检索结果-内蒙古大学图书馆

Predictive Analysis of the Building Damage From the 2011 Great East Japan Tsunami Using Decision Tree classification Related algorithms

引用

IEEE ACCESS 2021年 9卷 31065-31077页

作者： Saengtabtim, Kumpol Leelawat, Natt Tang, Jing Treeranurat, Wanit Wisittiwong, Narunporn Suppasri, Anawat Pakoksung, Kwanchai Imamura, Fumihiko Takahashi, Noriyuki Charvet, Ingrid Chulalongkorn Univ Dept Ind Engn Fac Engn Bangkok 10330 Thailand Chulalongkorn Univ Disaster & Risk Management Informat Syst Res Grp Bangkok 10330 Thailand Chulalongkorn Univ Int Sch Engn Fac Engn Bangkok 10330 Thailand Thammasat Univ Sirindhorn Int Inst Technol Sch Management Technol Pathum Thani 12121 Thailand Tohoku Univ Int Res Inst Disaster Sci Sendai Miyagi 9808572 Japan Tohoku Univ Sch Engn Dept Architecture & Bldg Sci Sendai Miyagi 9808579 Japan Praedicat Inc Los Angeles CA 90016 USA

When considering a tsunami disaster, many researchers have considered the tsunami's flow depth and velocity as the primary contributors to the building damage. Additionally, the majority of these studies have used the maximum value as the measure of each of these two factors. However, building damage may not occur when the maximum flow depth and the maximum flow velocity of the tsunami are reached. This study addressed two objectives based on the 2011 Great East Japan Earthquake and Tsunami. Firstly, to find out whether the maximum values of the flow depth and flow velocity are the same as their critical values and, secondly, to verify which combination of the parameters is the best predictor of the building damage level. The data from 18,000 buildings in Ishinomaki City, Japan, with the cooperation of the Japanese joint survey team, were analyzed using the decision tree related algorithms. The critical variables were the simulated data at the time when the buildings collapsed. The analysis showed the accuracy of the prediction based on the group of variables. Finally, the findings showed that the combination of the critical flow depth and maximum flow velocity provided the highest accuracy for classifying the level of building damage.

关键词： Buildings Tsunami Earthquakes Decision trees Soil Prediction algorithms classification algorithms Building damages data mining decision tree algorithm 2011 Great East Japan earthquake and tsunami

来源：评论

学校读者我要写书评

暂无评论

Hybrid classification algorithms for Predicting Student Performance

Hybrid Classification Algorithms for Predicting Student Perf...

引用

Artificial Intelligence and Smart Systems (ICAIS), International Conference on

作者： A.Dinesh Kumar R.Pandi Selvam V. Palanisamy Alagappa University Karaikudi Ananda College Devakottai

ISBN: (数字)9781728195377

ISBN: (纸本)9781728195384

Data mining is the progress of instinctively find out valuable information from huge depot. In educational data mining predicting the academic performance of the student is a well-known research. The intention of the research work is to examine the classification algorithms as a hybrid classification. We used Radial Basis Function network, Multilayer Perceptron, C4.5 and Random Forest algorithms for classification. Initially classification accuracy was computed individually by the classification algorithms. The Radial Basis Function network, Multilayer Perceptron, C4.5 and Random Forest Algorithm's individual classification gave the accuracy of 72.9167%, 75.4167%, 75% and 73.125% vice versa. To increase more accuracy of classification algorithm the Radial Basis Function network is combined with multilayer perceptron. This hybrid algorithm provides 75.625% of classification accuracy. Then we combined C4.5 algorithm with random forest algorithm which gives 76.4583% classification accuracy. In this study we found hybrid classification algorithm gives more accuracy than individual classification algorithm.

关键词： Radial basis function networks Forestry Multilayer perceptrons Prediction algorithms classification algorithms Data mining Random forests

来源：评论

学校读者我要写书评

暂无评论

Comparison of classification algorithms Towards Subject-Specific and Subject-Independent BCI

Comparison of Classification Algorithms Towards Subject-Spec...

