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

31,714 篇 会议
4,982 篇 期刊文献
261 册 图书

馆藏范围

36,957 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

22,973 篇 工学
- 16,597 篇 计算机科学与技术...
- 13,930 篇 软件工程
- 6,082 篇 信息与通信工程
- 3,592 篇 控制科学与工程
- 2,786 篇 电子科学与技术（可...
- 2,726 篇 电气工程
- 2,460 篇 生物工程
- 2,029 篇 生物医学工程（可授...
- 1,617 篇 光学工程
- 1,400 篇 机械工程
- 1,263 篇 仪器科学与技术
- 1,254 篇 动力工程及工程热...
- 1,056 篇 化学工程与技术
- 876 篇 安全科学与工程
- 742 篇 交通运输工程
11,813 篇 理学
- 6,408 篇 数学
- 3,225 篇 物理学
- 2,696 篇 生物学
- 2,115 篇 统计学（可授理学、...
- 1,410 篇 系统科学
- 1,127 篇 化学
5,974 篇 管理学
- 3,443 篇 管理科学与工程(可...
- 2,825 篇 图书情报与档案管...
- 1,916 篇 工商管理
1,691 篇 医学
- 1,470 篇 临床医学
- 1,378 篇 基础医学(可授医学...
- 906 篇 药学(可授医学、理...
- 701 篇 公共卫生与预防医...
1,084 篇 法学
- 878 篇 社会学
681 篇 经济学
- 681 篇 应用经济学
510 篇 农学
463 篇 教育学
162 篇 文学
78 篇 军事学
42 篇 艺术学
6 篇 哲学
4 篇 历史学

主题

1,136 篇 accuracy
877 篇 computational mo...
874 篇 feature extracti...
740 篇 deep learning
725 篇 training
701 篇 computer science
544 篇 real-time system...
532 篇 support vector m...
519 篇 machine learning
468 篇 predictive model...
430 篇 internet of thin...
367 篇 data models
364 篇 convolutional ne...
353 篇 computer archite...
345 篇 reliability
316 篇 data mining
312 篇 optimization
289 篇 routing
277 篇 artificial intel...
263 篇 wireless sensor ...

机构

83 篇 institut univers...
82 篇 north-west unive...
80 篇 national centre ...
79 篇 infn sezione di ...
76 篇 infn sezione di ...
76 篇 institute of inf...
73 篇 tno technical sc...
66 篇 università di ca...
64 篇 université de st...
58 篇 dept. of informa...
57 篇 nikhef national ...
56 篇 dept. of compute...
56 篇 dept. of informa...
55 篇 dept. of compute...
52 篇 school of automa...
52 篇 leiden universit...
49 篇 dept. of compute...
44 篇 dept. of compute...
43 篇 dept. of electri...
42 篇 infn lnf via enr...

作者

86 篇 ferrara g.
85 篇 ardid m.
84 篇 calvo d.
84 篇 gal t.
84 篇 bozza c.
83 篇 chen a.
80 篇 kalekin o.
79 篇 díaz a.f.
79 篇 boumaaza j.
72 篇 coleiro a.
71 篇 gozzini s.r.
68 篇 huang f.
62 篇 dallier r.
53 篇 anguita m.
51 篇 kulikovskiy v.
44 篇 del burgo r.
41 篇 benoit d.m.
41 篇 eddymaoui a.
40 篇 gagliardini s.
40 篇 labalme m.

语言

33,086 篇 英文
3,448 篇 其他
319 篇 中文
63 篇 日文
45 篇 朝鲜文
2 篇 德文
2 篇 斯洛文尼亚文
1 篇 法文
1 篇 葡萄牙文

检索条件"机构=Dept. Informatics Engineering of Computer Science"

共 36957 条记录，以下是4761-4770 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：

Descriptive and Predictive Analytics of Energy Security in India 2

Descriptive and Predictive Analytics of Energy Security in I...

