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

22,626 篇 会议
3,225 篇 期刊文献
19 篇 学位论文
7 册 图书

馆藏范围

25,876 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

5,151 篇 工学
- 3,093 篇 计算机科学与技术...
- 2,542 篇 电气工程
- 1,383 篇 信息与通信工程
- 686 篇 控制科学与工程
- 593 篇 软件工程
- 285 篇 仪器科学与技术
- 218 篇 环境科学与工程（可...
- 189 篇 机械工程
- 183 篇 生物医学工程（可授...
- 163 篇 交通运输工程
- 149 篇 测绘科学与技术
- 130 篇 生物工程
- 122 篇 石油与天然气工程
- 121 篇 电子科学与技术（可...
- 117 篇 材料科学与工程（可...
- 111 篇 网络空间安全
- 98 篇 土木工程
- 92 篇 动力工程及工程热...
- 66 篇 水利工程
943 篇 理学
- 289 篇 生物学
- 188 篇 物理学
- 155 篇 化学
- 139 篇 地球物理学
- 132 篇 数学
- 84 篇 系统科学
612 篇 管理学
- 525 篇 管理科学与工程(可...
- 69 篇 图书情报与档案管...
437 篇 医学
- 284 篇 临床医学
- 95 篇 基础医学(可授医学...
227 篇 农学
- 148 篇 作物学
53 篇 经济学
52 篇 法学
49 篇 文学
47 篇 教育学
17 篇 艺术学
3 篇 军事学
2 篇 哲学

主题

25,877 篇 machine learning...
8,356 篇 machine learning
4,256 篇 support vector m...
4,174 篇 accuracy
4,172 篇 prediction algor...
3,590 篇 predictive model...
3,467 篇 training
3,208 篇 classification a...
3,170 篇 feature extracti...
1,941 篇 data models
1,801 篇 computational mo...
1,776 篇 deep learning
1,658 篇 random forests
1,466 篇 neural networks
1,387 篇 clustering algor...
1,384 篇 real-time system...
1,274 篇 data mining
1,133 篇 optimization
998 篇 decision trees
975 篇 measurement

机构

120 篇 chitkara univers...
39 篇 lovely professio...
36 篇 chitkara centre ...
30 篇 computer science...
30 篇 centre of resear...
28 篇 department of co...
26 篇 school of comput...
21 篇 univ chinese aca...
21 篇 computer science...
21 篇 school of comput...
20 篇 computer science...
18 篇 school of comput...
17 篇 school of comput...
17 篇 department of co...
16 篇 brac university ...
16 篇 khulna universit...
15 篇 symbiosis instit...
15 篇 department of co...
13 篇 school of comput...
13 篇 computer science...

作者

25 篇 ashwini kodipall...
24 篇 mahendra kumar g...
15 篇 rakesh kumar
13 篇 wei liu
13 篇 atul kumar
12 篇 qiangfu zhao
12 篇 amit kumar
12 篇 surendran r
12 篇 satvik vats
11 篇 jun zhang
11 篇 li li
11 篇 liu yang
11 篇 wei li
10 篇 sudeep tanwar
10 篇 himanshu sharma
10 篇 amiya ranjan pan...
9 篇 lei wang
9 篇 tohari ahmad
9 篇 shanmugasundaram...
9 篇 rishabh sharma

语言

25,672 篇 英文
147 篇 其他
33 篇 中文
5 篇 法文
2 篇 西班牙文
2 篇 荷兰文
2 篇 土耳其文
1 篇 德文

检索条件"主题词=Machine Learning Algorithms"

共 25877 条记录，以下是891-900 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 RSSI Based Indoor Positioning System by a Fusion of Multiple machine learning algorithms

Enhanced RSSI Based Indoor Positioning System by a Fusion of...

引用

International Conference on Green Technology and Sustainable Development (GTSD)

作者： Truong An Pham Manh Dat Chu Dai Phat Pham Thanh Tung Pham Duc Chinh Hoang School of Electrical and Electronic Engineering Hanoi University of Science and Technology Hanoi Vietnam University of Fire Prevention and Fighting Hanoi Vietnam

ISBN: (数字)9798350386073

ISBN: (纸本)9798350386080

Tracking an object in the indoor environment has attracted researchers due to the increased randomness and interference compared to the outdoor environment. These factors reduce the precision of popular localization algorithms in an indoor environment. This paper proposes a fusion of three machine-learning methods to address this issue. This method combines feedforward neural network (FFNN), eXtreme gradient boost (XGBoost) and support vector machine (SVM) to estimate location over a set of WiFi location fingerprints. The experimental results show that the method significantly improves the precision of the output position in both case studies, with high test accuracy. Nevertheless, the paper's limitation lies in the assumption of a consistent environment or with objects moving under 1 m/s.

