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

1,941 篇 会议
1,768 篇 期刊文献
36 册 图书

馆藏范围

3,745 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

2,102 篇 工学
- 1,401 篇 计算机科学与技术...
- 1,072 篇 软件工程
- 372 篇 信息与通信工程
- 281 篇 控制科学与工程
- 251 篇 电气工程
- 238 篇 光学工程
- 232 篇 电子科学与技术（可...
- 195 篇 生物工程
- 167 篇 生物医学工程（可授...
- 117 篇 材料科学与工程（可...
- 115 篇 仪器科学与技术
- 108 篇 化学工程与技术
- 93 篇 机械工程
- 86 篇 动力工程及工程热...
- 73 篇 核科学与技术
1,676 篇 理学
- 827 篇 物理学
- 674 篇 数学
- 245 篇 生物学
- 238 篇 统计学（可授理学、...
- 176 篇 地球物理学
- 170 篇 化学
- 118 篇 系统科学
469 篇 管理学
- 260 篇 管理科学与工程(可...
- 223 篇 图书情报与档案管...
- 130 篇 工商管理
205 篇 医学
- 172 篇 临床医学
- 144 篇 基础医学(可授医学...
- 87 篇 公共卫生与预防医...
- 83 篇 药学(可授医学、理...
93 篇 法学
- 84 篇 社会学
57 篇 农学
45 篇 经济学
34 篇 教育学
13 篇 文学
6 篇 艺术学
4 篇 哲学
3 篇 军事学
1 篇 历史学

主题

142 篇 computer science
133 篇 accuracy
107 篇 deep learning
89 篇 educational inst...
80 篇 computational mo...
80 篇 machine learning
79 篇 feature extracti...
69 篇 predictive model...
66 篇 training
56 篇 artificial intel...
54 篇 support vector m...
52 篇 real-time system...
51 篇 convolutional ne...
45 篇 internet of thin...
44 篇 data models
43 篇 optimization
39 篇 data mining
39 篇 machine learning...
38 篇 cloud computing
36 篇 semantics

机构

100 篇 university of sc...
89 篇 tianfu cosmic ra...
85 篇 hebei normal uni...
83 篇 faculty of scien...
83 篇 department for p...
83 篇 department of ph...
83 篇 state key labora...
80 篇 department of ph...
80 篇 west university ...
79 篇 department of ph...
78 篇 department of mo...
78 篇 department of ph...
78 篇 department of ph...
78 篇 yunnan observato...
77 篇 department of ph...
77 篇 research center ...
76 篇 university of ch...
75 篇 infn e laborator...
71 篇 kirchhoff-instit...
69 篇 centre de calcul...

作者

100 篇 bastieri d.
72 篇 fang j.
72 篇 feng c.f.
70 篇 axikegu
69 篇 jiang k.
63 篇 m. klein
62 篇 c. alexa
62 篇 j. m. izen
62 篇 g. bella
62 篇 j. strandberg
62 篇 d. calvet
62 篇 c. amelung
62 篇 n. orlando
62 篇 y. tayalati
62 篇 g. spigo
62 篇 v. chiarella
62 篇 f. siegert
61 篇 a. dimitrievska
61 篇 f. deliot
61 篇 r. ströhmer

语言

3,550 篇 英文
177 篇 其他
19 篇 中文
1 篇 德文
1 篇 日文

检索条件"机构=Department of Computer Science and Institute for Advanced Computing Studies"

共 3745 条记录，以下是141-150 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：

Mukhtasir-Khail-Net: An Ultra-Efficient Convolutional Neural Network for Sports Activity Recognition with Wearable Inertial Sensors 4

Mukhtasir-Khail-Net: An Ultra-Efficient Convolutional Neural...

