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12th European conference e-Learning (EcEL)

作者： Bouarab-Dahmani, Farida Tahi, Razika Univ Tizi Ouzou FGEI Fac Dept Comp Sci Tizi Ouzou Algeria Univ Boumerdes Dept Econ Lab Elect Ind Enterprises Boumerdes Algeria

ISBN: (纸本)9781909507845

One of the challenges of research on Technology for Human Learning (THL) and the competency based pedagogy is the personalization of learning. This customization begins with the design of educational systems, applying the strategies defined to monitor and assist the learner in his learning by evaluating his knowledge, skills and detecting and analyzing errors. In this respect, formative evaluation is the process used to capture data on the strengths and weaknesses of a learner during a learning period. These data, to be useful, must be objectively analyzed so that they can be used to manage the following sessions. Data mining is a field at the intersection of statistics and information technology (databases, artificial intelligence, learning etc) whose goal is to discover "interesting" knowledge structures from a set of data. Following our research work on evaluation process and THL, applied to e-learning systems [2] [3] [4], we discuss in this paper a learning cycle with feedback loop integrating formative evaluation followed by the application of data mining algorithms after each learning session. We experimented with a set of tests, the exploration of learners' errors, obtained from a self e-learning by doing tool for the algorithmic domain. We particularly present, in this work, the results given by the application of the c4.5 algorithm for classification and the A Priori one for association rules deduction. These two algorithms were executed via the tool Weka after a data preparing process integrating a relational algebra calculation and format adjustment actions to get adequate input data for Weka algorithms. The results given by these experiments have proved interesting knowledge to complete the learner's profile and facilitate decisions about further learning / teaching / tutoring particularly in the case of distance education.

关键词： data mining e-learning formative assessment a priori algorithm c4.5 algorithm educational data mining

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classification algorithm for Predicting the Graduation Level: case Study of e-Learning in Telkom University 6th

Classification Algorithm for Predicting the Graduation Level...

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6th World conference on Smart Trends in Systems, Security and Sustainability, WS4 2022

作者： Hasibuan, M. Azani Andreswari, Rachmadita Lubis, Muharman Perdana, Fikli Fakhrurroja, Hanif Department of Information System Telkom University Bandung Indonesia

ISBN: (纸本)9789811976599

E-learning is one of the technologies used to support the learning process in university level. Telkom University with the university base on technology, Telkom University so intensively apply the learning using e-learning in order to become effective and efficient method. E-learning is used to reduce the cost and time spent learning in an offline class. With e-learning a review system can be done anywhere, because students do not need to come to the classroom to study. Simply by passing the Internet network can already attend the class. With the existing data in the e-learning system can be obtained some information by processing the existing data. This research will be predictions of student data contained in e-learning to determine the performance of algorithms c4.5, Naïve Bayes and Bayesian network, which will be used to determine what factors affect the level of graduation of students to a course. The process will go through several stages of data retrieval, preprocessing and classification, and the process will be done gradually to get the results of all the process. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Bayesian network c4.5 algorithm classification e-learning Naïve bayes

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Detection of the Smart Phone Position on User using Inertial Sensors 24

Detection of the Smart Phone Position on User using Inertial...

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24th Signal Processing and communication Application conference (SIU)

作者： Bayar, Veli Yayan, Ugur Yazici, Ahmet Linoyasyon Muhendislik Ltd Sti ETGB Osmangazi Teknopk Eskisehir Turkey Eskisehir Osmangazi Univ Bilgisayar Muhendisligi Bolumu Eskisehir Turkey

ISBN: (纸本)9781509016792

Smart phones are widely used in indoor positioning and navigation systems. Thus, it is important to know the relative position of the smart phone on the user while calculating position with RSS based systems. The accuracy of position will change within this scope. In this study, an intermediary solution is proposed to find the position of the smart phone on the user. Several algorithms are evaluated for the feature selection process. The outputs of the process are estimated via three different classifiers, namely Naives Bayes, k-Nearest Neighborhood and c4.5 decision tree. k-Nearest Neighborhood algorithm achieves 96% accuracy with the output of correlation Feature Selection.

