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

2,644 篇 会议
61 篇 期刊文献
38 册 图书
3 篇 学位论文

馆藏范围

2,746 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

2,237 篇 工学
- 1,991 篇 计算机科学与技术...
- 779 篇 软件工程
- 488 篇 电气工程
- 437 篇 信息与通信工程
- 174 篇 控制科学与工程
- 167 篇 生物工程
- 119 篇 生物医学工程（可授...
- 56 篇 仪器科学与技术
- 55 篇 化学工程与技术
- 50 篇 安全科学与工程
- 46 篇 机械工程
- 42 篇 电子科学与技术（可...
- 38 篇 光学工程
- 26 篇 动力工程及工程热...
537 篇 理学
- 279 篇 数学
- 187 篇 生物学
- 114 篇 物理学
- 108 篇 系统科学
- 108 篇 统计学（可授理学、...
- 54 篇 化学
266 篇 管理学
- 141 篇 图书情报与档案管...
- 136 篇 管理科学与工程(可...
- 64 篇 工商管理
146 篇 医学
- 122 篇 临床医学
- 56 篇 基础医学(可授医学...
- 36 篇 药学(可授医学、理...
- 27 篇 特种医学
- 25 篇 医学技术（可授医学...
41 篇 法学
- 33 篇 社会学
35 篇 教育学
- 28 篇 教育学
22 篇 经济学
22 篇 农学
8 篇 文学
5 篇 艺术学
1 篇 军事学

主题

897 篇 machine learning
218 篇 deep learning
123 篇 feature extracti...
106 篇 training
103 篇 machine learning...
85 篇 classification
72 篇 cloud computing
70 篇 support vector m...
70 篇 neural networks
62 篇 big data
60 篇 data mining
60 篇 computational mo...
53 篇 artificial intel...
49 篇 sentiment analys...
45 篇 predictive model...
43 篇 intrusion detect...
40 篇 reinforcement le...
38 篇 data models
35 篇 neural network
34 篇 internet of thin...

机构

12 篇 univ chinese aca...
11 篇 institute of mac...
7 篇 school of comput...
7 篇 fudan university
7 篇 school of electr...
7 篇 guangxi academy ...
6 篇 università di ca...
6 篇 infn sezione di ...
6 篇 north-west unive...
6 篇 in2p3 lpc campus...
6 篇 institut univers...
6 篇 national centre ...
6 篇 tno technical sc...
6 篇 université de st...
6 篇 nikhef national ...
6 篇 university würzb...
6 篇 université de ha...
6 篇 university of jo...
6 篇 university moham...
6 篇 astrocent nicola...

作者

14 篇 qin xiao
8 篇 yuan chang-an
8 篇 huang de-shuang
7 篇 chen hao
7 篇 yuan changan
7 篇 wu hongjie
7 篇 zhao xingming
6 篇 chen a.
6 篇 benoit d.m.
6 篇 ferrara g.
6 篇 ardid m.
6 篇 calvo d.
6 篇 gagliardini s.
6 篇 eddymaoui a.
6 篇 kulikovskiy v.
6 篇 labalme m.
6 篇 gal t.
6 篇 bozza c.
6 篇 khatun a.
6 篇 kalekin o.

语言

2,639 篇 英文
69 篇 中文
39 篇 其他

检索条件"任意字段=2017 International Conference on Machine Learning and Soft Computing, ICMLSC 2017"

共 2746 条记录，以下是291-300 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：

Enhancing Differential Evolution Algorithm: Adaptation for CEC 2017 and CEC 2021 Test Suites

Enhancing Differential Evolution Algorithm: Adaptation for C...

