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

1,258 篇 会议
264 篇 期刊文献
1 册 图书

馆藏范围

1,523 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

769 篇 工学
- 599 篇 计算机科学与技术...
- 519 篇 软件工程
- 194 篇 信息与通信工程
- 151 篇 控制科学与工程
- 128 篇 电气工程
- 115 篇 生物医学工程（可授...
- 114 篇 生物工程
- 82 篇 光学工程
- 81 篇 电子科学与技术（可...
- 67 篇 网络空间安全
- 66 篇 安全科学与工程
- 57 篇 机械工程
- 52 篇 仪器科学与技术
- 51 篇 交通运输工程
- 40 篇 化学工程与技术
426 篇 理学
- 209 篇 数学
- 130 篇 生物学
- 116 篇 物理学
- 85 篇 统计学（可授理学、...
- 60 篇 化学
- 38 篇 系统科学
247 篇 管理学
- 165 篇 管理科学与工程(可...
- 101 篇 图书情报与档案管...
- 95 篇 工商管理
141 篇 医学
- 108 篇 临床医学
- 107 篇 基础医学(可授医学...
- 86 篇 公共卫生与预防医...
- 58 篇 药学(可授医学、理...
57 篇 法学
- 49 篇 社会学
46 篇 经济学
- 46 篇 应用经济学
36 篇 农学
20 篇 教育学
7 篇 文学
3 篇 艺术学
1 篇 哲学
1 篇 历史学
1 篇 军事学

主题

185 篇 accuracy
119 篇 real-time system...
114 篇 deep learning
81 篇 convolutional ne...
79 篇 machine learning
77 篇 predictive model...
75 篇 training
69 篇 feature extracti...
59 篇 support vector m...
59 篇 computational mo...
55 篇 internet of thin...
55 篇 machine learning...
54 篇 medical services
53 篇 data models
50 篇 optimization
47 篇 reviews
41 篇 reliability
41 篇 prediction algor...
40 篇 artificial intel...
36 篇 technological in...

机构

50 篇 nitte meenakshi ...
42 篇 department of ar...
39 篇 lovely professio...
31 篇 department of co...
21 篇 department of ar...
19 篇 east west instit...
19 篇 department of co...
18 篇 department of ar...
18 篇 division of arti...
17 篇 department of ar...
17 篇 machine learning...
16 篇 new horizon coll...
15 篇 college of techn...
15 篇 department of ar...
13 篇 institute of aer...
12 篇 artificial intel...
12 篇 datta meghe inst...
12 篇 department of ar...
12 篇 division of comp...
12 篇 vector institute...

作者

42 篇 prateek verma
42 篇 piyush kumar par...
41 篇 pareek piyush ku...
29 篇 mohsen guizani
28 篇 verma prateek
17 篇 guizani mohsen
14 篇 uma reddy
14 篇 swapnil gundewar
13 篇 amandeep nagpal
12 篇 aditya barhate
12 篇 madhura g.k.
11 篇 utkarsha pachara...
11 篇 xiaojiang du
11 篇 abhay tale
11 篇 rushikesh burle
10 篇 tale abhay
10 篇 reddy uma
10 篇 sundaravadivel p...
9 篇 von lilienfeld o...
9 篇 n v uma reddy

语言

1,278 篇 英文
242 篇 其他
3 篇 中文

检索条件"机构=Artificial Intelligence and Machine Learning Faculty of Engineering and Technology"

共 1523 条记录，以下是611-620 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：

An Empirical Study of CNN-Deep learning Models for Detection of Covid-19 Using Chest X-Ray Images 1

引用

5th International Conference on Smart Technologies in Data Science and Communication, SMART-DSC 2022

