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Journal articles on the topic 'Decision Tree Mode'

Author: Grafiati

Published: 5 June 2025

Last updated: 16 July 2025

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1

Kang, Donggil, WenXing Yu, and HyungJun Cho. "Decision Tree for Mode Estimation." Korean Data Analysis Society 25, no. 3 (2023): 903–11. http://dx.doi.org/10.37727/jkdas.2023.25.3.903.

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Decision trees are one of the data mining techniques that make predictions by recursively partitioning data structures based on split rules. Since the analysis results can be understood through the tree structure, it has the advantage of having high interpretation power as well as predictive power. In addition, it is used in many fields because it is able to identify nonlinear relationships between response and predictor variables. However, if the purpose of it is to predict the mode of the response variable, there is a limitation in that the previously proposed decision tree cannot be applied

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Syafitri, Nesi, Syarifah Farradinna, Wella Jayanti, and Yudhi Arta. "MACHINE LEARNING TO CREATE DECISION TREE MODEL TO PREDICT OUTCOME OF ENTERPRENEURSHIP PSYCHOLOGICAL READINESS (EPR)." Jurnal Teknik Informatika (Jutif) 4, no. 2 (2023): 381–90. http://dx.doi.org/10.52436/1.jutif.2023.4.2.590.

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This study aims to create a decision tree model using machine learning to predict psychological readiness for entrepreneurship in college graduates. This research was conducted through several stages of research. In the early stages, a survey was conducted on 700 students from several universities in Riau aged between 17-25 years. The survey was conducted using the Entrepreneur Psychology Readiness (EPR) instrument. Furthermore, the survey data was validated and obtained 604 valid data to be used in forming machine learning models The urgency of this research is to find a number of decision ru

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3

Li, Hongchan, Peng Zhang, Baohua Jin, and Qiuwen Zhang. "Fast CU Decision Algorithm Based on CNN and Decision Trees for VVC." Electronics 12, no. 14 (2023): 3053. http://dx.doi.org/10.3390/electronics12143053.

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Compared with the previous generation of High Efficiency Video Coding (HEVC), Versatile Video Coding (VVC) introduces a quadtree and multi-type tree (QTMT) partition structure with nested multi-class trees so that the coding unit (CU) partition can better match the video texture features. This partition structure makes the compression efficiency of VVC significantly improved, but the computational complexity is also significantly increased, resulting in an increase in encoding time. Therefore, we propose a fast CU partition decision algorithm based on DenseNet network and decision tree (DT) cl

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Li, Ye, Zhihao He, and Qiuwen Zhang. "Fast Decision-Tree-Based Series Partitioning and Mode Prediction Termination Algorithm for H.266/VVC." Electronics 13, no. 7 (2024): 1250. http://dx.doi.org/10.3390/electronics13071250.

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With the advancement of network technology, multimedia videos have emerged as a crucial channel for individuals to access external information, owing to their realistic and intuitive effects. In the presence of high frame rate and high dynamic range videos, the coding efficiency of high-efficiency video coding (HEVC) falls short of meeting the storage and transmission demands of the video content. Therefore, versatile video coding (VVC) introduces a nested quadtree plus multi-type tree (QTMT) segmentation structure based on the HEVC standard, while also expanding the intra-prediction modes fro

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Diao, Yuzhu, and Qing Zhang. "Optimization of Management Mode of Small- and Medium-Sized Enterprises Based on Decision Tree Model." Journal of Mathematics 2021 (December 17, 2021): 1–9. http://dx.doi.org/10.1155/2021/2815086.

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Decision tree algorithm is a common classification algorithm in data mining technology, and its results are usually expressed in the form of if-then rules. The C4.5 algorithm is one of the decision tree algorithms, which has the advantages of easy to understand and high accuracy, and the concept of information gain rate is added compared with its predecessor ID3 algorithm. After theoretical analysis, C4.5 algorithm is chosen to analyze the performance appraisal results, and the decision tree for performance appraisal is generated by collecting data, data preprocessing, calculating information

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Chen, Wenzheng, Guanglei Yang, and Dongkai Qi. "Comprehensive Evaluation of College Students’ Physical Health and Sports Mode Recommendation Model Based on Decision Tree Classification Model." Computational Intelligence and Neuroscience 2022 (July 22, 2022): 1–8. http://dx.doi.org/10.1155/2022/5504850.

