Relevant bibliographies by topics / Decision Tree Algorithm

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Decision Tree Algorithm'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Decision Tree Algorithm"

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Parlindungan and HariSupriadi. "Implementation Decision Tree Algorithm for Ecommerce Website." International Journal of Psychosocial Rehabilitation 24, no. 02 (2020): 3611–14. http://dx.doi.org/10.37200/ijpr/v24i2/pr200682.

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Babar, Kiran Nitin. "Performance Evaluation of Decision Trees with Machine Learning Algorithm." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem34179.

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Decision tree learning is a supervised learning approach used in statistics, data mining and machine learning. Decision trees are considered to be one of the most popular approaches for representing classifiers. Researchers from various disciplines such as statistics, machine learning, pattern recognition and Data Mining have dealt with the issue of growing a decision tree from available data. Decision trees in machine learning will be used for classification problems, to categorize objects to gain an understanding of similar features. Decision trees helps in decision-making by representing co
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Tetteh, Evans Teiko, and Beata Zielosko. "Greedy Algorithm for Deriving Decision Rules from Decision Tree Ensembles." Entropy 27, no. 1 (2025): 35. https://doi.org/10.3390/e27010035.

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This study introduces a greedy algorithm for deriving decision rules from decision tree ensembles, targeting enhanced interpretability and generalization in distributed data environments. Decision rules, known for their transparency, provide an accessible method for knowledge extraction from data, facilitating decision-making processes across diverse fields. Traditional decision tree algorithms, such as CART and ID3, are employed to induce decision trees from bootstrapped datasets, which represent distributed data sources. Subsequently, a greedy algorithm is applied to derive decision rules th
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Xu, Yuwei. "Comparative study on early risk warning for corporate bond defaults." Intelligent Decision Technologies 19, no. 3 (2025): 1675–82. https://doi.org/10.1177/18724981241309549.

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Accurate prediction of default risk in corporate bonds is essential for maintaining market stability. This paper briefly introduces the decision tree and random forest (RF) algorithms. The RF algorithm was applied to predict default risk in corporate bonds. Moreover, simulation experiments were conducted. Firstly, the feature indices’ effectiveness for predicting default risk in corporate bonds was verified. Secondly, the impact of the number of decision trees on the RF algorithm was tested. Finally, the performance of the support vector machine (SVM), decision tree, back-propagation neural ne
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Sharma, Dr Nirmla, and Sameera Iqbal Muhmmad Iqbal. "Applying Decision Tree Algorithm Classification and Regression Tree (CART) Algorithm to Gini Techniques Binary Splits." International Journal of Engineering and Advanced Technology 12, no. 5 (2023): 77–81. http://dx.doi.org/10.35940/ijeat.e4195.0612523.

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Decision tree study is a predictive modelling tool that is used over many grounds. It is constructed through an algorithmic technique that is divided the dataset in different methods created on varied conditions. Decisions trees are the extreme dominant algorithms that drop under the set of supervised algorithms. However, Decision Trees appearance modest and natural, there is nothing identical modest near how the algorithm drives nearby the procedure determining on splits and how tree snipping happens. The initial object to appreciate in Decision Trees is that it splits the analyst field, i.e.
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Dr., Nirmla Sharma, and Iqbal Muhmmad Iqbal Sameera. "Applying Decision Tree Algorithm Classification and Regression Tree (CART) Algorithm to Gini Techniques Binary Splits." International Journal of Engineering and Advanced Technology (IJEAT) 12, no. 5 (2023): 77–81. https://doi.org/10.35940/ijeat.E4195.0612523.

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<strong>Abstract: </strong>Decision tree study is a predictive modelling tool that is used over many grounds. It is constructed through an algorithmic technique that is divided the dataset in different methods created on varied conditions. Decisions trees are the extreme dominant algorithms that drop under the set of supervised algorithms. However, Decision Trees appearance modest and natural, there is nothing identical modest near how the algorithm drives nearby the procedure determining on splits and how tree snipping happens. The initial object to appreciate in Decision Trees is that it spl
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PURDILA, V., and S. G. PENTIUC. "Fast Decision Tree Algorithm." Advances in Electrical and Computer Engineering 14, no. 1 (2014): 65–68. http://dx.doi.org/10.4316/aece.2014.01010.

