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

1,044 篇 会议
195 篇 期刊文献
12 册 图书

馆藏范围

1,251 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

726 篇 工学
- 612 篇 计算机科学与技术...
- 526 篇 软件工程
- 229 篇 信息与通信工程
- 140 篇 控制科学与工程
- 102 篇 生物工程
- 89 篇 电气工程
- 88 篇 生物医学工程（可授...
- 83 篇 网络空间安全
- 77 篇 安全科学与工程
- 63 篇 电子科学与技术（可...
- 63 篇 交通运输工程
- 50 篇 机械工程
- 50 篇 光学工程
- 44 篇 动力工程及工程热...
- 39 篇 化学工程与技术
- 37 篇 仪器科学与技术
358 篇 理学
- 181 篇 数学
- 114 篇 生物学
- 86 篇 物理学
- 51 篇 统计学（可授理学、...
- 48 篇 系统科学
- 44 篇 化学
262 篇 管理学
- 168 篇 管理科学与工程(可...
- 115 篇 图书情报与档案管...
- 82 篇 工商管理
103 篇 医学
- 85 篇 临床医学
- 77 篇 基础医学(可授医学...
- 55 篇 公共卫生与预防医...
- 48 篇 药学(可授医学、理...
53 篇 法学
- 40 篇 社会学
31 篇 经济学
19 篇 农学
16 篇 教育学
8 篇 文学
6 篇 军事学

主题

79 篇 accuracy
64 篇 machine learning
62 篇 deep learning
48 篇 convolutional ne...
47 篇 feature extracti...
47 篇 training
44 篇 computational mo...
38 篇 real-time system...
37 篇 support vector m...
34 篇 predictive model...
30 篇 data models
29 篇 machine learning...
28 篇 security
28 篇 technological in...
27 篇 artificial intel...
25 篇 adversarial mach...
23 篇 reliability
22 篇 medical services
22 篇 analytical model...
21 篇 internet of thin...

机构

47 篇 lovely professio...
28 篇 chitkara univers...
20 篇 amity school of ...
20 篇 department of co...
19 篇 amity school of ...
18 篇 school of comput...
18 篇 mlr institute of...
17 篇 department of co...
14 篇 amity school of ...
13 篇 amity school of ...
12 篇 school of comput...
11 篇 department of co...
11 篇 amity school of ...
10 篇 school of comput...
10 篇 amity university...
10 篇 department of co...
10 篇 department of co...
9 篇 amity university...
9 篇 school of engine...
9 篇 lloyd institute ...

作者

24 篇 keshav kaushik
24 篇 obaidat mohammad...
21 篇 arora deepak
21 篇 kaushik keshav
19 篇 hazela bramah
19 篇 gupta sumita
17 篇 bramah hazela
14 篇 mohammad s. obai...
14 篇 rana ajay
14 篇 bhattacharya pro...
14 篇 singh pawan
14 篇 ajay rana
13 篇 singh prabhishek
12 篇 dutta pushan kum...
11 篇 tanwar sudeep
10 篇 sudeep tanwar
10 篇 pronaya bhattach...
10 篇 ather danish
10 篇 kaur harleen
10 篇 chaurasia brijes...

语言

1,107 篇 英文
137 篇 其他
13 篇 中文
1 篇 法文
1 篇 斯洛文尼亚文

检索条件"机构=Student of Computer Science and Engineering Amity School of Engineering and Technology"

共 1251 条记录，以下是521-530 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：

A review of the classification of neuroscience problems with the help of deep learning framework 5

A review of the classification of neuroscience problems with...

引用

5th International Conference on Information Systems and computer Networks, ISCON 2021

作者： Pathak, Dharmendra Kashyap, Ramgopal Amity School of Engineering and Technology Department of Computer Science Engineering Raipur India

ISBN: (纸本)9781665403412

Electroencephalographic signals (EEG signals) processing has become very popular nowadays due to its effectiveness in dealing with and treating various disorders associated with the field of neuroscience. As per the recent trends, deep learning has shown many promising results as compared to machine learning due to its ability to extract end-to-end features automatically from the raw input data and subsequently providing better performance in classification results. This paper has reviewed research papers that implemented various deep learning methodologies i.e., CNN, R-CNN, LSTM, GAN, etc. for the classification of EEG signals specific to epilepsy, sleep stage, and mental stages disorders. The review has been carried out considering various parameters i.e., objectives, datasets, models, results, etc. This paper has also discussed the common challenges associated with EEG signals classification related to neuroscience problems and proposed a framework to overcome the same for future studies. © 2021 IEEE.

