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

272 篇 会议
54 篇 期刊文献

馆藏范围

326 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

182 篇 工学
- 139 篇 计算机科学与技术...
- 124 篇 软件工程
- 44 篇 信息与通信工程
- 31 篇 控制科学与工程
- 26 篇 生物工程
- 25 篇 生物医学工程（可授...
- 20 篇 光学工程
- 20 篇 电气工程
- 16 篇 电子科学与技术（可...
- 15 篇 安全科学与工程
- 9 篇 化学工程与技术
- 9 篇 交通运输工程
- 8 篇 机械工程
- 8 篇 仪器科学与技术
- 6 篇 力学（可授工学、理...
- 6 篇 石油与天然气工程
- 6 篇 环境科学与工程（可...
97 篇 理学
- 55 篇 数学
- 29 篇 物理学
- 28 篇 生物学
- 18 篇 统计学（可授理学、...
- 16 篇 系统科学
- 11 篇 化学
57 篇 管理学
- 33 篇 图书情报与档案管...
- 29 篇 管理科学与工程(可...
- 16 篇 工商管理
13 篇 医学
- 11 篇 临床医学
- 10 篇 基础医学(可授医学...
- 8 篇 公共卫生与预防医...
8 篇 经济学
- 8 篇 应用经济学
6 篇 法学
5 篇 教育学
3 篇 农学
2 篇 文学

主题

22 篇 deep learning
20 篇 accuracy
14 篇 feature extracti...
12 篇 real-time system...
11 篇 internet of thin...
11 篇 face recognition
11 篇 data models
10 篇 image segmentati...
10 篇 computational mo...
9 篇 machine learning
9 篇 training
8 篇 support vector m...
8 篇 reviews
8 篇 machine learning...
8 篇 robustness
7 篇 computer science
7 篇 classification a...
6 篇 computer archite...
6 篇 security
6 篇 medical diagnost...

机构

6 篇 department of co...
5 篇 mlr institute of...
5 篇 computer science...
5 篇 telecommunicatio...
5 篇 the department o...
4 篇 department of el...
4 篇 university of lo...
4 篇 chittagong unive...
4 篇 department of co...
3 篇 department of co...
3 篇 cse aiml avniet ...
3 篇 university of ce...
3 篇 department of in...
3 篇 federal universi...
3 篇 department of cs...
3 篇 department of co...
3 篇 department of el...
3 篇 department of co...
3 篇 energy engineeri...
3 篇 department of me...

作者

6 篇 zavlanos michael...
6 篇 johansson karl h...
6 篇 m. a. h. akhand
6 篇 wang zifan
5 篇 ajmeera kiran
4 篇 bahar anamul
4 篇 hasan nowshad
4 篇 shen yi
4 篇 md. maskat shari...
4 篇 nowshad hasan
4 篇 kiran ajmeera
4 篇 anamul bahar
3 篇 nandhini t.j.
3 篇 islam md saiful
3 篇 nandhini t j
3 篇 renata lopes ros...
3 篇 demostenes zegar...
3 篇 md saiful islam
3 篇 karl h. johansso...
3 篇 md tohedur raham...

语言

254 篇 英文
67 篇 其他
5 篇 中文
1 篇 西班牙文

检索条件"机构=Computer Science and Enginnering"

共 326 条记录，以下是1-10 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：

Unveiling Prognostic Pattern: A Hybrid Approach For Predictive Analytics in Medical Image Data

Unveiling Prognostic Pattern: A Hybrid Approach For Predicti...

引用

2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Parmar, Uttkarsh Dubey, Anil Kumar Abes Enginnering College Computer Science and Enginnering Department Ghaziabad India Abes Enginnering College Faculty of Computer Science and Enginnering Department Ghaziabad India

ISBN: (纸本)9798350378092

Currently, medical image data are being processed using deep learning technology to revolutionize disease-related research. Deep learning-based medical image analysis involves the application of various essential tasks, including medical image reformation, enhancement of image quality, segmentation, classification, and computer-Aided Detection (CAD). These tasks aim to discern differences between healthy cells and tumour cells, facilitating more accurate diagnoses and effective treatment planning. This paper focuses on employing a hybrid approach for medical image analysis, integrating both clustering and classification techniques. The proposed methodology leverages k-means clustering, Visual Geometry Group (VGG), and Recurrent Neural Network (RNN) on distinct datasets related to cardiovascular diseases, breast cancer, and brain diseases. The hybrid approach aims to capitalize on the strengths of both clustering and classification methods, enhancing the overall accuracy and interpretability of the medical image analysis process. By utilizing diverse datasets, the research endeavours to contribute valuable insights into the identification and understanding of various diseases, ultimately advancing the field of medical image analysis for improved healthcare outcomes. © 2024 IEEE.

