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

64,565 篇 会议
577 册 图书
325 篇 期刊文献

馆藏范围

65,466 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

38,377 篇 工学
- 35,097 篇 计算机科学与技术...
- 23,067 篇 软件工程
- 8,726 篇 信息与通信工程
- 5,271 篇 电气工程
- 4,761 篇 生物工程
- 4,756 篇 控制科学与工程
- 3,813 篇 生物医学工程（可授...
- 1,632 篇 网络空间安全
- 1,578 篇 安全科学与工程
- 1,451 篇 光学工程
- 1,326 篇 机械工程
- 1,256 篇 动力工程及工程热...
- 1,242 篇 电子科学与技术（可...
- 1,192 篇 仪器科学与技术
- 1,187 篇 化学工程与技术
14,081 篇 理学
- 7,955 篇 数学
- 5,257 篇 生物学
- 3,617 篇 统计学（可授理学、...
- 3,113 篇 系统科学
- 2,333 篇 物理学
- 1,213 篇 化学
8,772 篇 管理学
- 5,070 篇 管理科学与工程(可...
- 4,398 篇 图书情报与档案管...
- 2,233 篇 工商管理
4,424 篇 医学
- 3,697 篇 临床医学
- 3,227 篇 基础医学(可授医学...
- 2,071 篇 药学(可授医学、理...
- 1,730 篇 公共卫生与预防医...
1,854 篇 法学
- 1,604 篇 社会学
1,181 篇 农学
- 1,154 篇 作物学
991 篇 经济学
733 篇 教育学
338 篇 文学
45 篇 艺术学
39 篇 军事学
5 篇 哲学
5 篇 历史学

主题

17,102 篇 machine learning
5,347 篇 machine learning...
4,566 篇 deep learning
4,067 篇 accuracy
3,975 篇 support vector m...
3,447 篇 computational mo...
3,313 篇 training
3,214 篇 predictive model...
2,935 篇 feature extracti...
2,038 篇 data models
1,946 篇 cloud computing
1,760 篇 real-time system...
1,714 篇 neural networks
1,603 篇 adversarial mach...
1,555 篇 prediction algor...
1,326 篇 artificial intel...
1,303 篇 data mining
1,282 篇 decision trees
1,209 篇 random forests
1,156 篇 internet of thin...

机构

201 篇 chitkara univers...
127 篇 school of comput...
80 篇 karnataka bangal...
77 篇 school of comput...
65 篇 department of co...
55 篇 lovely professio...
48 篇 chennai
47 篇 centre of interd...
44 篇 univ chinese aca...
42 篇 school of comput...
40 篇 graphic era deem...
35 篇 shenyang institu...
34 篇 graphic era deem...
31 篇 department of co...
30 篇 department of in...
29 篇 department of co...
29 篇 shanghai jiao to...
28 篇 computer science...
27 篇 department of co...
27 篇 delhi technologi...

作者

36 篇 anjali t.
26 篇 isman kurniawan
25 篇 de-shuang huang
24 篇 rakesh kumar
23 篇 kumar anil
22 篇 hui wang
21 篇 amandeep nagpal
21 篇 kumar ajay
21 篇 gill kanwarparta...
20 篇 qin zengchang
20 篇 sharif mhd saeed
19 篇 yu wang
19 篇 ajay kumar
18 篇 qomariyah nunung...
18 篇 pareek piyush ku...
16 篇 schuurmans dale
16 篇 andersson karl
16 篇 kumar manish
15 篇 shiva mehta
15 篇 lakshmanarao a.