引用

International Winter Workshop on Brain-Computer Interface (BCI)

作者： Parisa Ghane Narges Zarnaghinaghsh Ulisses Braga-Neto Texas A&M University College Station United States

ISBN: (数字)9781728184852

ISBN: (纸本)9781728184869

Motor imagery brain computer interface designs are considered difficult due to limitations in subject-specific data collection and calibration, as well as demanding system adaptation requirements. Recently, subject-independent (SI) designs received attention because of their possible applicability to multiple users without prior calibration and rigorous system adaptation. SI designs are challenging and have shown low accuracy in the literature. Two major factors in system performance are the classification algorithm and the quality of available data. This paper presents a comparative study of classification performance for both SS and SI paradigms. The present classification algorithms include two parametric (LDA and SVM) and two non-parametric (k-NN and CART) methods. Our results show that classification algorithms for SS models display large variance in performance. Therefore, distinct classification algorithms per subject may be required. SI models display lower variance in performance but should only be used if a relatively large sample size is available. For SI models, LDA and CART had the highest accuracy for small and moderate sample size, respectively. The source code of this paper is available at ***/parisaghane/eegbciMI.

关键词： Support vector machines Training System performance Brain modeling Brain-computer interfaces classification algorithms Calibration

来源：评论

学校读者我要写书评

暂无评论

Evaluation of classification algorithms for Software Defect Prediction

Evaluation of Classification Algorithms for Software Defect ...

引用

International Conference on Mechatronics, Electronics and Automotive Engineering (ICMEAE)

作者： Ma. José Hernández-Molinos Ángel J. Sánchez-García R. Erandi Barrientos-Martínez Juan Carlos Pérez-Arriaga Facultad de Estadística e Informátic Universidad Veracruzana Universidad Veracruzana Institute de Investigaciones en Inteligencia Artificial Xalapa México

ISBN: (数字)9781665495400

ISBN: (纸本)9781665495417

For Software Engineering industry is essential to build software with high quality, but this has become difficult these days with the fact that size and complexity of developed software is high. For this reason, it is important to predict the quality of software in early phases, by this way, it helps to reduce testing resources. Some classification algorithms have been used for software defect prediction. In this paper five algorithms were evaluated and compared according to accuracy metric using three PROMISE datasets. For the evaluation, two classifiers were used which are focus on decision trees and three Bayesian classifiers. Results shows that even when percentages of accuracy are high on each classifier, decision trees got the highest percentage compared to Bayesian classifiers.

关键词： Measurement Mechatronics Software algorithms Prediction algorithms Software classification algorithms Bayes methods

来源：评论

学校读者我要写书评

暂无评论

Chronic Kidney Disease for Collaborative Healthcare Data Analytics using Random Forest classification algorithms

Chronic Kidney Disease for Collaborative Healthcare Data Ana...

引用

International Conference on Computer Communication and Informatics (ICCCI)

作者： V. Shanmugarajeshwari M. Ilayaraja Kalasalingam Academy of Research and Education Kalasalingam University Krishnankoil Tamil Nadu India

Collaborative Healthcare data analytics is a method of methodical data analysis that allows healthcare specialists to discovery opportunities used for development in health system management processing the various information are stored. This proposed approach entails three parts comparable to preprocessing, attribute selection, classification algorithms. The goal of this work is to plan a machine-based diagnostic approach using machine learning technique. This method is developed to mining the risk factors of chronic kidney diseases. There are many improved algorithms consume stayed proposed in every recent years. These algorithms improve the effectiveness of mining risk of Chronic Kidney Disease, but there are also shortcomings. In this work, Random forest, SVM and ANN algorithms were used to identify an early diagnosis of CKD patients with good accuracy.