引用

international conference on soft computing and machine Intelligence (ISCMI)

作者： Rohit Salgotra Seyedali Mirjalili Amir H Gandomi Department of Computer Science Swansea University Swansea UK Center for Artificial Intelligence Research and Optimization Torrens University Australia Faculty of Engg. IT University of Technology Sydney Australia

Differential evolution (DE) has proved its significance for optimizing various real-world applications and standard benchmarks. In this work, a self-adaptive version of DE is proposed namely LSHADESPA by employing three major modifications, i) proportional shrinking population mechanism for reducing computational burden, ii) simulated annealing-based scaling factor (F) for improving the exploration properties, and iii) oscillating inertia weight-based crossover rate (CR) for a balancing exploitation and exploration. The proposed algorithm has been experimentally tested on IEEE CEC 2017 and IEEE CEC 2021 benchmarks. For performance evaluation, a comparison with respect to JADE, SaDE, SHADE, LSHADE, MVMO, and others has been performed. Experimental and statistical results affirm the superior performance of the proposed LSHADESPA algorithms with respect to other algorithms.

关键词： Performance evaluation Sequential analysis Sociology Clustering algorithms Simulated annealing Benchmark testing Statistics

来源：评论

学校读者我要写书评

暂无评论

Data-Driven learning of Feedforward Neural Networks with Different Activation Functions 20th

Data-Driven Learning of Feedforward Neural Networks with Dif...

引用

20th international conference on Artificial Intelligence and soft computing (ICAISC)

作者： Dudek, Grzegorz Czestochowa Tech Univ Czestochowa Poland

ISBN: (纸本)9783030879860;9783030879853

This work contributes to the development of a new data-driven method (D-DM) of feedforward neural networks (FNNs) learning. This method was proposed recently as a way of improving randomized learning of FNNs by adjusting the network parameters to the target function fluctuations. The method employs logistic sigmoid activation functions for hidden nodes. In this study, we introduce other activation functions, such as bipolar sigmoid, sine function, saturating linear functions, reLU, and softplus. We derive formulas for their parameters, i.e. weights and biases. In the simulation study, we evaluate the performance of FNN data-driven learning with different activation functions. The results indicate that the sigmoid activation functions perform much better than others in the approximation of complex, fluctuated target functions.

关键词： Data-driven learning Feedforward neural networks Randomized learning algorithms

来源：评论

学校读者我要写书评

暂无评论

Blockchain and machine learning Security for the Internet of Things: A Bibliometric Analysis

Blockchain and Machine Learning Security for the Internet of...

引用

Pervasive computing and Social Networking (ICPCSN), international conference on

作者： Jaideep Kaur Mudhar Jyoteesh Malhotra Ajay Kumar Singh Monika Anand Computer Engineering Guru Nanak Dev University Amritsar India Electronics Engineering Regional Campus Guru Nanak Dev University Jalandhar India Computer Science and Engineering Chandigarh University Mohali India

The Internet of Things has developed as a catalyst for innovation across numerous industries, from home automation to smart cities to 4IR primarily Industry 4.0. Through the internet, IoT devices interact and exchange information. Beyond its potential for development, IoT devices are divergent, ubiquitous, memory-and computational-power-constrained, making them vulnerable to a range of security and privacy risks. Blockchain, a decentralised and tamper-proof technology, has recently been introduced in the BIoT domain for securing data handling activities and inculcating trusted transactions. In anticipation of IoT System vulnerabilities, machine learning (ML) algorithms yield accurate findings for large and complicated databases. This study conducts a bibliometric study from the Web of Science (WoS) database from 2017 to 2022 in the area of ML-based BIoT security using Prisma guidelines. Rankings of the most productive and influential writers, articles, and sources are based on total publications and citations. Text data content analysis, Author keywords, index keywords are utilized to highlight research hotspots and future trends in the ML-Based BIoT security domain.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Enhancing Intrusion Detection in The Internet of Vehicles: An Ensemble and Optimized machine learning Approach

Enhancing Intrusion Detection in The Internet of Vehicles: A...

引用

Sensing, Measurement, Communication and Internet of Things Technologies (SMC-IoT), international conference on

作者： Jiangjian Tu Wenli Shang School of Electronic and Communication Engineering Guangzhou University Guangzhou China Key Laboratory of On-Chip Communication and Sensor Chip of Guangdong Higher Education Institutes Guangzhou China