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Nowadays, more and more college students’ physical health is getting worse because of their living habits and self-consciousness. In order to improve the physical quality of college students as much as possible, the experiment uses the improved iterative dichotomiser III (ID3) decision tree to make decisions on the physical condition of some college students and the corresponding sports mode recommendation, and compares the results with the traditional ID3 algorithm. In the experimental results, the information entropy ratio of the improved ID3 algorithm is 89.5%, the operation information los

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Basak, Jayanta. "Online Adaptive Decision Trees." Neural Computation 16, no. 9 (2004): 1959–81. http://dx.doi.org/10.1162/0899766041336396.

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Decision trees and neural networks are widely used tools for pattern classification. Decision trees provide highly localized representation, whereas neural networks provide a distributed but compact representation of the decision space. Decision trees cannot be induced in the online mode, and they are not adaptive to changing environment, whereas neural networks are inherently capable of online learning and adpativity. Here we provide a classification scheme called online adaptive decision trees (OADT), which is a tree-structured network like the decision trees and capable of online learning l

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8

Huang, Pingge. "Construction of College Chinese Blended Teaching Mode Based on Decision Tree Classification Model in New Media Context." Computational Intelligence and Neuroscience 2022 (September 26, 2022): 1–10. http://dx.doi.org/10.1155/2022/4608631.

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Based on the context of new media and big data, this article uses the decision tree classification model to construct the college Chinese hybrid teaching mode. In order to verify the accuracy of ID3 algorithm prediction, the comparison of the ID3 algorithm, K-means algorithm, and support vector machine classification algorithm was made, and the experimental results show that the ID3 decision tree classification algorithm has better prediction and classification ability, for the construction of college Chinese hybrid teaching mode provides certain practical value and reference basis.

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9

Burduk, Robert, and Michal Wozniak. "Different decision tree induction strategies for a medical decision problem." Open Medicine 7, no. 2 (2012): 183–93. http://dx.doi.org/10.2478/s11536-011-0142-x.

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AbstractThe paper presents a comparative study of selected recognition methods for the medical decision problem -acute abdominal pain diagnosis. We consider if it is worth using expert knowledge and learning set at the same time. The article shows two groups of decision tree approaches to the problem under consideration. The first does not use expert knowledge and generates classifier only on the basis of learning set. The second approach utilizes expert knowledge for specifying the decision tree structure and learning set for determining mode of decision making in each node based on Bayes dec

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10

Chasani, Paraskevi, and Aristidis Likas. "Unsupervised Decision Trees for Axis Unimodal Clustering." Information 15, no. 11 (2024): 704. http://dx.doi.org/10.3390/info15110704.

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The use of decision trees for obtaining and representing clustering solutions is advantageous, due to their interpretability property. We propose a method called Decision Trees for Axis Unimodal Clustering (DTAUC), which constructs unsupervised binary decision trees for clustering by exploiting the concept of unimodality. Unimodality is a key property indicating the grouping behavior of data around a single density mode. Our approach is based on the notion of an axis unimodal cluster: a cluster where all features are unimodal, i.e., the set of values of each feature is unimodal as decided by a

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MARTIS, ROSHAN JOY, U. RAJENDRA ACHARYA, JEN HONG TAN, et al. "APPLICATION OF EMPIRICAL MODE DECOMPOSITION (EMD) FOR AUTOMATED DETECTION OF EPILEPSY USING EEG SIGNALS." International Journal of Neural Systems 22, no. 06 (2012): 1250027. http://dx.doi.org/10.1142/s012906571250027x.

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Epilepsy is a global disease with considerable incidence due to recurrent unprovoked seizures. These seizures can be noninvasively diagnosed using electroencephalogram (EEG), a measure of neuronal electrical activity in brain recorded along scalp. EEG is highly nonlinear, nonstationary and non-Gaussian in nature. Nonlinear adaptive models such as empirical mode decomposition (EMD) provide intuitive understanding of information present in these signals. In this study a novel methodology is proposed to automatically classify EEG of normal, inter-ictal and ictal subjects using EMD decomposition.

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Pineda-Jaramillo, Juan D. "A review of Machine Learning (ML) algorithms used for modeling travel mode choice." DYNA 86, no. 211 (2019): 32–41. http://dx.doi.org/10.15446/dyna.v86n211.79743.

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In recent decades, transportation planning researchers have used diverse types of machine learning (ML) algorithms to research a wide range of topics. This review paper starts with a brief explanation of some ML algorithms commonly used for transportation research, specifically Artificial Neural Networks (ANN), Decision Trees (DT), Support Vector Machines (SVM) and Cluster Analysis (CA). Then, these different methodologies used by researchers for modeling travel mode choice are collected and compared with the Multinomial Logit Model (MNL) which is the most commonly-used discrete choice model.