语言

55,276 篇 英文
10,017 篇 其他
635 篇 中文
3 篇 法文
2 篇 葡萄牙文
2 篇 俄文
1 篇 西班牙文
1 篇 土耳其文

检索条件"任意字段=International Conference on Machine Learning and Computing"

共 65467 条记录，以下是4731-4740 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：

Android Malware Detection System using machine learning 2024

Android Malware Detection System using Machine Learning

引用

16th international conference on Contemporary computing, IC3 2024

作者： Kaur, Amanpreet Lal, Sangeeta Goel, Shruti Pandey, Mrinal Agarwal, Astha Department of Computer Science and Engineering & IT Jaypee Institute of Information Technology India School of computing and mathematics Keele University Newcastle-under-Lyme United Kingdom

ISBN: (纸本)9798400709722

Detecting Android malware is imperative for safeguarding user privacy, securing data, and preserving device performance. Consequently, numerous studies have underscored the complexities associated with Android malware detection, prompting a multidimensional approach to tackle these challenges effectively. This research leverages machine learning techniques, emphasizing feature extraction, classification algorithms, and both supervised and unsupervised learning methodologies. The exploration begins with in-depth Exploratory Data Analysis (EDA) to gain insights into the dataset, paving the way for informed decision-making. Principal Component Analysis (PCA) is employed for dimensionality reduction, a pivotal step in handling the multivariate nature of the data. The integration of API calls, clustering, and anomaly detection further enriches the model's capability to discern between benign and malicious applications. Crucially, the study delves into the intricacies of sampling, evaluation, and the Confusion Matrix to quantify the model's performance accurately. The utilization of diverse classification algorithms, including Support Vector machines (SVM), Multi-Layer Perceptrons (MLP), Random Forest, GaussianNB, Decision Tree, and Logistic Regression, underscores the comprehensive nature of the approach. These algorithms collectively contribute to a robust and versatile Android malware detection model capable of adapting to varying threat scenarios. The dataset employed for training and evaluation is sourced from Kaggle, encompassing 29,999 Android applications categorized as benign or malicious based on permissions sought. Current detection methods, deemed resource-intensive and exhaustive, face the challenge of keeping pace with the relentless evolution of new malware strains. This research seeks to address this gap by proposing a sophisticated, machine learning-driven model that not only enhances accuracy but also demonstrates efficiency and adaptability in the face

关键词： Android malware

来源：评论

学校读者我要写书评

暂无评论

Enhancing Intrusion Detection in Education Sector Through Advanced machine learning Techniques: A Comparative Study 1

Enhancing Intrusion Detection in Education Sector Through Ad...

引用

1st international conference for Women in computing, InCoWoCo 2024

作者： Alegre, Glorybe E. Cerna, Patrick D. College of Computer Studies Central Philippines State University Kabankalan City Philippines College of Information and Communication Technology and Engineering State University of Northern Negros Sagay City Philippines

ISBN: (纸本)9798331518943

Intrusion Detection Systems (IDS) are important for identifying potential security threats, recording relevant data, reporting suspicious activity, and facilitating continuous improvement of security protocols. This study presents an IDS model developed using a range of machine learning (ML) techniques, including Naïve Bayes, Decision Tree, Support Vector machines (SVM), and Random Forest. Each model's performance was assessed using standard metrics like F1-score, recall, accuracy, and precision. The KDD Cup 99 dataset, which contains a comprehensive set of audit data simulating various types of network intrusions in a military environment, was employed for training and testing the models. The primary objective was to enhance the effectiveness of intrusion detection and safeguard computer networks in the education sector. Deploying an Intrusion Detection System (IDS) in schools and universities improves security by preventing unauthorized access, detecting potential cyber threats, and protecting sensitive information of students and faculty. Results indicate that the Random Forest algorithm, followed closely by SVM and Decision Tree, demonstrated the highest reliability and efficiency in detecting intrusions. The results emphasize the vital significance of carefully choosing machine learning models to enhance cybersecurity resilience in educational infrastructures. © 2024 IEEE.

关键词： Decision Tree IDS Naïve Bayesian Random Forest Support Vector machines (SVM)

来源：评论

学校读者我要写书评

暂无评论

CCSF: Clustered Client Selection Framework for Federated learning in non-IID Data 16

CCSF: Clustered Client Selection Framework for Federated Lea...

