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

13,381 篇 会议
22 册 图书
17 篇 期刊文献

馆藏范围

13,420 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

7,102 篇 工学
- 6,395 篇 计算机科学与技术...
- 5,092 篇 软件工程
- 1,730 篇 信息与通信工程
- 1,007 篇 控制科学与工程
- 735 篇 生物工程
- 721 篇 电气工程
- 667 篇 生物医学工程（可授...
- 582 篇 网络空间安全
- 511 篇 光学工程
- 451 篇 安全科学与工程
- 398 篇 电子科学与技术（可...
- 344 篇 交通运输工程
- 328 篇 动力工程及工程热...
- 288 篇 机械工程
- 267 篇 仪器科学与技术
2,667 篇 理学
- 1,298 篇 数学
- 879 篇 生物学
- 637 篇 统计学（可授理学、...
- 535 篇 物理学
- 328 篇 系统科学
- 242 篇 化学
1,877 篇 管理学
- 1,300 篇 管理科学与工程(可...
- 789 篇 图书情报与档案管...
- 573 篇 工商管理
947 篇 医学
- 687 篇 临床医学
- 681 篇 基础医学(可授医学...
- 659 篇 公共卫生与预防医...
- 264 篇 药学(可授医学、理...
508 篇 法学
- 471 篇 社会学
262 篇 经济学
- 262 篇 应用经济学
223 篇 农学
210 篇 教育学
198 篇 文学
9 篇 军事学
1 篇 艺术学

主题

2,088 篇 accuracy
1,690 篇 deep learning
1,202 篇 machine learning
1,188 篇 computational mo...
1,044 篇 training
1,028 篇 data models
971 篇 predictive model...
724 篇 real-time system...
687 篇 convolutional ne...
685 篇 feature extracti...
676 篇 machine learning...
585 篇 support vector m...
531 篇 federated learni...
484 篇 adversarial mach...
330 篇 contrastive lear...
312 篇 medical services
296 篇 random forests
289 篇 analytical model...
278 篇 prediction algor...
271 篇 neural networks

机构

105 篇 lovely professio...
101 篇 chitkara univers...
57 篇 department of co...
47 篇 school of comput...
45 篇 school of comput...
45 篇 department of co...
42 篇 the islamic univ...
42 篇 department of co...
40 篇 shandong provinc...
36 篇 college of compu...
32 篇 university of ch...
28 篇 department of co...
27 篇 department of co...
26 篇 computer science...
26 篇 graphic era deem...
25 篇 department of co...
25 篇 amrita vishwa vi...
25 篇 the islamic univ...
23 篇 department of co...
23 篇 college of intel...

作者

23 篇 satvik vats
18 篇 shiva mehta
18 篇 kukreja vinay
18 篇 vats satvik
17 篇 anjali t.
17 篇 rishabh sharma
17 篇 deepak upadhyay
16 篇 senthil kumar th...
15 篇 singh jagendra
15 篇 venugopalan manj...
13 篇 manjunatha
13 篇 rakesh kumar
12 篇 ramya maranan
11 篇 selvanayaki kola...
11 篇 s. kaliappan
10 篇 andersson karl
10 篇 muthukumaravel a...
10 篇 yang yang
10 篇 ayodeji olalekan...
10 篇 surendran r.

语言

12,153 篇 英文
1,266 篇 其他
306 篇 中文
1 篇 西班牙文

检索条件"任意字段=2024 International Conference on Computing, Machine Learning and Data Science, CMLDS 2024"

共 13420 条记录，以下是251-260 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：

Attention to Retention: Exploring the Dynamics of data Scientist Job Mobility

Attention to Retention: Exploring the Dynamics of Data Scien...