作者： Muqeet, Mohd. Abdul Hameeduddin, Quazi Mateenuddin Mohammed Ismail, B. Mohammad, Ali Baig Qadeer, Shaik Muzammil Parvez, M. Electrical Engineering Department Muffakham Jah College of Engineering and Technology Hyderabad India Faculty of Electronics and Communication Engineering Indian Naval Academy Kerala Ezhimala India Department of Artificial Intelligence & Machine Learning P.A. College of Engineering Karnataka Mangalore India School of Electronics and Communication Engineering REVA University Bengaluru India Electrical Engineering Department Muffakham Jah College of Engineering and Technology Hyderabad India Electronics and Communication Engineering Department KLEF Deemed to Be University A.P Vaddeswaram India

ISBN: (数字)9789811968808

ISBN: (纸本)9789811968792

The Covid-19 spun into a pandemic and has affected routine lives and global health. It is crucial to identify the infectious Covid-19 subjects as early as possible to avert its spread. The CXR images processed with deep learning (DL) processes have newly become an earnest method for early Covid-19 detection along with the regular RT-PCR test. This paper examines the deep learning (DL) models to detect Covid-19 from CXR images for early analysis of Covid-19. We conducted an empirical study to assess the efficacy of the proposed convolutional neural network DL model (CNN-DLM), pre-trained with some eminent networks such as MobileNet, InceptionNet-V3, ResNet50, Xception, and DenseNet121 for initial detection of Covid-19 for an openly accessible dataset. We also exhibited the accuracy and loss value curves for the selected number of epochs for all these models. The results indicate that with the proposed CNN model pre-trained with the DenseNet121 greater results were achieved compared to other pre-trained CNN-DLMs applied in a transfer learning approach. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Chest X-Ray (CXR) Images Convolutional neural network (CNN) Covid-19 Deep learning models (DLM)

来源：评论

学校读者我要写书评

暂无评论

A Review on Automated Facial Expression Recognition Using Image Emotion Analysis

A Review on Automated Facial Expression Recognition Using Im...

引用

engineering and technology (PICET), Parul International Conference on

作者： Sarthak Gaurkhede Aman Dhole Praveen Kumar Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India Department of Computer Science and Medical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India

ISBN: (数字)9798350369748

ISBN: (纸本)9798350369755

In order to build stronger relationships and make wiser decisions, it is essential to get a better understanding of human emotions through the complex language of facial expressions. This paper travels you through busy streets, the throbbing boardrooms, and the peaceful homes where expressions whisper unsaid tales. In order to provide a foundation for a more profound knowledge of emotions, Ekman and Friesen (1971) established six fundamental emotions that cut across cultural barriers. Driven by artificial intelligence and computer vision, Facial Expression Recognition (FER) technology emerges as a game-changing tool that allows for the exceptional precision of emotional reaction analysis. However, problems persist, including cultural sensitivity, ethical dilemmas, and accuracy across a variety of populations. This work imagines a future where FER improves human well-being, encourages empathy, and ushers in a transformational era of emotion analysis by exploring the most recent developments and suggesting ways for responsible development. Respecting individual privacy and cultural subtleties, FER has the potential to transform psychology research, education, human-computer interaction, and social well-being via cooperative efforts and ethical concerns.

关键词： Ethics Emotion recognition Privacy Sensitivity Reviews Face recognition Education Transforms Cultural differences artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

Dynamic Playlist Recommendations based on Multi-Modal Mood Detection and Contextual learning 8

Dynamic Playlist Recommendations based on Multi-Modal Mood D...

引用

8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Sundaravadivel, P. Raj, J. Relin Francis Kumar, S. Senthil Krishnan, R. Santhana Muthu, A. Essaki Karthikeyan, M. Saravana Saveetha Engineering College Department of Artificial Intelligence and Machine Learning Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Scad College of Engineering and Technology Department of Computer Science and Engineering Cheranmahadevi India Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai Avadi India

ISBN: (纸本)9798350376425

This paper presents a novel system for dynamic playlist recommendations by integrating multi-modal mood detection with contextual learning. The system utilizes three primary data sources: text-based sentiment analysis using BERT, facial expression recognition through ResNet-50, and physiological data analysis with LSTM networks. By processing user input from social media, capturing facial expressions, and analyzing physiological signals such as heart rate and skin conductance, the system effectively determines the user's current mood. Playlist recommendations are then generated through a hybrid approach combining collaborative and content-based filtering, augmented by contextual bandits to adapt dynamically to changing user preferences and time-of-day patterns. The proposed system provides a personalized music experience by continuously learning from user interactions and mood states. Comparative evaluations against existing recommendation systems demonstrate the system's efficacy in enhancing user satisfaction and engagement through accurate and context-aware music suggestions. © 2024 IEEE.