引用

16th IEEE/ACM international conference on Utility and Cloud computing (UCC) / 10th IEEE/ACM international conference on Big Data computing, Applications and Technologies (BDCAT)

作者： Mohamed, Aissa H. de Souza, Allan M. da Costa, Joahannes B. D. Villas, Leandro A. Dos Reis, Julio C. Univ Campinas UNICAMP Campinas SP Brazil

ISBN: (纸本)9798400702341

Federated learning (FL) is a distributed approach where numerous devices train a shared global model for machine learning (ML) tasks. At every training round, the client devices must share their new local gradients with the central server to update the global model. Hence, FL requires high communication costs in terms of bandwidth and the number of messages exchanged between FL clients and the central server, leading to many issues, such as communication bottlenecks and scaling in the network. Consequently, having all devices participating in every training round is not practical. Moreover, the devices' local datasets are usually not Independent and Identically Distributed (IID), posing additional challenges for training the global model. In this sense, we introduce a Clustered Client Selection Framework (CCSF) to decrease the overall communication costs for training an ML model in the FL environment. CCSF clusters the client devices and employs a biased client selection strategy with two main objectives: (i) reducing the number of devices training at every round;and (ii) the number of rounds required to reach convergence. Our experimental evaluations, conducted on two well-known datasets, MNIST and MotionSense, show that CCSF is highly efficient, where the clients' local datasets can be grouped into homogeneous clusters. In MNIST, CCSF reaches an accuracy score above 60% in less than 50 rounds compared to FedAvg at 50% after 100 rounds. The performance gap is wider in the MotionSense data. CCSF reaches an accuracy score of 70% in a little more than 20 training rounds compared to FedAvg below 30% of accuracy in the first 100 FL rounds.

关键词： Federated learning Communication machine learning

来源：评论

学校读者我要写书评

暂无评论

Quantum Many-Body Problems: Quantum machine learning Applications 1

引用

international conference on Internet of Everything and Quantum Information Processing, IEQIP 2023

作者： Medisetty, Pramoda Vulavalapudi, Veda Manohara Sunanda Evuru, Poorna Chand Mahmud, Mufti Prakash, Kolla Bhanu Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Computing and Technology Nottingham Trent University Nottingham United Kingdom

ISBN: (数字)9783031619298

ISBN: (纸本)9783031619281

A groundbreaking approach has been developed to accurately depict and predict the essential traits of quantum many-body systems. This inventive technique merges quantum ground state computations, classical shadow representations, and sophisticated machine learning approaches. The newly engineered software precisely identifies the ground state of a 2D antiferromagnetic Heisenberg model and forms a classical shadow representation for the quantum state. machine learning models are deployed to forecast ground-state correlation functions with remarkable accuracy. This pioneering technique shows enormous potential in transforming quantum simulations and quantum machine learning, offering flexible solutions for complex quantum *** implications extend to fields such as material science and drug discovery. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Utilizing Big Data and machine learning to Improve Aerobic Exercise Data Collection and Analysis 3

Utilizing Big Data and Machine Learning to Improve Aerobic E...

引用

3rd international conference on Data Analytics, computing and Artificial Intelligence, ICDACAI 2024

作者： Qiao, Shu Wuhan Qingchuan University Hubei Wuhan420204 China

ISBN: (纸本)9798331519032

Traditional data collection and analysis methods are manually recorded and analyzed, with low efficiency and poor accuracy. Additionally, due to limitations in the number of athletes and equipment, only a small number of data can be recorded, which cannot fully reflect the effectiveness of aerobic exercise. This poses significant limitations for the development of scientific fitness programs and the evaluation of their effectiveness. This article aims to improve the accuracy, real-time performance, and personalization of exercise data, in order to better guide and improve the exercise effectiveness and health status of users. This article utilizes BD (Big Data) and ML (machine learning) techniques to improve the collection and analysis of aerobic exercise data. This can improve the efficiency and accuracy of data collection and analysis, more comprehensively reflect the effects of aerobic exercise, and provide stronger support for scientific research and practice in the field of health. The accuracy rates of collecting athlete data for the control group and experimental group were 72.41% and 85.56%, respectively. The accuracy rate of collecting data for the experimental group was higher. The results of this article can optimize personalized exercise recommendations and predict health risks, improve exercise effectiveness and health management level. © 2024 IEEE.