引用

2024 international conference on Advances in computing Research on science Engineering and Technology, ACROSET 2024

作者： Panda, Amiya Ranjan Sanyal, Debdeep Sur, Anikat Mishra, Manoj Kumar School of Computer Engineering Kiit Deemed to Be University Bhubaneswar India

ISBN: (纸本)9798350388800

High turnover rates are a significant challenge in the data science industry, leading to concerns about talent retention and long-term growth. This study tackles the pressing challenge of predicting job change behavior among data scientists using machine learning to inform effective retention strategies. We analyze the 'HR Analytics: Job Change of data Scientists' dataset on Kaggle, employing a rigorous pipeline that addresses categorical data, missing values, and class imbalance through techniques such as Label Encoding, KNN Imputation, and up-sampling. Seven machine learning models are evaluated, with Extreme Gradient Boosting emerging as the frontrunner, attaining a test score of 84.4% and a noteworthy F1 score of 84.5%. To delve deeper into model interpretability, we leverage feature importance analysis, revealing key insights into factors influencing job change. By proactively identifying individuals at risk and implementing targeted interventions based on predictive insights, organizations can foster a more engaging and fulfilling work environment, leading to enhanced retention rates, employee satisfaction, and overall lower employee attrition. © 2024 IEEE.

关键词： Predictive analytics

来源：评论

学校读者我要写书评

暂无评论

Review on data Balancing Approaches for Skewed data Classification 7

Review on Data Balancing Approaches for Skewed Data Classifi...

引用

7th international conference on Contemporary computing and Informatics, IC3I 2024

作者： Patel, Vaibhavi Bhavsar, Hetal The Maharaja Sayajirao University of Baroda Department of Computer Science and Engineering Baroda India

ISBN: (纸本)9798350350067

Addressing the skewed data set is a significant concern nowadays. In machine learning, using such data to learn distributions poses a significant challenge. This problem arises when there are significantly more samples of one class, also known as a majority class, than of the other classes. The more important class is known as a minority. As a result of its use in so many real-world applications, researchers' interest in it is increasing. This issue of class imbalance has been studied using a variety of methods over the past few years, including methods such as data resampling, ensemble techniques, cost-sensitive analysis, hybrid methods, etc. With an emphasis on binary-class and multi-class problems, the goal of this study is to discuss the current state of the art on various strategies to comprehend the state of machine learning strategies for enhancing classification output and addressing class imbalance issues. © 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

Predicting College Dropout Rates using machine learning: A Student Success Initiative 1

Predicting College Dropout Rates using Machine Learning: A S...

引用

1st IEEE international conference on computing and data science, ICCDS 2024

作者： Karpagalakshmi, P. Dhanashree, S.R. Wahidh, M. Abdul Rajesh, A. VISTAS Chennai India

ISBN: (纸本)9798350365337

A student who departs from a college without completing a mandatory credit course is referred to as a "dropout". College dropout rates are a major global problem, with over 40% of students abandoning their studies before completing their degrees. This can have an impact on an individual's life and well-being of society. machine learning has the potential to reduce college dropout rates by predicting which students are at risk of dropping out and providing them with early intervention support. We have identified and framed the features of the dataset into Academic, Demographic and Socioeconomic features and the ensembled output from these modules have been combined to predict the final dropout. We have used SVM model to train, evaluate and predict the dropout rates with 91.61% of accuracy. © 2024 IEEE.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Prevention of Security Attacks at Wireless Network Layers using machine learning Techniques 18

Prevention of Security Attacks at Wireless Network Layers us...

引用

18th INDIAcom;11th international conference on computing for Sustainable Global Development, INDIACom 2024

作者： Ramisetty, Sowjanya Ghantasala, G. S. Pradeep Gupta, Gaurav Sonia Kg Reddy College of Engineering & Technology Department of Computer Science and Engineering Hyderabad India Alliance University Department of Computer Science and Engineering Bengaluru India Shoolini University Yogananda School of Artificial Intelligence Computers and Data Sciences Solan India

ISBN: (纸本)9789380544519

Wireless sensor networks have great potential for use in flood control, weather forecasting systems, the military, and the healthcare industry. A WSN's nodes are connected to one another and share information. When sending data between nodes, data security is essential. In the world of networking, security is a top priority. data travels along a path when it is transferred from one location to another, increasing the likelihood that a rogue node would enter the network. Detecting malicious nodes within a network presents a formidable challenge, particularly due to external assaults on data packets during their transmission between nodes. Hackers take advantage of weaknesses to manipulate and alter data as it travels from the source node to the destination node. This paper looked at machine learning algorithm-based preventive measures and layer-level security challenges. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： machine learning Security Security attacks Wireless Sensor Networks

来源：评论

学校读者我要写书评

暂无评论

Bias and Generalizability Challenges in machine learning Models for Leptospirosis

Bias and Generalizability Challenges in Machine Learning Mod...

