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检索条件"主题词=classification Algorithm"

共 549 条记录，以下是231-240 订阅

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Analysis of Factors Contributing to Vehicle-Pedestrian Crash Severity Incorporating Data Imbalance Treatment 21

Analysis of Factors Contributing to Vehicle-Pedestrian Crash...

引用

20th and 21st Joint COTA International Conference of Transportation Professionals - Advanced Transportation, Enhanced Connection

作者： Liu, Jinming Wang, Yuanqing Zhou, Bei Changan Univ Sch Highway Xian Peoples R China

ISBN: (纸本)9780784483565

For road users, walking is the safest and greenest way to travel. However, with the rapid development of motorization, vehicle-pedestrian traffic accidents occur frequently. In response to this phenomenon, this paper is devoted to analyzing the factors contributing to the severity of vehicle-pedestrian accidents. Based on the data of vehicle-pedestrian accidents in Chicago, Illinois, a dataset of 10 influencing factors is constructed. Considering the severity of accident as the classification label, and considering the imbalanced distribution of samples with different labels, a 2-classification decision tree and an SVM are established to identify the severity of the accident. Then, through model comparison, it is found that the decision tree has the best classification performance, and the overall classification accuracy can reach more than 70%. Finally, nine eigenvalues were found that had a significant impact on the severity of the accident.

关键词： traffic accident severity vehicle-pedestrian crash classification algorithm machine learning data imbalance treatment

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Design of Image Classifier for Web Application

Design of Image Classifier for Web Application

引用

11th International Conference of Computer Communication and Informatics (ICCCI)

作者： Shivani Gaur, Nidhi Amity Univ Dept Elect & Commun Engn ASET Noida Uttar Pradesh India

ISBN: (纸本)9781728158754

An image classifier uses machine learning algorithm to recognize the images. A classifier allocates class labels to specific data points and classifies the images using supervised learning. It recognizes the target classes using labelled sample images by training a Convolutional neural network model (CNN). This paper aims to design an image classifier for web application. Image classification will eliminate the need of manual search in the web application. CNN is an efficient algorithm to classify the images according to the category label. An image classification model has been designed using CNN to classify the images of fruits and vegetables. Accuracy of the model is based on the classification technique. For successful classification data pre-processing, data augmentation and feature extraction are involved. Positive results were obtained by the CNN model after testing it with the input images. The model can classify the 90483 images of 131 fruits and vegetables. This paper involves the description and methodology for designing the image classifier.

关键词： Machine learning Object recognition Convolutional neural network Mufti class classification Fruits classifier Vegetables classifier classification algorithm

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Evaluation based Approaches for Liver Disease Prediction using Machine Learning algorithms

Evaluation based Approaches for Liver Disease Prediction usi...

引用

11th International Conference of Computer Communication and Informatics (ICCCI)

作者： Geetha, C. Arunachalam, A. R. Bharath Inst Higher Educ & Res Dept CSE Chennai Tamil Nadu India Dr MGR Univ Dept CSE Chennai Tamil Nadu India

ISBN: (纸本)9781728158754

The life of humans living without liver tumors is one of the fundamental care of human livelihood. Therefore, for better care, detection of liver disease at a primitive phase is necessary. For medical experts, predicting the illness in the early stages due to subtle signs is a very difficult task Many, when it is too late, the signs become evident. The current work aims to augment the perceive nature of liver disease by means of machine learning methods to solve this epidemic. The key purpose of the present work focused on algorithms for classification of healthy people from liver datasets. Centered on their success variables, this research also aims to compare the classification algorithms and to provide prediction accuracy results.

关键词： Human life classification algorithm Linear Regression SVM Machine Learning

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A DDoS Attack Mitigation Framework for IoT Networks using Fog Computing 17

A DDoS Attack Mitigation Framework for IoT Networks using Fo...