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Hajjej, Fahima, Manal Abdullah Alohali, Malek Badr, and Md Adnan Rahman. "A Comparison of Decision Tree Algorithms in the Assessment of Biomedical Data." BioMed Research International 2022 (July 7, 2022): 1–9. http://dx.doi.org/10.1155/2022/9449497.

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By comparing the performance of various tree algorithms, we can determine which one is most useful for analyzing biomedical data. In artificial intelligence, decision trees are a classification model known for their visual aid in making decisions. WEKA software will evaluate biological data from real patients to see how well the decision tree classification algorithm performs. Another goal of this comparison is to assess whether or not decision trees can serve as an effective tool for medical diagnosis in general. In doing so, we will be able to see which algorithms are the most efficient and
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Pratiwi, Reni, Memi Nor Hayati, and Surya Prangga. "PERBANDINGAN KLASIFIKASI ALGORITMA C5.0 DENGAN CLASSIFICATION AND REGRESSION TREE (STUDI KASUS : DATA SOSIAL KEPALA KELUARGA MASYARAKAT DESA TELUK BARU KECAMATAN MUARA ANCALONG TAHUN 2019)." BAREKENG: Jurnal Ilmu Matematika dan Terapan 14, no. 2 (2020): 273–84. http://dx.doi.org/10.30598/barekengvol14iss2pp273-284.

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Decision tree is a algorithm used as a reasoning procedure to get answers from problems are entered. Many methods can be used in decision trees, including the C5.0 algorithm and Classification and Regression Tree (CART). C5.0 algorithm is a non-binary decision tree where the branch of tree can be more than two, while the CART algorithm is a binary decision tree where the branch of tree consists of only two branches. This research aims to determine the classification results of the C5.0 and CART algorithms and to determine the comparison of the accuracy classification results from these two met
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Kumar, Sunil, Saroj Ratnoo, and Jyoti Vashishtha. "HYPER HEURISTIC EVOLUTIONARY APPROACH FOR CONSTRUCTING DECISION TREE CLASSIFIERS." Journal of Information and Communication Technology 20, Number 2 (2021): 249–76. http://dx.doi.org/10.32890/jict2021.20.2.5.

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Decision tree models have earned a special status in predictive modeling since these are considered comprehensible for human analysis and insight. Classification and Regression Tree (CART) algorithm is one of the renowned decision tree induction algorithms to address the classification as well as regression problems. Finding optimal values for the hyper parameters of a decision tree construction algorithm is a challenging issue. While making an effective decision tree classifier with high accuracy and comprehensibility, we need to address the question of setting optimal values for its hyper pa
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Dissertations / Theses on the topic "Decision Tree Algorithm"

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Shi, Haijian. "Best-first Decision Tree Learning." The University of Waikato, 2007. http://hdl.handle.net/10289/2317.

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In best-first top-down induction of decision trees, the best split is added in each step (e.g. the split that maximally reduces the Gini index). This is in contrast to the standard depth-first traversal of a tree. The resulting tree will be the same, just how it is built is different. The objective of this project is to investigate whether it is possible to determine an appropriate tree size on practical datasets by combining best-first decision tree growth with cross-validation-based selection of the number of expansions that are performed. Pre-pruning, post-pruning, CART-pruning can be perfo
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Kassim, M. E. "Elliptical cost-sensitive decision tree algorithm (ECSDT)." Thesis, University of Salford, 2018. http://usir.salford.ac.uk/47191/.

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Cost-sensitive multiclass classification problems, in which the task of assessing the impact of the costs associated with different misclassification errors, continues to be one of the major challenging areas for data mining and machine learning. The literature reviews in this area show that most of the cost-sensitive algorithms that have been developed during the last decade were developed to solve binary classification problems where an example from the dataset will be classified into only one of two available classes. Much of the research on cost-sensitive learning has focused on inducing d
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Girardini, Davide <1985&gt. "Efficient implementation of Treant: a robust decision tree learning algorithm." Master's Degree Thesis, Università Ca' Foscari Venezia, 2020. http://hdl.handle.net/10579/17423.

