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

1,734 篇 会议
45 册 图书
12 篇 期刊文献

馆藏范围

1,790 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

1,229 篇 工学
- 1,097 篇 计算机科学与技术...
- 629 篇 软件工程
- 257 篇 信息与通信工程
- 201 篇 电气工程
- 177 篇 生物工程
- 102 篇 控制科学与工程
- 98 篇 生物医学工程（可授...
- 60 篇 光学工程
- 46 篇 电子科学与技术（可...
- 46 篇 化学工程与技术
- 42 篇 机械工程
- 41 篇 仪器科学与技术
- 40 篇 安全科学与工程
- 29 篇 动力工程及工程热...
- 29 篇 交通运输工程
- 27 篇 材料科学与工程（可...
457 篇 理学
- 200 篇 数学
- 197 篇 生物学
- 144 篇 物理学
- 84 篇 统计学（可授理学、...
- 51 篇 系统科学
- 46 篇 化学
237 篇 管理学
- 171 篇 图书情报与档案管...
- 89 篇 管理科学与工程(可...
- 46 篇 工商管理
182 篇 医学
- 160 篇 临床医学
- 72 篇 基础医学(可授医学...
- 39 篇 药学(可授医学、理...
- 32 篇 公共卫生与预防医...
34 篇 法学
- 30 篇 社会学
17 篇 经济学
14 篇 教育学
11 篇 农学
2 篇 艺术学
1 篇 军事学

主题

240 篇 machine learning
185 篇 deep learning
140 篇 pattern recognit...
110 篇 feature extracti...
91 篇 support vector m...
88 篇 data mining
79 篇 accuracy
75 篇 training
59 篇 convolutional ne...
51 篇 machine learning...
48 篇 data models
46 篇 artificial intel...
41 篇 computational mo...
40 篇 neural networks
39 篇 face recognition
36 篇 learning systems
36 篇 adversarial mach...
33 篇 predictive model...
32 篇 classification
32 篇 image recognitio...

机构

6 篇 college of compu...
5 篇 xi'an university...
5 篇 shanghai jiao to...
4 篇 badji mokhtar un...
4 篇 institute of big...
4 篇 chitkara centre ...
4 篇 chitkara univers...
4 篇 library shanghai...
3 篇 university of mi...
3 篇 school of comput...
3 篇 univ chittagong ...
3 篇 chongqing univer...
3 篇 centre of resear...
3 篇 school of comput...
3 篇 school of comput...
3 篇 school of design...
3 篇 korea univ dept ...
3 篇 school of automa...
3 篇 university of mi...
3 篇 shenyang jianzhu...

作者

5 篇 venkatesh svetha
4 篇 ashwini kodipall...
4 篇 zhang yan
4 篇 srinivasa gowri
4 篇 kamel mohamed s.
4 篇 lall brejesh
4 篇 trupthi rao
3 篇 andersson karl
3 篇 peng yang
3 篇 malerba donato
3 篇 karl andersson
3 篇 chaudhury santan...
3 篇 de-shuang huang
3 篇 sekhar c. chandr...
3 篇 wang shuo
3 篇 kamata sei-ichir...
3 篇 mohammad shahada...
3 篇 azizi nabiha
3 篇 phung dinh
3 篇 krasotkina olga

语言

1,515 篇 英文
267 篇 其他
26 篇 中文

检索条件"任意字段=4th International Conference on Machine Learning and Data Minining in Pattern Recognition"

共 1791 条记录，以下是711-720 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：

Early Stage Oral Cancer Detection using machine learning and Deep learning Algorithms

Early Stage Oral Cancer Detection using Machine Learning and...