引用

17th International Learning and Technology Conference

作者： Lawal, Muhammad Aminu Shaikh, Riaz Ahmed Hassan, Syed Raheel King Abdulaziz Univ Fac Comp & Informat Technol Dept Comp Sci Jeddah 21589 Saudi Arabia

The advent of 5G which strives to connect more devices with high speed and low latencies has aided the growth IoT network. Despite the benefits of IoT, its applications in several facets of our lives such as smart health, smart homes, smart cities, etc. have raised several security concerns such as Distributed Denial of Service (DDoS) attacks. In this paper, we propose a DDoS mitigation framework for IoT using fog computing to ensure fast and accurate attack detection. The fog provides resources for effective deployment of the mitigation framework, this solves the deficits in resources of the resource-constrained IoT devices. The mitigation framework uses an anomaly-based intrusion detection method and a database. The database stores signatures of previously detected attacks while the anomaly-based detection scheme utilizes k-NN classification algorithm for detecting the DDoS attacks. By using a database containing the attack signatures, attacks can be detected faster when the same type of attack is executed again. The evaluations using a DDoS based dataset show that the k-NN classification algorithm proposed for our framework achieves a satisfactory accuracy in detecting DDoS attacks. (C) 2021 The Authors. Published by Elsevier B. V.

关键词： Internet of Things (IoT) Anomaly mitigation DDoS Fog computing classification algorithm

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Research on classification algorithm of News Pages Based on Domain Ontology

Research on Classification Algorithm of News Pages Based on ...

引用

2nd International Conference on Measurement, Instrumentation and Automation (ICMIA 2013)

作者： Xie, Caiyun Hu, Xiaorong Nanchang Teachers Coll Dept Informat Sci Nanchang Peoples R China

ISBN: (纸本)9783037857519

This paper proposes the classification algorithm of news pages based on domain Ontology. In order to improve the shortage of current classification algorithm that only considers the content similarity, this paper presents the semantic classification method which considers both content similarity and structural correlation. Firstly, it parses the Ontology to get Ontology category vector, extracts keywords of news pages' texts and drops semantic dimension. At this time, finding out the same vocabulary and ontology category vector in page texts to constitute the text expectation vector, and then calculating the content similarity between ontology category vector and expectation vector of text by using the law of cosines. Secondly, the common vocabularies are mapped to the ontology hierarchy chart, and the structural relevancy is obtained by calculating weighted path of this directed acyclic graph. Finally, it calculates the correlation degree of the news pages and Ontology by combining both, and determines the category of news pages by judging the size relationship between the result and the initial threshold value.

关键词： classification algorithm Ontology Integrated Correlation Degree

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Study of Procrastination in Higher Vocational Education Based on Online Learning Data 22

Study of Procrastination in Higher Vocational Education Base...

引用

Proceedings of the 6th International Conference on Digital Technology in Education

作者： Fang Feng Mengxin Tang Wen Lei School of Information Technology in Education South China Normal University China

ISBN: (纸本)9781450398091

Academic procrastination is a common phenomenon in China's higher vocational education. Due to the weakening of the role of teacher supervisors and the lack of students' self-control, the academic procrastination of students in online learning is more likely to occur. At present, it has become a trend to use educational data mining and artificial intelligence technology to evaluate, predict and intervene in online learning, so as to solve the problem of practical teaching lag and improve the teaching effect of vocational education. In this paper, the data of "Computer Application Foundation" course of higher vocational students on Chaoxing platform is used to process the data by using K-means and DBSCAN clustering algorithms, and the performance of the two algorithms is evaluated by using the contour coefficient. The results show that the K-means algorithm has better performance. The students were divided into active learners, mild procrastinators and severe procrastinators by K-means clustering algorithm. Then, combined with decision tree (DT), neural network (NN) and Naive Bayes (NB) algorithm to verify the accuracy of K-means clustering algorithm in identifying the classification of students' procrastination tendency, this paper hopes to provide some advises for online learning procrastinators and encourage students to keep learning initiative and enthusiasm.

关键词： Academic procrastination classification algorithm Clustering algorithm Educational data mining

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A Microblog classification algorithm for Real-time Search

A Microblog Classification Algorithm for Real-time Search

引用

2nd International Conference on Information Technology and Management Innovation (ICITMI 2013)

作者： Yuan, Jingbo Wang, Bairong Yang, Jihao Ding, Shunli Northeastern Univ Qinhuangdao Peoples R China Northeastern Univ Sch Business Adm Qinhuangdao Peoples R China Northeastern Univ Coll Informat Sci & Engn Qinhuangdao Peoples R China Environm Management Coll Beijing Peoples R China

ISBN: (纸本)9783037858646

As a social network, microblog has obtained great attention and gotten wide application. Applications of microblog need to retrieve quickly information with the support of real-time search technology in order to implement information sharing. A query classification algorithm of microblog for real-time search was put forward. Based on question classification mechanism, the algorithm divides queries into two categories: the candidate queries and the popular queries, and takes separate storage strategy. Test results show that the classification algorithm can reduce real-time search time and improve the efficiency of retrieval.