关键词： Support vector machines Location awareness machine learning algorithms Accuracy Interference Fingerprint recognition IP networks

来源：评论

学校读者我要写书评

暂无评论

Study of Electric Vehicle Charging Prediction using Different machine learning algorithms

Study of Electric Vehicle Charging Prediction using Differen...

引用

International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： Satyam Sharma Deepa K S. V. Tresa Sangeetha Department of Electrical and Electronics Engineering Amrita School of Engineering Bengaluru Amrita Vishwa Vidyapeetham India Department of Engineering University of Technology and Applied Sciences-AIMusanna Sultanate of Oman

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

Electric Vehicles (EV s) have become a necessary companion of humans in the last 5–6 years since these solely work on electricity. People are preferring EVs over regular vehicles since they are more energy efficient. However, there is a major problem of constant charging requirements and a limitation of charging stations. The basic purpose of the proposed work is to predict the average charging time through different machine learning algorithms, which would provide a precise idea about the best algorithm that could be used for the prediction. The next decades will depend on EVs, so having such a model would be a boon for the EV owners and the charging stations, too, as it would help set up new charging stations and see the average charging time of each vehicle.

关键词： machine learning algorithms Communication systems Electricity Computational modeling Charging stations Prediction algorithms Energy efficiency Electric vehicle charging

来源：评论

学校读者我要写书评

暂无评论

Tail Risk Alert Based on Conditional Autoregressive VaR by Regression Quantiles and machine learning algorithms

Tail Risk Alert Based on Conditional Autoregressive VaR by R...

引用

Artificial Intelligence and Computer Engineering (ICAICE), International Conference on

作者： Zong Ke Yuchen Yin Faculty of Science National University of Singapore Singapore Teachers College Columbia University New York NY

ISBN: (数字)9798331528911

ISBN: (纸本)9798331528928

As the increasing application of AI in finance, this paper will leverage AI algorithms to examine tail risk and develop a model to alter tail risk to promote the stability of US financial markets, and enhance the resilience of the US economy. Specifically, the paper constructs a multivariate multilevel CAViaR model, optimized by gradient descent and genetic algorithm, to study the tail risk spillover between the US stock market, foreign exchange market and credit market. The model is used to provide early warning of related risks in US stocks, US credit bonds, etc. The results show that, by analyzing the direction, magnitude, and pseudo-impulse response of the risk spillover, it is found that the credit market's spillover effect on the stock market and its duration are both greater than the spillover effect of the stock market and the other two markets on credit market, placing credit market in a central position for warning of extreme risks. Its historical information on extreme risks can serve as a predictor of the VaR of other markets.

关键词： Reactive power Analytical models machine learning algorithms Finance Tail Stability analysis Stock markets Artificial intelligence Genetic algorithms Resilience

来源：评论

学校读者我要写书评

暂无评论

Premature Birth Prediction Using machine learning algorithms – A Comparative Analysis

Premature Birth Prediction Using Machine Learning Algorithms...

引用

International Conference on Contemporary Computing and Informatics (IC3I)

作者： A Padmavathi Laxmi Maurya Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

Premature birth (PB) presents a significant global health challenge, often leading to enduring developmental issues and neonatal mortality. Approximately 4-16% of new-born worldwide is born prematurely. Leveraging machine learning, a predictive model has been developed to anticipate preterm birth risk. This model evaluates maternal characteristics and other pertinent factors to provide timely alerts to healthcare providers. Through the analysis of extensive datasets, the system offers valuable insights for forecasting and classifying preterm birth risks. First and foremost, this study aims to enhance prenatal care and outcomes for high-risk pregnancies by harnessing artificial intelligence to refine the accuracy and promptness of preterm birth prediction. Employing machine learning algorithms, our study achieves a remarkable 99% accuracy rate throughout all the techniques.

关键词： Pregnancy Pediatrics machine learning algorithms Accuracy Computational modeling Mortality machine learning Predictive models Public healthcare Informatics

来源：评论

学校读者我要写书评

暂无评论

Student Placement Prediction and Skill Recommendation System using machine learning algorithms

Student Placement Prediction and Skill Recommendation System...