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Xiang, Jian Hua, Jun Li Li, Yang Lou, and Gang Yi Jiang. "A Fast Mode Decision Algorithm from MPEG-2 to H.264 Transcoding." Advanced Materials Research 204-210 (February 2011): 985–88. http://dx.doi.org/10.4028/www.scientific.net/amr.204-210.985.

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In order to reduce the complexity of MPEG-2 to H.264 transcoding, an efficient fast mode decision algorithm is proposed. We apply machine learning principles by building a decision tree (the relationship between the information gathered during the MPEG-2 decoding stage and the H.264 coding modes of MBs) enabling the development of very low complexity transcoding mechanism. The decision tree is used to determine the coding modes of the P-frames MBs of the output H.264 encoded video sequences. Experimental results show that the proposed algorithm can dramatically reduce the transcoding time, wit

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Xu, Yao-Zhi, Jian-Lin Zhang, Ying Hua, and Lin-Yue Wang. "Dynamic Credit Risk Evaluation Method for E-Commerce Sellers Based on a Hybrid Artificial Intelligence Model." Sustainability 11, no. 19 (2019): 5521. http://dx.doi.org/10.3390/su11195521.

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Credit risk evaluation is important for e-commerce platforms, due to the uncertainty and transaction risk associated with buyers and sellers. Moreover, it is the key ingredient for the development of the e-commerce ecosystem and sustainability of the financial market. The main objective of this paper is to develop an effective and user-friendly system for seller credit risk evaluation. Three hybrid artificial intelligent models, including (1) decision tree—artificial neural network (ANN), (2) decision tree—logistic regression, and (3) decision tree—dynamic Bayesian network have been investigat

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Ma, Wenwen, Kunjuan Jing, Ruotong Zhang, Xuefei Li, and Zheng Li. "Predictive factors of stigma in stroke patients based on logistic regression and decision tree mode." Pakistan Journal of Medical Sciences 41, no. 5 (2025): 1482–87. https://doi.org/10.12669/pjms.41.5.9946.

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Objective: Logistic regression and decision tree model were used to analyze the predictive factors of stigma in stroke patients, and to explore the application value of the two models. Methods: This was a retrospective study. The data of 342 stroke patients were collected from Baoding No.1 Central Hospital from December 2023 to March 2024. Data were retrospectively retrieved from the hospital information and management system. The regression model and decision tree model of influencing factors of stroke patients’ sense of stigma were established, to analyze the influencing factors of the sense

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Li, Xianghong, Hong Yuan, Guang Yang, Yingkui Gong, and Jiajia Xu. "A Novel Algorithm for Scenario Recognition Based on MEMS Sensors of Smartphone." Micromachines 13, no. 11 (2022): 1865. http://dx.doi.org/10.3390/mi13111865.

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The scenario is very important to smartphone-based pedestrian positioning services. The smartphone is equipped with MEMS(Micro Electro Mechanical System) sensors, which have low accuracy. Now, the methods for scenario recognition are mainly machine-learning methods. The recognition rate of a single method is not high. Multi-model fusion can improve recognition accuracy, but it needs to collect many samples, the computational cost is high, and it is heavily dependent on feature selection. Therefore, we designed the DT-BP(decision tree-Bayesian probability) scenario recognition algorithm by intr

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17

Yao, C., X. Zhang, and H. Liu. "SECTION-BASED TREE SPECIES IDENTIFICATION USING AIRBORNE LIDAR POINT CLOUD." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7 (September 13, 2017): 1001–7. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w7-1001-2017.

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The application of LiDAR data in forestry initially focused on mapping forest community, particularly and primarily intended for largescale forest management and planning. Then with the smaller footprint and higher sampling density LiDAR data available, detecting individual tree overstory, estimating crowns parameters and identifying tree species are demonstrated practicable. This paper proposes a section-based protocol of tree species identification taking palm tree as an example. Section-based method is to detect objects through certain profile among different direction, basically along X-ax

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18

Gue, I. H. V., J. Soliman, M. De Guzman, et al. "Decision tree analysis of commuter mode choice in Baguio City, Philippines." IOP Conference Series: Materials Science and Engineering 1109, no. 1 (2021): 012059. http://dx.doi.org/10.1088/1757-899x/1109/1/012059.

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KIM, J. K., and N. S. KIM. "Improved Frame Mode Selection for AMR-WB+ Based on Decision Tree." IEICE Transactions on Information and Systems E91-D, no. 6 (2008): 1830–33. http://dx.doi.org/10.1093/ietisy/e91-d.6.1830.