关键词： social network real-time search technology microblog classification algorithm

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Research on Pulsar classification Based on Machine Learning 3

Research on Pulsar Classification Based on Machine Learning

引用

3rd International Conference on Intelligent Autonomous Systems (ICoIAS)

作者： Chen, Zhiyu Xu, Aiting Zhou, Yingying Gai, Yingjie Zhejiang Gongshang Univ Coll Stat & Math 18 Xuezheng Str Hangzhou 310018 Peoples R China

ISBN: (纸本)9781728160788

This paper uses pulsar signal data for data mining, on the basis of exploratory analysis, constructs a variety of classification models, such as Random Forest, SVM, Logical Regression, K-Nearest Neighbor, Naive Bayes, Decision tree, AdaBoost classifier, GBDT and XGBoost, to classify pulsar candidate samples. It is hoped that valuable suspected pulsar samples can be effectively screened from massive data for further observation and confirmation.

关键词： pulsar machine learning classification algorithm random forest

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Trends Identification in Medical Care 10th

Trends Identification in Medical Care

引用

10th International Conference on Operations Research and Enterprise Systems (ICORES)

作者： Sena, Ines Pereira, Ana Inst Politecn Braganca Res Ctr Digitalizat & Intelligent Robot CeDRI Braganca Portugal

ISBN: (纸本)9789897584855

Daily, health professionals are sought out by patients, motivated by the will to stay healthy, making numerous diagnoses that can be wrong for several reasons. In order to reduce diagnostic errors, an application was developed to support health professionals, assisting them in the diagnosis, assigning a second diagnostic opinion. The application, called ProSmartHealth, is based on intelligent algorithms to identify clusters and patterns in human symptoms. ProSmartHealth uses the Support Vector Machine ranking algorithm to train and test diagnostic suggestions. This work aims to study the application's reliability, using two strategies. First, study the influence of pre-processing data analysing the impact in the accuracy method when data is previously processed. The second strategy aims to study the influence of the number of training data on the method precision. This study concludes the use of pre-processing data and the number of training data influence the precision of the model, improving the precision on 8%.

关键词： Artificial Intelligence Machine Learning Support Vector Machine classification algorithm Reliability

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Trustworthiness of Spam Email Addresses using Machine Learning 21

Trustworthiness of Spam Email Addresses using Machine Learni...

引用

21st ACM Symposium on Document Engineering (DocEng)

作者： Janez-Martino, Francisco Alaiz-Rodriguez, Rocio Gonzalez-Castro, Victor Fidalgo, Eduardo Univ Leon Dept Elect Syst & Automat INCIBE Spanish Natl Cybersecur Inst Leon Spain

ISBN: (纸本)9781450385961

Cybercriminals have increasingly used spam email to send scams, phishing, malware and other frauds to organisations and people. They design sophisticated and contextualised emails to make them look trustworthy for users, being the sender addresses an essential part. Although cybersecurity agencies and companies develop products and organise courses for people to detect emails patterns, spam attacks are not totally avoided yet. This work presents a proof-of-concept methodology to give the user more meaningful information about trustworthiness to detect these harmful emails. For the first time in the literature, we present an email address dataset manually labelled into two classes, low and high quality. Moreover, we extracted 18 handcrafted features based on social engineering techniques and natural language properties. We evaluated four popular machine learning classifiers and obtained the best performance with Naive Bayes, i.e., 88.17% of accuracy and 0.808 of F1-Score. Additionally, we applied the InterpretML framework to find out the most relevant properties to eventually implement an automatic system able to inform about the trustworthiness of email addresses.

关键词： Spam Email Detection Machine Learning classification algorithm Cybersecurity Feature Extraction

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