引用

Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE international conference on

作者： G. Manoj Kumar P. Satyanarayana B. Sridhar K. Tejo Rohith V. Vinay Kumar V. Gokula Krishnan Department of ECE Siddhartha Academy of Higher Education(Deemed to be University) Velagapudi Ramakrishna Siddhartha School of Engineering Vijayawada India Department of ECE Anurag University School of Engineering Ghatkesar Hyderabad India Department of CSE-CS Easwari Engineering College Ramapuram Chennai India

ISBN: (数字)9798331533663

ISBN: (纸本)9798331533670

Oral cancer remains a significant global health challenge with high mortality rates. the major cause is late recognition of oral cancer, making treatment less effective. this condition is particularly prevalent in low- and middle-income countries. Early-stage detection and accurate diagnosis are critical for improving treatment outcomes, reducing deaths, and minimizing recurrence. Enabling automation in the identification of malignant cells in the oral cavity could lead to low-cost and early diagnosis of the disease. this study aims to detect early cancer stages accurately using machine learning and Deep learning techniques. A histopathological dataset with cellular-level images of different regions in the oral cavity helps in detecting cancer cells more effectively. Preprocessing and image enhancement techniques are used to leverage spatial and temporal features from imaging data to distinguish damaged cancer cells from normal cells. Wavelet Decomposition and edge detection techniques play a crucial role in identifying damaged regions in images. the ensembling technique with the voting approach independently combines multiple CNN models and binary classifiers providing the highest accuracy of 98% shown in the results section. Ensembling approach produces significant improvements in cancer cell identification and enhancing system robustness.

关键词： Deep learning Accuracy Image edge detection Object detection Cancer detection Robustness Biological cells Image enhancement Cancer Image classification

来源：评论

学校读者我要写书评

暂无评论

DSDE 2021 - 2021 4th international conference on data Storage and data Engineering

DSDE 2021 - 2021 4th International Conference on Data Storag...

引用

4th international conference on data Storage and data Engineering, DSDE 2021

ISBN: (纸本)9781450389303

the proceedings contain 25 papers. the topics discussed include: performance evaluation of the JK-nearest neighbor online signature verification parameters;a review of Arabic text steganography;security management for human resource data based on blockchain technology;a credible individual behavior profiling method for online payment fraud detection;grid-based source location privacy protection schemes in IoT wireless sensor networks;adversarial attacks in banknote recognition systems;the impact of histogram equalization and color mapping on ResNet-34’s overall performance for COVID-19 detection;GraphicalAI: a user-centric approach to develop artificial intelligence and machine learning applications using a visual and graphical language;and multi-criteria recommender system with hybrid deep tensor decomposition.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Classification method of birdsong based on Gabor_WT feature image and convolutional neural network 4

Classification method of birdsong based on Gabor_WT feature ...

引用

4th international conference on pattern recognition and Artificial Intelligence, PRAI 2021

作者： Liu, Jiang Zhang, Yan Lv, Danjv Lu, Jing Xu, Haifeng Xie, Shanshan Xiong, Yanjiao School of Big Data and Intelligent Engineering Southwest Forestry University Kunming China School of Mathematics and Physics Southwest Forestry University Kunming China

ISBN: (纸本)9781665413220

In the classification of birdsong, the effect of traditional feature extraction methods and identifying birds through deep learning methods is still not ideal. therefore, this paper proposes a birdsong classification method based on Gabor_Wt feature image and convolutional neural network. According to the two-dimensional wavelet spectrogram signal can present more detailed information characteristics of birdsong. the two-dimensional wavelet spectrogram is obtained by using wavelet transform from birdsong audio. the Gabor_Wt feature images are extracted texture information from wavelet spectrogram with 2D-Gabor filters. With the convolutional neural network structure, WT_CNN and Gabor Wt Cnn classification models are built. the experimental results show that the Gabor_Wt_Cnn method can achieve a classification accuracy of 94%, outperforming WT_CNN of 87% and random forest with MFCC features of 80%. the proposed method provides a new reference to birdsong classification. © 2021 IEEE.

关键词： Wavelet transforms

来源：评论

学校读者我要写书评

暂无评论

Fine-Tuning-Powered Plant Disease Detection Based on Cloud-Edge Computing in Smart Farms

Fine-Tuning-Powered Plant Disease Detection Based on Cloud-E...