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The thesis focuses on the optimization of an existing algorithm called Treant for the generation of robust decision trees. Despite its good performances from the machine learning point of view, unfortunately, the code presented some strong limitations when employed with big datasets. The algorithm was originally written in Python, a very good programming language for fast prototyping but, as well as many other interpreted languages, it can lead to poor performances when it is asked to crunch a big amount of numbers if not supported by appropriated libraries. The code has been translated to the
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Trivedi, Ankit P. "Decision tree-based machine learning algorithm for in-node vehicle classification." Thesis, California State University, Long Beach, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10196455.

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<p> This paper proposes an in-node microprocessor-based vehicle classification approach to analyze and determine the types of vehicles passing over a 3-axis magnetometer sensor. The approach for vehicle classification utilizes J48 classification algorithm implemented in Weka (a machine learning software suite). J48 is Quinlan's C4.5 algorithm, an extension of decision tree machine learning based on an ID3 algorithm. The decision tree model is generated from a set of features extracted from vehicles passing over the 3-axis sensor. The features are attributes provided with correct classification
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Hari, Vijaya. "Empirical Investigation of CART and Decision Tree Extraction from Neural Networks." Ohio University / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1235676338.

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Krook, Jonatan. "Predicting low airfares with time series features and a decision tree algorithm." Thesis, Uppsala universitet, Statistiska institutionen, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-353274.

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Airlines try to maximize revenue by letting prices of tickets vary over time. This fluctuation contains patterns that can be exploited to predict price lows. In this study, we create an algorithm that daily decides whether to buy a certain ticket or wait for the price to go down. For creation and evaluation, we have used data from searches made online for flights on the route Stockholm – New York during 2017 and 2018. The algorithm is based on time series features selected by a decision tree and clearly outperforms the selected benchmarks.
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Feychting, Sara. "Incredible tweets : Automated credibility analysis in Twitter feeds using an alternating decision tree algorithm." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-186711.

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This project investigates how to determine the credibility of a tweet without using human perception. Information about the user and the tweet is studied in search for correlations between their properties and the credibility of the tweet. An alternating decision tree is created to automatically determine the credibility of tweets. Some features are found to correlate to the credibility of the tweets, amongst which the number of previous tweets by a user and the use of uppercase characters are the most prominent.
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Gerdes, Mike. "Predictive Health Monitoring for Aircraft Systems using Decision Trees." Licentiate thesis, Linköpings universitet, Fluida och mekatroniska system, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-105843.

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Unscheduled aircraft maintenance causes a lot problems and costs for aircraft operators. This is due to the fact that aircraft cause significant costs if flights have to be delayed or canceled and because spares are not always available at any place and sometimes have to be shipped across the world. Reducing the number of unscheduled maintenance is thus a great costs factor for aircraft operators. This thesis describes three methods for aircraft health monitoring and prediction; one method for system monitoring, one method for forecasting of time series and one method that combines the two oth
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Doubleday, Kevin. "Generation of Individualized Treatment Decision Tree Algorithm with Application to Randomized Control Trials and Electronic Medical Record Data." Thesis, The University of Arizona, 2016. http://hdl.handle.net/10150/613559.

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With new treatments and novel technology available, personalized medicine has become a key topic in the new era of healthcare. Traditional statistical methods for personalized medicine and subgroup identification primarily focus on single treatment or two arm randomized control trials (RCTs). With restricted inclusion and exclusion criteria, data from RCTs may not reflect real world treatment effectiveness. However, electronic medical records (EMR) offers an alternative venue. In this paper, we propose a general framework to identify individualized treatment rule (ITR), which connects the subg
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McNamara, Nathan Patrick. "Using Decision Trees to Predict Intent to Use Passive Occupational Exoskeletons in Manufacturing Tasks." Ohio University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1605720844135027.

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Books on the topic "Decision Tree Algorithm"

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Barros, Rodrigo C., André C. P. L. F. de Carvalho, and Alex A. Freitas. Automatic Design of Decision-Tree Induction Algorithms. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14231-9.

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Z, Hochberg, ed. Practical algorithms in pediatric endocrinology. Karger, 1999.