引用

4th International Conference on Digital Futures and Transformative Technologies, ICoDT2 2024

作者： Imran, Hamza Ali Muhammad, Shaida Wazir, Saad Ikram, Ataul Aziz Arshad, Obaidullah National University of Sciences & Technology Department of Computing Islamabad Pakistan Korea Advanced Institute of Science & Technology School of Computing Daejeon Korea Republic of National University of Computer & Emerging Sciences School of Engineering Islamabad Pakistan

ISBN: (纸本)9798350391862

The current prevalent approach of the Internet of Health and Medical Things entails proactively preventing disease onset through routine monitoring of individuals' physical activities, making Human Activity Recognition (HAR) and Behaviour Analysis an important field of study. Sports Activity Recognition (SAcR) is a subset of HAR that focuses on identifying athletic movements. computer Vision, Environmental Sensors, and Wearable Sensors are the three main methods for monitoring such activities. When the merits and limitations of these methods are considered, Wearable Sensors emerge as the most practical option. This study introduces the Mukhtasir-Khail-Net, a model with only 651 parameters that efficiently uses addition layers. When applied to six sports activities, the model achieves an impressive average accuracy of 98.865% using inertial sensor data generated by wearable sensors. For benchmark purposes, the presented model is also evaluated on the WISDM 11 dataset which is a Human Activity Recognition dataset from Fordham University and has achieved an accuracy of 94.24%. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Sparse Autoencoder (SAE) based Image Classification with Enhanced Security for College Attendance System

Sparse Autoencoder (SAE) based Image Classification with Enh...

引用

Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： Sangeetha Karunakaran Akila Department of Computer Science School of Computing Sciences VELS Institute of Science Technology and Advanced Studies (VISTAS) Chennai India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

An innovative attendance system that uses image fusion techniques to combine facial and iris images to improve recognition accuracy. Using a sparse autoencoder (SAE), we efficiently combine these two distinct biometric methods to create a comprehensive fused image that preserves the important features of both. This fusion solves the disadvantages of traditional recognition methods, such as sensitivity to illumination changes and face expressions, while leveraging the consistent uniqueness of iris patterns. The merged images are then processed by a Convolutional Neural Network (CNN), which excels at extracting hierarchical features for accurate classification. By combining facial and iris images, the system benefits from the complementary strengths of the two modalities. The SAE and CNN are produced with an accuracy of 92.74 % of fusion images. The proposed system not only improves overall authentication accuracy but also improves security by reducing the risk of unauthorized access through dual biometric verification

关键词： Accuracy Image recognition Face recognition Autoencoders Lighting Feature extraction Convolutional neural networks Security Image fusion Iris recognition

来源：评论

学校读者我要写书评

暂无评论

A Research on the Perspective of Exploring Restricted Decentralized Blockchain by Applying PoFE: Proof of Familiarity and Existence to Reinforce Multiple Domains 3

A Research on the Perspective of Exploring Restricted Decent...

引用

3rd International Conference on Smart Electronics and Communication, ICOSEC 2022

作者： Sheela, K. Priya, C. Vels Institute of Science Technology and Advanced Studies Department of Computer Science Chennai India Vels Institute of Science Technology and Advanced Studies Department of Information Technology Chennai India

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

Incorporation of Blockchain technology into our real-life scenarios serves in many aspects to improvise their services. Blockchain is a node-to-node decentralized distributed ledger system which provides clarity and unchangeable records of any digital assets while avoiding the intervention of a middleman. It is a new and revolutionary technology that is gaining a lot of attention because of its ability to eliminate threats and frauds on a large scale. The complete decentralized nature of blockchain has certain drawbacks wherein a restricted decentralized network can be accomplished in order to direct the overall process. The centralized server will be maintained along with its basic distributed nature with an authorized controller or miner. Every transaction will be stored in all the nodes and one node will be considered as a chief node wherein certain authenticating powers are provided to monitor and direct the operation in a successful manner. This process eliminates the involvement of multiple parties called miners to mine the block which reduces certain complications like time consumption for choosing miners, finding a fault node in case of breakdown and so on. Certain procedures are implemented to prove the presence of the records along with a process which encourages synergistic decision making for users using the Proof of Familiarity and Existence algorithm. Also, an approach of utilizing the nodes of any sector will be considered for smooth maintenance of an entire data in a blockchain platform. An approach of collaborating Proof of Familiarity and Proof of Existence will be carried out to enrich the decision-making system in both private and public sectors. © 2022 IEEE.

