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

30,935 篇 会议
19,511 篇 期刊文献
460 册 图书

馆藏范围

50,906 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

30,286 篇 工学
- 19,629 篇 计算机科学与技术...
- 16,057 篇 软件工程
- 7,521 篇 信息与通信工程
- 5,293 篇 控制科学与工程
- 4,361 篇 电气工程
- 4,051 篇 电子科学与技术（可...
- 3,682 篇 生物工程
- 2,998 篇 光学工程
- 2,931 篇 生物医学工程（可授...
- 2,216 篇 机械工程
- 2,087 篇 化学工程与技术
- 1,843 篇 仪器科学与技术
- 1,736 篇 动力工程及工程热...
- 1,554 篇 材料科学与工程（可...
- 1,514 篇 网络空间安全
- 1,252 篇 安全科学与工程
18,430 篇 理学
- 8,241 篇 数学
- 6,864 篇 物理学
- 4,280 篇 生物学
- 2,904 篇 统计学（可授理学、...
- 2,363 篇 化学
- 1,816 篇 系统科学
7,833 篇 管理学
- 5,117 篇 管理科学与工程(可...
- 3,073 篇 图书情报与档案管...
- 2,232 篇 工商管理
3,061 篇 医学
- 2,472 篇 临床医学
- 2,215 篇 基础医学(可授医学...
- 1,359 篇 公共卫生与预防医...
- 1,313 篇 药学(可授医学、理...
1,443 篇 法学
- 1,210 篇 社会学
897 篇 经济学
808 篇 农学
434 篇 教育学
219 篇 文学
116 篇 军事学
65 篇 艺术学
4 篇 历史学
3 篇 哲学

主题

1,958 篇 accuracy
1,577 篇 deep learning
1,304 篇 feature extracti...
1,190 篇 training
1,099 篇 machine learning
1,072 篇 computational mo...
912 篇 convolutional ne...
883 篇 real-time system...
840 篇 internet of thin...
794 篇 predictive model...
745 篇 computer science
711 篇 support vector m...
628 篇 optimization
623 篇 data models
597 篇 artificial intel...
463 篇 security
459 篇 neural networks
430 篇 reliability
388 篇 semantics
355 篇 scalability

机构

1,100 篇 cavendish labora...
1,032 篇 department of ph...
989 篇 physikalisches i...
976 篇 oliver lodge lab...
774 篇 university of ch...
605 篇 fakultät physik ...
562 篇 school of physic...
549 篇 chitkara univers...
543 篇 department of ph...
542 篇 horia hulubei na...
535 篇 school of physic...
528 篇 school of comput...
526 篇 iccub universita...
524 篇 imperial college...
524 篇 nikhef national ...
516 篇 h.h. wills physi...
512 篇 stfc rutherford ...
511 篇 school of physic...
504 篇 physik-institut ...
497 篇 henryk niewodnic...

作者

277 篇 barter w.
268 篇 a. gomes
259 篇 beiter a.
249 篇 c. alexa
249 篇 c. amelung
247 篇 j. m. izen
247 篇 g. bella
247 篇 j. strandberg
247 篇 d. calvet
242 篇 g. spigo
242 篇 bellee v.
235 篇 brundu d.
234 篇 f. siegert
234 篇 bowcock t.j.v.
234 篇 s. chen
231 篇 blake t.
224 篇 y. tayalati
223 篇 betancourt c.
220 篇 amato s.
219 篇 borsato m.

语言

46,192 篇 英文
3,967 篇 其他
699 篇 中文
98 篇 日文
8 篇 德文
8 篇 法文
6 篇 朝鲜文
3 篇 斯洛文尼亚文
2 篇 荷兰文

检索条件"机构=School of Computer Science and Engineering Vellore Institute of Technology"

共 50906 条记录，以下是91-100 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：

Enhanced Malware Image Classification through Ensemble model 3

Enhanced Malware Image Classification through Ensemble model

引用

3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Kotha, Eshwar Palivela, Lakshmi Harika Begum, Afruza Vellore Institute of Technology School of Computer Science and Engineering Chennai India Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350375190

Classification of malwares and viruses is a very important work in the cyber security field to protect the computers and systems from threats and attacks. In this paper, we proposed a novel approach for the classification of the malwares by combining 2 models EfficientNet B0 and EffiCBNet models. Here we have used Malimg dataset which is available in Kaggle and it contains around 9,339 malware images and these malwares are from 25 different families, for evaluating the models. EffcientNetB0 is a Convolution Neural Network (CNN) model, which showed great and high results on various image classification works. We used transfer learning to fine tune the EfficientNet model;whereas the proposed EffiCBNet model is a deep learning model which contains of convolution layers and batch normalization layers. The results have shown that the proposed model has given the highest accuracy of 99.70 on the dataset we have used, exceeding all other models. The proposed model is the combination of EfficientNet and EffiCBNet, which showed great results for the classification. The proposed model can also be used in real time applications for detecting malware attacks and threats © 2024 IEEE.