引用

Electronic Information Engineering and Computer Science (EIECS), 2021 international conference on

作者： Da Meng Wanxing Jiang Wenquan Jin Department of Electronic & Communication Engineering Yanbian University Yanji China

ISBN: (数字)9798331531409

ISBN: (纸本)9798331531416

Plants are an indispensable part of agricultural production, and the study of plant disease classification and recognition has an important impact on garden horticulture, environmental protection, crop growth and the continuous development of agricultural economy. Moreover, it plays a very important role in crop production intelligence and crop big data management applications. Traditional machine learning relies too much on expert knowledge, and the form of artificial extraction of plant leaf information cannot meet the recognition of complex diseases. In this paper, we come up with an edge-cloud plant disease classification approach by using fine-tuning to improve the performance continuously. Methods of crop disease identification leverages convolutional neural networks (CNN) that achieves successful performance in various computer vision tasks. the improved leaf recognition model performs the collection, analysis, processing and prediction of heterogeneous data in the process of crop planting. the proposed fine-tunning-improved CNN model is used for searching three common potato diseases in real time. In the cloud, the dataset is deployed to derive the prediction model using CNN for performing fine-tuning in the edge device. Based on the presented approach, the performance of the model is improved and the time consuming decreased by fine-tuning method in the edge environment.

关键词： Performance evaluation Plant diseases Computational modeling Image edge detection Crops Production Predictive models data models Convolutional neural networks Tuning

来源：评论

学校读者我要写书评

暂无评论

SPEAR: Design and Implementation of an Advanced Virtual Assistant

SPEAR: Design and Implementation of an Advanced Virtual Assi...

引用

Sustainable Expert Systems (ICSES), international conference on

作者： Garima Jain Amita Shukla Nitesh Kumar Bairwa Anamika Chaudhary Ashish Patel Ankush Jain Department of Computer Science and Buisness Systems Noida Institute of Engineering and Technology Greater Noida India Department of Artificial Intelligence Noida Institute of Engineering and Technology Greater Noida India Department of Data Science Noida Institute of Engineering and Technology Greater Noida India Department of Artificial Intelligence and Machine Learning Dronacharya Group of Institutions Greater Noida India Department of Computer Science and Enginnering Netaji Subhash University of Technology Delhi India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

this research presents the development and evaluation of SPEAR, an advanced voice-activated personal desktop assistant designed to address challenges in existing virtual assistant technology, such as limited language support, low recognition accuracy, and inadequate task automation. Despite recent advancements in Natural Language Processing (NLP) and machine learning, implementation in current systems remains subpar. SPEAR achieves an 88% overall command execution success rate, with a speech recognition accuracy of 92% and an average response time of 1.8 seconds, demonstrating efficiency in real-time interactions. Developed using Python and machine learning libraries, SPEAR supports 15 voice commands across multiple platforms, excelling in note creation (95% success), web searching (94%), and information retrieval (92%). Its natural language understanding score of 0.86 and context retention rate of 75% highlight its conversational capabilities. While the assistant performs well in messaging functionality (85% success) and currently supports three languages, expanding its multilingual capabilities is essential for broader user engagement. Metrics indicate a satisfaction score of 8.2/10 and an average of 12 daily interactions per user, showcasing its practical utility. Additionally, the system's text-to-speech quality (8.5/10) and error recovery rate (82 %) affirm its robustness. these findings contribute valuable insights for future research, focusing on enhancing multilingual support, broadening task categories, and improving context retention, ultimately positioning SPEAR to rival established industry leaders by offering a comprehensive suite of services.

关键词： Accuracy Personal voice assistants machine learning Information retrieval Natural language processing Real-time systems Robustness Text to speech Time factors Web search

来源：评论

学校读者我要写书评

暂无评论

An unsupervised discriminative random vector functional link network for efficient data clustering 4

An unsupervised discriminative random vector functional link...

