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Journal articles on the topic 'Applied learning'

Author: Grafiati

Published: 4 June 2021

Last updated: 19 February 2022

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1

Rudrappa, Gujanatti. "Machine Learning Models Applied for Rainfall Prediction." Revista Gestão Inovação e Tecnologias 11, no. 3 (June 30, 2021): 179–87. http://dx.doi.org/10.47059/revistageintec.v11i3.1926.

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Yusfiani, Marnida. "Teacher performance on students learning outcomes in applied chemistry." Jurnal Pendidikan Kimia 12, no. 1 (April 25, 2020): 20–25. http://dx.doi.org/10.24114/jpkim.v12i1.17709.

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3

Israel, Steven A., Philip Sallee, Franklin Tanner, Jonathan Goldstein, and Shane Zabel. "Applied Machine Learning Strategies." IEEE Potentials 39, no. 3 (May 2020): 38–42. http://dx.doi.org/10.1109/mpot.2019.2927899.

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4

Pehrson, Lea, Carsten Lauridsen, and Michael Nielsen. "Machine learning and deep learning applied in ultrasound." Ultraschall in der Medizin - European Journal of Ultrasound 39, no. 04 (August 2018): 379–81. http://dx.doi.org/10.1055/a-0642-9545.

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Fabián A. Aldana Herrera et al.,, Fabián A. Aldana Herrera et al ,. "Machine Learning Applied to Networking." International Journal of Mechanical and Production Engineering Research and Development 10, no. 6 (2020): 347–52. http://dx.doi.org/10.24247/ijmperddec202039.

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Dr Dara Hamid Mohammed. "APPLIED LINGUISTICS AND LANGUAGE LEARNING." Researchers World : Journal of Arts, Science and Commerce VIII, no. 3(1) (July 1, 2017): 123–28. http://dx.doi.org/10.18843/rwjasc/v8i3(1)/18.

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Saravanan, T. "ICT Applied Cooperative Learning Environment." International Journal for Research in Applied Science and Engineering Technology V, no. VIII (August 30, 2017): 1630–35. http://dx.doi.org/10.22214/ijraset.2017.8231.

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Lipovetsky, Stan, and Jong-Min Kim. "Machine Learning in Applied Statistics." Model Assisted Statistics and Applications 12, no. 3 (August 31, 2017): 193–94. http://dx.doi.org/10.3233/mas-170404.

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Stewart, Jim. "Work-applied learning for change." Action Learning: Research and Practice 12, no. 2 (May 4, 2015): 244–47. http://dx.doi.org/10.1080/14767333.2015.1049461.

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Santos, Denise. "Conceptualising ‘Learning’ in Applied Linguistics." System 39, no. 4 (December 2011): 576–78. http://dx.doi.org/10.1016/j.system.2011.10.005.

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Pridham, Bruce, Simon O’Mallon, and Vaughan Prain. "Insights into Vocational Learning from an Applied Learning Perspective." Vocations and Learning 5, no. 2 (August 9, 2011): 77–97. http://dx.doi.org/10.1007/s12186-011-9063-8.

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Costa, Roberto D., Gustavo F. Souza, Ricardo A. M. Valentim, and Thales B. Castro. "The theory of learning styles applied to distance learning." Cognitive Systems Research 64 (December 2020): 134–45. http://dx.doi.org/10.1016/j.cogsys.2020.08.004.

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Akiyama, T. S., J. Marcato Junior, W. N. Gonçalves, P. O. Bressan, A. Eltner, F. Binder, and T. Singer. "DEEP LEARNING APPLIED TO WATER SEGMENTATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2020 (August 14, 2020): 1189–93. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2020-1189-2020.

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Abstract. The use of deep learning (DL) with convolutional neural networks (CNN) to monitor surface water can be a valuable supplement to costly and labour-intense standard gauging stations. This paper presents the application of a recent CNN semantic segmentation method (SegNet) to automatically segment river water in imagery acquired by RGB sensors. This approach can be used as a new supporting tool because there are only a few studies using DL techniques to monitor water resources. The study area is a medium-scale river (Wesenitz) located in the East of Germany. The captured images reflect different periods of the day over a period of approximately 50 days, allowing for the analysis of the river in different environmental conditions and situations. In the experiments, we evaluated the input image resolutions of 256 × 256 and 512 × 512 pixels to assess their influence on the performance of river segmentation. The performance of the CNN was measured with the pixel accuracy and IoU metrics revealing an accuracy of 98% and 97%, respectively, for both resolutions, indicating that our approach is efficient to segment water in RGB imagery.