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L, Bready Lois, Noorily Susan H, and Dillman Dawn, eds. Decision making in anesthesiology: An algorithmic approach. 4th ed. Mosby/Elsevier, 2007.

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Shaoul, Ron. Practical algorithms in pediatric gastroenterology. Karger, 2014.

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Israel, Zelikovic, and Eisenstein Israel 1964-, eds. Practical algorithms in pediatric nephrology. Karger, 2008.

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Khouzam, Nelly. GenID3: A hybrid approach to feature construction in decision trees using genetic algorithms. UMIST, 1997.

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ADT 2011 (2011 Piscataway, N.J.). Algorithmic decision theory: Second International Conference, ADT 2011, Piscataway, NJ, USA, October 26-28, 2011 : proceedings. Springer, 2011.

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An Algorithm (decision tree) for the management of Parkinson's Disease: Treatment guidelines. Lippincott-Raven, 1998.

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Mola, Francesco. Evolutionary Algorithms in Decision Tree Induction. INTECH Open Access Publisher, 2008.

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Tree-based Machine Learning Algorithms: Decision Trees, Random Forests, and Boosting. CreateSpace Independent Publishing Platform, 2017.

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Book chapters on the topic "Decision Tree Algorithm"

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Yates, Darren, Md Zahidul Islam, and Junbin Gao. "SPAARC: A Fast Decision Tree Algorithm." In Communications in Computer and Information Science. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6661-1_4.

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Jankowski, Dariusz, and Konrad Jackowski. "Evolutionary Algorithm for Decision Tree Induction." In Computer Information Systems and Industrial Management. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-45237-0_4.

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Zhu, Lin, and Yang Yang. "Improvement of Decision Tree ID3 Algorithm." In Collaborate Computing: Networking, Applications and Worksharing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59288-6_59.

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Rebitschek, Felix G. "Boosting Consumers: Algorithm-Supported Decision-Making under Uncertainty to (Learn to) Navigate Algorithm-Based Decision Environments." In Knowledge and Digital Technology. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-39101-9_4.

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AbstractFinding information that is quality assured, objectively required, and subjectively needed is essential for consumers navigating uncertain and complex decision environments (e.g., retail or news platforms) and making informed decisions. This task is particularly challenging when algorithms shape environments and choice sets in the providers’ interest. On the other side, algorithms can support consumers’ decision-making under uncertainty when they are transparent and educate their users (boosting). Exemplary, fast-and-frugal decision trees as interpretable models can provide robust clas
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Manjula, R., and R. Anitha. "Identification of Encryption Algorithm Using Decision Tree." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-17881-8_23.

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Islam, Md Zahidul. "EXPLORE: A Novel Decision Tree Classification Algorithm." In Data Security and Security Data. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-25704-9_7.

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Kim, Myung Won, and Joung Woo Ryu. "Optimized Fuzzy Decision Tree Using Genetic Algorithm." In Neural Information Processing. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11893295_88.

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Salem, Abdel-Badeeh M., and Abeer M. Mahmoud. "A Hybrid Genetic Algorithm — Decision Tree Classifier." In Intelligent Information Processing and Web Mining. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-36562-4_23.

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Potharst, Rob, and Jan C. Bioch. "A Decision Tree Algorithm for Ordinal Classification." In Advances in Intelligent Data Analysis. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48412-4_16.

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Kura, Satoshi, Hiroshi Unno, and Ichiro Hasuo. "Decision Tree Learning in CEGIS-Based Termination Analysis." In Computer Aided Verification. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81688-9_4.

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AbstractWe present a novel decision tree-based synthesis algorithm of ranking functions for verifying program termination. Our algorithm is integrated into the workflow of CounterExample Guided Inductive Synthesis (CEGIS). CEGIS is an iterative learning model where, at each iteration, (1) a synthesizer synthesizes a candidate solution from the current examples, and (2) a validator accepts the candidate solution if it is correct, or rejects it providing counterexamples as part of the next examples. Our main novelty is in the design of a synthesizer: building on top of a usual decision tree lear
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Conference papers on the topic "Decision Tree Algorithm"

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Salim, Khiat, and Ould Ali Yasmine. "Mining Big Data with Decision Tree Algorithm." In 2024 4th International Conference on Embedded & Distributed Systems (EDiS). IEEE, 2024. https://doi.org/10.1109/edis63605.2024.10783227.