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

Innovative Solutions for Sustainable IoT in Smart City Infrastructure

Innovative Solutions for Sustainable IoT in Smart City Infra...

引用

Smart Electronics and Communication (ICOSEC), International Conference on

作者： S. Arul P. Kavitha S. Kamalakkannan Department of Computer Science School of Computing Sciences Vels Institute of Science Technology & Advanced Studies (VISTAS) Pallavaram Chennai India Department of Computer Applications School of Computing Sciences Vels Institute of Science Technology & Advanced Studies (VISTAS) Pallavaram Chennai India Department of Information Technology School of Computing Sciences Vels Institute of Science Technology & Advanced Studies (VISTAS) Pallavaram Chennai India

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

Smart cities are being created all over the world to enhance the safety and quality of life for their residents via the use of technology. Video surveillance, which includes placing cameras at key locations across the city to keep an eye on public areas and give law enforcement and other local authorities real-time access to recordings, is a crucial part of the architecture of smart cities. Cuttingedge technologies like deep learning, blockchain, edge computing, and cloud computing are used in modern video surveillance systems. This paper offers a comprehensive review of video surveillance systems for smart cities, examining both their benefits and drawbacks. In order to improve inhabitants’ safety, security, and overall well-being, this article aims to show the benefits of video surveillance systems in smart cities and offers suggestions for their application.

关键词： Smart cities Reviews Video surveillance Cameras Robustness Real-time systems Safety Blockchains Recording Research and development

来源：评论

学校读者我要写书评

暂无评论

Building NIDS for IoT Network using Ensemble Approach 7

Building NIDS for IoT Network using Ensemble Approach

引用

7th International Conference on Communication and Electronics Systems, ICCES 2022

作者： Sivasankari, N. Kamalakkannan, S. Vels Institute of Science Technology and Advanced Studies Department of Computer Science Chennai India Vels Institute of Science Technology and Advanced Studies Department of Information Technology Chennai India

ISBN: (数字)9781665496346

ISBN: (纸本)9781665496346

The term 'IoT' is a trendy word in current era due to its usage in day to day live of human beings. Its applications are widespread across many areas such as home, agriculture, health care, transportation, etc., Due to the vast development of IoT services, cyber-attacks against IoT application and devices also increased. Thus, Security and Privacy are two major concerns in IoT environment. Building a Network Intrusion Detection System (NIDS) is one of the ways for securing the IoT system against cyber-attacks. So, an Ensemble based Network Intrusion Detection System using machine learning algorithms has been proposed to detect botnet attacks against HTTP and DNS protocol in network traffic. The goal of this proposed work is to higher the detection rate and decrementing the false positive rate. UNSW_NBl5 dataset was used for evaluating the performance of the proposed NDS. In this work, an Io T environment architecture using fog node proposed and the performance of various classifiers included in the framework compared with the ensemble methodbased on detection rate and false positive rate. © 2022 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Towards Sustainable Waste Management: Exploring Machine Learning and Deep Learning Solutions for Biodegradable and Non-Biodegradable Waste Identification

Towards Sustainable Waste Management: Exploring Machine Lear...

引用

Pervasive computing and Social Networking (ICPCSN), International Conference on

作者： S. Sasi Rekha P. Kavitha S. Kamalakkannan Department of Computer Science School of Computing Sciences Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai Pallavaram India Department of Computer Applications School of Computing Sciences Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai Pallavaram India Department of Information Technology School of Computing Sciences Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai Pallavaram India

ISBN: (数字)9798350386349

ISBN: (纸本)9798350386356

This study examines 20 research articles focused on AI-based waste management techniques, including machine learning, deep learning, and IoT sensors. Researchers aim to develop sustainable solutions for waste sorting and management. The articles highlight the significant role of deep learning and computer vision in waste classification, with a growing interest in recent years, particularly in 2022. These advanced technologies offer promising avenues for enhancing waste sorting accuracy, reducing manual labor reliance, and promoting recycling efforts. Moreover, the incorporation of IoT sensors plays a vital role in continuously monitoring waste levels and environmental parameters, thereby optimizing the entire waste collection and management workflow. These outcomes underscore the escalating adoption of sophisticated AI methodologies in waste management, suggesting a paradigm shift towards enhanced and environmentally conscious waste management practices. This research provides valuable insights for policymakers, waste management practitioners, and researchers seeking to address the challenges of waste management while promoting environmental sustainability.