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20

Guo, Yanqi. "University Classroom Teaching Model Based on Decision Tree Analysis and Machine Learning." Mobile Information Systems 2021 (November 22, 2021): 1–12. http://dx.doi.org/10.1155/2021/6926013.

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The existing teaching evaluation system partially reflects the teaching effect and other related conditions through statistical reports, but it is difficult to find the useful knowledge hidden in the database, and it cannot effectively assist the decision-making support. In order to improve the evaluation effect of college classroom teaching mode, this paper mainly uses decision tree algorithm and data mining technology of association rules to construct the effectiveness evaluation system of college classroom teaching mode based on decision tree analysis. Moreover, this paper analyzes the teac

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Miao, Xiyuan, and Shi Zhang. "Research on Identifying Important Factors and Prediction of Online Service Satisfaction for Mobile Phone Users." Frontiers in Science and Engineering 3, no. 5 (2023): 27–46. http://dx.doi.org/10.54691/fse.v3i5.5005.

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Our lives cannot do without the internet. How to improve the network quality has always been an essential problem. The paper explores the important factors of online service satisfaction and the best predicting model. Based on the data offered by Beijing Mobile Company, we identify main factors affecting online service satisfaction by calculating their mutual information values. The factors include signal problem factors, scene factors and software usage factors. Additionally, based on decision tree model and models with decision tree as base learner, we predict the online service satisfaction

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Gao, Gangyi, Cuixia Ou, and Linian Shi. "Multi-Degree-of-Freedom Manipulator Joint Trajectory Tracking Control Method Based on Decision Tree." Journal of Physics: Conference Series 2066, no. 1 (2021): 012026. http://dx.doi.org/10.1088/1742-6596/2066/1/012026.

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Abstract For industrial-grade manipulators, the study of trajectory tracking control issues provides an important guarantee for accurate and safe work. Therefore, the trajectory control input driving torque can meet the requirements of the robot arm to accurately track a given target trajectory, and the process of building a decision tree is a process of dividing the feature space. For a given training data set, a set of if-then is summarized the rule of. Based on this, this paper launches the research of multi-degree-of-freedom manipulator joint trajectory tracking control method based on dec

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Song, Wenjun, Congxian Li, and Qiuwen Zhang. "Rapid CU Partitioning and Joint Intra-Frame Mode Decision Algorithm." Electronics 13, no. 17 (2024): 3465. http://dx.doi.org/10.3390/electronics13173465.

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H.266/Versatile Video Coding (VVC) introduces new techniques that build upon previous standards, proposing a nested multi-type tree quadtree (QTMT). The introduction of this structure significantly enhances video coding efficiency; additionally, the number of directional modes in H.266 has increased by 32 compared to H.265, accommodating a greater variety of texture patterns. However, the changes in the related structures have also led to a significant increase in encoding complexity. To address the issue of excessive computational complexity, this paper proposes a targeted rapid Coding Units

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Okpor, Margaret Dumebi, Fidelis Obukohwo Aghware, Maureen Ifeanyi Akazue, et al. "Comparative Data Resample to Predict Subscription Services Attrition Using Tree-based Ensembles." Journal of Fuzzy Systems and Control 2, no. 2 (2024): 117–28. https://doi.org/10.59247/jfsc.v2i2.213.

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The digital market today, is rippled with a variety of goods/services that promote monetization and asset exchange with clients constantly seeking improved alternatives at lowered cost to meet their value demands. From item upgrades to their replacement, businesses are poised with retention strategies to help curb the challenge of customer attrition. Such strategies include the upgrade of goods and services at lesser cost and targeted improved value chains to meet client needs. These are found to improve client retention and better monetization. The study predicts customer churn via tree-based

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Yuanyuan, Zhang. "MOOC Teaching Model of Basic Education Based on Fuzzy Decision Tree Algorithm." Computational Intelligence and Neuroscience 2022 (June 8, 2022): 1–7. http://dx.doi.org/10.1155/2022/3175028.

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In recent years, the development of science and technology in China has greatly affected people’s ways of entertainment. In the traditional industrial model, new industries and Internet industries represented by the Internet have emerged, and the Internet video business is an emerging business that has been gradually emerging in the Internet industry in recent years. Moreover, this new teaching method has been gradually noticed in simple education, such as MOOC, I want to self-study network, and Smart Tree, and other online learning websites have sprung up. At present, the epidemic environment

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Wets, Geert, Koen Vanhoof, Theo Arentze, and Harry Timmermans. "Identifying Decision Structures Underlying Activity Patterns: An Exploration of Data Mining Algorithms." Transportation Research Record: Journal of the Transportation Research Board 1718, no. 1 (2000): 1–9. http://dx.doi.org/10.3141/1718-01.