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Chi, Dianwei, Zehao Jia, Tiantian Huang, et al. "An improvement of a decision tree ID3 algorithm." In International Conference on Algorithms, High Performance Computing and Artificial Intelligence, edited by Pavel Loskot and Liang Hu. SPIE, 2024. http://dx.doi.org/10.1117/12.3051346.

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Liu, Lidong, Rui Huang, Xiaoqin Li, Yongbin Luo, and Rong Zhang. "Analysis of mini program user usage market based on decision tree classification and decision tree regression algorithm." In Fourth International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2024), edited by Grigorios Beligiannis and Daniel-Ioan Curiac. SPIE, 2024. http://dx.doi.org/10.1117/12.3045543.

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Thaiparnit, Sattarpoom, Thanakrit Janchidfah, and Adil Farooq. "Brain Tumor Classification System Using Decision Tree Algorithm." In 2025 IEEE International Conference on Cybernetics and Innovations (ICCI). IEEE, 2025. https://doi.org/10.1109/icci64209.2025.10987380.

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Shivappa, Nagesha. "An autoML algorithm to select suitable decision tree algorithm for classification." In 2024 Global Conference on Communications and Information Technologies (GCCIT). IEEE, 2024. https://doi.org/10.1109/gccit63234.2024.10862540.

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Meng, Qing-wu, Qiang He, Ning Li, Xiang-ran Du, and Li-na Su. "Crisp Decision Tree Induction Based on Fuzzy Decision Tree Algorithm." In 2009 First International Conference on Information Science and Engineering. IEEE, 2009. http://dx.doi.org/10.1109/icise.2009.440.

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Xudong, Song, and Cheng Xiaolan. "Decision Tree Algorithm based on Sampling." In 2007 IFIP International Conference on Network and Parallel Computing Workshops (NPC 2007). IEEE, 2007. http://dx.doi.org/10.1109/npc.2007.133.

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Qingyun, Chi. "Research on incremental decision tree algorithm." In Mechanical Engineering and Information Technology (EMEIT). IEEE, 2011. http://dx.doi.org/10.1109/emeit.2011.6022930.

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Chen Jin, Luo De-lin, and Mu Fen-xiang. "An improved ID3 decision tree algorithm." In Education (ICCSE). IEEE, 2009. http://dx.doi.org/10.1109/iccse.2009.5228509.

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Xudong, Song, and Cheng Xiaolan. "Decision Tree Algorithm based on Sampling." In 2007 IFIP International Conference on Network and Parallel Computing Workshops (NPC 2007). IEEE, 2007. http://dx.doi.org/10.1109/icnpcw.2007.4351564.

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Reports on the topic "Decision Tree Algorithm"

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Lorenz, Markus. Auswirkungen des Decoy-Effekts auf die Algorithm Aversion. Sonderforschungsgruppe Institutionenanalyse, 2022. http://dx.doi.org/10.46850/sofia.9783947850013.

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Limitations in the human decision-making process restrict the technological potential of algorithms, which is also referred to as "algorithm aversion". This study uses a laboratory experiment with participants to investigate whether a phenomenon known since 1982 as the "decoy effect" is suitable for reducing algorithm aversion. For numerous analogue products, such as cars, drinks or newspaper subscriptions, the Decoy Effect is known to have a strong influence on human decision-making behaviour. Surprisingly, the decisions between forecasts by humans and Robo Advisors (algorithms) investigated
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Montiel, Peter J. Capital Flows: Issues and Policies. Inter-American Development Bank, 2013. http://dx.doi.org/10.18235/0011498.

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This paper presents an analytical overview of recent contributions to the literature on the policy implications of capital flows in emerging and developing countries, focusing specifically on capital inflows as well as on the links between inflows and subsequent capital-flow reversals. The objective is to clarify the policy challenges that such inflows pose and to evaluate the policy alternatives available to the recipient countries to cope with those challenges. A large menu of possible policy responses to large capital inflows is considered, and experience with the use of such policies is re
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Liu, Hongrui, and Rahul Ramachandra Shetty. Analytical Models for Traffic Congestion and Accident Analysis. Mineta Transportation Institute, 2021. http://dx.doi.org/10.31979/mti.2021.2102.