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Barone, T., and Beth Ritter. "Applied Anthropology and Community Service-Learning." Practicing Anthropology 32, no. 2 (March 22, 2010): 21–25. http://dx.doi.org/10.17730/praa.32.2.c19n7216230p5r5j.

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Community Service-Learning (CSL) in higher education is a pedagogical approach embraced by many disciplines as a form of experiential learning that is rewarding for students, faculty and community partners. Although applied anthropology and CSL seem natural partners, applied anthropologists have published very little on CSL experiences. In this presentation of our experience, we argue that applied anthropologists should consider participating and publishing on CSL. We believe with a little preparation applied anthropologists are ready to jump into CSL, and that this will benefit their communities, their students, their careers and the discipline.

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Umachandran, Krishnan, Amuthalakshmi P, and Roosefert Mohan T. "Technological Approaches in Applied Social learning." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 19 (March 1, 2019): 7485–92. http://dx.doi.org/10.24297/ijct.v19i0.8161.

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Industry 4.0 covers the readiness of achieving expertise that impacts the society, strategy, talent and technology. Individual, social and economic demands pose ever-changing challenges for education and training even in today’s world. In spite of the shrinking rapidity, which progressively affects the cognitive, biological, and information offerings into the new insights on people learning, for an effective reformation to an even more miniature of scientific and technological connect, ultimately on the knowledge content. Today’s students will compete in a technological, diverse, multi-cultural world and must be prepared to thrive in that futuristic environment; therefore, it is vital that today’s pedagogy produce lifelong learners, who can succeed in a global arena. To ensure our educational technology progresses at the rate demanded by today’s ubiquitous digital learners, we are reviewing emerging technologies and traditional teaching methods and propose desirable changes. Further enhancement in student learning and outcomes through technology enabled learning builds alliances among institutional units, employers, community partners and provides the effective delivery of learning services and opportunities to students. Technology depends on the cleverness and the innovative application of theoretical tools from control and estimation for scalability through hierarchy and multi-modality. Educational technology enhances the learning experience for students, faculty, the university and community at large. Hence, to develop a richer context for student learning, covering societal inadequacies, injustices in empowerment to learning access among students, cross-cultural involvements, and opportunity to learn, we deliberate on the latest trends in pedagogy - intelligent tutoring systems and ICT integration that can be modified during their implementation to suit future needs for industry 4.0 requirements.

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Mullainathan, Sendhil, and Jann Spiess. "Machine Learning: An Applied Econometric Approach." Journal of Economic Perspectives 31, no. 2 (May 1, 2017): 87–106. http://dx.doi.org/10.1257/jep.31.2.87.

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Machines are increasingly doing “intelligent” things. Face recognition algorithms use a large dataset of photos labeled as having a face or not to estimate a function that predicts the presence y of a face from pixels x. This similarity to econometrics raises questions: How do these new empirical tools fit with what we know? As empirical economists, how can we use them? We present a way of thinking about machine learning that gives it its own place in the econometric toolbox. Machine learning not only provides new tools, it solves a different problem. Specifically, machine learning revolves around the problem of prediction, while many economic applications revolve around parameter estimation. So applying machine learning to economics requires finding relevant tasks. Machine learning algorithms are now technically easy to use: you can download convenient packages in R or Python. This also raises the risk that the algorithms are applied naively or their output is misinterpreted. We hope to make them conceptually easier to use by providing a crisper understanding of how these algorithms work, where they excel, and where they can stumble—and thus where they can be most usefully applied.

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Franco Martinez, Maria Teresa, Amparo Vazquez Saldaña, and Jose de Jesus Negrete Redondo. "Information technologies applied to acoustics learning." Journal of the Acoustical Society of America 128, no. 4 (October 2010): 2343. http://dx.doi.org/10.1121/1.3508297.

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Bryan, R. Nick. "Machine Learning Applied to Alzheimer Disease." Radiology 281, no. 3 (December 2016): 665–68. http://dx.doi.org/10.1148/radiol.2016162151.

