity, and specificity values of 86.14%, 87.53%, and 99.45%, respectively. Simulation results show that CBDCM outperforms, demonstrating superior performance in a detailed comparison with both deep neural networks and traditional machine learning techniques. The findings of this study, with significant implications for addressing class imbalance in datasets, hold promise for standardizing the classification of bone marrow cells, potentially revolutionizing the diagnosis of blood diseases. © 2024 IEEE.

关键词： bone marrow cell CBDCM data imbalance deep learning EfficientNet gaussian smoothing

来源：评论

学校读者我要写书评

暂无评论

Enhancing Traffic Control Strategies through Dynamic Simulation and Reinforcement Learning 3

Enhancing Traffic Control Strategies through Dynamic Simulat...

引用

3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Sebastian, Alona Mary Athulram, K.R. Michael, Colin Sunil, Diya Anna Preetha, K.G. Rajagiri School of Engineering and Technology Dept. of Computer Science and Engineering Ernakulam India

ISBN: (纸本)9798350375190

In urban areas, traffic congestion is a major problem causing increased travel times, higher consumption of fuel, and environmental pollution. The traditional traffic control systems cannot adapt to dynamic traffic conditions, causing inefficient traffic flow and inefficient use of road infrastructure. In this paper, we propose a novel method to enhance traffic management strategies through dynamic simulation and reinforcement learning (RL). A simulation framework is presented that models the complex interactions among vehicles, traffic signals, and infrastructure in a realistic urban environment. Within this framework, RL agents are deployed to learn adaptive traffic control policies that minimize congestion and optimize traffic flow. This approach uses the ability of RL to continuously learn and adjust to shifting traffic circumstances, which results in improved efficiency and responsiveness compared to static or rule-based control systems. Finally, evaluate the effectiveness of the approach through extensive simulations and demonstrate its capability to significantly reduce congestion, decrease travel times, decrease average waiting time and enhance overall traffic management. By integrating dynamic simulation and RL-based control strategies, the proposed approach offers a guaranteed solution for addressing the challenges of urban traffic congestion and advancing the field of intelligent transportation systems. © 2024 IEEE.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Binary Classification of Breast Tumours using CBAM-Enhanced Collaborative Network 3

Binary Classification of Breast Tumours using CBAM-Enhanced ...

引用

3rd International Conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Das, Soumit Nivel, Mst Fabia Akter Rajshahi University of Engineering & Technology Dept. of Computer Science and Engineering Rajshahi Bangladesh

ISBN: (纸本)9798350388282

For women breast cancer is a life-threatening problem, making the importance of early detection crucial. Expert pathologists assess cell shapes in breast histopathology images at various magnification levels for accurate diagnosis. Unfortunately, a shortage of pathologists in many countries heightens the risk of human error in disease identification and treatment. To address this challenge, this study proposed a CBAM-Enhanced Collaborative Network for classifying benign and malignant tumors in breast cancer histopathological images, offering a potential solution to enhance accuracy and efficiency in diagnosis. The model included a transfer learning branch using a pretrained DenseNet121 structure with integrated CBAM (Convolutional Block Attention Module). Simultaneously, a collaborative branch employed Inception-ResNet-v2 with CBAM for feature extraction from histopathological images. The feature fusion module then optimized information from both branches. The proposed method achieved impressive binary classification accuracies on the BreaKHis dataset: 98.31% overall, with 98.33%, 98.08%, 99.67%, and 97.80% for magnification factors of 40x, 100x, 200x, and 400x, respectively. CBAM-Enhanced Collaborative Network also exhibits strong generalization capabilities, as evidenced by its superior performance on the ICIAR2018 BACH Challenge dataset. © 2024 IEEE.

关键词： Image classification

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 << < 5 6 7 8 9 10 11 12 13 14 > >>

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

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：