关键词： Collaborative filtering

来源：评论

学校读者我要写书评

暂无评论

A Review of machine learning Algorithms for Detection of Breast Cancer

A Review of Machine Learning Algorithms for Detection of Bre...

引用

Ubiquitous Computing and Intelligent Information Systems (ICUIS), International Conference on

作者： Abhay Tale Aditya Barhate Prateek Verma Palash Gourshettiwar Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India Department of Computer Science and Medical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

Breast cancer is still a global health concern, chiefly affecting women, as it is one of the major causes of cancer mortality. For the therapy to be effective, early diagnosis of cancer is critical to raising the survival level and increasing the effectiveness of the procedure. The advancement in the applied technique of machine learning (ML) has brought further opportunities for early and effective diagnosis of breast cancer. This review paper provides insights into how different ML algorithms including the Support Vector machine (SVM), Convolutional Neural Network (CNN), Random Forests, and many more have contributed to the detection of breast cancer. These algorithms have proved effective in providing solutions to various medical data and imaging including early diagnosis and treatment. The paper also looks at the use of ML with precision medicine to show that these technologies will help minimize false diagnoses and offer better treatment options. This review consolidates recent works and advancements to emphasize the prospect of ML in breast cancer management and calls for more studies to enhance its implementation in practice

关键词： Support vector machines machine learning algorithms Sensitivity Reviews Precision medicine Ubiquitous computing Breast cancer Vectors Convolutional neural networks Random forests

来源：评论

学校读者我要写书评

暂无评论

Blockchain-based Booth-less Tolling System using GPS and Image Processing

Blockchain-based Booth-less Tolling System using GPS and Ima...

引用

AI IoT Congress (AIIoT), World

作者： Keshav Thosar Hargobind Singh Sreejit Chatterjee Dayanand Ambawade Electronics and Telecomm. Engineering Sardar Patel Institute of Technology Mumbai India Artificial Intelligence and Machine Learning Engineering Sardar Patel Institute of Technology Mumbai India

India as a country is aggressively investing in building better roads and highways, but due to the costly nature of these projects, they are done in the form of a Public Private Partnership. This makes the efficient collection of toll tax highly important to incentivize further development of infrastructure. Currently, the FASTag system of tolling is employed in India which is resulting in unorganized and inaccurate revenue collection, physical problems like tags getting damaged, traffic congestion and reduced fuel efficiency. To solve these problems, a blockchain-based virtual tolling system using GPS and Image processing is proposed. In this paper, appropriate hardware and software designs were involved to develop a GPS-based highway toll collection system further complemented with image processing to get the number plate data. A blockchain-based technology to implement a decentralized, secure toll collection mechanism that uses blockchain smart contracts to store data.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Harnessing NLP and Deep learning to Decode Disease–Gene Interactions in Lung Cancer

Harnessing NLP and Deep Learning to Decode Disease–Gene Int...

