检索结果-内蒙古大学图书馆

International Conference on Information and Communication Technologies (ICICT)

作者： Kumar, Ashish Bhatnagar, Roheet Srivastava, Sumit Manipal Univ Jaipur Jaipur 303007 Rajasthan India

k-Nearest Neighbor (k-NN) classification technique is one of the most elementary and straightforward classification methods. Although distance learning is in the core of k-NN classification, similarity can be preferred upon distance in several practical applications. This paper proposes a novel algorithm for learning a class of an instance based on a similarity measure which does not calculate distance, for k-Nearest Neighbor (k-NN) classification. (C) 2015 The Authors. Published by Elsevier B.V.

关键词： classification algorithm Data Mining Nearest Neighbor Similarity Measures

来源：评论

学校读者我要写书评

暂无评论

Improving Spam Detection in Online Social Networks 1

Improving Spam Detection in Online Social Networks

引用

2015 International Conference on Cognitive Computing and Information Processing (CCIP)

作者： Gupta, Arushi Kaushal, Rishabh Indira Gandhi Delhi Tech Univ Women Dept Informat Technol Delhi India

ISBN: (纸本)9781479971718

Online Social Networks (OSNs) are deemed to be the most sought-after societal tool used by the masses world over to communicate and transmit information. Our dependence on these platforms for seeking opinions, news, updates, etc. is increasing. While it is true that OSNs have become a new medium for dissemination of information, at the same time, they are also fast becoming a playground for the spread of misinformation, propaganda, fake news, rumors, unsolicited messages, etc. Consequently, we can say that an OSN platform comprises of two kinds of users namely, Spammers and Non-Spammers. Spammers, out of malicious intent, post either unwanted (or irrelevant) information or spread misinformation on OSN platforms. As part of our work, we propose mechanisms to detect such users (Spammers) in Twitter social network (a popular OSN). Our work is based on a number of features at tweet-level and user-level like Followers/Followees, URLs, Spam Words, Replies and HashTags. In our work, we have applied three learning algorithms namely Naive Bayes, Clustering and Decision trees. Furthermore, to improve detection of Spammers, a novel integrated approach is proposed which "combines" the advantages of the three learning algorithms mentioned above. Improvement of spam detection is measured on the basis of Total Accuracy, Spammers Detection Accuracy and Non-Spammers Detection Accuracy. Results, thus obtained, show that our novel integrated approach that combines all algorithms outperforms other classical approaches in terms of overall accuracy and detect Non-Spammers with 99% accuracy with an overall accuracy of 87.9%.

关键词： Spam detection classification algorithm twitter online social networking

来源：评论

学校读者我要写书评

暂无评论

Traffic Pattern Recognition System Design and Development Based on Smart Phones 7

Traffic Pattern Recognition System Design and Development Ba...

引用

7th International Conference Intelligent Computation Technology Automation (ICICTA)

作者： Yu Ning Liu-xiaoxing Hebei Coll Sci & Technol Baoding 071000 Peoples R China

ISBN: (纸本)9781479966363

With the development of social intelligence and information, it is of more and more great significance in recognizing traffic patterns automatically. Traffic patterns recognition, refers to identifying the user's currently traffic pattern, which can be widely used in the transportation planning, location based services, social networks and many other fields. With the development and popularization of smart terminals, mobile phones have more and more powerful perception, computation, storage and communication abilities, making recognize user traffic patterns with the mobile phones a hot topic of current research. This paper firstly introduces the current research situation of the traffic patterns recognition. Then the positioning algorithms and classification algorithms are presented, and appropriate algorithms are selected for our systems. Based on the selected algorithms, the traffic patterns recognition system is developed with smart mobile phones, which has three main functions: sustaining traffic patterns recognition, record the current traffic information and view the history traffic locus.

关键词： Traffic Patterns positioning algorithm classification algorithm Smart Mobile Phone

来源：评论

学校读者我要写书评

暂无评论

A Chinese Text Classier Based on Strong Class Feature Selection and Bayesian algorithm

A Chinese Text Classier Based on Strong Class Feature Select...

引用

International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS)

作者： Chen, Yanqiu Sun, Peili Dalian Neusoft Inst Informat Dept Comp Sci Dalian Peoples R China Dalian Ocean Univ Econ Management Inst Dalian Peoples R China

ISBN: (纸本)9781728113074

For improve the efficiency and accuracy of Chinese text categorization, this paper presents a new Chinese text classier, in which word segmentation system is based on forward scan corpus, and word frequency statistics method is different in the training stage and the test stage, after that, a novel feature selection is proposed according to word frequency, mutual information and classificatory information, Finally a fast Bayes theory classier is designed. Experiments prove this classier is simple and effective.