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The utility-maximizing framework—in particular, the logit model—is the dominantly used framework in transportation demand modeling. Computational process modeling has been introduced as an alternative approach to deal with the complexity of activity-based models of travel demand. Current rule-based systems, however, lack a methodology to derive rules from data. The relevance and performance of data-mining algorithms that potentially can provide the required methodology are explored. In particular, the C4 algorithm is applied to derive a decision tree for transport mode choice in the context of

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D., Kavitha*1& T. Balasubramanian2. "PREDICTING THE MODE OF DELIVERY AND THE RISK FACTORS ASSOCIATED WITH CESAREAN DELIVERY USING DECISION TREE MODEL." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 7, no. 8 (2018): 116–24. https://doi.org/10.5281/zenodo.1336707.

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<strong>Background:</strong> The rate of cesarean section has been increasing worldwide and particularly Tamil Nadu has more than 60% of cesarean section. <strong>Objective:</strong> The purpose of this study is to affirm and suggest that decision tree model can be used to predict the mode of delivery and the risk factors associated with cesarean delivery. <strong>Study design:</strong> This is a study of women delivered live-born neonates in 2015 through 2017 (4043). The frequency of cesarean delivery is 61.61%; 33 variables are used for analysis. Decision tree model is applied to the 50% of

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Ko, Hyun-Suk, Ki-Won Yoo, Jung-Dong Seo, and Kwang-Hoon Sohn. "Fast Intra-Mode Decision for H.264/AVC using Inverse Tree-Structure." Journal of Broadcast Engineering 13, no. 3 (2008): 310–18. http://dx.doi.org/10.5909/jbe.2008.13.3.310.

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Baihaqi, Ahmad Luthfi, Tegar Palyus Fiqar, and Boby Mugi Pratama. "Klasifikasi Kematangan Musa Paradisiaca L Berbasis Warna Kulit Menggunakan Metode Decision Tree." Jurnal Borneo Informatika dan Teknik Komputer 3, no. 2 (2023): 14–22. http://dx.doi.org/10.35334/jbit.v3i2.3317.

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Bananas are one of the cultivated products that contribute significantly to domestic fruit production. With the increasing market demand for bananas, farmers have the opportunity to further optimize the quality of bananas they produce in their gardens. In terms of meeting the market share standards in the horticultural sector is a goal that needs to be achieved. The technique used is Hue Saturation Value (HSV) used to classify banana images. Then the maturity is determined using a decision tree. The image data of 150 fruits were divided into 2 categories, namely 100 training data and 50 test d

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Julianto, Muhammad Fahmi, Yesni Malau, Wahyutama Fitri Hidayat, Wawan Nugroho, and Fintri Indriyani. "COMPARATION OF DECISION TREE MODEL AND SUPPORT VERCTOR MACHINE IN SENTIMENT ANALYSIS OF REVIEW DATASET SAMSUNG SSD 850 EVO AT NEW EGG SHOP." Jurnal Riset Informatika 3, no. 4 (2021): 319–26. http://dx.doi.org/10.34288/jri.v3i4.278.

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The development of information technology is currently growing very rapidly, including the impact on the hardware used. This can be exemplified in the use of hard drives that are starting to switch to SSDs. The process of selecting an SSD product to be used cannot be separated from the sources of information found on the internet. Through the internet, every user can provide reviews, both positive and negative reviews. With the many reviews regarding the review of the Samsung 850 Evo SSD on the NewEgg Store, the author uses it to be processed into information, which will have new knowledge. Ba

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Yang, Weiwei, and Jie Yang. "Construction of College Physical Education MOOCS Teaching Model Based on Fuzzy Decision Tree Algorithm." Mathematical Problems in Engineering 2022 (September 27, 2022): 1–11. http://dx.doi.org/10.1155/2022/3315872.