引用

2024 IEEE international conference on Big data, Bigdata 2024

作者： Jadhav, Arya Sawesi, Suhila Rashrash, Bushra College of Computing Grand Valley State University Grand Rapids United States

ISBN: (纸本)9798350362480

This paper highlights the presence of bias in machine learning (ML) and deep learning (DL) models used in leptospirosis research. Of the 17 studies reviewed, only four showed low analysis bias, while the remaining 13 had issues such as data imbalance, small sample sizes, and geographic bias. These biases negatively affect model performance and limit generalizability. We explore the sources of these biases - such as feature selection, dataset composition, and model design - and discuss their impact on predictive accuracy. Finally, we propose strategies to reduce these biases, aiming to improve the fairness, reliability, and performance of ML/DL models in leptospirosis prediction and diagnosis. © 2024 IEEE.

关键词： Contrastive learning

来源：评论

学校读者我要写书评

暂无评论

A Multi-Classifier Approach to Landslide Susceptibility Mapping Using Geospatial data and machine learning in the Ghats Regions of Karnataka 5

A Multi-Classifier Approach to Landslide Susceptibility Mapp...

引用

5th IEEE international conference on Communication, computing and Industry 6.0, C2I6 2024

作者： Anil, D. Manjula, S.H. University Visvesvaraya College of Engineering Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350379884

Landslides pose a significant threat to life, infrastructure and the environment in the Western Ghats region of Karnataka. This study develops a comprehensive Landslide Susceptibility Mapping (LSM) model by integrating machine learning algorithms Random Forest, CART, Naive Bayes and Support Vector machine (SVM) with remote sensing and geospatial data. The model incorporates critical geographic and environmental factors such as slope, aspect, elevation and rainfall to predict landslide susceptibility across six districts: Kodagu, Shimoga, Chikkamagaluru, Uttara Kannada, Dakshina Kannada and Udupi. The Random Forest model demonstrated the highest accuracy (94.2%) in predicting landslide-prone areas. Results indicate that Shimoga, Chikkamagaluru and Uttara Kannada have higher susceptibility, particularly in the Moderate and High categories, underscoring the need for targeted mitigation strategies. This study highlights the effectiveness of machine learning in geohazard mapping and provides a scalable approach that can be adapted for other regions vulnerable to landslides. © 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

Evolutional Study on KNN and K-means Algorithms

Evolutional Study on KNN and K-means Algorithms

引用

2024 international conference on Electrical, Electronics and computing Technologies, ICEECT 2024

作者： Vaishnav, Hritika Choudhary, Aanamika JIET Computer Science and Engineering Rajasthan Jodhpur India

ISBN: (纸本)9798350378092

We are currently in a technological age when fresh innovations are making human job quick and easy. One of these subjects is machine learning, which enables a machine to carry out tasks in accordance with what it has learned and understood. machine learning is being utilized to guide a lot of programmers online. These devices can recognize our traditions and preferences in the same way that people do. machine learning is now applied in a wide range of industries, including finance, medicine, retail, social media, robotics, automation, and game apps. These various sectors employ various algorithms. The objective of the work is a comparison of the KNN and K-means algorithms. They differ from one another marginally, according to the evaluation research. K-means clustering is a tool for organizing data into groups without predefined categories, like sorting a mixed bag of items based on their similarities. Conversely, KNN (K-Nearest Neighbors) is a useful machine learning technique in which fresh instances are classified or predictions are made by the algorithm based on labeled examples. We have also implemented and compared the performance of these algorithms using a real-world dataset. © 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

Future for Cleaner Environment: A Hybrid machine learning Approach for Intelligent Waste Management

Future for Cleaner Environment: A Hybrid Machine Learning Ap...

