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19th International Conference on Advanced Communication Technology (ICACT)

作者： Yang, Eujin Gu, Bokyung Yoon, Taeseon Hankuk Acad Foreign Studies Nat Sci Young In South Korea Hankuk Acad Foreign Studies Fac Young In South Korea

ISBN: (纸本)9788996865094

Flavivirus is spreaded with the help of intermediary, especially mosquitoes. In preceding research, we found out that Leucine has high frequency. Wanting to know specific relationship between 5 flaviviruses;Yellow fever, West Nile virus, Dengue virus, Tick borne encephalitis, decision tree and support vector machine algorithm were used. Analyzing results of the algorithms, difference or similarity about the viruses and a group as flavivirus were found.

关键词： Zika virus Yellow fever West Nile virus Dengue virus Tick borne encephalitis Flavivirus decision tree algorithm Support Vector Machine(SVM)

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Power System Islanding Detection & Identification using Topology Approach and decision tree

Power System Islanding Detection & Identification using Topo...

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General Meeting of the IEEE-Power-and-Energy-Society (PES)

作者： Sun, Rui Wu, Zhongyu Centeno, Virgilio A. Virginia Polytech Inst & State Univ Dept Elect & Comp Engn Blacksburg VA 24061 USA

ISBN: (纸本)9781457710018

Power system islanding contingency has always been one of the most severe wide area system failures. The recent Hurricane Gustav landed in west coast in 2008 was one exact case to illuminate the importance and severity of this kind of contingencies. Longtime the researches of islanding situations focus on studying the coherence groups of the generations under an islanding contingency. In this paper, a new method in detecting power system islanding contingencies using both the system's topological structure and real-time system dynamic state variables is presented and tested. An islanding severity index concept is given for ranking the severity of the islanding cases.

关键词： Phasor measurements PMU placement strategy Islanding contingency decision tree algorithm islanding severity index

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Social Media Devices' Influence on User Neck Pain during the COVID-19 Pandemic: Collaborating Vertebral-GLCM Extracted Features with a decision tree

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JOURNAL OF IMAGING 2023年第1期9卷 14-14页

作者： Al-Naami, Bassam Badr, Bashar E. A. Rawash, Yahia Z. Owida, Hamza Abu De Fazio, Roberto Visconti, Paolo Hashemite Univ Fac Engn Dept Biomed Engn Zarqa 13133 Jordan Al Ahliyya Amman Univ Dept Elect Engn Amman 19328 Jordan Al Ahliyya Amman Univ Dept Med Engn Amman 19328 Jordan Univ Salento Dept Innovat Engn I-73100 Lecce Italy

The prevalence of neck pain, a chronic musculoskeletal disease, has significantly increased due to the uncontrollable use of social media (SM) devices. The use of SM devices by younger generations increased enormously during the COVID-19 pandemic, being-in some cases-the only possibility for maintaining interpersonal, social, and friendship relationships. This study aimed to predict the occurrence of neck pain and its correlation with the intensive use of SM devices. It is based on nine quantitative parameters extracted from the retrospective X-ray images. The three parameters related to angle_1 (i.e., the angle between the global horizontal and the vector pointing from C7 vertebra to the occipito-cervical joint), angle_2 (i.e., the angle between the global horizontal and the vector pointing from C1 vertebra to the occipito-cervical joint), and the area between them were measured from the shape of the neck vertebrae, while the rest of the parameters were extracted from the images using the gray-level co-occurrence matrix (GLCM). In addition, the users' ages and the duration of the SM usage (***) were also considered. The decision tree (DT) machine-learning algorithm was employed to predict the abnormal cases (painful subjects) against the normal ones (no pain). The results showed that angle_1, area, and the image contrast significantly increased statistically with the time of SM-device usage, precisely in the range of 2 to 9 h. The DT showed a promising result demonstrated by classification accuracy and F1-scores of 94% and 0.95, respectively. Our findings confirmed that the objectively detected parameters, which elucidate the negative impacts of SM-device usage on neck pain, can be predicted by DT machine learning.