关键词： Machine learning algorithms Collaboration Medical services classification algorithms Kidney Random forests Diseases

来源：评论

学校读者我要写书评

暂无评论

Prediction Performance of Crime Against Women Using Rule Based Decision Tree J48 classification algorithms in various states of India

Prediction Performance of Crime Against Women Using Rule Bas...

引用

International Conference on Computation, Automation and Knowledge Management (ICCAKM)

作者： S. Lavanya D. Akila Vels Institute of Science Technology & Advanced Studies Chennai India

ISBN: (数字)9781728194912

ISBN: (纸本)9781728194929

Crimes against women be situated collective societal difficult distressing eminence of lifecycle in addition monetary development of a civilization. Through expanding violations, rule implementations organizations remain proceeding towards request propelled frameworks and new ways to deal with improving wrongdoing investigation and better ensure their networks. Rule Based Decision tree (RBDT J48) and Naive Bayesian algorithm functional happening framework of commandment implementation then cleverness investigation embraces capacity of improving problems. Information Mining stands method in the direction of citation information available, frequently huge record sets; the widest collection of machine learning solicitations consumes prepared this one significant field of exploration. Crime type stands progression intentions towards classify law-breaking individualities. Exploration measured improvement in wrongdoing expectation model utilizing Rule Based Decision Tree (RBDT J48) calculation and Naive bayes since it has been considered as the most effective AI calculation for a forecast of wrongdoing information. And furthermore think about the two distinctive grouping calculations to be specific, Naive bayes and Rule Based Decision Tree J48 for anticipating wrongdoing type for various states in India.

关键词： Organizations Tools Propulsion Predictive models Bayes methods classification algorithms Decision trees

来源：评论

学校读者我要写书评

暂无评论

Performance Analysis of Various Supervised Binary classification algorithms and their Optimized Variants on High-Dimension Limited-Sample-Size Data

Performance Analysis of Various Supervised Binary Classifica...

引用

IEEE UP Section Conference on Electrical Computer and Electronics (UPCON)

作者： Rohan Kumar Lala School of Computer Engineering Kalinga Institute of Industrial Technology Bhubaneshwar India

ISBN: (纸本)9781665409636

In modern times, high-dimension limited-sample-size (HDLSS) data have become a lot prevalent in data mining research areas. Performing analysis on HDLSS data is inherently difficult because of large number of features and low sample size. Dimensionality reduction is a recommended step before working on HDLSS data because of a phenomenon known as the ‘curse of dimensionality’. However in medical and psychiatric areas, these large number of features often prove to be extremely important in cases of life threatening disease detection or identifying biomarkers. The purpose of this study is to perform a comparative performance analysis of various supervised classification algorithms and their optimized variants on HDLSS data without any dimensionality reduction to recreate the aforementioned situations. Various hyper-parameter optimization techniques and AdaBoost meta-estimator classifier are implemented on these algorithms to find their optimized variants. Optimized Random Forest and CatBoost classifiers produced the most optimal results on the used HDLSS datasets. Relevant arguments and findings regarding the used eight different supervised classification algorithms, both base and optimized, are evaluated and summarized in this paper.

关键词： Dimensionality reduction Measurement Learning systems Predictive models classification algorithms Performance analysis Naive Bayes methods

来源：评论

学校读者我要写书评

暂无评论

Compare and Contrast of Machine Learning classification algorithms to Predict Accuracy and Performance of Lung Cancer Disease

Compare and Contrast of Machine Learning Classification Algo...

引用

Technological Advancements and Innovations (ICTAI), International Conference on

作者： Pawan Kumar Singh Vinod Chaudhary Sheetu Jain Gannavarapu Ananth Kumar Computer Science and Engineering G L Bajaj Institute of Technology and Management Greater Noida India Computer Science and Engineering Chandigarh University Mohali India

ISBN: (纸本)9781665420884

Lung cancer becoming one of dangerous disease that occur in human life due to unmanageable growth of cells in lungs and it will cause the serious breathing problems occurs. The medical industries are providing high-tech facilities and efficient medical treatment for procure lung cancer diseases, so, it will control the lung cancer disease up to good extent and focus to take early precaution also including symptoms for better diagnosis. In today’s Artificial Intelligence and Machine Learning algorithms has great influence in medical sectors at the early prediction of the diseases and other measures with accurate outcomes along with data analysis. During this research, we apply various classification algorithms of machine learning to classify the lung cancer disease along with lung cancer dataset. Above classifiers are created and data is prepossessed in rapid-miner tool, and it has transformed into binary form to detect accuracy and performance of lung cancer disease.