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Cohn, Ellen S., Nancy Robert Dooley, and Lynn A. Simmons. "Collaborative Learning Applied to Fieldwork Education." Occupational Therapy In Health Care 15, no. 1-2 (January 2002): 69–83. http://dx.doi.org/10.1080/j003v15n01_08.

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Cohn, Ellen, Nancy Robert Dooley, and Lynn Simmons. "Collaborative Learning Applied to Fieldwork Education." Occupational Therapy In Health Care 15, no. 1 (May 20, 2002): 69–83. http://dx.doi.org/10.1300/j003v15n01_08.

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21

Loes, Chad N. "Applied Learning through Collaborative Educational Experiences." New Directions for Higher Education 2019, no. 188 (December 2019): 13–21. http://dx.doi.org/10.1002/he.20341.

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22

Ibrahim, Rania, and Akila Sarirete. "Constructivist Approach applied to Leadership Learning." International Journal of Knowledge Society Research 4, no. 4 (October 2013): 27–35. http://dx.doi.org/10.4018/ijksr.2013100104.

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This paper introduces student-initiated projects outside the classroom as an effective method for developing intended learning outcomes (ILOs) based on Effat university graduate characteristics and national accreditation standard for learning and teaching. The characteristics of the university graduate are based on the IQRA values (core values that stand for: adopting research for life, social values, responsible and creative leadership, and effective communication). Furthermore, the academic programs ILOs are designed according to National Qualification Framework (NQF) of the National Commission of Academic Assessment and Accreditation (NCAAA). Effat Ambassadors Program (EAP) is a co-extra-curricular program with ILOs that focus on developing Effat student as ambassadors of IQRA values. ILOs related to social and educational values and responsible leadership are the most difficult to achieve and assess; therefore a construtivist approach towards leadership learning is described as “Life Immersion Learning Learning Model” (LIM). This paper demonstrates LIM by focusing on one of the EAP projects initated by the Student Government, which was to launch a TEDx EffatU chapter for Effat University in Saudi Arabia. This project, as a real life project, was found to yield excellent results in terms of meeting characteristics of Effat graduate, especially for social & educational values and responsible leadership. Another result is the optimum student and teacher satisfaction and involvement.

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Lleo, Sébastien. "Machine Learning: An Applied Mathematics Introduction." Quantitative Finance 20, no. 3 (February 28, 2020): 359–60. http://dx.doi.org/10.1080/14697688.2020.1725610.

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Borsting, Emily, Robert DeSimone, Mustafa Ascha, and Mona Ascha. "Applied Deep Learning in Plastic Surgery." Journal of Craniofacial Surgery 31, no. 1 (2020): 102–6. http://dx.doi.org/10.1097/scs.0000000000005905.

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25

Hang, Weiqiang, and Timothy Banks. "Machine learning applied to pack classification." International Journal of Market Research 61, no. 6 (April 10, 2019): 601–20. http://dx.doi.org/10.1177/1470785319841217.

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Pack or product classification is a quite common task in market research, particularly for sales tracking audits and related services. Electronic data sources have led to increased volumes, both in the sales volume being tracked and also the number of packs (or stock keeping units). The increase in packs needing to be classified presents a problem, in that, it needs to be done accurately and quickly. Traditional solutions using people for the classifications can be costly, due to the large number of people required to process the classifications in a timely and accurate manner. Reducing the manual work is a priority for audit-based market research businesses, leading to interest in automation, such as through machine learning techniques. In this article, we apply such methods. These include support vector machine, decision tree, XGBoost, AdaBoost, random forest, and neural network–based methods that are trained on the textual descriptions of already classified packs. We also implement a hierarchical classification method to take advantage of the structure of classes of the products. Once the models are trained, they can be used on unclassified data. Where the methods are not confident in their classifications, humans can be asked to classify. The hope is that the methods can learn to classify accurately enough that the manual workloads are reduced to manageable levels. This article reviews various methods and then outlines tests using these methods on two datasets collected by Nielsen, showing good performance.

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Cox, E. Sam. "A Practical Aid for Applied Learning." Review of Communication 9, no. 2 (April 2009): 182–84. http://dx.doi.org/10.1080/15358590802650248.

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Carapuço, João, Rui Neves, and Nuno Horta. "Reinforcement learning applied to Forex trading." Applied Soft Computing 73 (December 2018): 783–94. http://dx.doi.org/10.1016/j.asoc.2018.09.017.