引用

2nd IEEE International Conference on Advances in Computing, Communication and Applied informatics, ACCAI 2023

作者： Justus, J. Jean Mannish, S. St. Joseph's College of Engineering Computer Science and Engineering Tamil Nadu Chennai India St. Joseph's College of Engineering Dept. of Cse Tamil Nadu Chennai India

ISBN: (纸本)9798350315905

Energy security for a country is essential as energy management is crucial for a country's endurance and progress. For every development scheme, growth in the economy, or improving the exports to other countries in trade the most fundamental requirement is to match the energy production of the country to that of the growth of the country. This action is extremely crucial as it forms a basis where growth efficiently takes place. Another important aspect of energy security in today's age is overcoming the dependence on non-renewable forms of energy such as coal or natural gas. There are two prime reasons why this is significant. One is that these types of energy will not be exhausted in the upcoming years. Another reason is the long-term consequence on the environment producing and utilizing this energy conceives. Since this requires infrastructure and planning it is necessary to begin scheming on the renewable aspect. Therefore, it is imperative to focus on energy management on one hand and on the other focus on moving towards energy generation from non-renewable sources of energy to renewable sources. Furthermore, energy distribution management will benefit from the inequitable distribution of energy within a country. © 2023 IEEE.

关键词： Energy security

来源：评论

学校读者我要写书评

暂无评论

Pruning Binarized Neural Networks Enables Low-Latency, Low-Power FPGA-Based Handwritten Digit Classification

Pruning Binarized Neural Networks Enables Low-Latency, Low-P...

引用

IEEE Conference on High Performance Extreme Computing (HPEC)

作者： Syamantak Payra Gabriel Loke Yoel Fink Joseph D. Steinmeyer Dept. of Electrical Engineering Stanford University (formerly Dept. of Electrical Engineering and Computer Science Massachusetts Institute of Technology) Dept. of Materials Science and Engineering Massachusetts Institute of Technology Dept. of Materials Science and Engineering Dept. of Electrical Engineering and Computer Science and Institute for Soldier Nanotechnologies Massachusetts Institute of Technology Dept. of Electrical Enigneering and Computer Science Massachusetts Institute of Technology

As neural networks are increasingly deployed on mobile and distributed computing platforms, there is a need to lower latency and increase computational speed while decreasing power and memory usage. Rather than using FPGAs as accelerators in tandem with CPUs or GPUs, we directly encode individual neural network layers as combinational logic within FPGA hardware. Utilizing binarized neural networks minimizes the arithmetic computation required, shrinking latency to only the signal propagation delay. We evaluate size-optimization strategies and demonstrate network compression via weight quantization and weight-model unification, achieving 96% of the accuracy of baseline MNIST digit classification models while using only 3% of the memory. We further achieve 86% decrease in model footprint, 8mW dynamic power consumption, and $< 9\text{ns}$ latency, validating the versatility and capability of feature-strength-based pruning approaches for binarized neural networks to flexibly meet performance requirements amid application resource constraints.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Prediction and Modelling of Suicidal Mental Tendencies

Prediction and Modelling of Suicidal Mental Tendencies

引用

IEEE Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)

作者： Sunitha Guruprasad Arnav G Rao Shreeraksha Shivani K Varsha S N Yashoda K Dept. of Computer Science & Engineering St Joseph Engineering College Mangaluru India Visvesvaraya Technological University Belagavi Karnataka India Dept. of Computer Science & Engineering Ramaiah Institute of Technology Bengaluru India