引用

International Conference on Inventive Computation Technologies (ICICT)

作者： Rakesh Kadu Purshottam J. Assudani Tanmay Mukewar Jatin Kapgate Rajat Bijekar Information Technology Shri Ramdeobaba College of Engineering and Management Nagpur India

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359305

The Student Placement Prediction and Skill Recommendation System is an innovative application designed to assist students in enhancing their employability and securing suitable job placements. In today's competitive job market, students often face challenges in understanding their strengths, identifying required skills, and effectively positioning themselves for job opportunities. This research work aims to address these challenges by leveraging machine learning algorithms to predict a student's likelihood of placement and recommend skills to improve their employability. The dataset used in this proposed system comprises various attributes such as academic performance (CGPA, marks), internship experiences, projects undertaken, certifications earned, aptitude test scores, soft skills ratings, and extracurricular activities. Leveraging this dataset, machine learning models, particularly the Random Forest algorithm, were employed to predict the probability of a student getting placed and the potential offers from different companies based on their academic, skill, and experiential profiles. Moreover, the system also offers personalized skill recommendations to students based on their current profile. For students who are not yet placed, the system generates skill recommendations using cosine similarity vectorization techniques, analyzing various attributes, including academic achievements, internship experiences, projects, certifications, aptitude scores, and soft skills. Recommendations are tailored to bridge skill gaps and enhance the chances of securing suitable job placements. The research work demonstrates the effectiveness of machine learning techniques in predicting placement outcomes and recommending personalized skill improvements, thus providing valuable insights for students to strategize and improve their employability prospects. The system serves as a useful tool for students seeking career guidance and advancement in the competitive job market.

关键词： machine learning algorithms Engineering profession Navigation Predictive models Prediction algorithms Data models Trajectory

来源：评论

学校读者我要写书评

暂无评论

UAV Security logs processing using machine learning algorithms

UAV Security logs processing using Machine Learning Algorith...

引用

Technologies for Defense and Security (TechDefense), IEEE International Workshop on

作者： Cristian Bucur Gabriela Nicolescu Departement of Computer Engineering Ecole Polytechnique Montreal Canada

ISBN: (数字)9798331505585

ISBN: (纸本)9798331505592

In this paper, we propose an evaluation of machine learning algorithms for security logs collected from an Intrusion Detection System (IDS) integrated in a Unmanned Aerial Vehicles (UAV) architecture. machine learning (ML) methodologies in the analysis of security logs presents a promising strategy for enhancing the capabilities of cybersecurity experts in addressing cyber threats. Currently, the utilization and the processing of system security logs presents a significant challenge for organizations due to its time-consuming nature and the extensive human resources required. In this study, we utilized UAV security logs generated by a publicly accessible log simulator. The investigation is focused on evaluating the efficiency of five ML algorithms: Unsupervised Support Vector machines, Logistic Regression, Random Forest, K-nearest neighbors (KNN) and Gradient Boosting, utilizing a simulated dataset that has been pre-processed. Five use cases were examined utilizing security logs content, and our analysis revealed that Logistic Regression demonstrated exceptional performance in all the studied areas.

关键词： Support vector machines Logistic regression machine learning algorithms Organizations Nearest neighbor methods Boosting Autonomous aerial vehicles Vectors Security Random forests

来源：评论

学校读者我要写书评

暂无评论

Epileptic Seizure Recognition using machine learning algorithms

Epileptic Seizure Recognition using Machine Learning Algorit...

引用

Self Sustainable Artificial Intelligence Systems (ICSSAS), International Conference on

作者： C. Jamunadevi P Arul M D Dineshkumar Department of Computer Technology PG Kongu Engineering College Erode Tamilnadu Department of Computer Science Government Arts College (Affiliated to Bharathidasan University Trichy-24) Tiruchirappalli India

ISBN: (数字)9798350368413

ISBN: (纸本)9798350368420

The project entitled “Epileptic Seizure Recognition Using machine learning algorithms” is used to identify the epileptic seizure at the early stage. Epileptic seizure is a significant health concern affecting millions worldwide, necessitate precise and timely identification for effective medical intervention and management. The present system for epileptic seizure recognition often relies on traditional methods, including expert analysis of electroencephalogram (EEG) data and manual review. While these methods have proven effective, they are labor-intensive, prone to subjectivity and may not be suitable for real-time monitoring. This gap led to the proposed machine learning-based approach which offer potential seizure recognition, allowing for more timely and accurate intervention. This study propose a novel approach for epileptic seizure recognition by combining dimensionality reduction techniques, namely Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE), with the K-Means clustering algorithm, then integrate this unsupervised clustering approach with four popular machine learning algorithms: Support Vector machine classifier, Logistic Regression, Naive Bayes classifier and Random Forest classifier to enhance the performance of seizure recognition. Each cluster is labeled as either seizure or non-seizure based on the majority class within it. The method involves the development and implementation of these algorithms within the context of epileptic seizure. The primary objective is to explore the feasibility and performance of these classifiers, aiming to enhance the efficiency of seizure recognition system. The work emphasizes the potential of machine learning in automating the identification of epileptic seizures, ultimately contributing to improved patient care and medical diagnosis. The improved accuracy of $95 \%$ is achieved by Random Forest model.