关键词： Waste management Deep learning Pervasive computing Social networking (online) Green products Manuals Sensors

来源：评论

学校读者我要写书评

暂无评论

Analysis of Cardiac Disease based on AI Prediction Techniques using data analytics approach

Analysis of Cardiac Disease based on AI Prediction Technique...

引用

Soft computing for Security Applications (ICSCSA), International Conference on

作者： R. Sivanathbabu S. Kamalakkannan Department of Computer Science School of Computing Sciences Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai India Department of Information Technology School of Computing Sciences Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai India

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515737

Patients who have an increased likelihood of coronary heart disease can reduce their consequences by changing their lifestyle, with the support of early diagnosis. Healthcare expenses are rising above, both company budgets and the average cost of medical treatment nationally, due to asymptomatic conditions like cardiovascular disorders. Early detection and treatment of these illnesses are essential. Utilizing the data that is accessible in healthcare systems has been significantly impacted by recent technological advancements. As part of this, patients' Heart Disease (HD) may be predicted using the massive amount of data. However, due to concerns with prediction and diagnosis performance and accuracy, projecting HD has emerged as one of the main obstacles facing the healthcare sector. The main challenges were to apply homogeneous data mining techniques to define the most significant causes of heart disease and to accurately predict the overall risk. Using Data Analytics (DA) to speed up the prediction process is one way to make use of massive amounts of health care data. Selecting the optimal analytical methods, however, is the most crucial step as it will impact the final result of the diagnostic and forecasting. In order to assess the effectiveness of the methods for HD forecasting, this study intends to implement DA approach in collecting of databases. In this research work, Best First Search (BFS) has been used to identify the most important attributes for HD prediction. This study employs a range of ML approaches based on AI to predict HD. The Random Forest (RF) method, which uses specific characteristics, has the highest accuracy rate (98.63%) in this experiment. The BFS extracted 14 relevant features and then compared them using AI approaches. Based on the analysis, feature selection has enhanced the DA technique by improving the efficiency and precision of the classification model's prediction.

关键词： Heart Radio frequency Analytical models Accuracy Data analysis Medical services Predictive models Feature extraction Forecasting Diseases

来源：评论

学校读者我要写书评

暂无评论

A Novel Method to Predict Chronic Kidney Disease using Optimized Deep Learning Algorithm 21

A Novel Method to Predict Chronic Kidney Disease using Optim...

引用

21st International Learning and Technology Conference, L and T 2024

作者： Preethi, I. Dharmarajan, K. Sharma, Bhisham Chowdhury, Subrata Dhaou, Imed Ben Vistas School of Computing Sciences Chennai India Vistas Department of Information Technology Chennai India Chitkara University Institute of Engineering and Technology Chitkara University Department of Computer Science and Engineering Punjab India Sreenivasa Institute of Technology and Management Studies Department of Computer Science and Engineering Andhra Pradesh Chittoor India Dar Al-Hekma University Hekma School of Engineering Computing Design Department of Computer Science Jeddah Saudi Arabia

ISBN: (纸本)9798350393569

Chronic Kidney Disease (CKD), a significant global health issue and has a major impact on a vast number of people. In the early stages, it is impossible to identify the disease's symptoms. Very few people are aware of this illness and are able to foresee the symptoms earlier. The disorder is considered as potential risk factor. As a result, a potent deep learning framework is used in this paper to reduce the risk of acquiring these diseases as a way to prevent them. The machine learning algorithms used in early disease prediction, has been found to be computationally expensive, frequently overfit, and underperform in terms of accuracy since they must examine the large amount of clinical data until the model converges. Therefore, in this paper three novel work has been proposed an efficient novel hybrid feature selection strategy RFITLO algorithm is used to find the optimal features that gives the major contribution is classifying the CKD disease. Then two proposed classification algorithms namely Enhanced Multi-Layer Perceptron (HW-MLP) and Optimized Multi-Layer Perceptron (PKD-OMLP) are used in prediction model to capture the complex patterns and optimize the learning algorithm to predict the CKD at prior stage from the data gathered in Kaggle, Real Time and UCI Machine Learning Repository Dataset. In order to measure the classifications of disease, performance measures including accuracy, precision and recall are analyzed. The experimental findings show that the PKD-OMLP strategy produces better results than the proposed HW-MLP and other conventional approaches like Support Vector Machine (SVM), Linear Regression (LR) and Multi-Layer Perceptron (MLP). Among the preceding four models PKD-OMLP renders the best outcome as per its performance level producing a high accuracy of 94.89% on testing Real Time (RT) CKD dataset comparatively with other datasets such as Kaggle and UCI repository. Therefore, these proposed algorithms can support clinicians to enable secured an