引用

4th international conference on pattern recognition and Artificial Intelligence, PRAI 2021

作者： Zhang, Yikai Zhu, Qi Peng, Yong Kong, Wanzeng School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China Provincial Key Lab. for Computer Information Processing Technology Soochow University Suzhou215123 China

ISBN: (纸本)9781665413220

Random Vector Functional Link (RVFL) is a single hidden layer feed forward network which can be trained with non-iterative learning methods. Concretely, the input weights and hidden biases in RVFL can be randomly generated and then the calculation of the output weights can be reduced to solve a regularized least squares regression formula. though RVFL network obtained promising performance in diverse applications, most of its current variants work mainly for supervised tasks such as classification and regression while less effort was made to utilize the unlabeled data. In this paper, we propose an unsupervised RVFL model by simultaneously considering the local manifold structure and the global discriminative information of data. the newly formulated unsupervised discriminative RVFL (UDRVFL) model can be efficiently solved by handling an eigen-value decomposition operation. We conduct experiments by comparing UDRVFL with several state-of-the-arts on representative benchmark data sets and the results demonstrate that it has very competitive performance in data clustering. © 2021 IEEE.

关键词： Iterative methods

来源：评论

学校读者我要写书评

暂无评论

Classification of Flower dataset using machine learning Models

Classification of Flower Dataset using Machine Learning Mode...

引用

Artificial Intelligence and Speech Technology (AIST), international conference on

作者： Tina Gupta Puja Arora Ritu Rani Garima Jaiswal Poonam Bansal Amita Dev Computer Science Engineering Indira Gandhi Delhi Technical University for Women Delhi India Center of Excellence - Artificial Intelligence Indira Gandhi Delhi Technical University for Women Delhi India Computer Science Engineering Bennet University Noida Artificial Intelligence and Data Sciences Indira Gandhi Delhi Technical University for Women Delhi India

Modern day machine learning aims to categorize data based on developed models and predict future outcomes according to these models. Today machine learning finds its application in various fields such as facial recognition, speech recognition, medical diagnosis for example predicting potential heart failure, sentiment analysis, product recommendations etc. this paper proposes 3 classification models to efficiently predict the Iris flower species. the proposed model uses Exploratory data Analysis (EDA) to analyse and pre-process the dataset and the prediction is performed by the three classification models namely- "Logistic Regression, Support Vector machine (SVM) and K-Nearest Neighbours (KNN)". All the proposed models are tested on Iris dataset and achieved maximum accuracy of 96.43, 98.21 and 94.64 percent respectively. this paper provides a thorough analysis of the various supervised machine learning models that are suitable for predicting the species of Iris flower based on the various attributes like sepal width, sepal length, petal width and petal length.

关键词： Analytical models Sentiment analysis data analysis Support vector machine classification machine learning Speech recognition Predictive models

来源：评论

学校读者我要写书评

暂无评论

Comprehensive Performance Evaluation of Association Rule Mining Algorithms for Border Gateway Protocol Anomaly Detection

Comprehensive Performance Evaluation of Association Rule Min...

引用

Emerging Smart Technologies and Applications (eSmarTA), international conference on

作者： Mubarak Altamimi Nehad Ramaha Akram A. Almohammedi Almaafer Communıty College Taiz Yemen Karabük University Karabük Turkey Dept. of Computer Engineering Karabuk University Karabük Turkey Dept. of Electrical & Electronics Engineering Karabük University Karabük Turkey

ISBN: (数字)9798350354133

ISBN: (纸本)9798350354140

Association rule mining is a data mining technique that concentrates on uncovering noteworthy connections or associations within a vast database of items. the process entails identifying frequently occurring sets of items and deriving association rules from those sets. Border Gateway Protocol (BGP) serves as the prevalent gateway protocol facilitating communication between autonomous systems, enabling the exchange of routing and reachability information. the occurrence of anomalous behavior in protocol attributes can be attributed to diverse factors such as insufficient provisioning, malicious attacks, traffic or equipment problems, and errors made by network operators. Based on the association rule, the dataset is examined and partitioned into sub-frequent pattern datasets. Each pattern dataset selects a single anomalous frequent itemset for comparison to determine the itemset with the highest anomaly value. the commonly used rule-based machine learning algorithms for anomaly detection include Frequent pattern (FP) growth and Apriori algorithms. this study aims to use unsupervised algorithms based on association rules in select features to detect BGP anomalies. Additionally, the performance of these algorithms will be evaluated in terms of support, confidence, and accuracy values. Furthermore, the anomaly detection results were presented using a tailored and efficient tool and framework, enhancing the clarity and providing a more precise and detailed understanding of the performance.

