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Journal articles on the topic 'Modeling Learning'

Author: Grafiati

Published: 5 June 2025

Last updated: 16 July 2025

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1

Jha, Ritambhara. "Statistical Modeling vs. Machine Learning Modeling: A Comparative Research." International Journal of Science and Research (IJSR) 10, no. 6 (2021): 1793–95. http://dx.doi.org/10.21275/sr24203181719.

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2

EDMONDS, SCOTT MOSS BRUCE. "MODELING ECONOMIC LEARNING AS MODELING." Cybernetics and Systems 29, no. 3 (1998): 215–47. http://dx.doi.org/10.1080/019697298125696.

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3

Kudryavtsev, V. B., P. A. Aliseichik, K. Vashik, J. Knap, A. S. Strogalov, and S. G. Shekhovtsov. "Modeling learning process." Journal of Mathematical Sciences 172, no. 5 (2011): 700–733. http://dx.doi.org/10.1007/s10958-011-0214-0.

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4

Neifachas, Sergejus, Tomas Butvilas, and Kristina Kovaitė. "Virtual Learning Environments: Modeling a New Learning Policy Strategy." Acta Paedagogica Vilnensia 49 (December 30, 2022): 98–114. http://dx.doi.org/10.15388/actpaed.2022.49.7.

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The paper examines the modelling dimension of the virtual teaching/learning environment and its implementation practices. Changing trends of the educational processes become apparent; thus, it is necessary to reflect and justify the contours of modeling the virtual educational environment. When looking for new ways of conceptualizing teaching/learning strategies, it is especially important to understand the inclusive, enabling context of the open learning spaces, to adequately assess the content elements and to predict the possible characteristics of their expression in the context of inclusiv

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5

A, Tanko. "A Machine Learning Approach to Modeling Pore Pressure." Petroleum & Petrochemical Engineering Journal 4, no. 1 (2020): 1–6. http://dx.doi.org/10.23880/ppej-16000213.

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Machine Learning techniques and applications have lately gained a lot of interest in many areas, including spheres of arithmetic, finances, engineering, dialectology, and a lot more. This is owing to the upwelling of ground-breaking and sophisticated machine learning procedures to exceedingly multifaceted complications along with the prevailing advances in high speed computing. Numerous usages of Machine learning in daily life include pattern recognition, automation, data processing and analysis, and so on. The Petroleum industry is not lagging behind also. On the contrary, machine learning ap

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6

P, Dinesh, Aakash Prabhu P, and Rajaesh Kanna R. "Modeling of Social Distancing Detection UsingDeep Learning Approach." International Journal of Research Publication and Reviews 4, no. 5 (2023): 1646–52. http://dx.doi.org/10.55248/gengpi.234.5.40503.

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7

E, Weinan, Jiequn Han, and Linfeng Zhang. "Machine-learning-assisted modeling." Physics Today 74, no. 7 (2021): 36–41. http://dx.doi.org/10.1063/pt.3.4793.

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8

Lane, David C., John D. W. Morecroft, and John D. Sterman. "Modeling for Learning Organizations." Journal of the Operational Research Society 46, no. 6 (1995): 793. http://dx.doi.org/10.2307/2584318.

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9

Wolfgand Matsui Siqueira, Sean, Maria Helena Lima Baptista Braz, and Rubens Nascimento Melo. "Modeling e‐learning content." International Journal of Web Information Systems 3, no. 1/2 (2007): 140–52. http://dx.doi.org/10.1108/17440080710829261.

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10

Nick, Mitchel Res. "Learning Through Computational Modeling." Computers in the Schools 14, no. 1-2 (1997): 143–52. http://dx.doi.org/10.1300/j025v14n01_11.

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11

Lane, David C. "Modeling for Learning Organizations." Journal of the Operational Research Society 46, no. 6 (1995): 793–95. http://dx.doi.org/10.1057/jors.1995.111.