关键词： Recurrent neural networks

来源：评论

学校读者我要写书评

暂无评论

Prognostic Pattern: A Hybrid Methodology For Predictivity in Medical Imaging Using VGG-16 4

Prognostic Pattern: A Hybrid Methodology For Predictivity in...

引用

4th International Conference on Advancement in Electronics and Communication Engineering, AECE 2024

作者： Parmar, Uttkarsh Dubey, Anil Kumar M.Tech Abes Enginnering College Computer Science and Enginnering Department Ghaziabad India Abes Enginnering College Faculty of Computer Science and Enginnering Department Ghaziabad India

ISBN: (纸本)9798350364729

At present, deep learning is being used to compute data related to medical images with the purpose of improving disease-related research. Deep learning based medical image analysis includes various important tasks such as medical image reformation, quality enhancement in different types of images, segmentation and classification of images (computer-Aided Detection) CAD, registration to distinguish between normal cells and tumor cells. This article focuses on the hybrid approach for analyzing medical images that makes use of both clustering and classification via k-mean, VGG, RNN) techniques on different diseases datasets like cardio-vascular diseases, breast cancer and brain disease dataset. This paper utilizes predictive analytics in medical image data which is spread across a combination of clustering and classification. The objective of this study is to demonstrate that integrating these two methods will help improve accuracy as well as save time in diagnosing illnesses from image data by avoiding the process where labeling the data for feature detection takes place. Furthermore, this paper aims at presenting an overview of the methodology employed during investigation along with results emanating from this analysis highlighting how predictive analytics can be applied in healthcare. © 2024 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Machine Learning-Based Classification of ASD for Improved Diagnostic Accuracy 2

Machine Learning-Based Classification of ASD for Improved Di...

引用

2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Singla, Ishdeep Onkesh Singla, Sanjay Yadav, Narinder Singla, Priti Rani, Jyoti Chandigarh University Computer Science & Enginnering Mohali India

ISBN: (纸本)9798331519056

Autism Spectrum Disorder(ASD) is a neuro-developmental disorder that influences how people interact with others and communicate hence affecting their ability to interact with others. This is an indication that with the use of primary symptoms it is quite difficult for humans to predict/estimate the present condition and stage of ASD. The main goal is to achieve early diagnosis of ASD in a person using different machine learning and deep learning models. In fact, for humans, it becomes challenging to compiled such amount of data as well as use it to predict via various results. But with the help of machine learning, this task becomes more efficient and less time consuming. This paper aims to show several measurements related to ASD using various classifiers such as Support Vector Machines(SVM), Random Forest Classifier (RFS) and Decision trees. The main purpose to use Random forest algorithm is because it contains the object called feature importance which calculates the importance of the various parameters used in the model between 0(low) to 1(high). Also, random forest helps in prediction using multiple decision trees by selecting the output with high probability. SVM algorithm is used because it is efficent in handling dataset of high dimension i.e. Dataset having large number of features. SVM is also considered best algorithm in terms of memory managament. As one of the main problems in making machine learning model is overfitting, SVM serves it the best in case of handling and reducing overfitting of the dataset. Decision trees are used because the main advantage of decision tree is that it goes through several inputs, make multiple predictions based on the result of each decision tree and gives the best output that follows. There are various advantages of using decision trees such as ease of understanding and easy to interpret and understand by others. Deep learning is also used by using layers like Sequential, Dense and Dropout which goes deep into data to fin

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Robust Object Detection in Jam-Packed Scenes Through Enhanced YOLOv8: A Deep Learning Method 5

Robust Object Detection in Jam-Packed Scenes Through Enhance...