ISBN: (数字)9798350373257

ISBN: (纸本)9798350373264

In contemporary intelligent transportation systems, particularly within autonomous vehicles and connected cars, a growing array of functionalities has been integrated through interactions with other vehicles, smart devices, and infrastructure. However, the increasing connectivity in the Internet of Vehicles (IoV) has concurrently heightened the risk of network attacks. To fortify IoV systems against these threats, intrusion detection systems (IDSs) utilizing machine learning techniques prove effective in identifying malicious network attacks. This study proposes a novel optimized ensemble IDS employing soft voting for the detection of cyber threats in both in-vehicle networks and external vehicular networks within modern intelligent transportation systems. The system framework is established on three machine learning algorithms (logistic regression, random forest, and decision tree) optimized by Bayesian optimization, creating an integrated structure capable of efficiently detecting intrusions from both in-vehicle and external networks. The proposed framework undergoes evaluation using two benchmark datasets: the CICIDS2017 dataset for external network communication and the Car-Hacking dataset for in-vehicle communication. The findings indicate that the proposed framework attains an accuracy of 99.997% on the Car-Hacking dataset and 99.515% on the CICIDS2017 dataset, surpassing the performance of existing intrusion detection systems.

关键词： Logistic regression machine learning algorithms Intrusion detection Sensors Decision trees Smart devices Random forests

来源：评论

学校读者我要写书评

暂无评论

Lan: learning to Augment Noise Tolerance for Self-report Survey Labels 19

Lan: Learning to Augment Noise Tolerance for Self-report Sur...

引用

19th IEEE international conference on Pervasive computing and Communications (IEEE PerCom)

作者： Lin, Suwen Faust, Louis Chawla, Nitesh, V Univ Notre Dame Notre Dame IN 46556 USA

ISBN: (纸本)9781665404181

The prevalence of mobile sensors makes it possible for researchers to collect and analyze the pervasive sensed human activity data with machine learning tools. These analyses and applications heavily rely on the self-report survey data that reflect human physical and psychological behaviors. However, many factors can affect the reliability of such self-report surveys, such as participants' trusty and ambiguous descriptions in questionnaires. These inaccurate survey labels challenge the development of the models, especially when it comes to the case where deep learning approaches tend to memorize the noisy information from training data. In this paper, we set out to deal with the noisy self-report survey labels. We aim to address the following challenges: 1)the limited access to clean labeled data in real-world scenarios;2)different noise ratio of the data from different participants;3)various types of noise that appear in the data;4)how to evaluate the reliability of participants. As such, we propose a generalizable model, Lan, for learning from multi-modal sensory data and noisy labels. The proposed model incorporates the temporal information from sensory data to guide the model training and thus to augment noise tolerance. Extensive experiments on a real-world dataset were conducted to show our model outperforms state-of-the-art benchmarks.

关键词： noise tolerance self-report surveys multi-modal sensory data

来源：评论

学校读者我要写书评

暂无评论

xIDS-EnsembleGuard: An Explainable Ensemble learning-based Intrusion Detection System

xIDS-EnsembleGuard: An Explainable Ensemble Learning-based I...

引用

IEEE international conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Muhammad Adil Mian Ahmad Jan Safayat Bin Hakim Houbing Herbert Song Zhanpeng Jin Deptartment of Computer Science University at Buffalo Buffalo USA College of Computing and Informatics University of Sharjah Sharjah UAE School of Information Systems University of Maryland Baltimore County USA School of Future Technology South China University of Technology Guangdong China And University at Buffalo Buffalo USA

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

In this paper, we focus on addressing the challenges of detecting malicious attacks in networks by designing an advanced Explainable Intrusion Detection System (xIDS). The existing machine learning and deep learning approaches have invisible limitations, such as potential biases in predictions, a lack of interpretability, and the risk of overfitting to training data. These issues can create doubt about their usefulness, and transparency, and decrease the trust of involved stakeholders. To overcome these challenges, we propose an ensemble learning technique called the "EnsembleGuard". This approach uses the predicted outputs of multiple models, including tree-based (LightGBM, GBM, Bagging, XGBoost, CatBoost) and deep learning models such as neural network (LSTM (long short-term memory networks) and GRU (gated recurrent unit), to maintain a balance and achieve trustworthy results. Our work is unique because it combines both tree-based and deep learning models to design an interpretable and explainable meta-model through model distillation. By considering the predictions of all individual models, our neta-model effectively addresses key challenges, and ensures both explainable and reliable results. We evaluate our model using well-known datasets, including UNSW-NB15, NSL-KDD, and CIC-IDS-2017, to assess its reliability against various types of attacks. During analysis, we found that our model outperforms both tree-based models and other comparative approaches when it comes to different kinds of attack scenarios.