关键词： Data accuracy

来源：评论

学校读者我要写书评

暂无评论

The Role of AI in University Course Registration in the Middle East: AI and machine learning Approaches to Improve Academic Performance 2

The Role of AI in University Course Registration in the Midd...

引用

2nd international conference on computing and Data Analytics, ICCDA 2024

作者： Shannaq, Boumedyen University of Buraimi Dep. Managment of Information Systems Al Buraimi Oman

ISBN: (纸本)9798331542573

This study investigates the use of machine learning in improving course selection for Middle Eastern university students, with a focus on the University of Oman. We offer an intelligent application that uses classification techniques to select the best course combinations based on individual student characteristics. The technology forecasts probable grades based on historical data from 1,972 student records with 26 characteristics, allowing students to make educated course selections. The research employs the (CRISP-DM) methodology, which ensures a methodical approach to data analysis and model creation. The findings demonstrate that with an accuracy rate of 96.37% vs. RandomTree's 84.10%, the J48 algorithm outperforms RandomTree by 12.27%. RandomTree has a misclassification rate of 15.90%, but J48 has a far lower rate of misclassification of 3.63%. J48 outperforms RandomTree in prediction accuracy, with a (MAE) of 0.0287 and a (RMSE) of 0.1259. J48 outperforms all other classes in terms of true positive rates, precision, and ROC areas, as evidenced by class-specific accuracy statistics. This study fills a vacuum in current academic advising systems, which frequently lack tailored, data-driven suggestions. Our suggested system improves academic achievement, lowers dropout rates, and streamlines course preparation, representing a substantial improvement in educational technology. The application's ability to replace traditional advising with a more efficient, personalized method is especially significant at educational institutions throughout the Gulf area. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Proceedings of the 2024 10th international conference on Biosignals, Images and Instrumentation, ICBSII 2024

Proceedings of the 2024 10th International Conference on Bio...

引用

10th international conference on Biosignals, Images and Instrumentation, ICBSII 2024

ISBN: (纸本)9798350350951

The proceedings contain 71 papers. The topics discussed include: image analysis for diagnosis of glaucoma using soft computing methods;multimodal analysis of bone mineral density classification;chitosan based biopatches impregnated with Amaranthus spinosus herbal drug for wound healing application;comparison of DL model for retinal diseases classification using OCT images;structural MR brain image based diagnosis of Alzheimer’s disease using extreme learning machine;machine learning-based early seizure detection: a random forest classifier approach for pre-ictal stage prediction in epilepsy;a novel miniaturized hexagonal-shaped patch antenna for medical applications;and emotion–based media playback system for autistic children.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Bayesian Approach for Raisin Data Classification

A Bayesian Approach for Raisin Data Classification

引用

2023 international Research conference on Smart computing and Systems Engineering, SCSE 2023

作者： Kumari, H.M.N.S. Nawarathne, U.M.M.P.K. University of Peradeniya Computing Centre Faculty of Engineering Peradeniya Sri Lanka