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With the continuous development of the MOOCS model in college physical education, the corresponding teaching evaluation has also been widely concerned by the community. The development of a traditional education mode in college physical education cannot meet the current teaching requirements. In order to solve the problem of narrow application and insufficient accuracy in traditional education, on the basis of the Kohonen fuzzy decision tree algorithm and the MOOCS fuzzy decision tree algorithm, a fuzzy evaluation model of P.E. teaching is proposed. The results show that the fuzzy ID3 algorith

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Elouafi, Abdelamine, Ilyas Tammouch, Souad Eddarouich, and Raja Touahni. "Evaluating various machine learning methods for predicting students' math performance in the 2019 TIMSS." Indonesian Journal of Electrical Engineering and Computer Science 34, no. 1 (2024): 565. http://dx.doi.org/10.11591/ijeecs.v34.i1.pp565-574.

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The growth of a country strongly depends on the quality of its educational system. All over the world, the education sectors are experiencing a fundamental evolution of their mode of operation. The greatest challenge for education today is the low success rate of learners and the abandonment of education in institutions at a premature age. Early prediction of student failure can help administrators provide timely guidance and supervision to enhance student success and retention. We propose a performance prediction model based on students' social and academic integration using several classific

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Elouafi, Abdelamine, Ilyas Tammouch, Souad Eddarouich, and Raja Touahni. "Evaluating various machine learning methods for predicting students' math performance in the 2019 TIMSS." Indonesian Journal of Electrical Engineering and Computer Science 34, no. 1 (2024): 565–74. https://doi.org/10.11591/ijeecs.v34.i1.pp565-574.

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The growth of a country strongly depends on the quality of its educational system. All over the world, the education sectors are experiencing a fundamental evolution of their mode of operation. The greatest challenge for education today is the low success rate of learners and the abandonment of education in institutions at a premature age. Early prediction of student failure can help administrators provide timely guidance and supervision to enhance student success and retention. We propose a performance prediction model based on students' social and academic integration using several classific

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Basak, Jayanta. "Online Adaptive Decision Trees: Pattern Classification and Function Approximation." Neural Computation 18, no. 9 (2006): 2062–101. http://dx.doi.org/10.1162/neco.2006.18.9.2062.

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Recently we have shown that decision trees can be trained in the online adaptive (OADT) mode (Basak, 2004), leading to better generalization score. OADTs were bottlenecked by the fact that they are able to handle only two-class classification tasks with a given structure. In this article, we provide an architecture based on OADT, ExOADT, which can handle multiclass classification tasks and is able to perform function approximation. ExOADT is structurally similar to OADT extended with a regression layer. We also show that ExOADT is capable not only of adapting the local decision hyperplanes in

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Wu, Ping, Yun Fei Huang, Qian Qian Cao, and Feng Xiong. "Research on Mining of E-Procurement Model Parameters Based on Decision Tree." Applied Mechanics and Materials 397-400 (September 2013): 2655–61. http://dx.doi.org/10.4028/www.scientific.net/amm.397-400.2655.

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More and more enterprises adopt e-business for procurement. Varieties of procurement modes can be controlled by different parameters, and different business processes are generated by the combination of parameters, but numerous optional parameters will increase the difficulty of the customer's choice. In order to solve it, we will use the C4.5 algorithm to analyze a procurement mode parameters correlation with the bidding results, and mine parameters combinations that suit for certain type of materials to provide customers with recommended parameter selection guide. Thereby it generates the e-

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Wang, Caixia, Xiaoyun Wei, Aiqian Yang, and Haiyan Zhang. "Construction and Analysis of Discrete System Dynamic Modeling of Physical Education Teaching Mode Based on Decision Tree Algorithm." Computational Intelligence and Neuroscience 2022 (July 19, 2022): 1–11. http://dx.doi.org/10.1155/2022/2745146.

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Physical education is not only an important part of national education but also one of the important means to improve the physical quality of students and citizens. Therefore, the reform of physical education is of great significance to the development of physical education. With the application of data mining technology in the field of physical education, the scale of relevant data increases rapidly. The traditional data analysis methods cannot meet the needs of physical education data analysis. Traditional data analysis methods still have many basic problems to be solved. For example, the pr

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Aghaabbasi, Mahdi, Muhammad Zaly Shah, and Rosilawati Zainol. "Investigating the Use of Active Transportation Modes Among University Employees Through an Advanced Decision Tree Algorithm." Civil and Sustainable Urban Engineering 1, no. 1 (2021): 26–49. http://dx.doi.org/10.53623/csue.v1i1.28.