关键词： Support vector machines Logistic regression machine learning algorithms Accuracy Clustering algorithms Stochastic processes Electroencephalography Classification algorithms Random forests Principal component analysis

来源：评论

学校读者我要写书评

暂无评论

Comparative Analysis of Fake Account Detection Using machine learning algorithms

Comparative Analysis of Fake Account Detection Using Machine...

引用

Artificial Intelligence and Signal Processing (AISP), INDIA, International Conference on

作者： A Padmavathi K.B. Vaisshnavi Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9798350350654

ISBN: (纸本)9798350350661

With the rise of Social Media platforms and new applications, the Rapid Expansion of fake accounts has become an important concern, posing threats to security, privacy and trust-worthiness. In response, this research explores the application of machine learning techniques for the detection and reduction of fake accounts. By utilizing datasets containing, user behavior patterns, network characteristics and various parameters, we explore the efficiency of various machine learning algorithms in selective genuine users from false ones. This study includes attribute manipulation, model training and evaluation techniques made for the unique challenges of fake account detection. We examine the effects of different features, such as interaction measures, language patterns and network structures on the output of detection models. Systematic testing and validation reveal effective methods for identifying fake accounts, enhancing information security and user trust on online platforms.

关键词： Training Privacy Analytical models machine learning algorithms Accuracy Systematics Social networking (online) machine learning Artificial neural networks Testing

来源：评论

学校读者我要写书评

暂无评论

Comparative Analysis of machine learning algorithms for Predicting Undergraduate Academic Performance

Comparative Analysis of Machine Learning Algorithms for Pred...

引用

Creative Communication and Innovative Technology (ICCIT), IEEE

作者： Euis Sitinur Aisyah Danny Manongga Ade Iriani Sugeng Santoso Faculty of Science and Technology University of Raharja Tangerang Indonesia Faculty of Information Technology Satya Wacana Christian University Salatiga Indonesia

ISBN: (数字)9798350367492

ISBN: (纸本)9798350367508

Accurate prediction of student academic performance is crucial for educational institutions and parents to implement proactive intervention strategies. This study employs machine learning algorithms to predict the graduation outcomes of undergraduate students, focusing on the effectiveness of various algorithms. We used the Random Forest algorithm and compared its performance with Decision Tree, Nave Bayes, Artificial Neural Network, and Support Vector machine, utilizing the Orange data mining tool. Our evaluation metrics included Area Under Curve (AUC), accuracy, F1-score, precision, and recall, applied to a dataset of 387 students from the class of 2017 who graduated on time in 2021 and 2022. The features considered were ID, study program, age, sex, GPA of the first semester and GPA of the second semester. The Random Forest algorithm achieved the highest accuracy at 95%. This research contributes to the early identification of students at risk of academic failure and highlights the most effective machine learning algorithm for predicting academic outcomes in higher education.

关键词： Support vector machines machine learning algorithms Accuracy Education Predictive models Prediction algorithms Robustness Socioeconomics Random forests Testing

来源：评论

学校读者我要写书评

暂无评论

Leveraging machine learning algorithms for Improved Road Safety

Leveraging Machine Learning Algorithms for Improved Road Saf...

引用

International Conference on Advanced Systems and Electric Technologies (IC_ASET)

作者： Muhammad Rafsan Kabir Md. Samin Yasar Department of Electrical and Computer Engineering North South University Dhaka Bangladesh Department of Civil and Environmental Engineering North South University Dhaka Bangladesh

ISBN: (数字)9798350384895

ISBN: (纸本)9798350384901

In recent times, road accidents have emerged as a growing concern, resulting in numerous deaths and injuries. Each day, a significant number of individuals become victims of road accidents, emphasizing the critical need to address this issue and its severity. To confront this serious problem, this research dives into the realm of road accidents, with a particular focus on the states of North Carolina and New York. The article begins by introducing a predictive approach to road accident severity, employing six machine learning algorithms: Decision Tree, Random Forest, XGBoost, AdaBoost, Logistic Regression, and K-Nearest Neighbors. Subsequently, it identifies and analyzes the most influential factors contributing to road accidents in North Carolina and New York using Explainable AI (Permutation Feature Importance). Following the identification and analysis of these factors, the study also focuses on comparing the accident factors between the two aforementioned states. The article also presents some practical, real-life-based recommendations from a safety and transportation engineering perspective that can be implemented to improve road safety.

关键词： Logistic regression Road accidents machine learning algorithms Explainable AI Transportation Nearest neighbor methods Road safety

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 << < 86 87 88 89 90 91 92 93 94 95 > >>

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：