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12

Sebastian, G., Ricardo Tesoriero, and Jose A. Gallud. "Modeling Language-Learning Applications." IEEE Latin America Transactions 15, no. 9 (2017): 1771–76. http://dx.doi.org/10.1109/tla.2017.8015084.

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13

Draskovic, I., R. Holdrinet, J. Bulte, S. Bolhuis, and J. Van Leeuwe. "Modeling small group learning." Instructional Science 32, no. 6 (2004): 447–73. http://dx.doi.org/10.1007/s11251-004-2276-6.

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14

Dwijonagoro, Suwarna, and Suparno Suparno. "PRANATACARA LEARNING: MODELING, MIND MAPPING, E-LEARNING, OR HYBRID LEARNING?" Jurnal Cakrawala Pendidikan 38, no. 1 (2019): 156–73. http://dx.doi.org/10.21831/cp.v38i1.23034.

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This research was aimed to examine the most effective learning model for Pranatacara (Master of Ceremony) course. By having effective learning, the students can master the skills at being a professional Pranatacara. The study used the experimental method with the pretest-posttest nonequivalent group design. Four parallel classes were given learning treatments each with modeling, mind mapping, e-learning, and hybrid learning. The data of the practice test were analyzed by the one-way Anova. The result indicates that there is a significant difference among the learning models. The hybrid learnin

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15

Jakfar, M., Jasrial Jasrial, and Mukhaiyar Mukhaiyar. "PENGARUH PENDEKATAN MODELLING DAN PENGETAHUAN AWAL TERHADAP HASIL BELAJAR (Studi Eksperimen pada Pelajaran Bahasa Inggris kelas V SD Cendana Pekanbaru)." Lingua Didaktika: Jurnal Bahasa dan Pembelajaran Bahasa 5, no. 1 (2011): 39. http://dx.doi.org/10.24036/ld.v5i1.9973.

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The learning process should be presented more varied and ivonative. Modeling approach is one alternative that is used. This study aims to reveal Effect of Initial modeling approach and ability to learn English Results, whereas the problems presented in this study are: (1) whether there are differences in student learning outcomes of the modeling approach to student learning in the conventional?, (2) whether students who have prior knowledge modeling approach to obtain high with higher learning outcomes of the students' learning in the conventional?, (3) whether the student has the ability to l

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16

Mohanapriya, D., and Dr R. Beena. "Predicting Drug Indications and Side Effects Using Deep Learning and Transfer Learning." Alinteri Journal of Agriculture Sciences 36, no. 1 (2021): 281–89. http://dx.doi.org/10.47059/alinteri/v36i1/ajas21042.

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In the area of biology, text mining is commonly used since it obtains the unknown relationship among medicines, phenotypes and syndromes from much information. Enhanced Topic modeling with Improved Predict drug Indications and Side effects using Topic modelling and Natural language processing (ETP-IPISTON) has been employed to predict the drug-phenotype and drug-side effect association. Initially, corpus documents are collected from the literature data and the topics in the data are modeled using logistic Linear Discriminative Analysis (LDA) and Bi-directional Long-Short Term Memory-Conditiona

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17

Hassanzadeh, Zeinab, and Jaleh Bagherli. "Effects of three Observational Learning Models with Contextual Interference on the Performance and Learning of Handball Skills." Physical Activity in Children 1, no. 1 (2025): 65–67. https://doi.org/10.61186/pach.2024.466455.1017.

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Introduction: Observational modeling combined with physical training involving contextual interference can impact the acquisition and execution of complex sports movement skills. Objective: The purpose of this study was to determine the effects of three type of observational modeling accompanied without and with contextual interference on acquisition, retention, and transfer of handball skills. Methods: 90 right-handed female students in the age range of 12 to 16 years old who had no participation experience in handball competitive training, randomly located into six experimental groups. Group

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18

Fatimah, Siti, Sholahudin Al Ayubi, and Tri Novita Irawati. "The Collaboration of Mind Mapping Learning With Modeling The Way To Improve Student Learning Outcomes." Jurnal Axioma : Jurnal Matematika dan Pembelajaran 5, no. 2 (2020): 137–43. http://dx.doi.org/10.36835/axi.v5i2.741.