ISBN: (数字)9798350350593

ISBN: (纸本)9798350350609

Suicide remains a pressing public health concern worldwide, necessitating effective predictive and preventive strategies. Predicting suicidal tendencies is a complex endeavor, as it involves multifaceted interactions between biological, psychological, and environmental factors. Advances in computational modeling and machine learning have provided promising avenues for understanding and forecasting these tendencies. Such models often integrate clinical data, including psychiatric history, symptom severity, and demographic information, with biological markers and psychosocial variables. The objective of our work is to identify patterns and risk factors that may indicate heightened suicidal risk. Ongoing research seeks to refine predictive models, incorporate real-time monitoring, and develop targeted interventions, aiming to reduce the incidence of suicide and provide timely support to those at risk. The application of suicide text analysis extends to user-friendly interfaces, allowing individuals to input text, receive predictions, and access relevant support. The analysis involves preprocessing techniques such as text normalization, stop-word removal, and stemming to enhance the quality of input data. Various machine learning algorithms, ranging from Naive Bayes to sophisticated ensemble methods, are explored to determine their effectiveness in predicting suicidal ideation.

关键词： Text analysis Computational modeling Soft sensors Predictive models Very large scale integration Natural language processing Real-time systems Bayes methods Wearable devices Robots

来源：评论

学校读者我要写书评

暂无评论

Adversarial Machine Learning Attack on Machine Learning-based Controller for Solar Inverters

Adversarial Machine Learning Attack on Machine Learning-base...

引用

IEEE Energy Conversion Congress and Exposition, ECCE

作者： Joaquin Massa Kyoung-Tak Kim Taesic Kim BoHyun Ahn Kumar Venayagamoorthy Jianwu Zeng Dept. of Electrical Engineering and Computer Science Texas A&M University-Kingsville Kingsville USA Dept. of Electrical Engineering Soongsil University Seoul Republic of Korea Dept. of Electrical Engineering and Computer Science University of Missour-Columbia Columbia USA Dept. of Electrical and Computer Engineering Clemson University Clemson USA Dept. of Electrical and Computer Engineering and Technology Minnesota State University Mankato Mankato USA

ISBN: (数字)9798350376067

ISBN: (纸本)9798350376074

Adversarial machine learning (ML) attacks are stealthy attacks designed to mislead the ML model results. This paper explores adversarial ML attacks that generate adversarial noisy input data in an ML-based controller in a solar inverter. Three types of ML models, long short-term memory (LSTM), gated recurrent unit (GRU)), and bidirectional-LSTM (Bi-LSTM), are designed to replace proportional-integral (PI) controller-based vector control for a solar inverter and two white-box adversarial ML attacks (Basic Iterative Method (BIM) attack and Fast Sign-Gradient Method (FGSM)) are applied to the ML controllers. It is observed that the adversary ML attacks designed in stealthy way do not affect the PI controller, while significantly degrading performance of the ML-based controllers. Moreover, the BIM attack is more effective than FGSM and Bi-LSTM-based controller is relatively robust to the attacks compared to peer.

关键词： PI control Inverters Adversarial machine learning Hardware Robustness Real-time systems Noise measurement Long short term memory Machine vector control Microprogramming

来源：评论

学校读者我要写书评

暂无评论

RL-Driven Fine-Grained Power Gating for Modern Processors Using Multi-Layer Perceptron Models

RL-Driven Fine-Grained Power Gating for Modern Processors Us...

引用

IEEE International Symposium on Quality Electronic Design

作者： Naman Kalra Jaynarayan T Tudu Dept. of Electrical Engineering Indian Institute of Technology Tirupati India Dept. of Computer Science Engineering Indian Institute of Technology Tirupati India

ISBN: (数字)9798331509422

ISBN: (纸本)9798331509439

This paper introduces an RL-driven algorithm for fine-grained power gating of functional units in superscalar processors. The proposed algorithm leverages a Multi-Layer Perceptron (MLP) neural network as a policy model, trained by a reinforcement learning (RL) agent, to optimize power states across four modes: On, Sleep-on, Sleep-off, and Off. By statically analyzing features extracted from the control flow graph (CFG) and execution profile of the program, the RL-driven model selects the most energy-efficient power states for each functional unit without compromising performance. Traditional power gating techniques often struggle with tradeoffs between minimizing performance loss (wake-up latency) and reducing power consumption, necessitating a reevaluation of power management strategies. Next-generation complex SoC designs at < 7nm technology nodes require a finer granularity approach to effectively manage power states. Our AI-based algorithm provides an optimized trade-off between power efficiency and performance by enabling power gating across multiple power states and selecting the power state that has the effectively least energy-delay product. Additionally, we propose an architectural circuit design for power gating multiple FU's within a superscalar processor, enabling efficient state transitions. Preliminary experimental results show that the proposed AI-based method achieves 10-20% energy savings while improving performance by 1.5% compared to traditional power gating algorithms.