关键词： Technological innovation Machine learning algorithms Lung cancer Medical treatment Lung Machine learning classification algorithms

来源：评论

学校读者我要写书评

暂无评论

A context-sensitive offloading system using machine-learning classification algorithms for mobile cloud environment

引用

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2019年 90卷 503-520页

作者： Junior, Warley Oliveira, Eduardo Santos, Albertinin Dias, Kelvin Univ Fed Pernambuco Informat Ctr Rd Jorn Anibal FernandesCidade Univ BR-50740560 Recife PE Brazil

Computational offloading in Mobile Cloud Computing (MCC) has attracted attention due to benefits in energy saving and improved mobile application performance. Nevertheless, this technique underperforms if the offloading decision ignores contextual information. While recent studies have highlighted the use of contextual information to improve the computational offloading decision, there still remain challenges regarding the dynamic nature of the MCC environment. Most solutions design a single reasoner for the offloading decision and do not know how accurate and precise this technique is, so that when applied in real-world environments it can contribute to inaccurate decisions and consequently the low performance of the overall system. Thus, this paper proposes a Context-Sensitive Offloading System (CSOS) that takes advantage of the main machine-learning reasoning techniques and robust profiling system to provide offloading decisions with high levels of accuracy. We first evaluate the main classification algorithms under our database and the results show that JRIP and J48 classifiers achieves 95% accuracy. Secondly, we develop and evaluate our system under controlled and real scenarios, where context information changes from one experiment to another. Under these conditions, CSOS makes correct decisions as well as ensuring performance gains and energy efficiency. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Mobile cloud Context-sensitive Machine-learning classification algorithms

来源：评论

学校读者我要写书评

暂无评论

DDoS Detection System: Using a Set of classification algorithms Controlled by Fuzzy Logic System in Apache Spark

引用

IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2019年第3期16卷 936-949页

作者： Alsirhani, Amjad Sampalli, Srinivas Bodorik, Peter Dalhousie Univ Fac Comp Sci Halifax NS B3H 1W5 Canada

Distributed denial of service (DDoS) attacks are a major security threat against the availability of conventional or cloud computing resources. Numerous DDoS attacks, which have been launched against various organizations in the last decade, have had a direct impact on both vendors and users. Many researchers have attempted to tackle the security threat of DDoS attacks by combining classification algorithms with distributed computing. However, their solutions are static in terms of the classification algorithms used. In fact, current DDoS attacks have become so dynamic and sophisticated that they are able to pass the detection system thereby making it difficult for static solutions to detect. In this paper, we propose a dynamic DDoS attack detection system based on three main components: 1) classification algorithms;2) a distributed system;and 3) a fuzzy logic system. Our framework uses fuzzy logic to dynamically select an algorithm from a set of prepared classification algorithms that detect different DDoS patterns. Out of the many candidate classification algorithms, we use Naive Bayes, Decision Tree (Entropy), Decision Tree (Gini), and Random Forest as candidate algorithms. We have evaluated the performance of classification algorithms and their delays and validated the fuzzy logic system. We have also evaluated the effectiveness of the distributed system and its impact on the classification algorithms delay. The results show that there is a trade-off between the utilized classification algorithms' accuracies and their delays. We observe that the fuzzy logic system can effectively select the right classification algorithm based on the traffic status.

关键词： DDoS attack DDoS detection fuzzy logic system machine learning classification algorithms apache spark apache Hadoop cloud computing

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：