关键词： Chinese text categorization feature selection feature weighting classification algorithm

来源：评论

学校读者我要写书评

暂无评论

Data mining: will first-year results predict the likelihood of completing subsequent units in accounting programs?

引用

ACCOUNTING EDUCATION 2023年第4期32卷 409-444页

作者： Sithole, Seedwell T. M. Ran, Guang de Lange, Paul Tharapos, Meredith O'Connell, Brendan Beatson, Nicola Univ Tasmania Tasmanian Sch Business & Econ Accounting Hobart Tas Australia RMIT Univ Sch Accounting Informat Syst & Supply Chain Melbourne Vic Australia Univ Otago Dept Accountancy & Finance Dunedin New Zealand

This study introduces data mining methods to accounting education scholarship to explore the relationship between accounting students' current academic performance (grades), demographic information, pre-university entrance scores and predicted academic performance. It adopts a C4.5 classification algorithm based on decision-tree analysis to examine 640 accounting students enrolled in an undergraduate accounting program at an Australian university. A significant contribution of this study is improved prediction of academic performance and identification of characteristics of students deemed to be at risk. By partitioning students into sub-groups based on tertiary entrance scores and employing clustering of study units, this study facilitates a more nuanced understanding of predictor attributes. Key findings were the dominance of a cluster of second year units in predicting students' later academic performance;that gender did not influence performance;and that performance in first year at university, rather than secondary school grades, was the most important predictor of subsequent academic performance.

关键词： Academic performance accounting students classification algorithm educational data mining performance prediction

来源：评论

学校读者我要写书评

暂无评论

An Heuristic Feature Selection algorithm to Evaluate Academic Performance of Students 10

An Heuristic Feature Selection Algorithm to Evaluate Academi...

引用

IEEE 10th Control and System Graduate Research Colloquium (ICSGRC)

作者： Ajibade, Samuel-Soma M. Ahmad, Nor Bahiah Shamsuddin, Siti Mariyam UTM Comp Sci Dept Johor Baharu Malaysia

ISBN: (纸本)9781728107554

The value of schooling and academic performance of student is the topmost priority of all academic institutions. Educational Data Mining (EDM) is an evolving area of research which aids academic institutions to enhance their student's performances. Feature Selection algorithms eradicates inapt and unrelated data from the dataset, thereby increasing the classifiers performances that are utilized in EDM. This aim of this paper is to evaluate the performance of students utilizing a heuristic technique known as Differential Evolution for feature selection algorithms on the dataset of students and some other feature selection algorithms have also been used which have never been used before on the dataset. Also, classification techniques such as Naive Bayes (NB), Decision Tree (DT), K-Nearest Neighbor (KNN) and Discriminant Analysis (DISC) were used to evaluate. The Differential Evolution (DE) algorithm is proposed as a better feature selection algorithm for evaluating the academic performance of students and this gave a better accuracy than other feature selection algorithm that were used. The outcome of the different feature selection algorithms and classification techniques will help researchers to find the finest combinations of the classifiers and feature selection algorithms. This paper is a step towards playing an important role in enhancing the standard of education in academic institutions and also to carefully guide researchers in strategically interfering in academic issues.

关键词： educational data mining differential evolution student performance feature selection algorithm classification algorithm

来源：评论

学校读者我要写书评

暂无评论

Investigating the Impacts of Weather and Personalisation on Asthma Exacerbations using Machine Learning 2022

Investigating the Impacts of Weather and Personalisation on ...

引用

11th International Conference on Software and Computer Applications, ICSCA 2022

作者： Haque, Radiah Ho, Sin-Ban Chai, Ian Abdullah, Adina Faculty of Computing and Informatics Multimedia University Malaysia Faculty of Medicine University of Malaya Malaysia

ISBN: (纸本)9781450385770

This paper reports the results of investigating the impacts of weather and personalisation on asthma exacerbations. Additionally, several machine learning classification algorithms were applied and evaluated to develop an intelligent mHealth solution that can offer accurate personalised estimations of asthma exacerbations based on weather and demography and provide tailored precautionary guidelines for selfcare to each user. Consequently, the proposed solution will enhance asthma self-management by assisting patients with chronic asthma to plan their daily outdoor activities and limit exposure to weather triggers, thus controlling their asthma exacerbations. © 2022 ACM.