关键词： Electroencephalography

来源：评论

学校读者我要写书评

暂无评论

Navigating the Unseen: Proposing an IoT-Based Smart Shoe with Obstacle Detection for the Visually Impaired 3

Navigating the Unseen: Proposing an IoT-Based Smart Shoe wit...

引用

3rd International Conference on Technological Advancements in Computational sciences, ICTACS 2023

作者： Arora, Kavita Pathak, Sonal Quraishi, Suhail Javed Singh, Anupam Singh, Malikhan Ather, Danish School of Computer Applications Manav Rachna International Institute of Research and Studies Haryana Faridabad India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India BSA College of Engineering & Technology Mathura India Amity University in Tashkent Uzbekistan

ISBN: (纸本)9798350342338

Navigating daily life remains a substantial challenge for the visually impaired, despite technological advancements. This paper presents an IoT-based Smart Shoe equipped with ultrasonic sensors, accelerometers, and gyroscopes to detect obstacles, providing real-time feedback to enhance navigation. The system communicates with a mobile application to offer auditory or haptic alerts, focusing on reliability, comfort, and affordability. Tested across varied terrains and lighting conditions, the prototype showed strong performance and accuracy. Visually impaired participants in trials reported increased mobility and independence. The paper also explores potential integration with navigation tools such as GPS and ethical considerations like user privacy. 'Navigating the Unseen' showcases the potential of technology in creating a more inclusive future for the visually impaired, emphasizing human centered design. © 2023 IEEE.

关键词： Assistive technology Human-Centered Design IoT-Based Smart Shoe Obstacle Detection Visually Impaired Navigation

来源：评论

学校读者我要写书评

暂无评论

A Comparative Analysis of Machine Learning Models: SVM, Naïve Bayes, Random Forest, and LSTM in Predictive Analytics 3

A Comparative Analysis of Machine Learning Models: SVM, Naï...

引用

3rd International Conference on Technological Advancements in Computational sciences, ICTACS 2023

作者： Pathak, Sonal Quraishi, Suhail Javed Singh, Anupam Singh, Malikhan Arora, Kavita Ather, Danish School of Computer Applications Manav Rachna International Institute of Research and Studies Haryana Faridabad India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India BSA College of Engineering & Technology Mathura India Amity University in Tashkent Uzbekistan

ISBN: (纸本)9798350342338

Machine learning models have recently developed into a crucial predictive analytics tool, driving progress in a variety of industries. Support Vector Machine (SVM), Naive Bayes, Random Forest, and Long Short-Term Memory are four well-known machine learning algorithms that are thoroughly examined and compared in this work (LSTM). By assessing these models on many parameters, including accuracy, precision, recall, and computing efficiency, the study conducts an in-depth investigation. To ensure the validity and relevance of the findings, a broad dataset is used. The comparison analysis highlights the distinct advantages and disadvantages of each model, providing information about how well suited they are for various applications. The analysis explores efficiency and interpretability as well, offering helpful direction for scholars and practitioners. The work also advances the field of predictive analytics by providing actual data on the effectiveness of these algorithms, paving the path for more in-depth and efficient application of machine learning models across a range of fields. © 2023 IEEE.

关键词： Long Short-Term Memory Machine Learning Naive Bayes Random Forest Support Vector Machine

来源：评论

学校读者我要写书评

暂无评论

Churn Analysis for Constructive Employment Using Machine Learning Classifiers 22

Churn Analysis for Constructive Employment Using Machine Lea...