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This study aims to improve student learning outcomes through the collaboration of Mind Mapping learning with Modeling The Way. This type of research is classroom action research (CAR). The subjects of this study were students of class VII MTs. Nurul Islam. The data collection methods used was test, observation, interview and documentation methods. This research was conducted in two cycles, namely cycle I and cycle II. Based on the results of the research conducted, it was obtained data on student learning outcomes before the study was 42.3%, in the first cycle it was 65%, and in the second cyc

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19

Slimani, Nadia, Ilham Slimani, Nawal Sbiti, and Mustapha Amghar. "Machine Learning and statistic predictive modeling for road traffic flow." International Journal of Traffic and Transportation Management 03, no. 01 (2021): 17–24. http://dx.doi.org/10.5383/jttm.03.01.003.

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Traffic forecasting is a research topic debated by several researchers affiliated to a range of disciplines. It is becoming increasingly important given the growth of motorized vehicles on the one hand, and the scarcity of lands for new transportation infrastructure on the other. Indeed, in the context of smart cities and with the uninterrupted increase of the number of vehicles, road congestion is taking up an important place in research. In this context, the ability to provide highly accurate traffic forecasts is of fundamental importance to manage traffic, especially in the context of smart

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20

Konkathi, Manikanta. "Predictive Modeling in Business Analytics: Leveraging AI & Machine Learning." International Journal of Science and Research (IJSR) 13, no. 4 (2024): 875–89. http://dx.doi.org/10.21275/sr24403173852.

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21

Singh, Upma, Sandeep Singh, Tripti Rathee, and Manav Vaish. "Customer Retention Modeling over the OTT Platform using Machine Learning." Indian Journal Of Science And Technology 17, no. 42 (2024): 4365–71. http://dx.doi.org/10.17485/ijst/v17i42.2619.

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Objectives: Customer retention, a multifaceted issue that plagues the digital entertainment industry, particularly within the realm of Over-the-Top (OTT) platforms, poses a significant challenge, impacting revenue and sustainability. With the discontinuation of subscriptions or usage, retention not only impacts immediate revenue streams but also threatens the long-term sustainability of these platforms. Recognizing this challenge, this paper undertakes a comprehensive examination of predictive modeling methods tailored explicitly for forecasting customer retention within OTT platforms. Method:

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22

Nurmakhmatovich, Eshtemirov Akhror. "LEARNING TO SOLVE PROBLEMS USING MODELING METHODS FOR TECHNICAL SPECIALIZATION REQUIREMENTS." European International Journal of Pedagogics 4, no. 11 (2024): 226–30. https://doi.org/10.55640/eijp-04-11-46.

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Modeling is the study of cognitive objects (physical phenomena and processes) using their models, the creation and study of models of existing objects and phenomena. The modeling method is widely used in modern science. It simplifies the process of scientific research, and in some cases becomes the only means of studying complex objects. The importance of modeling in the study of abstract objects, distant objects, and very small objects is great. The use of physical and logical modeling plays a significant role in the theoretical and practical development of physics.

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23

Roy, Urmimala, Tanmoy Pramanik, Subhendu Roy, Avhishek Chatterjee, Leonard F. Register, and Sanjay K. Banerjee. "Machine Learning for Statistical Modeling." ACM Transactions on Design Automation of Electronic Systems 26, no. 3 (2021): 1–17. http://dx.doi.org/10.1145/3440014.