关键词： correlation feature selection k-nearest neighborhood naive bayes c4.5 algorithm J48 algorithm RSS variations wi-fi indoor positioning

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Twitter noun-verb relation thesaurus tree 7

Twitter noun-verb relation thesaurus tree

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7th IIAI International congress on Advanced Applied Informatics (IIAI-AAI)

作者： Urabe, Naoki Rokui, Jun Shimane Univ Grad Sch Nat Sci & Technol Matsue Shimane 6908504 Japan

ISBN: (纸本)9781538674475

The thesaurus is a representation of words and concepts in tree structure [1]. Methods for automatically constructing a thesaurus are being actively studied. Thesauri constructed using conventional methods cannot deal with changes in word usage. Therefore, in this research, we build a thesaurus applying the c4.5 algorithm for Twitter data. The word use varies depending on when the Tweet data was generated. Therefore, we can capture the changes in how words are used. By applying the c4.5 algorithm to Twitter data, it is possible to efficiently derive the relationship of words. We verified the effectiveness of the Twitter thesaurus.

关键词： c4.5 algorithm Decision tree Thesaurus Twitter

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Application of Decision Tree classification algorithm in college Students' Employment Guidance 3

Application of Decision Tree Classification Algorithm in Col...

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3rd International conference on Smart Generation computing, communication and Networking, SMART GENcON 2023

作者： Lang, Huang Yan, Du Ze School of Software Shenyang University of Technology Sichuan Province Chengdu City611743 China

ISBN: (纸本)9798350319125

With the increasingly severe employment situation of college students, how to help college graduates better plan their career paths and achieve employment goals has become the focus of attention of universities and society. As an important method in data mining technology, decision tree classification algorithm has important application value in college students' career guidance. This paper introduces the application status of decision tree classification algorithm in college students' career guidance, and analyzes its advantages and disadvantages. © 2023 IEEE.

关键词： c4.5 algorithm classification Data mining Decision tree Employment of college Students

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Research on campus Digital Teaching System of Smart u Based on Data Mining algorithm 3

Research on Campus Digital Teaching System of Smart u Based ...

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3rd IEEE International conference on Electrical Engineering, Big Data and algorithms, EEBDA 2024

作者： Wang, Guoyun Baise University Guangxi Baise533000 China

ISBN: (纸本)9798350380989

With the continuous development of data mining technology, the informatization level of colleges and universities is gradually improving. However, the traditional digital teaching system has many problems such as low utilization rate of data resources, non-sharing of information and data between systems, and it is difficult to meet the emerging new needs of colleges and universities. Therefore, it is of great significance to study the smart u campus digital teaching system based on data mining. Aiming at the problems of many links, serious information islands and low data utilization rate in traditional university teaching, this paper studies the smart u campus digital teaching system based on data mining. The core module of the system is the decision analysis module, which is based on the c4.5 algorithm framework, reduces the decision number of threshold points according to the Fayyad boundary point decision principle, and uses the equivalent infinitesimal principle to simplify the calculation of information entropy, overcoming the disadvantages of c4.5 algorithm. The module generates decision tree, generates classification rules through IF-THEN form, and realizes achievement prediction. The experimental data in this paper is the teaching data of a university. The experimental results show that the designed system can effectively realize data preprocessing, complete the learning effect prediction, provide personalized guidance for students, and realize the digital teaching work of smart u campus. © 2024 IEEE.

关键词： c4.5 algorithm Data mining Digital teaching Wisdom u campus

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Influence of environmental factors on human-like decision-making for intelligent ship

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OcEAN ENGINEERING 2019年 186卷 106060-000页

作者： Xue, Jie chen, Zhijun Papadimitriou, Eleonora Wu, chaozhong Van Gelder, P. H. A. J. M. Wuhan Univ Technol ITSC 1040 Heping Ave Wuhan 430063 Hubei Peoples R China Natl Engn Res Ctr Water Transport Safety WTSC Wuhan Hubei Peoples R China Delft Univ Technol S3G Fac Technol Policy & Management Jaffalaan 5 NL-2628 BX Delft Netherlands Minist Educ Engn Res Ctr Transportat Safety Wuhan Hubei Peoples R China Wuhan Univ Technol Sch Energy & Power Engn Wuhan Hubei Peoples R China

To date, the increasing density of water traffic has caused the ship's navigation environment to deteriorate, resulting in frequent water traffic accidents. In addition, a majority of maritime accidents are caused by human factors, and one of the important ways to solve the ship accidents caused by human factors is to utilize intelligent maneuvering of ships. Based on the actual crews' operational data from full-task handling simulation platform, this study combines a 30,000-ton bulk carrier inbound navigation scenario and uses the decision tree method to propose a knowledge learning model under multiple environmental constraints to give intelligent ships the ability to make decisions like a human: An intelligent ship Human-like Decision-making Maneuvering Decision Recognition (HDMDR) model. The decision-making mechanism for the maneuvering behavior of Officer On Watch (OOW) under the influence of the specific water traffic environment in the inbound scenario is analyzed, and the OOW's decision-making knowledge is automatically acquired and represented. The validation tests and the comparative analysis with the classic classification algorithms of k-Nearest Neighbours (k-NN) and Support Vector Machine (SVM) are performed to demonstrate the accuracy of the proposed HDMDR model. This paper provides a feasible basis for the human-like decision-making analysis of intelligent ships.