关键词： Performance evaluation machine learning algorithms Itemsets Logic gates Routing Feature extraction Border Gateway Protocol

来源：评论

学校读者我要写书评

暂无评论

Voice Sentiment Analysis System Using Librosa

Voice Sentiment Analysis System Using Librosa

引用

Ubiquitous Computing and Intelligent Information Systems (ICUIS), international conference on

作者： Ashok Reddy Kandula Rayala Nathaniel thota Bhanu Vissamsetti Sai Vignesh Meduri Leela Naga Sai Dept of AI&DS Seshadri Rao Gudlavalleru Engineering College Gudlvalleru India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

the Voice Sentiment Analysis System is an innovative solution that can analyze the emotional content of spoken audio data in real-time. By using advanced machine learning algorithms and signal processing techniques, the system can accurately detect and categorize the emotions expressed by speakers. this provides valuable insights into the sentiment conveyed in conversations, speeches, or customer interactions. By processing streaming audio data, the system allows for immediate feedback and actionable insights in various applications. In business settings, it can be used to analyze customer service calls or feedback recordings in real-time to assess satisfaction levels, identify areas for improvement, and prioritize follow-up actions. Integration with voice-enabled assistants enhances user interactions by interpreting emotional cues and personalizing responses. In healthcare, the system can assist in monitoring patients' emotional well-being during therapy sessions or remote consultations. this enables healthcare professionals to assess progress, intervene when necessary, and customize treatment plans effectively. With its easy-to-use interface and strong performance, the Voice Sentiment Analysis System offers a versatile solution for understanding and interpreting human emotions through voice communication channels

关键词： Sentiment analysis Emotion recognition Speech analysis Customer services machine learning Feature extraction Ubiquitous computing Real-time systems Monitoring Spectrogram

来源：评论

学校读者我要写书评

暂无评论

A Review of Advancements in Driver Emotion Detection: Deep learning Approaches and dataset Analysis

A Review of Advancements in Driver Emotion Detection: Deep L...

引用

Innovative Research in Applied Science, Engineering and Technology (IRASET),conference on

作者： Mohammed Shukur Alfaras Oğuz Karan Electrical and Computer Engineering Altinbas University Istanbul Turkey

ISBN: (数字)9798350309508

ISBN: (纸本)9798350309515

this review paper delves into the rapidly evolving field of driver emotion detection, with a specific focus on the contributions of deep learning methodologies and the diverse datasets that facilitate this research. Facial emotion detection and recognition is a highly dynamic and challenging field in machine learning (ML) and Artificial Intelligence (AI). It has gained attraction for several decades, but it is extremely challenging due to the intrinsic complexities associated with understanding and interpreting human emotions. Understanding and responding to a driver's emotions is increasingly important when autonomous or assisted driving becomes common. To ensure safety, comfort, and optimal interaction between the driver and the car's systems, predicting the emotional state of the driver is essential, also of great significance in practical applications. We give a comprehensive survey of the state-of-the-art driver emotion recognition works that can effectively make use of the recent deep-learning approaches to identify complex emotional cues. Moreover, we explain a variety of datasets that play a vital role in flourishing this field along with the analysis of their effect, like AffectNet, CK+, and EMOTIC. Via this survey we try to investigate the challenges authors are faced with involving this field, e.g., concerns about data privacy, real-time processing demands, and the need for interdisciplinary collaboration. More importantly, the potential of these technologies to improve the driving experience and road safety has been highlighted. We hope this survey can benefit researchers and practitioners to have more insights and provide directions for advancing drivers’ emotions.

关键词： Deep learning Surveys Emotion recognition Protocols Reviews Road safety Safety

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180页 << < 68 69 70 71 72 73 74 75 76 77 > >>

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：