引用

Women in Computing (InCoWoCo), International Conference for

作者： K. Mary Sudha Rani V. Kamakshi Prasad Department of Artificial Intelligence and Machine Learning Chaitanya Bharathi Institute of Technology Hyderabad India Department of Computer Science and Engineering Jawaharlal Nehru Technological University Hyderabad Hyderabad India

ISBN: (数字)9798331518943

ISBN: (纸本)9798331518950

Lung cancer remains a critical global health challenge, necessitating advanced predictive models to enhance diagnostic and prognostic accuracy. This study employs natural language processing (NLP) and deep learning to advance personalized lung cancer prognosis and treatment response prediction. Using a synthetic dataset, our novel neural network model integrates clinical data with patient narratives to extract meaningful insights, correlating patient transcripts with existing datasets. The model interprets lung cancer histology types, gene sets, and genetic mutations, achieving 85% accuracy in predicting treatment responses, with a sensitivity of 0.88 and specificity of 0.83. By merging clinical and transcript-based data, this research provides clinicians with a reliable tool to support personalized medicine and informed decision-making. The results highlight the potential of AI-driven approaches to improve disease-gene association analysis, paving the way for innovative computational methodologies in lung cancer research.

关键词： Deep learning Ethics Accuracy Neural networks Genetic mutations Lung cancer Predictive models Natural language processing Data models Synthetic data

来源：评论

学校读者我要写书评

暂无评论

Animal Repellent Smart System using AI based Edge Computing 2

Animal Repellent Smart System using AI based Edge Computing

引用

2nd International Conference on Sustainable Computing and Data Communication Systems, ICSCDS 2023

作者： Mythili, E. Harsha, R. Rajeshkumar, G. Gomathi, B. Satheshkumar, K. Jamunadevi, C. Bannari Amman Institute of Technology Dept of Information Technology Sathyamangalam638 401 India Bannari Amman Institute of Technology Department of Information Science and Engineering Sathyamangalam638 401 India Sri Eshwar College of Engineering Department of Computer Science and Engineering India Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Sathyamangalam638 401 India Kongu Engineering College Department of Computer Technology-PG Erode Perundurai India

ISBN: (纸本)9781665491990

During recent times, edge computing is becoming an essential technology for the development of real time applications which have the capabilities to move, process, and store the data close to end devices. It helps to improve the response time, to reduce the latency and ensures secure data exchange. This study focuses on an application, which is used as a smart farming tool to protect the crops from ungulate attacks and thereby reducing the crop damage by creating fences (virtual) with the help of computer vision and ultrasound emission. It begins with a device which generates ultrasound waves to drive the ungulates away from the crop field. In this work the design, development, and assessment of an intelligent animal repulsion system using ultrasound waves by detecting, recognizing the ungulates has been provided. Here, the proposed method deploys and evaluates various edge devices running real time objection detection with customized training models to recognize and detect the ungulates with high precision. © 2023 IEEE.

关键词： Digital storage

来源：评论

学校读者我要写书评

暂无评论

Optimal Kernel Choice for Score Function-based Causal Discovery 41

Optimal Kernel Choice for Score Function-based Causal Discov...

引用

41st International Conference on machine learning, ICML 2024

作者： Wang, Wenjie Huang, Biwei Liu, Feng You, Xinge Liu, Tongliang Zhang, Kun Gong, Mingming School of Mathematics and Statistics The University of Melbourne Australia Department of Machine Learning Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates University of California San Diego United States School of Computing and Information Systems The University of Melbourne Australia Huazhong University of Science and Technology China School of Computer Science Faculty of Engineering The University of Sydney Australia Department of Philosophy Carnegie Mellon University United States

Score-based methods have demonstrated their effectiveness in discovering causal relationships by scoring different causal structures based on their goodness of fit to the data. Recently, Huang et al. (2018) proposed a generalized score function that can handle general data distributions and causal relationships by modeling the relations in reproducing kernel Hilbert space (RKHS). The selection of an appropriate kernel within this score function is crucial for accurately characterizing causal relationships and ensuring precise causal discovery. However, the current method involves manual heuristic selection of kernel parameters, making the process tedious and less likely to ensure optimality. In this paper, we propose a kernel selection method within the generalized score function that automatically selects the optimal kernel that best fits the data. Specifically, we model the generative process of the variables involved in each step of the causal graph search procedure as a mixture of independent noise variables. Based on this model, we derive an automatic kernel selection method by maximizing the marginal likelihood of the variables involved in each search step. We conduct experiments on both synthetic data and real-world benchmarks, and the results demonstrate that our proposed method outperforms heuristic kernel selection methods. Copyright 2024 by the author(s)

关键词：

来源：评论

学校读者我要写书评

暂无评论

Advanced Process Control in Manufacturing Using IoT Devices and artificial Neural Networks 4

Advanced Process Control in Manufacturing Using IoT Devices ...