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Now more than ever, the health and economic benefits of active transportation (AT) are evident and several planning efforts and programs are particularly targeted at improving active transportation options for different populations, such as students and seniors. Administrative employees at universities received less attention in the literature than other population groups.This population spends a lot of time doing sedentary activities and behaviors during their working time. Thus, the present study used a C5 decision tree to examine the usage of university employees’ AT modes when they are out

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Gong, Yujuan, and Jie Zhou. "MOOC and Flipped Classroom Task-Based English Teaching Model for Colleges and Universities Using Data Mining and Few-Shot Learning Technology." Computational Intelligence and Neuroscience 2022 (June 13, 2022): 1–11. http://dx.doi.org/10.1155/2022/9770747.

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As a revolutionary education model, the flipped classroom teaching model has unique advantages over the traditional education model. How to change the teaching method of flipped classroom into a teaching method suitable for college English courses is a problem. The goal of this research is to investigate how to use data mining and few-shot learning technology to investigate the impact of MOOC and flipped classroom task-based college English teaching modes. This work provides a data mining-based decision tree algorithm and examines the enhanced decision tree method. The experimental results of

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Champahom, Thanapong, Sajjakaj Jomnonkwao, Vuttichai Chatpattananan, Ampol Karoonsoontawong, and Vatanavongs Ratanavaraha. "Analysis of Rear-End Crash on Thai Highway: Decision Tree Approach." Journal of Advanced Transportation 2019 (November 27, 2019): 1–13. http://dx.doi.org/10.1155/2019/2568978.

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Objective. Among crash types on Thai highways, rear-end crashes have been found to cause the largest number of fatalities. This study aims to find ways to decrease rear-end crashes and fatal rear-end crashes. Methods. Classification and regression tree (CART) was used to analyze the complicated relationship of variables of big data. The analysis was conducted by creating two models: (1) a model which indicates the causes of rear-end crashes by applying Quasi-Induced Exposure to at-fault driver characteristics; (2) a determined model which studies fatal crashes. Results. Predictor variables in

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Tang, Liang, Chenfeng Xiong, and Lei Zhang. "Decision tree method for modeling travel mode switching in a dynamic behavioral process." Transportation Planning and Technology 38, no. 8 (2015): 833–50. http://dx.doi.org/10.1080/03081060.2015.1079385.

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Shah, Dhwanir, and Lokesh Kumar Sharma. "Credit Card Fraud Detection using Decision Tree and Random Forest." ITM Web of Conferences 53 (2023): 02012. http://dx.doi.org/10.1051/itmconf/20235302012.

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It is the time of technology advancement. Due to internet everything is available at the touch of a finger. There is a benefit of online shopping: first it saves lots of time and second it does not demand to go to market to buy anything. There exists various mode of payments and credit card payment is one of them. Today, there exists a good number of credit card users in the world. Every day so many credit cards transactions are taken place. Some of these transactions are fraudulent. Due to such fraudulent transactions banks and customers need to suffer. In order to prevent financial losses du

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Wang, Yu Min, and Lin Xue. "Application of Decision Tree Algorithm in Early Entrepreneurial Project Screening." Scientific Programming 2022 (March 31, 2022): 1–9. http://dx.doi.org/10.1155/2022/3584196.

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Venture capital firms are always faced with insufficient information and insufficient time when evaluating whether startups are worth investing in. This paper focuses on how to combine the public information of startups with the decision tree algorithm to assist investors in project screening. By extracting the public information of 1104 AI and big data companies from January 2016 to June 2017 and the financing progress in the following 18 months, this paper finds that: the six indicators of having a working background in well-known companies, being reported by well-known media, having patents

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Zhang, Qianwu, Zicong Wang, Shuaihang Duan, et al. "An Improved End-to-End Autoencoder Based on Reinforcement Learning by Using Decision Tree for Optical Transceivers." Micromachines 13, no. 1 (2021): 31. http://dx.doi.org/10.3390/mi13010031.

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In this paper, an improved end-to-end autoencoder based on reinforcement learning by using Decision Tree for optical transceivers is proposed and experimentally demonstrated. Transmitters and receivers are considered as an asymmetrical autoencoder combining a deep neural network and the Adaboost algorithm. Experimental results show that 48 Gb/s with 7% hard-decision forward error correction (HD-FEC) threshold under 65 km standard single mode fiber (SSMF) is achieved with proposed scheme. Moreover, we further experimentally study the Tree depth and the number of Decision Tree, which are the two

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Depp, R., L. G. Keith, and J. J. Sciarra. "The Northwestern University Twin Study. VII: The Mode of Delivery in Twin Pregnancy - North American Considerations." Acta geneticae medicae et gemellologiae: twin research 37, no. 1 (1988): 11–18. http://dx.doi.org/10.1017/s0001566000004190.