引用

Proceedings of the 4th International Conference on Information Management & Machine Intelligence

作者： Pravek Sharma Tushar Agarwal Aarti Chugh Charu Jain CSE Amity School of Engineering and Technology Amity University Gurugram India CSE Amity school of engineering and technology Amity university India CSE Amity School of Engineering and Technology Amity University Haryana India Computer science Manav Rachna University India

ISBN: (纸本)9781450399937

In today's world, initial and further business development is a challenge. It's a challenge that governs survival or failure of any practical business. The most important resource for any business is Human Resource i.e., its people/employees. Thus, a successful business model is one that ensures a minima of churn rate. As a result, it is important to reduce churn rate and hence increase business growth. The paper talks of a system that helps employers find out factors affecting employee attrition and hence achieve constructive employment. While preparation of proposed system important aspects like data size, type, and number of features are taken into consideration. Models like SVM, Logistic Regression, K Nearest neighbors etc. are implemented and compared against each other to find best fit model in the scope of this research. According to this research random forest is found out to be the best algorithm to investigate employee behavior and finally help in well-timed decision-making *** CONCEPTS • Applied Computing • Enterprise Computing • Business Process Management • Business Intelligence

关键词： Churn

来源：评论

学校读者我要写书评

暂无评论

A Machine Learning Approach to Rock and Mine Classification in Sonar Systems Using Logistic Regression 3

A Machine Learning Approach to Rock and Mine Classification ...

引用

3rd International Conference on Technological Advancements in Computational sciences, ICTACS 2023

作者： Quraishi, Suhail Javed Singh, Malikhan Prasad, Sujit Kumar Arora, Kavita Pathak, Sonal Singh, Anupam School of Computer Applications Manav Rachna International Institute of Research and Studies Haryana Faridabad India BSA College of Engineering & Technology Mathura India Amity University Noida India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350342338

The classification of underwater objects into rocks or mines is a vital task in naval security, marine exploration, and environmental studies. The current work introduces a machine learning-based approach to distinguish between rocks and mines in sonar systems utilizing logistic regression. The novelty of this approach lies in its combination of feature extraction, selection, and a logistic regression model to create an efficient and interpretable binary classifier. The data consists of acoustic signals reflected off underwater objects, which are then processed to identify relevant features. The logistic regression model is trained on a sizeable dataset made up of both hypothetical and actual examples, enabling an objective assessment of performance. The model was refined through a number of trials, which led to an improvement in the model's ability to distinguish between rocks and mines. A comparison of the suggested method with other machine learning methods highlights its effectiveness and simplicity. The study's conclusions have important ramifications for enhancing real-time decision-making in maritime navigation and naval defense. The suggested approach not only improves the precision of separating rocks from mines, but it also paves the way for more investigation into machine learning-based intelligent sonar systems. © 2023 IEEE.

关键词： Logistic Regression Machine Learning Naval Security Rock and Mine Classification Sonar Systems

来源：评论

学校读者我要写书评

暂无评论

Enhancing Security in Cloud Registration with Multi Dimensional Features Fusion 5

Enhancing Security in Cloud Registration with Multi Dimensio...

引用

5th International Conference on Information Management and Machine Intelligence, ICIMMI 2023

作者： Mutha, Rakhi Reddy Gantla, Harish Pandey, Sunil Kr Sayeed Ahmad, Sayed Ahuja, Vandana Bhattacherjee, Abhishek Mamodiya, Udit Dept. of Information Technology Amity University Rajasthan Jaipur India Department of Computer Science and Engineering Vignan Institute of Technology and Science Hyderabad India Department of Information Technology Institute of Technology & Science Uttar Pradesh Ghaziabad India School of Engineering and Computing De Montfort University Dubai United Arab Emirates Dept. of CSE Swami Vivekanand Institute of Engineering and Technology Punjab Ramnagar India Department of Computer Science Engineering Lovely Professional University Punjab India Poornima University Jaipur India