引用

4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Sumathi, V. Ramesh, S. Basavaraddi, Chethan Chandra S. Visumathi, J. Ishwarya, M.V. Srinivasan, S. Sri Sairam Engineering College Department of Mathematics Tamil Nadu Chennai India School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Tamil Nadu Chennai India R&d Don Bosco Institute of Technology Vtu Department of Artificial Intelligence and Machine Learning Karnataka Belagavi India Veltech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Information Technology Tamil Nadu Chennai India Agni College of Technology Artificial intelligence and Data science Department Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331540364

Optimization of industrial activities is significantly helped by Advanced Process Control (APC), which increases efficiency, decreases costs, and improves product quality. artificial Neural Networks (ANNs) and the Internet of Things (IoT) have greatly improved APC in manufacturing. It examines how APC systems may include ANNs and IoT devices in industrial processes. By connecting to a wide range of sensors placed strategically throughout a production facility, IoT devices may gather data in real time, allowing for managing and monitoring crucial parameters. To APC systems, ANNs are invaluable for predictive modeling and control due to their adaptability to changing process conditions and capacity to represent complicated nonlinear interactions. Manufacturers may gain better control over their operations, improving productivity and product quality, by using data from the IoT to train and update ANN models. Recent advances in APC using IoT devices and ANNs are reviewed in this study. Their applications in various industrial industries are discussed, and case examples are shown to demonstrate their usefulness. Further improvements in APC for manufacturing using IoT devices and ANNs may be guided by highlighting the field's problems and future research goals. Challenges with data security, integration challenges, and reliability impact the accuracy of predictive modeling, scalability, and performance of IoT systems. © 2024 IEEE.

关键词： Adaptive control systems

来源：评论

学校读者我要写书评

暂无评论

machine learning-Enabled Data-Driven Fault Detection for Predictive Maintenance in HVAC Systems

Machine Learning-Enabled Data-Driven Fault Detection for Pre...

引用

Frontiers of Information technology (FIT)

作者： Faizan Hamayat Rana Fayyaz Ahmad Amin Ud Din Syed Zubair Machine Learning and Deep Learning Lab Artificial Intelligence Technology Center (AITeC) National Center for Physics (NCP) Islamabad Pakistan Electrical Engineering Department University of Engineering and Technology Lahore Lahore Pakistan

Buildings consume around 40% of the total global energy consumption, while within the building, the HVAC systems are the most energy-hungry systems responsible for more than half of the total building’s energy consumption. In addition, the most challenging task for facility managers is the maintenance of the HVAC system. Since the HVAC system is one of the necessities in modern buildings to maintain a comfortable and healthy environment. This is necessary to ensure proper maintenance of the HVAC system, specifically the faults detection and diagnostic to avoid system downtime and energy wastage and to enhance the HVAC system’s lifespan. In this research work, we proposed a data-driven fault detection framework for predictive maintenance in HVAC systems of modern buildings using machine learning. In specific, the proposed data-driven framework is implemented using 8 different machine learning techniques for accurate fault detection, and their performance is evaluated using 4 different metrics including accuracy, precision, recall, and F-1 score. Extra Tree, Random Forest, and CatBoost classifiers outperformed the rest of the machine-learning techniques in all evaluation metrics. Further, the comparison of best-performer machine learning models is performed for each fault category detection. CatBoost and Random Forest are found more efficient in comparison to Extra Tree when evaluated for individual faults detection. Results demonstrate the effectiveness of the proposed machine learning- based data-driven framework for fault detection in HVAC systems.

关键词：

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153页 << < 58 59 60 61 62 63 64 65 66 67 > >>

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：