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AbstractNo consensus exists for the optimal mode of delivery for twin fetuses. Opinions vary by type of institution (university medical center vs community hospital), country or continent (North America vs Western Europe) and personal preference of individual physicians. This article lists clinical considerations in arriving at the decision and presents them in the form of a decision tree.

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Wang, Ping, Xiaodan Zhang, and Hua Huang. "Fast Inter Mode Decision Algorithm Based on Mode Mapping and Decision Tree for P Frames in MPEG-2 to H.264/AVC Transcoding." Journal of Signal Processing Systems 79, no. 1 (2013): 33–43. http://dx.doi.org/10.1007/s11265-013-0824-5.

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Realdo, Adam Meredita, Anggara Trisna Nugraha, and Shubhrojit Misra. "Design and Development of Electricity Use Management System of Surabaya State Shipping Polytechnic Based on Decision Tree Algorithm." Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics 3, no. 4 (2021): 179–84. http://dx.doi.org/10.35882/ijeeemi.v3i4.7.

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Electrical energy has become a primary need in society, including in education. This is related to the number of electrical equipment used to support teaching and learning activities. However, this is not directly proportional to the students' awareness of the importance of saving energy. From this, the authors create a management system for electric power, which aims to save electricity consumption by limiting the use of electrical loads. In this system, the method used is a decision tree; the goal is to limit and set priorities for electrical loads. When three classes are ON simultaneously,

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Wang, Jing Dong. "Design and Research of Intelligent Knowledge Push Model." Applied Mechanics and Materials 711 (December 2014): 293–96. http://dx.doi.org/10.4028/www.scientific.net/amm.711.293.

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In viewing of information overload environment,traditional search engine has inherent insufficiency about retrieval accuracy and query mode,together with the characteristics of decision tree algorithm and knowledge push,this paper presets an intelligent knowledge push model,and discuss the mechanism of this model.The model strive to improve the retrieval efficiency and realize the intelligent service.

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Pitombo, Cira Souza, Aline Schindler Gomes Da Costa, and Ana Rita Salgueiro. "PROPOSAL OF A SEQUENTIAL METHOD FOR SPATIAL INTERPOLATION OF MODE CHOICE." Boletim de Ciências Geodésicas 21, no. 2 (2015): 274–89. http://dx.doi.org/10.1590/s1982-21702015000200016.

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The main objective of this study is to propose a sequential method for spatial interpolation of mode choice for household locations where choices are unobserved based on Decision Tree analysis and Geostatistics. Initially, Decision Tree analysis was applied in order to estimate the probability of mode choice in surveyed households, thus determining the numeric variable to be estimated by Ordinary Kriging. The data used is from the Origin-Destination Survey and Urban Transportation Evaluation Survey, carried out in 2007/2008 in the city of São Carlos (São Paulo/Brazil). The study area selected

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Yauri, Ricardo, Luis Cuyubamba, and Stefano Nuñez. "Crop Monitoring System Using IoT, Solar Energy and Decision Tree Algorithm." Emerging Science Journal 9, no. 2 (2025): 603–14. https://doi.org/10.28991/esj-2025-09-02-06.

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Peru's diverse topographical regions offer optimal conditions for agriculture, but a lack of technology hinders efficiency, leading to food imports despite the country's potential. This paper aims to design an Internet of Things-based monitoring system where the specific objectives are focused on building a solar-powered power stage and integrating machine learning algorithms to help determine crop health. The development methodology includes the evaluation of the use of sensors to measure environmental and soil temperature and humidity, precipitation and hydrogen potential to help identify th

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Rukmana, Indra, Arvin Rasheda, Faiz Fathulhuda, Muh Rizky Cahyadi, and Fitriyani Fitriyani. "Analisis Perbandingan Kinerja Algoritma Naïve Bayes, Decision Tree-J48 dan Lazy-IBK." JURNAL MEDIA INFORMATIKA BUDIDARMA 5, no. 3 (2021): 1038. http://dx.doi.org/10.30865/mib.v5i3.3055.

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This research is focused on knowing the performance of the classification algorithms, namely Naïve Bayes, Decision Tree-J48 and K-Nearest Neighbor. The speed and the percentage of accuracy in this study are the benchmarks for the performance of the algorithm. This study uses the Breast Cancer and Thoracic Surgery dataset, which is downloaded on the UCI Machine Learning Repository website. Using the help of Weka software Version 3.8.5 to find out the classification algorithm testing. The results show that the J-48 Decision Tree algorithm has the best accuracy, namely 75.6% in the cross-validati

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