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We propose a methodology to perform process variation-aware device and circuit design using fully physics-based simulations within limited computational resources, without developing a compact model. Machine learning (ML), specifically a support vector regression (SVR) model, has been used. The SVR model has been trained using a dataset of devices simulated a priori, and the accuracy of prediction by the trained SVR model has been demonstrated. To produce a switching time distribution from the trained ML model, we only had to generate the dataset to train and validate the model, which needed ∼

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24

Pokidko, Daniil. "The Experiential Learning Modeling Method." Academy of Management Proceedings 2018, no. 1 (2018): 12357. http://dx.doi.org/10.5465/ambpp.2018.12357abstract.

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25

Bogers, Enide A. I., Michel Bierlaire, and Serge P. Hoogendoorn. "Modeling Learning in Route Choice." Transportation Research Record: Journal of the Transportation Research Board 2014, no. 1 (2007): 1–8. http://dx.doi.org/10.3141/2014-01.

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26

Burnaev, E. V., and A. V. Bernstein. "Manifold Modeling in Machine Learning." Journal of Communications Technology and Electronics 66, no. 6 (2021): 754–63. http://dx.doi.org/10.1134/s106422692106005x.

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27

Etzioni, Ruth, and Jane Lange. "Cancer Modeling as Learning Experience." Cancer Epidemiology, Biomarkers & Prevention 31, no. 4 (2022): 702–3. http://dx.doi.org/10.1158/1055-9965.epi-21-1409.

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Abstract Cancer modeling has become an accepted method for generating evidence about comparative effectiveness and cost-effectiveness of candidate cancer control policies across the continuum of care. Models of early detection policies require inputs concerning disease natural history and screening test performance, which are often subject to considerable uncertainty. Model validation against an external data source can increase confidence in the reliability of assumed or calibrated inputs. When a model fails to validate, this presents an opportunity to revise these inputs, thereby learning ne

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28

Ghiassi, Manoochehr, and Beatrice D. Simo-Kengne. "Machine Learning for Economic Modeling." International Journal of Public Administration in the Digital Age 8, no. 1 (2021): 1–17. http://dx.doi.org/10.4018/ijpada.294120.

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Accurate estimate of public expenditures is needed for budgetary planning and government decision making. Recent advances in machine learning offers the opportunity for modeling such problems. The paper introduces a novel modeling approach using a machine learning tool to forecast public expenditures and compare and contrast the effectiveness of this approach to traditional modeling alternatives. This research uses historical quarterly data from 1960-2016 to model public expenditures. Various accuracy measures (MAD, MAPE, and RSME) show that the machine learning model is the best alternative f

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29

Jukic, Nenad, and Boris Jukic. "Modeling-Centered Data Warehousing Learning." International Journal of Business Intelligence Research 3, no. 4 (2012): 74–95. http://dx.doi.org/10.4018/jbir.2012100104.

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Though data warehousing is widely recognized in the industry as the principal decision support system architecture and an integral part of the corporate information system, the majority of academic institutions in the US and world-wide have been slow in developing curriculums that reflect this. The authors examine the issues that have contributed to the lag in the coverage of data warehousing topics at universities and introduce methods, concepts and resources that can enable business educators to deal with these issues and conduct comprehensive, detailed, and meaningful coverage of data wareh

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30

Beloglazov, A. A., and L. B. Beloglazova. "MODELING TECHNOLOGIES OF INTERNET LEARNING." RUDN Journal of Informatization in Education 14, no. 1 (2017): 83–91. http://dx.doi.org/10.22363/2312-8631-2017-14-1-83-91.

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31

Drton, Mathias, and Marloes H. Maathuis. "Structure Learning in Graphical Modeling." Annual Review of Statistics and Its Application 4, no. 1 (2017): 365–93. http://dx.doi.org/10.1146/annurev-statistics-060116-053803.

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32

ÖZKURT SİVRİKAYA, Serpil. "RENEWABLE ENERGY: LEARNING BY MODELING." International Journal of Ecosystems and Ecology Science (IJEES) 9, no. 3 (2019): 543–48. http://dx.doi.org/10.31407/ijees9318.