关键词： Image classification

来源：评论

学校读者我要写书评

暂无评论

Lane Detection and Traffic Sign Detection for Autonomous Driving Systems 2

Lane Detection and Traffic Sign Detection for Autonomous Dri...

引用

2nd International Conference on computer Vision and Internet of Things, ICCVIoT 2024

作者： Biswas, Aishi Chowdhury, Anushka Roy Selvanambi, Ramani School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9781837242672

In the realm of autonomous driving, this study focuses on two critical tasks for scene understanding: lane detection and traffic sign detection. These tasks are essential to ensuring safe and reliable navigation in self-driving systems. A combination of traditional computer vision techniques and modern deep learning methods are used to achieve accurate detection and analysis of lanes and traffic signs in real-world driving scenarios. For lane detection, we utilize edge detection and the Hough Transform, while for traffic sign detection, we apply machine learning algorithms to recognize and classify various signs. This hybrid approach demonstrates the potential of integrating classical computer vision algorithms with deep learning models to enhance the perception capabilities of autonomous vehicles. The model achieves a balanced performance, with accuracy metrics ranging from 80-90% across both tasks, indicating a robust ability to detect lanes and signs effectively under varied conditions. This work serves as a foundation for developing more advanced scene understanding systems in autonomous driving technology. In the future, we aim to enhance the model's adaptability through continuous learning and incorporating additional sensory data, such as LiDAR and radar. Additionally, expanding the dataset and testing the models in diverse environmental conditions, including different lighting and weather scenarios, will further improve the robustness and reliability. © The Institution of engineering & technology 2024.

关键词： Hough transforms

来源：评论

学校读者我要写书评

暂无评论

LTC-AE: Liquid Time Constant Autoencoders for Time Series Anomaly Detection

LTC-AE: Liquid Time Constant Autoencoders for Time Series An...

引用

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

作者： Sailesh, Riya Srisailam Madhu Shree, K. Preethan, J. Sivaprakash, S. School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350382693

This research introduces a novel class of autoencoders, termed Liquid Time-Constant Autoencoder (LTC-AEs), for anomaly detection in time series data. Anomaly detection in real-time data streams is an extremely crucial task for various domains like healthcare, finance, and cyber security but it comes with a few challenges including missing values, class imbalances and noise. Recent research has proved optimal in addressing these challenges using sequential Autoencoders. Our proposed algorithm having the ability to capture temporal dynamics, learn from irregular sequences, improve interpretability and facilitate scalability, propose to help resolve some problems that delimits the potential of existing Autoencoder models. The experiments conducted in the study are utilized to emphasize the potential of our proposed model in handling multifaceted anomalies. The workflow follows semi-supervised learning to leverage the strengths of both supervised and unsupervised approaches. The utility of LTC-AEs are evaluated on various datasets and performance comparison is done with respect to architectures like Long Short-Term Memory (LSTM-AE), Gated Recurrent Unit (GRU-AE) and Temporal Convolutional Network (TCN-AE) Autoencoders. © 2024 IEEE.

关键词： Network security

来源：评论

学校读者我要写书评

暂无评论

InceptionV3 infused Neural Network for Brain Tumor Detection 5

InceptionV3 infused Neural Network for Brain Tumor Detection

引用

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

作者： Bharargava, Kunal Nanda, Harshil Basha, Niha Kamal Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (纸本)9798331504403

Automated identification and detection of brain tumors using Magnetic Resonance Imaging (MRI) is difficult, time-consuming and laborious because there exists many overlooked, misread, & deceptive lesions (similar to tumor) present in the brain tumor image scans. This paper evaluates the efficiency of the InceptionV3 infused convolutional neural network for brain tumor detection. Utilizing a comprehensive dataset Br35H with MRI brain tumor images, initially the model preprocessed and augmented the input to enhance training robustness. The novelty of our approach lies in the integration of advanced data augmentation techniques and a customized training pipeline. The proposed InceptionV3 infused neural network model achieved a validation accuracy of 98.50%, outperforming other models such as EfficientNetB4, VGG19, ResNet50, and MobileNetV2 in terms of key metrics. Our findings demonstrate that the proposed InceptionV3 infused neural network is highly effective and computationally efficient regarding brain tumor detection & classification, providing one reliable instrument for medical image analysis. © 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

Fostering Inclusive Communication: A Tool Integrating Machine Translation, NLP, and Audio-to-Sign-Language Conversion for the Deaf 2

Fostering Inclusive Communication: A Tool Integrating Machin...