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In the US, over 38,000 people die in road crashes each year, and 2.35 million are injured or disabled, according to the statistics report from the Association for Safe International Road Travel (ASIRT) in 2020. In addition, traffic congestion keeping Americans stuck on the road wastes millions of hours and billions of dollars each year. Using statistical techniques and machine learning algorithms, this research developed accurate predictive models for traffic congestion and road accidents to increase understanding of the complex causes of these challenging issues. The research used US Accident
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Lee, W. S., Victor Alchanatis, and Asher Levi. Innovative yield mapping system using hyperspectral and thermal imaging for precision tree crop management. United States Department of Agriculture, 2014. http://dx.doi.org/10.32747/2014.7598158.bard.

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Original objectives and revisions – The original overall objective was to develop, test and validate a prototype yield mapping system for unit area to increase yield and profit for tree crops. Specific objectives were: (1) to develop a yield mapping system for a static situation, using hyperspectral and thermal imaging independently, (2) to integrate hyperspectral and thermal imaging for improved yield estimation by combining thermal images with hyperspectral images to improve fruit detection, and (3) to expand the system to a mobile platform for a stop-measure- and-go situation. There were no
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Fessel, Kimberly. Machine Learning Essentials (Free Seminar). Instats Inc., 2024. http://dx.doi.org/10.61700/l6x4izy1bov9p1764.

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This comprehensive one-hour seminar provides PhD students, academics, and professional researchers with fundamental insights into machine learning concepts, crucial for modern data analysis in many disciplines. Led by data science expert Dr Kimberly Fessel, participants will explore key topics such as supervised and unsupervised learning, model performance (under- vs. overfitting), and popular algorithms like linear and logistic regression, decision trees, and neural networks.
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Alwan, Iktimal, Dennis D. Spencer, and Rafeed Alkawadri. Comparison of Machine Learning Algorithms in Sensorimotor Functional Mapping. Progress in Neurobiology, 2023. http://dx.doi.org/10.60124/j.pneuro.2023.30.03.

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Objective: To compare the performance of popular machine learning algorithms (ML) in mapping the sensorimotor cortex (SM) and identifying the anterior lip of the central sulcus (CS). Methods: We evaluated support vector machines (SVMs), random forest (RF), decision trees (DT), single layer perceptron (SLP), and multilayer perceptron (MLP) against standard logistic regression (LR) to identify the SM cortex employing validated features from six-minute of NREM sleep icEEG data and applying standard common hyperparameters and 10-fold cross-validation. Each algorithm was tested using vetted feature
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Hart, Carl R., D. Keith Wilson, Chris L. Pettit, and Edward T. Nykaza. Machine-Learning of Long-Range Sound Propagation Through Simulated Atmospheric Turbulence. U.S. Army Engineer Research and Development Center, 2021. http://dx.doi.org/10.21079/11681/41182.

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Conventional numerical methods can capture the inherent variability of long-range outdoor sound propagation. However, computational memory and time requirements are high. In contrast, machine-learning models provide very fast predictions. This comes by learning from experimental observations or surrogate data. Yet, it is unknown what type of surrogate data is most suitable for machine-learning. This study used a Crank-Nicholson parabolic equation (CNPE) for generating the surrogate data. The CNPE input data were sampled by the Latin hypercube technique. Two separate datasets comprised 5000 sam
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Enhancing quality for clients: The balanced counseling strategy. Population Council, 2003. http://dx.doi.org/10.31899/rh2003.1014.

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Abstract:
A central focus of high-quality family-planning care is the interaction between clients and the providers who serve them. In the ideal client-provider interaction, the provider treats all clients respectfully, responds to their reproductive needs and intentions, helps in the selection of the most appropriate family planning method, and offers sufficient information to use the method safely and effectively. To improve the quality of the client-provider interaction, Population Council staff developed a “Balanced Counseling Strategy,” a type of algorithm or decision tree, to be used in combinatio
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