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33

Mohd Tahir, Zurainee, Anitawati Mohd Lokman, Azhar Abd Aziz, Mohd Khairul Ikhwan Zolkefley, and Shamsudin Md Sarif. "Modeling Ubiquitous Language Learning Environment." Journal of Engineering and Applied Sciences 14, no. 6 (2019): 9251–57. http://dx.doi.org/10.36478/jeasci.2019.9251.9257.

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34

Lake, B. M., G. K. Vallabha, and J. L. McClelland. "Modeling Unsupervised Perceptual Category Learning." IEEE Transactions on Autonomous Mental Development 1, no. 1 (2009): 35–43. http://dx.doi.org/10.1109/tamd.2009.2021703.

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35

Jarosz, Gaja. "Computational Modeling of Phonological Learning." Annual Review of Linguistics 5, no. 1 (2019): 67–90. http://dx.doi.org/10.1146/annurev-linguistics-011718-011832.

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Recent advances in computational modeling have led to significant discoveries about the representation and acquisition of phonological knowledge and the limits on language learning and variation. These discoveries are the result of applying computational learning models to increasingly rich and complex natural language data while making increasingly realistic assumptions about the learning task. This article reviews the recent developments in computational modeling that have made connections between fully explicit theories of learning, naturally occurring corpus data, and the richness of psych

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36

WU, ZHICHAO, and ALEX H. B. DUFFY. "Modeling collective learning in design." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 18, no. 4 (2004): 289–313. http://dx.doi.org/10.1017/s0890060404040193.

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In this paper, a model of collective learning in design is developed in the context of team design. It explains that a team design activity uses input knowledge, environmental information, and design goals to produce output knowledge. A collective learning activity uses input knowledge from different agents and produces learned knowledge with the process of knowledge acquisition and transformation between different agents, which may be triggered by learning goals and rationale triggers. Different forms of collective learning were observed with respect to agent interactions, goal(s) of learning

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37

Valsamis, Epaminondas Markos, Rajna Golubic, Thomas E. Glover, Henry Husband, Adnan Hussain, and Amir-Reza Jenabzadeh. "Modeling Learning in Surgical Practice." Journal of Surgical Education 75, no. 1 (2018): 78–87. http://dx.doi.org/10.1016/j.jsurg.2017.06.015.

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38

Yassine, Benjelloun Touimi, Nourrdine EL Faddouli, Bennani Samir, and Mohammed Khalidi Idrissi. "Project-based Learning Modeling Language." Procedia - Social and Behavioral Sciences 106 (December 2013): 2159–78. http://dx.doi.org/10.1016/j.sbspro.2013.12.247.

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39

Carranza, Rogelio Ramos, Armando Aguilar Márquez, Frida M. León Rodríguez, and Omar García León. "Modeling in E-learning System." AASRI Procedia 8 (2014): 57–62. http://dx.doi.org/10.1016/j.aasri.2014.08.010.

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40

Chen, Wei James, and Ian Krajbich. "Computational modeling of epiphany learning." Proceedings of the National Academy of Sciences 114, no. 18 (2017): 4637–42. http://dx.doi.org/10.1073/pnas.1618161114.

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Models of reinforcement learning (RL) are prevalent in the decision-making literature, but not all behavior seems to conform to the gradual convergence that is a central feature of RL. In some cases learning seems to happen all at once. Limited prior research on these “epiphanies” has shown evidence of sudden changes in behavior, but it remains unclear how such epiphanies occur. We propose a sequential-sampling model of epiphany learning (EL) and test it using an eye-tracking experiment. In the experiment, subjects repeatedly play a strategic game that has an optimal strategy. Subjects can lea

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41

M. N. Nurgabylov, A.N. Narenova, Zh.A. Nazikova, N.B. Shamuratova, and M.T. Kenzhebaeva. "OBSERVATIONAL LEARNING AND MODELING THEORY." REPORTS 1, no. 329 (2020): 168–76. http://dx.doi.org/10.32014/2020.2518-1483.22.