关键词： Deep learning Analytical models Accuracy Intrusion detection Training data Predictive models NSL-KDD Reliability Ensemble learning Long short term memory

来源：评论

学校读者我要写书评

暂无评论

Evaluation of Centralized, Distributed and Federated learning for IoT Intrusion Detection Systems

Evaluation of Centralized, Distributed and Federated Learnin...

引用

IEEE international conference on Intelligent computing and Information Systems (ICICIS)

作者： Omar Elnakib Eman Shaaban Mohamed Mahmoud Karim Emara Department of Computer Systems Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt Electrical and Computer Engineering Tennessee Technological University Cookeville TN USA

Introducing smart intrusion detection systems (IDSs) in the IoT system was a crucial requirement in the last decades to secure these systems due to the high sensitivity of the sensed data. Smart IDS has often been built based on a machine/deep learning model trained at a centralized node in the cloud. However, the centralized learning approach violates data privacy since IoT nodes are required to share their data to the cloud to train the model. Distributed and federated learning approaches were introduced to solve this issue by keeping data within the local network and letting the edge devices train the model. In this paper, a comparative study among centralized, distributed, and federated learning approaches is presented. First, a deep learning model is proposed to identify traffic behavior using CICIDS2017 dataset. The three learning approaches are then applied with the proposed model to evaluate the effect of each approach on the model accuracy. In the centralized learning approach, an F-Score of 98% can be achieved. Moreover, the dataset is split over ten simulated nodes with various data distribution and entropies to evaluate the distributed and federated learning approaches. In distributed learning, the F-Score ranges between 63% to 93% with an average of 78% over the ten nodes. In the federated learning, the F-Score ranges between 73% to 98% with an average of 89% over the ten nodes. This result confirms that IDSs based on federated learning are a promising solution for both accuracy and preserving data privacy.

关键词：

来源：评论

学校读者我要写书评

暂无评论

An Extensive Examination of Methods for Detecting and Classifying Malignant and Benign Skin Cancers Using Deep learning Techniques

An Extensive Examination of Methods for Detecting and Classi...

引用

Distributed computing and Electrical Circuits and Electronics (ICDCECE), IEEE international conference on

作者： Rambabu Pasumarthy Lakshmeeswari Gondi Department of Computer Science and Engineering GITAM School of Technology GITAM Deemed to be University Vishakhapatnam Andhra Pradesh India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

Skin cancer is a pressing health concern responsible for numerous fatalities in today's world, often diagnosed in its advanced stages, making treatment planning and patient survival challenging. Medical image interpretation, such as MRI as well as CT scans, is crucial for skin cancer diagnosis, but the manual analysis is costly, time-consuming, and can be influenced by biases. DL (Deep learning), a cutting-edge technology, is gaining attention as it allows for the automatic evaluation and interpretation of medical images, reducing the need for human intervention. Deep learning approaches have demonstrated remarkable outcomes in various domains by automatically extracting intrinsic image features, excluding the need for manual feature extraction. This review delves into the application of DL approaches in the diagnosis & detection of cancer, with a particular focus on skin cancer. Detecting melanoma from samples of dermoscopic skin is a formidable task, but employing a machine vision tool, like a deep learning approach, can address some of these challenges. The study introduces an automated melanoma classifier on the basis of DCNN (Deep Convolutional Neural Network) to accurately differentiate malignant from benign melanoma. It also outlines the various deep learning models and steps involved in utilizing these models for skin cancer detection. Recent advancements in the skin cancer detection Deep learning (DL) methods are explored and summarized, shedding light on the critical challenges that have been faced in accurately detecting the cancer of skin in its early stages. For this research, dermoscopic images from the international Skin Imaging Collaboration datastores (ISIC 2016, ISIC 2017, and ISIC 2020), containing diverse cancer samples, were obtained and evaluated based on metrics like precision, accuracy, specificity, recall, and F1-score.