关键词： Program processors Power demand Power system management Reinforcement learning Performance gain Feature extraction Energy efficiency Circuit synthesis Flow graphs Next generation networking

来源：评论

学校读者我要写书评

暂无评论

Advanced Forecasting and Analysis Techniques for Optimizing Website Performance: A Comparative Study of Time Series Models

Advanced Forecasting and Analysis Techniques for Optimizing ...

引用

IoT, Communication and Automation Technology (ICICAT), International Conference on

作者： Gayathri Raikar Harshith D N. Guruprasad Dept. of Computer Science and Engineering Global Academy of Technology Bangalore India Computer Science and Engineering Buddha Institute of Technology Gorakhpur India

ISBN: (数字)9798350368109

ISBN: (纸本)9798350368116

Analyzing website performance is crucial for optimizing a site's functionality, enhancing user engagement, and aligning with business goals. This evaluation focuses on essential metrics such as average engagement time per session, engagement rate, and events per session, which significantly impact user experience and overall business success. Through effective performance analysis, organizations can improve user satisfaction, boost conversion rates, and enhance their profitability and reputation. This paper reviews different approaches for analyzing these key metrics, explores their effects on user behavior and business performance, and offers best practices for optimizing website performance. By employing detailed performance analysis, businesses can make informed decisions and realize their strategic goals in the digital environment.

关键词： Measurement Reviews Profitability Time series analysis Organizations Predictive models User experience Data models Performance analysis Forecasting

来源：评论

学校读者我要写书评

暂无评论

Multimodal Representation Learning:Cross-modality and Shared Representation

Multimodal Representation Learning:Cross-modality and Shared...

引用

2022 International Conference on Industry 4.0 Technology, I4Tech 2022

作者： Mohan, Paul Brilley, Batley C Kumar A. A, Nippun Amrita School of Engineering Dept. of Computer Science & Engineering Bengaluru India Ai and Automation Litmus 7 Kochi India

ISBN: (数字)9781665471961

ISBN: (纸本)9781665471961

Various kinds of Information retrieval or processing task can be difficult basis on how the information is viewed or represented. Representation learning is a technique that allows a system to discover the representation of the data which used for detecting features and classifying things. As compared with learning from one representation, learning from different representations make the task easier. For example, learning something from images and their texts or learning from video and their audio produces good results than learning from one source (either image or text)or (video or audio). Learning from multiple modalities helps to achieve the target correctly within short time. This paper tries to design a Multimodal Autoencoder with minimum loss that can produce shared representation of images and its texts © 2022 IEEE.

关键词： Learning systems

来源：评论

学校读者我要写书评

暂无评论

Detecting Ransomware Threats Using Machine Learning Approach

Detecting Ransomware Threats Using Machine Learning Approach

引用

IEEE Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)

作者： Sunitha Guruprasad Arnav G Rao Sanmitha Chitharanjan Shetty Rose Bastian Sarah Abdul Khader Dept. of Computer Science & Engineering St Joseph Engineering College Mangaluru India Visvesvaraya Technological University Belagavi Karnataka India Dept. of Computer Science & Engineering Ramaiah Institute of Technology Bengaluru India