关键词： c4.5 algorithm Ship maneuvering Decision-making Intelligent ship classification rule Data mining

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A New Framework for Scholarship Predictor Using a Machine Learning Approach

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Intelligent Automation & Soft computing 2024年第5期39卷 829-854页

作者： Bushra Kanwal Rana Saud Shoukat Saif Ur Rehman Mahwish Kundi Tahani AlSaedi Abdulrahman Alahmadi School of Science and Technology Foundation University IslamabadRawalpindi46000Pakistan University Institute of Information Technology PMAS-University of Arid AgricultureRawalpindi46000Pakistan Maynooth International Engineering College Maynooth UniversityMaynoothW23 F2H6Irland Applied College Taibah UniversityMadina42353Saudi Arabia

Education is the base of the survival and growth of any state,but due to resource scarcity,students,particularly at the university level,are forced into a difficult *** are the most significant financial aid mechanisms developed to overcome such obstacles and assist the students in continuing with their higher *** this study,the convoluted situation of scholarship eligibility criteria,including parental income,responsibilities,and academic achievements,is *** an attempt to maximize the scholarship selection process,numerous machine learning algorithms,including Support Vector Machines,Neural Networks,K-Nearest Neighbors,and the c4.5 algorithm,were *** c4.5 algorithm,owing to its efficiency in the prediction of scholarship beneficiaries based on extraneous factors,was capable of predicting a phenomenal 95.62%of predictions using extensive data of a well-esteemed government sector university from *** percentage is 4%and 15%better than the remainder of the methods tested,and it depicts the extent of the potential for the technique to enhance the scholarship selection *** Decision Support Systems(DSS)would not only save the administrative cost but would also create a fair and transparent process in *** a world where accessibility to education is the key,this research provides data-oriented consolidation to ensure that deserving students are helped and allowed to get the financial assistance that they need to reach higher studies and bridge the gap between the demands of the day and the institutions of intellect.

关键词： Education data mining c4.5 algorithm decision support system scholarship guarantee machine learning

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A Geographic Information System for Hotspot Occurrences classification in Riau Province Indonesia

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Procedia Environmental Sciences 2015年 24卷 127-131页

作者： Khairil Amri Imas Sukaesih Sitanggang Department of Computer Science Bogor Agricultural University Jl. Meranti Wing 20 Level 5 – 6 Bogor 16680 Indonesia

Forest fire is a serious problem because it causes damages to forest ecosystem and surrounding environment in many areas in Sumatera and Kalimantan including Riau Province. One of activities in forest fire prevention is to provide relevant information about hotspot and characteristics of areas where hotspots probably occur. Such information can be presented in a geographic information system (GIS) for classifying hotspot occurrences. This study aims to develop a classification module for hotspot occurrences in a GIS using OpenGeo Suite 3.0. The classification module was made using the decision tree method namely the c4.5 algorithm. Applying the c4.5 algorithm on the forest fire data generated 109 classification rules with accuracy of 69.56%. The SIG has major features such as displaying map, pan map, zoom, in, zoom out, scale, mouse position and a classification module for hotspot occurrences based on characteristics of areas. The GIS enables users to determine whether an area is potential for hotspot incidence or not based on its characteristics. Potential hotspot occurrences can be used in decision making related to forest fire prevention.

关键词： classification c4.5 algorithm decision tree geographic information system hotspot

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Application comparison of Association Rules and c4.5 Rules in Land Evaluation

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Agricultural Science & Technology 2010年第4期11卷 144-147页

作者：李亭杨敬锋陈志民中山火炬职业技术学院广东中山528436 广东融讯信息科技有限公司广东广州510656 广东瑞图万方科技股份有限公司广东顺德528305 华南农业大学公共基础课实验教学中心广东广州510642

Association rules and c4.5 rules can overcome the shortage of the traditional land evaluation methods and improve the intelligibility and efficiency of the land evaluation *** order to compare these two kinds of classification rules in the application,two fuzzy classifiers were established by combining with fuzzy decision algorithm especially based on Second General Soil Survey of Guangdong *** results of experiments demonstrated that the fuzzy classifier based on association rules obtain a higher accuracy rate,but with more complex calculation process and more computational overhead;the fuzzy classifier based on c4.5 rules obtain a slightly lower accuracy,but with fast computation and simpler calculation.

关键词： Land evaluation Association rules c4.5 algorithm Fuzzy decision

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