引用

2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Sen, Madhura Rajkumar, R. Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (纸本)9798350306415

Approximately 70 million individuals worldwide grapple with deafness or muteness, presenting challenges in communication. This article presents a novel solution: an audio-to-sign-language converter. Sign language, a visual medium for speech, is pivotal in bridging this communication gap. This Python-based converter interprets user input, transforming it into gestures, facilitating interaction between the hearing and the deaf. It also serves as a valuable tool for learning sign language. Live voice input, captured through a microphone, undergoes advanced Natural Language Processing techniques to be converted into text. Recognized phrases trigger relevant gesture videos or display corresponding letters. The application also features a termination command, "goodbye."With potential applications in web and mobile platforms, two-way interaction, and video integration, this converter holds promise in enhancing accessibility for the deaf community, fostering inclusivity and effective communication. This paper is a significant step towards facilitating meaningful connections between individuals with disabilities and their non-disabled peers. © 2024 IEEE.

关键词： accessibility facial expressions gestures GIF ISL vocabulary natural language processing Sign language speech recognition

来源：评论

学校读者我要写书评

暂无评论

Reverse engineering of Android Malware Classification Using Semi-Supervised Learning 10

Reverse Engineering of Android Malware Classification Using ...

引用

10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Sujaykumar Reddy, M. Thota, Phanitha Sri Vellore Institute of Technology School of Computer Science Engineering Vellore India

ISBN: (纸本)9798350384369

The escalating prevalence of Android malware poses a substantial threat to online security, necessitating innovative approaches to combat this growing menace. Identifying novel and intricate malware variants remains a formidable challenge in the realm of software security. Existing malware detection methods primarily relying on static features often prove inadequate in countering sophisticated modern malware. While dynamic detection methods hold promise, they frequently fail to leverage the full spectrum of malware characteristics. This paper proposes a novel malware classification framework utilizing semi-supervised learning methods utilizing both unsupervised and supervised learning algorithms. We use the CCCS-CIC-AndMal-2020 dataset encompassing 13 prominent malware categories and 191 eminent malware families. Our results suggested that PCA+XGBoost accomplished by reducing the original dimensionality from 142 features to a more compact set of 48 features. The utilization of PCA in conjunction with the XGBoost model not only enhances the computational efficiency but also preserves the essential information required for classification. © 2024 IEEE.

关键词： Self-supervised learning

来源：评论

学校读者我要写书评

暂无评论

Privacy-Protected Data Mining and CNN for E-Healthcare Systems 5

Privacy-Protected Data Mining and CNN for E-Healthcare Syste...

引用

5th IEEE Global Conference for Advancement in technology, GCAT 2024

作者： Nevatia, Isha Chaudhary, Vraj Pandian, M. Thurai Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (纸本)9798350376685

The E-Health care system is crucial in modern health care. These systems are designed to secure medical data repositories. The main goal of this study is to ensure both security and privacy by enhancing fully homomorphic data collection. It also aims to improve classification accuracy in disease detection. The existing method of Privacy-Preserving Data Mining (PPDM) is utilized to maintain privacy in healthcare, and the new method using a Convolutional Neural Network (CNN) algorithm is employed to identify diseases more accurately. This new approach enhances performance by achieving higher accuracy, recall, and reducing time complexity. © 2024 IEEE.

关键词： Differential privacy

来源：评论

学校读者我要写书评

暂无评论

A Novel RoPE-Enhanced Informer Approach for Accurate Remaining Useful Life Prediction of Lithium-Ion Batteries

A Novel RoPE-Enhanced Informer Approach for Accurate Remaini...