ISBN: (纸本)9798400709418

Data mining uses artificial intelligence and other methods to extract hidden potential information from large data sets, which provides an effective way to obtain valuable knowledge from a large amount of information. Data mining is also ubiquitous in the process of using deep learning to solve point cloud registration tasks. Global feature extraction and rigid body transformation estimation are two critical stages of uncorrelated point cloud registration. Mining the rich information hidden in the two stages is one of the essential tasks of point cloud registration. However, the recently proposed methods tend to ignore low-dimensional local features when extracting global features, resulting in the loss of a large amount of point cloud information, which makes the accuracy of solving transformation parameters in the subsequent rigid body transformation estimation stage unsatisfactory. In this paper, a feature mining network based on multi-dimensional information fusion is proposed, which thoroughly mines the high-dimensional global and low-dimensional local information in the point cloud, and effectively makes up for the lack of local features in the worldwide feature extraction stage of point cloud registration. Dual quaternions are used to estimate pose in the rigid body transformation estimation stage, which can simultaneously represent rotation and translation in a standard frame, providing a compact and accurate representation for pose estimation. Finally, extensive experiments on the ModelNet40 dataset show that compared with the existing state-of-the-art point cloud registration methods without corresponding points, the proposed method can achieve higher accuracy and is more robust to noise. © 2023 ACM.

关键词： Data mining

来源：评论

学校读者我要写书评

暂无评论

A Disaster Prediction Ensemble Classifier Using Artificial Intelligence

A Disaster Prediction Ensemble Classifier Using Artificial I...

引用

Modeling, Simulation & Intelligent Computing (MoSICom), International Conference on

作者： V. Bharathi A. Saranya Garima Shukla Deepika Shekhawat Vanshaj Awasthi C.N.S. Vinoth Kumar Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Mumbai India Department of Computer Science and Engineering Amity University Mumbai India Department of Networking and Communications SRM Institute of Science and TechnologyKattankulathur Chennai India

ISBN: (数字)9798331533311

ISBN: (纸本)9798331533328

Natural and man-made disasters may have severe impacts, but disaster prediction can help lessen such effects. The natural catastrophes that strike India on a regular basis cause immense devastation in terms of human lives and material possessions. Predicting the start and development of disasters in real-time is essential for reducing their effect. For more precise and trustworthy catastrophe predictions, this research presents an AI-powered catastrophe Ensemble B-Classifier network (DE BCN). The DPEC ensemble technique improves forecast accuracy over individual models by integrating various predictive outputs from numerous machine learning models that specialise in distinct kinds of disasters. This is where the data was originally pulled from Kaggle. To finish off the preprocessing, the norma clash filter was used. The next step is to use the optimisation method known as mustard twin swarm to extract characteristics that are connected to the tragedy. With any luck, the calamity Ensemble-B Classifier network will be able to forecast the calamity. Python and the Kaggle database were the environments used to carry out the investigation. According to the results of the simulations, the suggested approach is better than the current standard for predicting the catastrophic state.

关键词： Accuracy Disasters Soft sensors Predictive models Sensor phenomena and characterization Real-time systems Internet of Things Forecasting Socioeconomics Standards

来源：评论

学校读者我要写书评

暂无评论

Keypoint Detection for Identifying Body Joints using TensorFlow 22

Keypoint Detection for Identifying Body Joints using TensorF...

引用

Proceedings of the 4th International Conference on Information Management & Machine Intelligence

作者： Anuj Grover Deepak Arora Anant Grover Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Uttar Pradesh Lucknow Campus India

ISBN: (纸本)9781450399937

Keypoint Detection for the Body Joints is a process of finding and detecting all the important body joints of a human being. It takes an RGB image as input gives a list of keypoints as output. It can be applied for single-person body joints detection or multi-person body joints detection. The various architectures for detecting the human body joints can be compared using the Percentage of Detected Joints (PDJ), Object Keypoint Similarity (OKS) and mean Average Precision(mAP) evaluation metrics. The state-of-the-art body joint detection architectures like DeepPose, Higher Resolution Network and OpenPose architectures were studied and compared against each other. These architectures were evaluated on the COCO dataset for the 17 key body joints using the OKS and mAP evaluation metrics. The evaluation results on all the body joints were compared to each other for each architecture. The architectures were implemented using opensource implementations of the PyTorch and TensorFlow libraries. The HRNet architecture was the fastest and most accurate of all the architectures.