引用

5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Chitra, M. Shanmuganathan, C. Computer Science and Enginnering Srm Institute of Science and Technology Ramapuram India

ISBN: (纸本)9798331504403

Dimension variation, obstruction, and occlusion pose significant difficulties to object detection in heavily surrounded situations. The You Only Look Once (YOLO) v8 method is improved in this study by adding an optimized loss function, enhancing accuracy and robustness in such challenging circumstances. The model incorporates a number of breakthroughs, including a method that uses attention to concentrate on pertinent regions, a multi scale feature fusion technique to manage distinct object sizes, and a more advanced backbone network with new convolutional layers for improved extraction of features. The primary new feature is an improved loss algorithm that is intended to more successfully handle localization issues and class imbalance. This loss function uses metrics based on Intersection over Union (IoU) to improve localization accuracy and focal loss to address class imbalance. Full-blown tests are carried out on benchmark datasets such as Pascal VOC and COCO, in addition to a custom-designed dataset intended for dense situations. When compared to other cutting-edge object identification models and the baseline YOLO v8, the improved YOLO v8 performs better. Significant gains in mean Average Precision (mAP) and recall measures are demonstrated by the quantitative data. The suggested improvements to YOLO v8 maintain real-time processing capabilities while pushing the limits of object recognition in clogged environments, making it suitable for real-world applications in surveillance, autonomous driving, and urban scene analysis. © 2024 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Wick-Based Hydroponics System with Mobile Application

Wick-Based Hydroponics System with Mobile Application

引用

2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Prianka, D. Shilpa, G.V. Parvin, Hasina Prerana, B. Gauda, Rajashree Computer Science and Enginnering Vemana Institute of Technology Bengaluru India

ISBN: (纸本)9798350382693

The proposed system is designed to address resource efficiency, cost-effectiveness, and user convenience, thereby promoting accessible and eco-friendly agricultural practices. Employs readily available and reusable materials, emphasizing sustain ability and afford ability. Utilizing wick-based technology relying on capillary action to deliver nutrient-rich water directly to plant roots. The thyme seedlings are placed inside pots that are filled with an inert growing medium to support the plant, and a wick material is used for optimal nutrient absorption. The wick facilitates capillary action, delivering nutrient solution directly to the plant roots, thereby minimizing water wastage. The system utilizes sensors to measure key parameters such as pH, EC, and humidity to measure the nutrient concentration in the hydroponics solution. A mobile application serves as the central control interface, authorizing users to monitor nutrient levels, and water levels remotely. Through real-time data analysis by utilizing the sensor measurements, the application displays the nutrient delivery graph, humidity level, and water level parameters automatically for the user to monitor the plant growth. © 2024 IEEE.

关键词： Reusability

来源：评论

学校读者我要写书评

暂无评论

Activity Prediction for Localizing the Events in Imagined Speech EEG Signals 13

Activity Prediction for Localizing the Events in Imagined Sp...

引用

13th International Winter Conference on Brain-computer Interface, BCI 2025

作者： Balasubramanian, Arun Pandey, Kartik Veer, Gautam Samanta, Debasis Dept. of Computer Science and Enginnering Indian Institute of Technology Kharagpur India

ISBN: (纸本)9798331521929

Imagined speech electroencephalogram (EEG) signals are often collected for longer durations than necessary, leading to a difficulty in understanding the generation of EEG during the task as it is likely that most of the data collected is that of resting state. Developing a filter to identify the segments of the trials with actual information and remove those that contain the EEG from resting states could significantly advance our understanding of EEG signals in neuroscience and biomedical engineering. This work uses derivatives-based features to form the feature vectors used to train the classifiers. Based on feature importance analysis, it has been found that the derivative-based features contributed more to the classification than the traditionally preferred feature, band power in the alpha frequency band. Using the proposed features, the classification performance experienced a significant enhancement, surpassing the results reported in previous studies. The greater accuracy of the classifiers with the proposed features implies that they are effective at filtering out the resting state segments from imagined speech EEG signals. © 2025 IEEE.

关键词： Electroencephalography

来源：评论

学校读者我要写书评

暂无评论

Artificial Intelligence Based Emotion Recognition from Fuzzy EEG Signals: A Comprehensive Review 4

Artificial Intelligence Based Emotion Recognition from Fuzzy...