ISBN: (纸本)9798350341454

Raisin performs a decisive role in the commodity economy. Recently, low-quality raisin products have been introduced to agricultural markets worldwide. Therefore, it is crucial to identify a suitable classification method to distinguish between varieties of raisins. Previous research has employed various traditional machine learning methods to classify commodities. However, it is challenging to quantify uncertainties through traditional machine learning models. Therefore, this study employed a Bayesian Logistic Regression (BLR) model using seven morphological features of two varieties of raisins grown in Turkey. Initially, different machine learning techniques were employed on data. After that, four priors, such as Jefferys, Laplace, Cauchy, and Gaussian, were considered, and hyperparameters were tuned using the empirical Bayes method. Marginal posterior distributions of the model parameters were estimated, and the convergence of the models was checked. Then, evaluation metrics of the BLR model with different priors were compared to those of machine learning models. According to the results, the BLR model with Gaussian prior produced the highest accuracy of 93%. Finally, it can be concluded that the BLR model with Gaussian prior provides substantially better results when classifying raisin data. © 2023 IEEE.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

Leveraging Hybrid machine learning Technique For Effective House Rental Price Forecasting 11

Leveraging Hybrid Machine Learning Technique For Effective H...

引用

11th IEEE international conference on Advances in computing and Communications, ICACC 2024

作者： Komalavalli, D. Anbalagan, Naveenkumar Madhumitha, M. Najma, N. Kaviyanbu, K. Mathiyarasi, P. Sona College of Technology Department of Information Technology Tamil Nadu Salem India Sona College of Technology Department of Information Technology Salem India

ISBN: (纸本)9798350379136

These days, figuring out the house's sale price is crucial because both the land and the house's prices rise annually. Therefore, the next generation needs a simple approach for forecasting future rents for houses. Accurate and data-driven strategies are crucial for estimating house rental prices in the rapidly shifting real estate market. This study investigates the application of machine learning to accurate rental price forecasting. Conventional methods of rental estimation tend to rely on intuition and knowledge of the marketplace in the area, but they are incapable of taking into consideration the complex network of variables which influence rental rates. This study explores how rental prices are influenced by factors including the age of the property, whether it is furnished, the flat area, the size of the house etc. It also shows how machine learning models may be used to find hidden connections and get precise rental price predictions. The dearth of highly accurate models currently in use, the need to reduce overpricing, adjust to changing conditions in the market, maximize the return on investments, support data-driven decision-making, advance market transparency, and create a simple to use interface are among the reasons behind this research. The main goal is to create a reliable and accurate model for predicting the price of a rental home so that tenants and property owners can benefit from data-driven insights. © 2024 IEEE.

关键词： Regression analysis

来源：评论

学校读者我要写书评

暂无评论

An Efficient Cancer Detection Model Using ML and Transfer learning Techniques 2

An Efficient Cancer Detection Model Using ML and Transfer Le...

引用

2nd international conference on Distributed computing and Optimization Techniques, ICDCOT 2024

作者： Raja Rao, P.B.V. Prasad, M. Kiran Sree, P. Satyanarayana Murty, P.T. S Mallesh, A. Satyanarayana, B.V.V. A.P Bhimavaram India Vishnu Institute of Technology A.P Bhimavaram India

ISBN: (纸本)9798350382952

Cancer, a pervasive and challenging medical condition, demands innovative approaches for early detection and intervention. This paper explores the complex domain of breast cancer detection, recognizing its crucial impact on improving patient outcomes. Two machine learning algorithms, namely SVM and RF, along with three transfer learning strategies employing distinct pre-trained CNN architectures (ResNet50, VGG16, and InceptionV3), were employed to augment the accuracy of cancer detection. The proposed algorithms are implemented with a Kaggle breast cancer dataset with three labels namely benign, malignant and normal. The transfer learning models are trained on the dataset, with evaluations performed using a validation set. For comparison, conventional ML classifiers and also applied for the cancer detection. The proposed transfer learning showcases a comprehensive approach to cancer detection by leveraging the strengths of multiple pre-trained CNN architectures. The proposed transfer learning given accuracy of 98.9% for breast cancer detection. The results of the experiments revealed that transfer learning outperformed the conventional methods for cancer detection. © 2024 IEEE.

关键词： Diseases

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 << < 470 471 472 473 474 475 476 477 478 479 > >>

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：