关键词： Deep learning Image segmentation Melanoma Object segmentation Manuals Feature extraction Skin

来源：评论

学校读者我要写书评

暂无评论

machine learning Algorithms Performance Investigation in Fake News Detection

Machine Learning Algorithms Performance Investigation in Fak...

引用

international conference on Big Data Intelligence and computing, DataCom 2022

作者： Sami, Monesh Shawkat Ali, A.B.M. Department of Computer Science and Information Systems Fiji National University Nadi Fiji School of Science and Technology The University of Fiji Lautoka Fiji

ISBN: (纸本)9789819922321

Mass media has always been one of the most enriching forms of information dissemination and sharing across the world among millions of people. It has taken the news industry by storm where traditionally, news media was transmitted using printed materials, radios and television, whereas over its existence it has become more effective and powerful with the use of the Internet. Social media is a very powerful tool in today’s era that is being used to reach out to a larger audience which was not possible traditionally. This paper conducts a research on the top 6 algorithms that has been utilized for fake news detection in the recent 5 years (2017–2021). From the top 6 algorithms, the best performing algorithm was determined using the confusion matrix. The literature review shows that Naïve Bayes (NB), Decision Tree (DT), Random Forest (RF), Support Vector machine (SVM), K-Nearest Neighbor (KNN) and Logistic Regression (LR) were the best algorithms that have been utilized for fake news detection in the recent 5 years indicating that simple algorithms had better accuracy and performed much better compared to the complicated ones. The experimental outcomes indicate that out of the top 6 algorithms, the best performing algorithm is Decision Tree with an accuracy of 99.38% whereas the least performing algorithm was K-Nearest Neighbor with an accuracy of 86.59%. The confusion matrix determines the performance of algorithms that also indicates that Decision Tree got the highest score compared to other algorithms for True Positive (7008/13470), where the algorithm correctly identified fake news. Hence proving that the best algorithm is the Decision Tree from the top 6 algorithms to identify a fake news accurately from this study. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

Child Tracking and Prediction of Violence on Children In Social Media Using Natural Language Processing and machine learning 1

引用

22nd international conference on Artificial Intelligence and soft computing, ICAISC 2023

作者： Nallakaruppan, M.K. Srivastava, Gautam Gadekallu, Thippa Reddy Reddy, Praveen Kumar Krishnan, Sivarama Polap, Dawid School of Information Technology and Engineering Vellore Institute of Technology Vellore India Department of Mathematics and Computer Science Brandon University Brandon Canada School of Information Technology and Engineering Vellore Institute of Technology Department of Electrical and Computer Engineering Lebanese American University Lebanon Zhongda Group Zhejiang Jiaxing314312 China Faculty of Applied Mathematics Silesian University of Technology Kaszubska 23 Gliwice44-100 Poland

ISBN: (数字)9783031425059

ISBN: (纸本)9783031425042

Crimes against children are a direct threat to the world of tomorrow. The future of the world is in the hands of children who are the real wealth of mankind. The challenge for society is to address and handle challenges against children such as sexual abuse, trafficking, child labour, and harassment. Even though measures are taken by several governments globally, violence against children is an evident day-to-day process. This becomes the most challenging issue in the post-Internet era, where organized crime against children takes place around the world through a targeted group of people through virtual private networks and the dark web. What comes next is the emergence of social media, which provides an opportunity for these criminals to harm children globally through powerful social networks [15]. The existing methods focus on classifiers that do not work across multiple keywords [6] to look for sentiment analysis regarding a single classifier. There are only a few works that address the issue and these methods do not employ the multi-classification model [2]. In this paper, we propose a model which includes the Twitter API, the APIFY web scraper framework, as well as the VADER semantic analyzer in Python which possesses Decision tree analysis of sentiment prediction. The proposed work provides 99.6904% accuracy and predicts the classification of sentiment analysis. Our work has novelty in attribute selection, child trade, and violence analysis in social media and prediction of classification accuracy using the Decision Tree algorithm. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： machine learning

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275页 << < 26 27 28 29 30 31 32 33 34 35 > >>

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：