引用

2024 IEEE Conference on engineering Informatics, ICEI 2024

作者： Nair, Aayush Bhatnagar, Shashwat Srinivasan, Kathiravan Manoov, R. Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (纸本)9798331505776

Lithium-ion batteries are integral to various systems, including electric vehicles and warehouses, where estimating their operational lifespan is critical. This lifespan, known as Remaining Useful Life (RUL), helps in optimizing performance and maintenance. Accurate RUL prediction helps avoid unexpected breakdowns and lowers maintenance costs, making these systems more reliable. This paper introduces a novel approach using a Rotary Positional Embedding (RoPE) Enhanced Informer model to address challenges in long-term time series forecasting for RUL prediction. By integrating RoPE into the Informer, the model efficiently preserves positional information and leverages sparse attention mechanisms to reduce computational complexity, enabling it to handle longer sequences with minimal memory usage. The proposed model was tested on benchmark datasets, where it outperformed peer level approaches in terms of Mean Absolute Error and Root Mean Squared Error. The results show a significant reduction in validation errors, indicating the model's superior generalization ability and its robustness against overfitting. This approach offers a scalable solution suitable for real-time predictive maintenance, particularly in resource-constrained environments. The combination of advanced positional encoding with the Informer architecture demonstrates the potential for this method to enhance battery management and improve the accuracy of RUL prediction across various industrial applications. © 2024 IEEE.

关键词： Lithium-ion batteries

来源：评论

学校读者我要写书评

暂无评论

AI LipReader-Transcribing Speech from Lip Movements 6

AI LipReader-Transcribing Speech from Lip Movements

引用

6th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Ajitha, D. Dutta, Disha Saha, Falguni Giri, Parus Kant, Rohan School of Computer Science and Engineering Vellore Institute of Technology Vellore Department of Software Systems Vellore India School of Computer Science and Engineering Vellore Institute of Technology Department of Database Systems Vellore India School of Computer Science and Engineering Vellore Institute of Technology Vellore Department of Information Security Vellore India

ISBN: (纸本)9798350306613

Lip Reading AI is a discipline that is rapidly changing and has numerous applications in security, accessibility and human-computer interaction. This paper proposes a model which combines Convolutional Neural Networks (CNNs) to capture spatial capabilities, Long Short-Term Memory (LSTM) networks to examine temporal dependencies, and an adaptive interest mechanism. Meticulous preprocessing of the MIRACL VC-l dataset addressing challenges including one of a kind lip moves and occlusions accompanied with the aid of transitioning this study effortlessly to LRS2 dataset to complement lexemic versatility is one of its key function. The effects verify its robustness throughout unique datasets with superior overall performance towards cutting-edge techniques. Ablation checks suggest the crucial significance of every element in phrases of improving lip analyzing accuracy. Our proposed model version additionally suggests flexibility in restricted and naturalistic language situations. © 2024 IEEE.

关键词： Attention Mechanism Convolutional Neural Networks (CNNs) Deep Learning Lip Reading Long Short-Term Memory (LSTM) LRS2 Dataset MIRACLVC-J Dataset

来源：评论

学校读者我要写书评

暂无评论

Unveiling the Multidimensional Risk Landscape of Human Trafficking in India: A K-Means Approach 1

Unveiling the Multidimensional Risk Landscape of Human Traff...

引用

1st IEEE International Conference on Smart Power Control and Renewable Energy, ICSPCRE 2024

作者： Rajan, Nitya Dasararaju, Shalini School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350377002

Human trafficking, a modern form a slavery, causes extreme pain and trauma by depriving victims of their basic right and dignity. Within South Asia, India emerges as a significant focal point of human trafficking activities. Our research provides a multidimensional risk analysis on a state level by analyzing Human Trafficking Data using 5 categories: Total Number of Cases, Cases per Lakh Population, Child Trafficking, Trafficking for Forced Labor, and Sex Trafficking. Using the K-Means algorithm, we classify states into 3 risk levels: High, Medium and Low Risk. Our research shows that risk distribution varies significantly based on the type of Human Trafficking, showing different prevalence in different states and calls for a more nuanced analysis and intervention to combat Human Trafficking. Furthermore, it highlights the importance of analyzing the data using population normalized metrics, showing smaller states can have high prevalence of trafficking cases with respect to their population but lower numbers overall. Finally, we provide a ranked list of the most vulnerable states to Human Trafficking based on a multidimensional analysis over all categories, the top 5 states being Orissa, Delhi, Telangana, Rajasthan and Bihar. © 2024 IEEE.

关键词： K-means clustering

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500页 << < 6 7 8 9 10 11 12 13 14 15 > >>

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：