关键词： social media usage smartphones neck pain GLCM decision tree algorithm

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An SDN-based decision tree Detection (DTD) Model for Detecting DDoS Attacks in Cloud Environment

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2022年第7期13卷 54-64页

作者： Praba, J. Jeba Sridaran, R. Christ Coll Dept Comp Applicat Rajkot India Marwadi Univ Rajkot Gujarat India Marwadi Univ Fac Comp Applicat Rajkot Gujarat India

Detecting Distributed Denial of Service (DDoS) attacks has become a significant security issue for various network technologies. This attack has to be detected to increase the system's reliability. Though various traditional studies are present, they suffer from data shift issues and accuracy. Hence, this study intends to detect DDoS attacks by classifying the normal and malicious traffic. The study aims to solve the data shift issues by using the introduced decision tree Detection (DTD) model encompassing of Greedy Feature Selection (GFS) algorithm and decision tree algorithm (DTA). It also attempts to enhance the proposed model's detection rate (accuracy) above 90%. Various processes are involved in DDoS attack detection. Initially, the gureKddcup dataset is loaded to perform pre-processing. This process is essential for removing noisy data. After this, feature selection is performed to select only the relevant features, removing the irrelevant data. This is then fed into the train and test split. Following this, Software Defined Networking (SDN) based DTA is used to classify the normal and malicious traffic, then given to the trained model for predicting this attack. Performance analysis is undertaken by comparing the proposed model with existing systems in terms of accuracy, MCC (Matthew's Correlation Coefficient), sensitivity, specificity, error rate, FAR (False Alarm Rate), and AUC (Area under Curve). This analysis is carried out to evaluate the efficacy of the proposed model, which is verified through the results.

关键词： -Distributed denial of service attack greedy feature selection decision tree algorithm software defined networking cloud and decision tree detection

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A HYBRID APPROACH FOR MINIMIZING MAKESPAN IN PERMUTATION FLOWSHOP SCHEDULING

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Journal of Systems Science and Systems Engineering 2017年第1期26卷 50-76页

作者： Kannan Govindan R'Balasundaram N.Baskar e.Asokan Center for Sustainable Engineering Operations Management Department of Technology and Innovation University of Southern Denmark Odense M Denmark-5230 Department of Mechanical Engineering K.Ramakrishnan College of Engineering Tiruchirappalli Tamilnadu India Pin-621112 Department of Mechanical Engineering Saranathan College of Engineering Tiruchirappalli Tamilnadu India Pin 620012 Department of Production Engineering National Institute of Technology Tiruchirappalli Tamilnadu India Pin-620015

This work proposes a hybrid approach for solving traditional flowshop scheduling problems to reduce the makespan （total completion time）. To solve scheduling problems, a combination of decision tree （DT） and Scatter Search （SS） algorithms are used. Initially, the DT is used to generate a seed solution which is then given input to the SS to obtain optimal / near optimal solutions of makespan. The DT used the entropy function to convert the given problem into a tree structured format / set of rules. The SS provides an extensive investigation of the search space through diversification. The advantages of both DT and SS are used to form a hybrid approach. The proposed algorithm is tested with various benchmark datasets available for flowshop scheduling. The statistical results prove that the proposed method is competent and efficient for solving flowshop problems.

关键词： Flowshop scheduling makespan decision tree algorithm scatter search algorithm hybrid algorithm

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Safety performance prediction and modification strategies for construction projects via machine learning techniques

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ENGINEERING CONSTRUCTION AND ARCHITECTURAL MANAGEMENT 2023年第3期30卷 1146-1164页

作者： Abbasianjahromi, Hamidreza Aghakarimi, Mehdi KN Toosi Univ Technol Tehran Iran

Purpose Unsafe behavior accounts for a major part of high accident rates in construction projects. The awareness of unsafe circumstances can help modify unsafe behaviors. To improve awareness in project teams, the present study proposes a framework for predicting safety performance before the implementation of projects. Design/methodology/approach The machine learning approach was adopted in this work. The proposed framework consists of two major phases: (1) data collection and (2) model development. The first phase involved several steps, including the identification of safety performance criteria, using a questionnaire to collect data, and converting the data into useful information. The second phase, on the other hand, included the use of the decision tree algorithm coupled with the k-Nearest Neighbors algorithm as the predictive tool along with the proposing modification strategies. Findings A total of nine safety performance criteria were identified. The results showed that safety employees, training, rule adherence and management commitment were key criteria for safety performance prediction. It was also found that the decision tree algorithm is capable of predicting safety performance. Originality/value The main novelty of the present study is developing an integrated model to propose strategies for the safety enhancement of projects in the case of incorrect predictions.