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Zhou, Shijie, John L. Sapp, Amir AbdelWahab, and Natalia Trayanova. "Deep Learning Applied to Electrocardiogram Interpretation." Canadian Journal of Cardiology 37, no. 1 (January 2021): 17–18. http://dx.doi.org/10.1016/j.cjca.2020.03.035.

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Sailer, Maria, Florian Schiller, Thorsten Falk, Andreas Jud, Sven Arke Lang, Juri Ruf, and Michael Mix. "Applied machine learning for liver surgery." Current Directions in Biomedical Engineering 7, no. 1 (August 1, 2021): 154–57. http://dx.doi.org/10.1515/cdbme-2021-1033.

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Abstract Background and objectives: Both hepatic functional reserve and the underlying histology are important determinants in the preoperative risk evaluation before major hepatectomies. In this project we developed a new approach that implements cutting-edge research in machine learning and nevertheless is cheap and easily applicable in a routine clinical setting is needed. Methods: After splitting the study population into a training and test set we trained a convolutional neural network to predict the liver function as determined by hepatobiliary mebrofenin scintigraphy and single photon emission computer tomography (SPECT) imaging. Results: We developed a workflow for predicting liver function from routine CT imaging data using convolutional neural networks. We also evaluated in how far transfer learning and data augmentation can help to solve remaining manual data pre-processing steps and implemented the developed workflow in a clinical routine setting. Conclusion: We propose a robust semiautomatic end-to-end classification workflow for abdominal CT scans for the prediction of liver function based on a deep convolutional neural network model that shows reliable and accurate results even with limited computational resources.

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Junaidi, Junaidi, Prasetyo Wibowo, Dini Yuniasri, Putri Damayanti, Ary Mazharuddin Shiddiqi, and Baskoro Adi Pratomo. "APPLIED MACHINE LEARNING IN LOAD BALANCING." JUTI: Jurnal Ilmiah Teknologi Informasi 18, no. 2 (July 31, 2020): 76. http://dx.doi.org/10.12962/j24068535.v18i2.a940.

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Shah, Tristan, Linwei Zhuo, Peter Lai, Amaris De La Rosa-Moreno, Feruza Amirkulova, and Peter Gerstoft. "Reinforcement learning applied to metamaterial design." Journal of the Acoustical Society of America 150, no. 1 (July 2021): 321–38. http://dx.doi.org/10.1121/10.0005545.

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32

de Carvalho, Walisson Ferreira, and Luis Enrique Zárate. "A new local causal learning algorithm applied in learning analytics." International Journal of Information and Learning Technology 38, no. 1 (September 19, 2020): 103–15. http://dx.doi.org/10.1108/ijilt-04-2020-0046.

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PurposeThe paper aims to present a new two stage local causal learning algorithm – HEISA. In the first stage, the algorithm discoveries the subset of features that better explains a target variable. During the second stage, computes the causal effect, using partial correlation, of each feature of the selected subset. Using this new algorithm, the study aims to identify the actions that lead a student succeed or failure in a course.Design/methodology/approachThe paper presents a brief review of main concepts used in this study: Causal Learning and Causal effects. The paper also discusses the results of applying the algorithm in education data set. Data used in this study was extracted from the log of actions of a Learning Management System, Moodle. These actions represent the behavior of 229 engineering students that take Algorithm and Data Structure course offered in a blended model.FindingsThe algorithm proposed in the paper identifies that features with weak relevance to a target may become relevant when computing the direct effect.Research limitations/implicationsThe algorithm needs to be improved to automatically discard attributes that are under a specific threshold of direct effect. Researchers are also encouraged to test the proposed propositions further.Practical implicationsThe algorithm presented in this paper can be used to identify the mostly relevant features given a classification task.Originality/valueThis paper computes the direct effect of a selected subset of features in a target variable to evaluate if a variable in this subset is really a cause of the target or if it is a spurious correlation.

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Pires, et. al, Jorge Manuel. "Chi-Square Function Applied to Learning Objects Intelligent Learning Mechanisms." International Journal of Computing and Digital Systems 3, no. 3 (September 1, 2014): 197–206. http://dx.doi.org/10.12785/ijcds/030303.