关键词： Machine learning Neural networks Personalisation Asthma self-management classification algorithm

来源：评论

学校读者我要写书评

暂无评论

A novel gender classification method based on MapReduce

A novel gender classification method based on MapReduce

引用

IEEE International Conference on Software Engineering and Service Sciences (ICSESS)

作者： Tong Cui Haifeng Zhao Science and Technology on Information Systems Engineering Laboratory Nanjing China

ISBN: (纸本)9781479983544

A novel parallelize gender recognition method with MapReduce is presented, which successfully comprise several machine leaning algorithms which are employed for gender recognition. The mass of face sample images are gathered and separated as train dataset and test dataset, and Local Binary Pattern (LBP) features are extracted when those sample sets are pre-processed and made ready for following operations. And Principle Component Analysis (PCA) is applied to train dataset to extract the most distinguishing features. Three classification algorithms: Support Vector Machine(SVM), k-Nearest Neighborhood (k-NN) and Adaboost are implemented and compared to determine the most suitable and successful algorithm for gender parallelize machine learning (GPML). To achieve the shortest execution time, we propose to apply GPML with MapReduce to avoid parallelizing above three algorithms while also improving their scalability to big datasets. The results show that this method reduces the training computational complexity significantly when the number of computing nodes increases while gaining better speedup rates and extending performance than those on parallelize Adaboost.

关键词： face recognition feature extraction image classification learning (artificial intelligence) parallel processing principal component analysis support vector machines Adaboost GPML LBP features MapReduce PCA SVM classification algorithm face sample images gender classification method gender parallelize machine learning k-nearest neighborhood local binary pattern feature extraction machine leaning algorithms parallelized gender recognition method principle component analysis support vector machine test dataset train dataset training computational complexity gender recognition parallel meta-learning Support Vector Network principal components analysis Sorting algorithm Image classification PARALLEL PROCESSING (COMPUTERS) Facial Recognition Passive Cutaneous Anaphylaxis Feature extraction Dataset Gender ADABOOST execution time classification Methods

来源：评论

学校读者我要写书评

暂无评论

Liver Tumour Parameter Prediction Using Feature Extraction and Supervised Function 1

Liver Tumour Parameter Prediction Using Feature Extraction a...

引用

1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Kumar, Ankit Singh, Kamred Udham Singh, Teekam Banu, Nargis Rathore, Yogesh Kumar Pandey, Saroj Kumar Gla University Department of Computer Engineering & Apllications UP Mathura India School of Computing Graphic Era Hill University Dehradun India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Geetanjali Institute of Technical Studies Dabok Udaipur India India

ISBN: (纸本)9798350330915

The process of extracting features is essential to simplifying and condensing a wide variety of data dimensions while at the same time combining essential factors into a single influential parameter. In the field of medical research, predicting liver tumours represents a daunting task, which calls for the development of a more advanced detection mechanism that is focused towards achieving higher levels of identification precision. Even though experienced medical professionals can make preliminary predictions regarding liver tumours based on their clinical experience and diagnostic tools such as imaging and scans, there is still a compelling need for systematic assistance in making a final decision. This is especially true when evaluating patient symptoms. For the sake of our application, the approach of feature extraction that we have choose to use is called linear discriminant analysis (LDA). LDA displays its usefulness by being able to accommodate classification techniques with more than two classes and by exceeding logistic regression in terms of accuracy of classification. We make use of the power of supervised functions, in particular decision trees and the Naive Bayes algorithm, in order to make an accurate prediction about the existence of liver illness. These algorithms produce predictions regarding the existence of liver disease with a high degree of accuracy by using the factors discovered during LDA analysis. For the purposes of our simulation work, we make use of the flexible scikit-learn library in Python. This library offers a dependable infrastructure for the performance of machine learning activities. The part of this research under 'Methodology and Implementation' digs into the specifics of our approach and outlines the actions that are required to evaluate the effectiveness of our prediction model. © 2023 IEEE.

关键词： classification algorithm feature extraction LDA Linear Discriminant Analysis Liver tumor Supervised function Supervised function

来源：评论

学校读者我要写书评

暂无评论

Study on Decision classification Fusion Model

Study on Decision Classification Fusion Model

引用

2009 Ninth International Conference on Hybrid Intelligent Systems(第九届混合智能系统国际会议 HIS 2009)

作者： Xiao-dan Zhang Zhen-dong Niu School of Computer Science and Technology Beijing Institute of Technology University Beijing P.R. China

In this paper, a general decision layer classification fusion model, based on information fusion for improving classification precision, is proposed, that is, different multi - classification algorithms as the feature layer doing respective classification, and the results of classification algorithms are input into decision level, the last classification result is output. This model is applied into improving precision of text classification. And the model is used to the Computer Center of some department. Through the experiment, the text classification fusion model can improve the classification precision effectively.

关键词： text classification decision layer classification fusion model classification algorithm information fusion

来源：评论

学校读者我要写书评

暂无评论

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：

请选择收藏分类：

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

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：