ISBN: (数字)9798350350593

ISBN: (纸本)9798350350609

The rise of ransomware-related cyber-attacks poses a severe threat to organizations across various sectors. This research addresses the urgent need for robust detection mechanisms using advanced machine learning (ML) models. Ransomware, characterized by irreversible encryption of user data, demands proactive strategies for identification and prevention. This study leverages publicly available ransomware and benign executable files from sources such as VirusTotal which are run in a sandboxed environment to obtain a dataset to train an ML model capable of differentiating between diverse ransomware samples and classifying them based on specific characteristics. The process begins with meticulous data preprocessing ensuring the dataset's quality. Feature extraction algorithms are employed to identify relevant characteristics, streamlining the dataset and optimizing model performance. The model is trained on the dataset to effectively learn patterns. Evaluation metrics, including accuracy, precision, recall, F1-score, and the ROC curve, provide comprehensive insights into the model's effectiveness.

关键词： Analytical models Accuracy Computational modeling Stacking Telecommunication traffic Very large scale integration Feature extraction Ransomware Integrated circuit modeling Random forests

来源：评论

学校读者我要写书评

暂无评论

Novel Plant Leaf Disease Detection Approach using Hybrid Deep Learning Strategy 5

Novel Plant Leaf Disease Detection Approach using Hybrid Dee...

引用

5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Vamshi, Jalla Tanwar, Shashi Thatipudi, Jayendra Gopal Ponsangeetha, A. Mukherjee, Saptarshi Mandhare, Jagdish B. School of Agriculture Sr University Telangana Warangal India G D Goenka University Department- Computer Science and Engineering Haryana Gurugram India University of North Texas Dept. of Computer Science United States Velammal Institute of Technology Department of Computer Science and Engineering Panchetti India Goldsmith Institute of Advanced Research Department-Mtech Bioinformatics Giar West Bengal Kolkata India Bharati Vidyapeeth College of Engineering Department of Instrumentation Navi Mumbai India

ISBN: (纸本)9798350385649

The early detection and identification of plant diseases are pivotal for precision agriculture, aiming to mitigate crop losses and optimize yields. This study presents a novel approach to plant disease identification, leveraging Deep Learning (DL) techniques with a focus on transfer learning. Traditional methods often rely on complex image enhancement, disease region segmentation, and feature extraction, whereas our approach employs Convolutional Neural Networks (CNNs) for efficient and accurate disease detection. To enhance accessibility and diagnostic capabilities, we implement Hybrid DL Strategy, which consists of EfficientNetB0 and MobileNetV2 model, tailored for lightweight applications suitable for smartphone implementation. Efficient NetB0 model, which considers dept., width, and resolution during convolution, enhancing the model's capacity to capture intricate features critical for accurate disease diagnosis. The overall accuracy of proposed method is 98.44%, surpassing CNN, ResNet, and AlexNet. This high accuracy underscores the effectiveness of the proposed model in correctly classifying both diseased and healthy plant leaves. © 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

Game-Based Learning for engineering Education 6

Game-Based Learning for Engineering Education

引用

6th South-East Europe Design Automation, computer engineering, computer Networks and Social Media Conference, SEEDA-CECNSM 2021

作者： Velaora, Chrysoula Kakarountas, Athanasios Dept. of Computer Science and Biomedical Informatics University of Thessaly Lamia Greece

ISBN: (纸本)9781665427425

The issue addressed in this work is the impact of virtual lab to distance learning of the cognitive object of logic design course. Thirty four digital electronic circuits were designed using an online application for electronics. Students were asked to implement a game using most of them and watch relevant educational videos in order to achieve it. This paper reports our evaluation of how the motivation of students is affected by online lab. In the survey participated 15 undergraduate students of the University of Thessaly in Greece. When the semester came to an end, they filled out a questionnaire based on IMMS (Instructional Materials Motivation Survey) tool which includes 36 sentences on the components of the Keller's ARCS motivation model: students' attention, relevance, confidence and satisfaction. The survey justifies that the design of virtual lab and corresponding educational material based on gamification and educational videos strengthens students' motivation. © 2021 IEEE

关键词： computer circuits

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 << < 473 474 475 476 477 478 479 480 481 482 > >>

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：