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Vella, F. "Practice-based learning problem-based learning applied to medical education." Biochemical Education 22, no. 4 (October 1994): 218. http://dx.doi.org/10.1016/0307-4412(94)90020-5.

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35

Chen, Zhaojun. "Grammar Learning Strategies Applied to ESP Teaching." Theory and Practice in Language Studies 6, no. 3 (March 21, 2016): 617. http://dx.doi.org/10.17507/tpls.0603.23.

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There are difficulties in learning ESP because of the characteristics of ESP and learners’ low grammatical competence. Grammar knowledge plays important roles in cultivating grammar competence, especially for ESP learning. There is the connection between grammar and learning strategies. Cognitive approach (deductive and inductive learning), communicative approach, and drills are beneficial to grammar learning. ESP grammar learning strategies can be classified into cognitive strategies, metacognitive strategies, affective strategies for learning grammar, and social strategies for learning grammar. Teachers can apply some strategies to ESP teaching.

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36

Pange, Jenny, Aspasia Lekka, and Eugenia I. Toki. "Different learning theories applied to diverse learning subjects A pilot study." Procedia - Social and Behavioral Sciences 9 (2010): 800–804. http://dx.doi.org/10.1016/j.sbspro.2010.12.237.

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37

Bolibar, Jordi, Antoine Rabatel, Isabelle Gouttevin, Clovis Galiez, Thomas Condom, and Eric Sauquet. "Deep learning applied to glacier evolution modelling." Cryosphere 14, no. 2 (February 13, 2020): 565–84. http://dx.doi.org/10.5194/tc-14-565-2020.

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Abstract. We present a novel approach to simulate and reconstruct annual glacier-wide surface mass balance (SMB) series based on a deep artificial neural network (ANN; i.e. deep learning). This method has been included as the SMB component of an open-source regional glacier evolution model. While most glacier models tend to incorporate more and more physical processes, here we take an alternative approach by creating a parameterized model based on data science. Annual glacier-wide SMBs can be simulated from topo-climatic predictors using either deep learning or Lasso (least absolute shrinkage and selection operator; regularized multilinear regression), whereas the glacier geometry is updated using a glacier-specific parameterization. We compare and cross-validate our nonlinear deep learning SMB model against other standard linear statistical methods on a dataset of 32 French Alpine glaciers. Deep learning is found to outperform linear methods, with improved explained variance (up to +64 % in space and +108 % in time) and accuracy (up to +47 % in space and +58 % in time), resulting in an estimated r2 of 0.77 and a root-mean-square error (RMSE) of 0.51 m w.e. Substantial nonlinear structures are captured by deep learning, with around 35 % of nonlinear behaviour in the temporal dimension. For the glacier geometry evolution, the main uncertainties come from the ice thickness data used to initialize the model. These results should encourage the use of deep learning in glacier modelling as a powerful nonlinear tool, capable of capturing the nonlinearities of the climate and glacier systems, that can serve to reconstruct or simulate SMB time series for individual glaciers in a whole region for past and future climates.

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Brodley, Carla E., Umaa Rebbapragada, Kevin Small, and Byron Wallace. "Challenges and Opportunities in Applied Machine Learning." AI Magazine 33, no. 1 (March 15, 2012): 11–24. http://dx.doi.org/10.1609/aimag.v33i1.2367.

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Machine learning research is often conducted in vitro, divorced from motivating practical applications. A researcher might develop a new method for the general task of classification, then assess its utility by comparing its performance (such as accuracy or AUC) to that of existing classification models on publicly available datasets. In terms of advancing machine learning as an academic discipline, this approach has thus far proven quite fruitful. However, it is our view that the most interesting open problems in machine learning are those that arise during its application to real-world problems. We illustrate this point by reviewing two of our interdisciplinary collaborations, both of which have posed unique machine learning problems, providing fertile ground for novel research.

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39

Shelton, Mark D. "Enterprise Projects: Experiential Learning in Applied Entomology." American Entomologist 38, no. 4 (1992): 201–3. http://dx.doi.org/10.1093/ae/38.4.201.

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Hong, Jon-Chao, Mei-Lien Chen, and Jian-Hong Ye. "Acceptance of YouTube Applied to Dance Learning." International Journal of Information and Education Technology 10, no. 1 (2020): 7–13. http://dx.doi.org/10.18178/ijiet.2020.10.1.1331.