引用

4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Indraneel, K. Miriyala, Trinath Basu Koneru Lakshmaiah Education Foundation Department of Computer Science and Enginnering Telangana Hyderabad India

ISBN: (纸本)9798331523923

Affective Computing, a type of artificial intelligence that can detect and interpreting human emotions with an advancement technologies like non-invasive sensing, and brain-computer interfaces. Academics have been fascinated by the idea of developing systems that can identify human emotions using various facial expressions, gestures, and physiological signals. The first three techniques are typically ineffective because people may hide what they're feeling unconsciously or consciously. Emotion recognition can be improved using physiological signals. These signals are more sensitive to the changes in the emotional states of people. Implementing emotional recognition systems face several challenges including data acquisition, feature extraction, model development, model training, and generalization. Adoption of AI techniques can help in improving accuracy and robustness using fuzzy EEG signals. Advancements in deep learning and machine learning have made it possible to recognize, analyze, and interpret various emotional states. The present work reviews the available datasets and techniques such as the extraction of features, selection/reduction, and machine learning for eliciting emotional states. It also explores different types of neural networks with long-term memory or convolutional systems. The review also describes electroencephalogram signals that are associated with certain emotions and the relationship among various parts of the brain. The review also helps future research to gain insights towards efficient emotion recognition capturing human emotions. © 2025 IEEE.

关键词： Emotion Recognition

来源：评论

学校读者我要写书评

暂无评论

Alzheimer's Disease Prediction Using Deep Learning Techniques 3

Alzheimer's Disease Prediction Using Deep Learning Technique...

引用

3rd International Conference for Innovation in Technology, INOCON 2024

作者： Lavanya, V. Deepalakshmi, R. Aartheeswari, K.S. Nithika Sri, M. Velammal College of Enginnering and Technology Department of Computer Science and Engineering Madurai India

ISBN: (纸本)9798350381931

The brain anatomy is studied by Magnetic resonance imaging (MRI). In order to take preventive measures that are necessary, early diagnosis of patients affected by Alzheimer's disease(AD) is more essential. The most prevalent illness affecting the old people is AD. For the diagnosis of AD, numerous, intricate segmentation methods have been presented. Deep Learning (DL) techniques have attracted interest for the technique of segmenting the anatomy of the brain and categorizing AD because they can produce efficient results over a vast data collection. How the knowledge of brain anatomy can be used to identify AD using concepts from convolutional neural networks are explained. New methods, their outcomes on public datasets, and the advantages of MRI segmentation of brain for classification of AD is described. This work investigates the potential use of DL techniques to enhance and strengthen early diagnosis, predict AD phases as mild, moderate, low, or medium, and investigate strategies for strengthening overall performance analysis. © 2024 IEEE.

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

A Novel Approach in the Automatic Generation of Regional Language Subtitles for Videos in English 8

A Novel Approach in the Automatic Generation of Regional Lan...

引用

8th IEEE International Conference for Convergence in Technology, I2CT 2023

作者： Bilva Teja, R. Chinnari, Nischitha P Hadadi, Prerana Shanbhogh, Surabhi Prasad, V R Badri PES University Computer Science and Enginnering Banglore India

ISBN: (纸本)9798350334012

The usage of videos for communication has increased drastically in recent years. Non-native language speakers and those with hearing impairments, on the other hand, are unable to use this strong medium. Subtitles translate the speech to text and hence it makes the video available for a wider audience which includes those who don't understand the language and people with hearing problems. With the rapid development of e-Learning technology, geographical boundaries are no longer considered as a major barrier in learning. However, language becomes a barrier in the wave of knowledge globalization. Subtitles in a regional language like Kannada helps to understand concepts better. The major goal of designing the system is to give an automated method for creating Kannada subtitles for videos. © 2023 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

A Systematic Review of Supervised Learning and Deep Learning Algorithms for Diabetic Detection 4

A Systematic Review of Supervised Learning and Deep Learning...

引用

4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Sridevi, Pothumarthi Padmanaban, K. Koneru Lakshmaiah Education Foundation Department of Computer Science and Enginnering Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9798331523923

Lack of insulin production and utilization can lead to diabetes, which is a major health issue. Currently, diagnostic procedures are typically invasive and have various disadvantages, such as cost. Although various machine learning models can perform well in the field, they tend to perform poorly when under-performing on the data. Currently, most diabetes-related procedures depend on blood glucose levels. These include laboratory tests and glucose meters, commonly used at home and in hospitals. Although these methods are relatively accurate, they have various disadvantages. Some of these include the time and expense involved in performing the procedure, the lack of skilled technicians, and the pain that comes with testing. By utilizing the latest advancements in machine learning, deep learning, and sensor technology, the present research proposed to develop a novel method for non-invasive diabetes detection. The method addresses some of the challenges faced by traditional machine learning techniques, such as its limited sensitivity and accuracy. Finally, the future directions are highlighted to improve the reliability of the detection process. © 2025 IEEE.

关键词： Adversarial machine learning

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33页 << < 1 2 3 4 5 6 7 8 9 10 > >>

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：