关键词： Body Joints Detection

来源：评论

学校读者我要写书评

暂无评论

Leveraging XGBoost for Predictive Analytics in Healthcare: Enhancing Disease Diagnosis 7

Leveraging XGBoost for Predictive Analytics in Healthcare: E...

引用

7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Shrivastava, Anurag Kotiyal, Arnav Habelalmateen, Mohammed I. Rana, Ajay Devi, V.S Anusuya Rao, Bolleddu Devananda Bansal, Saloni Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamilnadu Chennai India Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India The Islamic university College of technical engineering Department of computers Techniques engineering Najaf Iraq Amity University Uttar Pradesh Greater Noida India New Horizon College of Engineering Department of Applied Sciences Bangalore India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Gla University Departyment of Computer Engineering and Applications Uttar Pradesh Mathura India

ISBN: (纸本)9798350350067

In this study, we look at how XGBoost (Extreme Gradient Boosting) may be used in healthcare predictive analytics to improve the speed and precision of illness detection. Thanks to its built-in regularisation algorithms that avoid overfitting and its capacity to handle big datasets, XGBoost has proved useful in a broad variety of prediction applications. It is a scalable and high-performance machine learning algorithm. In order to improve patient outcomes, healthcare providers must ensure that patients are diagnosed with chronic and life-threatening conditions including cancer, heart disease, and diabetes as soon as possible. To test how well XGBoost performs in comparison to more conventional machine learning techniques, the research makes use of healthcare datasets collected from the actual world. The effectiveness of the model in predicting illness outcomes is evaluated using critical metrics including accuracy, precision, recall, and F1-score. Based on the findings, XGBoost is the best classifier for real-time healthcare applications because to its quicker processing efficiency and greater predictive ability. Additionally, the article delves into the model's interpretability and how it may be integrated into clinical decision support systems. By demonstrating how XGBoost may improve diagnostic accuracy and bolster individualised treatment plans, this study adds to the expanding area of AI-driven healthcare. © 2024 IEEE.

关键词： Predictive analytics

来源：评论

学校读者我要写书评

暂无评论

Brain computer Interface Imagination based Thought Analyser Framwork to Generate and Classify Real Time Image Retrieval System for Space Images in Chandrayaan 3 Lunar Misson 10

Brain Computer Interface Imagination based Thought Analyser ...

引用

10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and computer engineering, UPCON 2023

作者： Sinha, Anurag Suman Alkhayyat, Ahmed Kumar, Biresh Kumari, Anita Kumar, Uttam School of Computing and Information Sciences Ignou New Delhi India Iitm Department of Computer Science India College of Technical Engineering The Islamic University Najaf Iraq Amity Institute of Information Technology Amity University Jharkhand Ranchi India Rkdf University Ranchi Department of English Literature India Amity School of Business Amity University Jharkhand India

ISBN: (纸本)9798350382471

We provide a ground-breaking architecture for the Chandrayaan 3 Lunar Mission that seamlessly combines Brain-computer Interface (BCI) technology with sophisticated real-time picture retrieval and classification algorithms, encouraging a paradigm change in lunar exploration. By giving mission personnel the ability to express their picture search criteria verbally rather than through manual search queries, this ground-breaking framework makes the most of the creative capacity of the human mind. This technology converts cognitive intent into executable commands by combining BCI with state-of-the-art machine learning methods, speeding up picture retrieval and reducing operator cognitive load. In parallel, a cutting-edge picture retrieval and classification system powered by computer vision and machine learning sorts through the massive collection of lunar images gathered by Chandrayaan 3 in order to find specific objects. This technique effectively classifies photos according to geological qualities, mineral composition, and topographical features, ensuring the quick identification of scientifically important data. Our system streamlines operations, decreases human error, and improves data quality by automating these procedures. As a result, lunar research is elevated to new levels, allowing for ground-breaking discoveries and a deeper knowledge of the Moon's mysterious geography and past. © 2023 IEEE.

关键词： Brain-computer Interface Chandrayaan 3 Lunar Mission Image Retrieval Imagination Thought Analyzer

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126页 << < 49 50 51 52 53 54 55 56 57 58 > >>

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：