引用

2024 international conference on Electrical, Electronics and computing Technologies, ICEECT 2024

作者： Anupriya, Anupriya Kumar, Ratish Chandiagrh University Computer Science and Engineering Punjab Mohali India

ISBN: (纸本)9798350378092

Intelligent waste management is becoming increasingly vital in addressing the challenges posed by escalating waste volumes and environmental concerns. The standard methods for handling waste are frequently economical and ineffective thus sophisticated technology alternatives must be used. Using the strengths of both convolutional & recurrent neural networks, this research suggests a hybrid machine learning strategy for intelligent trash management. The study highlights the opportunity of machine learning to transform the effective management of waste and also clarifies the shortcomings of traditional approaches through an extensive analysis of current approaches. Specifically, the focus is on RNN-based algorithms for sequential data processing. The experimental evaluation demonstrates the superiority of RNN-based architectures, particularly gated recurrent units (GRU) and bidirectional GRU models, in classifying waste images into distinct categories. These models exhibit robust performance and outperform other RNN and CNN algorithms, validating the efficacy of the proposed hybrid approach. © 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

ADVANCED CHAT APPLICATION INTEGRATING machine learning FOR SENTIMENTAL ANALYSIS

ADVANCED CHAT APPLICATION INTEGRATING MACHINE LEARNING FOR S...

引用

2024 international conference on computing and Intelligent Reality Technologies, ICCIRT 2024

作者： Layek, Santanu Sharma, Gauransh Agarwal, Vihan Chandru Vignesh, C. School of Computer Science and Engineering Vellore Institute of Technology Tami Nadu Vellore India School of Computer Science and Engineering Tami Nadu Vellore India

ISBN: (纸本)9798331510299

The "Advanced Chat Application Integrating machine learning for Sentimental Analysis" is a progressive interface that utilizes machine learning (ML) algorithm to analyze user's interaction and offer compatibility services with emotional intelligence in real-time. This platform offers the chat user an intelligent map of a comprehensive communication interface with the ability to also complete the human ability in the sense of the differentiation between emotions where it will show empathy based on the context of the user. By the implementation of sentiment analysis feature, the chat application is able to identify the emotions as happy, sad, angry or neutral that people portray when in a conversational mode of text. This is made possible by natural language processing (NLP) encoding using the artificial intelligence models that enjoy the capacity to determine the emotional content of the text messages by virtue of deep learning algorithms which were trained on large data. Therefore, the sentiment detection is able to increase the level of participation, improve on the services offered to patrons, and most importantly, the identification of symptoms of mental disorders in their early stages. © 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

Software Bug Prediction using machine learning 15

Software Bug Prediction using Machine Learning

引用

15th international conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2024

作者： Sathiyaraj, A. Angel, T.G. Ruby Turukmane, Anil V. Ramkumar, D. Guravaiah, Y. Department of Information Technology School of Computing Sathyabama Institute of Science and Technology Tamil Nadu Chennai600 119 India School of Computer Science and Engineering VIT-AP University Inavolu Andhra Pradesh Amaravati522237 India Department of Computer Science and Engineering MJR College of Engineering and Technology Diguvapokulavaripalli Pulicherla Andra Pradesh Chittoor District517214 India

ISBN: (纸本)9798331300579

The entire success of software is impacted by software bug prediction (SBP), a crucial part of the software development and maintenance life cycles. It is necessary to anticipate issues in order to increase the software's dependability, efficiency, and cost. In spite of the fact that a number of methods have been put out in the literature, it is difficult to create a reliable bug prediction model. In this paper, a machine learning (ML) based prediction method for software issues is introduced. Algorithms for guided machine learning that are based on historical data have been used to predict software errors. Two of the classifiers are Support Vector machine and Naive Bayes (NB). How accurate and useful ML methods can be employed was demonstrated through the evaluation process. Included is the utilization of a comparative measure. © Grenze Scientific Society, 2024.

关键词： Prediction 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500页 << < 22 23 24 25 26 27 28 29 30 31 > >>

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：