关键词： Safety performance decision tree algorithm KNN Construction industry

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Machine learning methods as auxiliary tool for effective mathematics teaching

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COMPUTER APPLICATIONS IN ENGINEERING EDUCATION 2024年第6期32卷

作者： Milicevic, Marina Marinovic, Budimirka Jeftic, Ljerka Univ East Sarajevo Fac Prod & Management Trebinje 89 101 TrebinjeStepe Stepanov bb Sarajevo Bosnia & Herceg

Seeing mathematics teaching as a very demanding and responsible process while having in mind the importance of mathematical knowledge for students of technical faculties, this paper aims to present heuristics for student classification according to their predicted mathematical success. Over the last few decades, the process of informatization of universities has resulted in new challenges universities are faced with. Due to the widespread use of educational databases, which opens new possibilities for educational data mining and analyses, machine learning algorithms have become a very popular tool for predicting students' academic performance. The decision tree algorithm is used in this paper for the classification and prediction of students' mathematical performance and it is trained on the data collected from the educational information system. The experimental results show that the model accuracy is 72% with an error rate of 0.28. The implementation of the decision tree Model to predict whether a student will pass, fail or be conditional in mathematical courses is important for both teachers and students, as well as for universities. Students' performance is one of the major keys in evaluating the quality of the teaching process, but also for evaluating the overall success of the university itself. As mathematics is considered a basic and important discipline, it is clear why predicting students' mathematical achievement is crucial for all levels of university organization.

关键词： decision tree algorithm machine learning tool mathematics teaching student classification

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Rapid and automated determination of rusted surface areas of a steel bridge for robotic maintenance systems

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AUTOMATION IN CONSTRUCTION 2014年 42卷 13-24页

作者： Son, Hyojoo Hwang, Nahyae Kim, Changmin Kim, Changwan Chung Aug Univ Dept Architectural Engn Seoul 156756 South Korea

There has been an increased interest in the use of robotic systems to automate the blasting tasks in steel bridge maintenance. To utilize such robotic systems effectively, an automated process for determining the rusted areas on a steel bridge to be blasted is a prerequisite. This study proposes a method to rapidly and accurately determine rusted surface areas on a steel bridge that are to be blasted, within current standards. The proposed method consists of three steps: color space conversion, classification of rusted area via the J48 decision tree algorithm, and determination of blasting area. The method was validated using 119 test images showing both normal states and various degrees of rusting and rust distribution types. The experimental results showed that the success rate for determining the rusted areas that needed blasting was 97.48%. The average processing time was 0.57 s/image. The results demonstrate that the proposed method rapidly and accurately indicated whether blasting was necessary, and if so, where blasting should be performed, based on current standards of practice. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Rust detection Rusted surface area determination Steel bridge inspection and maintenance Color space conversion decision tree algorithm

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Prediction of unplanned islanding using an energy based strategy

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IET GENERATION TRANSMISSION & DISTRIBUTION 2016年第1期10卷 183-191页

作者： Kamali, Sadegh Amraee, Turaj Bathaee, Seyed Mohammad Taghi KN Toosi Univ Technol Dept Elect Engn Tehran Iran

In this study a new index is proposed for power system islanding prediction. The total energy absorbed by coherent synchronous generators during unplanned islanding conditions is formulated as an islanding predictor. decision tree (DT) algorithm is utilised to extract the information gain of the proposed predictor over the input training samples. A comprehensive list of input scenarios, including island and non-island conditions is constructed. The rotor angles of synchronous generators are estimated by phasor measurements at generators' terminals and used for calculating the predictor over the input samples. The results of DT algorithm are rearranged as a set of if-then rules for practical applications. The proposed method is implemented in the 10-machine 39-bus New-England test system.

关键词： synchronous generators decision trees phasor measurement rotors power distribution faults power distribution protection unplanned islanding prediction energy based strategy power system islanding prediction coherent synchronous generators islanding predictor decision tree algorithm information gain extraction input training samples island condition non island condition DT algorithm rotor angle estimation phasor measurements 10-machine 39-bus New-England test system

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RETRACTED: Classification accuracy of basketball simulation training system based on sensor fusion and Bayesian algorithm (Retracted Article)

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020年第4期39卷 5965-5976页

作者： Zhu, Wei Guangzhou Inst Phys Educ Sports Training Dept Basketball Teaching & Res Off Guangzhou Guangdong Peoples R China

As a pattern recognition application direction, human body posture recognition provides decision-making basis for human body behavior pattern analysis of human-computer intelligent interactive control. Therefore, in a complete human-computer intelligent interaction system, human body posture recognition is a necessary link that can complete the human body's behavioral characterization and make humanized decision-making. This paper studies the athlete's posture recognition algorithm based on multi-sensor method and completes the whole process from data acquisition to data processing and model algorithm construction and verification. Moreover, this paper designs experiments to verify the model's recognition results for athletes, and discusses the results, and analyzes the advantages and disadvantages of the model in this experiment. In addition, this study takes basketball action as an example to take identification analysis. The results show that the proposed method has certain practical effects and can provide theoretical reference for subsequent related research.

关键词： decision tree algorithm improved algorithm athlete simulation training motion recognition

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