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

Diabetes Prediction using Enhanced and Optimized DRL-CNN Approaches 21

Diabetes Prediction using Enhanced and Optimized DRL-CNN App...

引用

21st International Learning and Technology Conference, L and T 2024

作者： Usha Nandhini, A. Dharmarajan, K. Sharma, Bhisham Chowdhury, Subrata Ben Dhaou, Imed Vistas School of Computing Chennai India Vistas Department of Information Technology Chennai India Chitkara University Institute of Engineering and Technology Chitkara University Department of Computer Science and Engineering Punjab India Sreenivasa Institute of Technology and Management Studies Department of Computer Science and Engineering Andra Pradesh Chittoor India Dar Al-Hekma University Hekma School of Engineering Computing and Design Department of Computer Science Jeddah Saudi Arabia

ISBN: (纸本)9798350393569

This study develops an advanced automated prediction system using Machine Learning (ML) techniques to identify diabetes early. The research employs the WBSMOTE method for data preprocessing, addresses class imbalances with the WB-SMOTE approach, and introduces two novel classification methods, DRL-CNN and DRL-OCNN. Reliable datasets from sources like The Data World repository, PIMA Indian Diabetes (PIDD) dataset, and Real Time (RT) Diabetes Dataset are used, with an emphasis on data consistency and feature relevance. If we examine these datasets, it contains general attributes and no internal specific attributes related to diabetes is used. So, prediction results will be what type of diabetes we won't get any specific characteristics and risk factors. Evaluation is based on Accuracy, Precision, and Recall criteria. The DRL-OCNN model, integrating SGD optimization and Delta Rule Learning, outperforms other ML models with a accuracy of 96%, precision of 0.93 and recall of 0.91, demonstrating its potential for accurate early diabetes identification and classification. © 2024 IEEE.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Deep Generative Decision Neural Network Approach for Effective Early Prediction of Meninges

Deep Generative Decision Neural Network Approach for Effecti...

引用

Sustainable computing and Smart Systems (ICSCSS), International Conference on

作者： A. Shabana P. Kavitha S. Kamalakkannan Department of Computer Science School of Computing Sciences Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai India Department of Information Technology School of Computing Sciences Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai India

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558

Meningitis is a contagious disease that leads to neurocognitive impairment. It is caused by inflammation in the protective membranes surrounding the brain and spinal cord and can be caused by various pathogens, such as viruses and bacteria. It is necessary to make predictions and classify meninges diseases. The conventional techniques for forecasting meninges are time-consuming and prone to errors, resulting in low accuracy rates with high false classifications. To tackle these problems, this study presents a Deep Learning (DL) method to aid in predicting meningitis. The novel introduces a Deep Generative Decision Neural Network (DGDN2) algorithm for predicting meninges. The Box-Plot Standardization (BPS) algorithm eliminates null and missing values to prepare the dataset. Next, the Mutual Gain Genetic Algorithm (MG2A) is utilized to select essential features of meninges diseases. Finally, the DGDN2 algorithm is used to predict meninges diseases efficiently. According to experimental results, the proposed model achieves higher accuracy, precision, recall, and F-measure than previous methods.

关键词： Pathogens Accuracy Spinal cord Computational modeling Standardization Medical services Predictive models

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375页 << < 11 12 13 14 15 16 17 18 19 20 > >>

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：