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Rus, Vasile. "Automated Learning Applied to Functional Argument Identification." Journal of Computing and Information Technology 12, no. 3 (2004): 211. http://dx.doi.org/10.2498/cit.2004.03.03.

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42

GÃ¸ytil, Petter H., Michael R. Hansen, and Geir Hovland. "Iterative Learning Applied to Hydraulic Pressure Control." Modeling, Identification and Control: A Norwegian Research Bulletin 39, no. 1 (2018): 1–14. http://dx.doi.org/10.4173/mic.2018.1.1.

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Parikh, Dhwaani, and Vineet Menon. "Machine Learning Applied to Cervical Cancer Data." International Journal of Mathematical Sciences and Computing 5, no. 1 (January 8, 2019): 53–64. http://dx.doi.org/10.5815/ijmsc.2019.01.05.

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Третьяков, Андрей, Andrey Tretyakov, Алексей Ткаченко, Alexey Tkachenko, Владимир Дрогомерецкий, and Vladimir Drogomeretskiy. "Learning the basics of applied swimming cadets." Vestnik of the St. Petersburg University of the Ministry of Internal Affairs of Russia 2019, no. 3 (October 15, 2019): 197–203. http://dx.doi.org/10.35750/2071-8284-2019-3-197-203.

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The paper presents the rationale for solving the problem of teaching the basics of applied swimming cadets of educational institutions of the Ministry of internal Affairs. The need for training these skills of cadets is high. Many students in the educational organizations of the Ministry of internal Affairs do not know how to assist and counteract the criminal in the water environment. Despite this, the future police during execution of official duties, the duty to assist, where appropriate, and within the framework of crime prevention, respectively, to the persecution, to oppose and delay in the aquatic environment of the offender. These circumstances determined the need to develop methods of teaching the basics of applied swimming cadets of educational institutions of the Ministry of internal Affairs. It was supposed to carry out the analysis of level of readiness of cadets in questions of knowledge of bases of applied swimming. To develop a program of training students in the basics of applied swimming and test its effectiveness. In the course of the study, pedagogical, practical, mathematical and statistical methods were used. In the course of the study it was found that the proposed program of practical training and methodology for teaching the basics of applied swimming is effective. This is indicated by the experimental data obtained as a result of testing of cadets. This fact allows us to recommend an experimental technique for use in the educational process of students.

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45

Ugaz, Simeón, Hugo Fernández, Liz Ugaz, Flor Vásquez, and Eduardo Quiroz. "Applied neurobiology: Bases of neurodevelopment and learning." SCIÉNDO 22, no. 2 (June 28, 2019): 169–73. http://dx.doi.org/10.17268/sciendo.2019.022.

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dos Santos, Eduardo Ferro, Bruna Caroline Marques Gonçalves, Karine Borges de Oliveira, and Messias Borges Silva. "Project Based Learning Applied to Technical Drawing." Creative Education 09, no. 03 (2018): 479–96. http://dx.doi.org/10.4236/ce.2018.93034.

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47

Možina, Martin, Jure Žabkar, Trevor Bench-Capon, and Ivan Bratko. "Argument Based Machine Learning Applied to Law." Artificial Intelligence and Law 13, no. 1 (March 2005): 53–73. http://dx.doi.org/10.1007/s10506-006-9002-4.

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48

Ordóñez, Celestino, Javier Taboada, Fernando Bastante, Jose María Matías, and Angel Manuel Felicísimo. "Learning Machines Applied to Potential Forest Distribution." Environmental Management 35, no. 1 (January 2005): 109–20. http://dx.doi.org/10.1007/s00267-003-0120-4.

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49

Ayala, Néstor Adrián Rodríguez, Eduardo González Mendívil, Patricia Salinas, and Horacio Rios. "Kinesthetic Learning Applied to Mathematics Using Kinect." Procedia Computer Science 25 (2013): 131–35. http://dx.doi.org/10.1016/j.procs.2013.11.016.

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Viloria, Amelec, Nelson Alberto Lizardo Zelaya, and Noel Varela. "Unsupervised learning algorithms applied to grouping problems." Procedia Computer Science 175 (2020): 677–82. http://dx.doi.org/10.1016/j.procs.2020.07.099.

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