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42

Boulesteix, Anne-Laure, and Matthias Schmid. "Machine learning versus statistical modeling." Biometrical Journal 56, no. 4 (2014): 588–93. http://dx.doi.org/10.1002/bimj.201300226.

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43

Raddatz, Manuel. "Threat Modeling in Machine Learning." Open Conference Proceedings 2 (December 15, 2022): 173–79. http://dx.doi.org/10.52825/ocp.v2i.161.

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Because of the increasing globalization, the technological progress and the degree of networking, the number of threats is constantly increasing. Security requirements often only play a minor role. With the new version of the IT Security Act, its scope has been extended and affected companies need to take actions to increase IT security. Threat modeling is a structured process that is already used in secure hardware and software development. Both the nature of the attacks and the life cycle time differ from traditional SW development. Starting from the machine learning structure, this article

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44

Niu, Ming Hui, and Joung Hyung Cho. "Ocean Modeling Analysis and Modeling Based on Deep Learning." Mobile Information Systems 2022 (July 30, 2022): 1–6. http://dx.doi.org/10.1155/2022/1019564.

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The ocean comprises an uninterrupted body of salt water confined within a vast basin on the earth’s surface. The ocean is the largest ecosystem on earth with rich and diverse biological resources. Organisms that reside in salty water are referred to as “marine life.” Plants, animals, and microorganisms including archaea and bacteria are examples of these. The existence of marine life is not only a biological resource but also an economic source. Toys and other industries that imitate marine life have emerged in the market. A different modeling design of marine life has improved with the passag

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45

Canbaloğlu, Gülay, Jan Treur, and Peter H. M. P. Roelofsma. "Computational modeling of organisational learning by self-modeling networks." Cognitive Systems Research 73 (June 2022): 51–64. http://dx.doi.org/10.1016/j.cogsys.2021.12.003.

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46

Brandriet, Alexandra R., Rose Marie Ward, and Stacey Lowery Bretz. "Modeling meaningful learning in chemistry using structural equation modeling." Chem. Educ. Res. Pract. 14, no. 4 (2013): 421–30. http://dx.doi.org/10.1039/c3rp00043e.

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47

Sulasmi, Emilda. "Effectiveness of Modeling Learning Strategies to improve Student Learning Outcomes." Budapest International Research and Critics Institute (BIRCI-Journal): Humanities and Social Sciences 4, no. 1 (2021): 926–36. http://dx.doi.org/10.33258/birci.v4i1.1694.

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This study aims to analyze the learning process at SMP Negeri 14 Medan. This research uses qualitative research, with a case study approach of SMP Negeri 14 Medan. Data collection techniques used in this study were observation, interviews, focus group discussions and documentation and then analyzed descriptively with descriptive analysis model. The results obtained are that the modeling learning strategy has a good impact on improving student learning outcomes, by making several figures into models who practice certain materials in the learning process, so that learning to dance starts from op

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AKYOL, Bertan. "Teacher Self-Efficacy Perceptions, Learning Oriented Motivation, Lifelong Learning Tendencies of Candidate Teachers: A Modeling Study." Eurasian Journal of Educational Research 16, no. 65 (2016): 1–35. http://dx.doi.org/10.14689/ejer.2016.65.02.

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49

Westi, Utami, Sugiyanto Catur, and Rahardjo Noorhadi. "Artificial intelligence in land use prediction modeling: a review." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 3 (2024): 2514–23. https://doi.org/10.11591/ijai.v13.i3.pp2514-2523.

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This study aims to review methods of artificial intelligence (AI) in land use modelling. Data were extracted from journals in the Scopus and Google Scholar databases using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) method. The review demonstrates that modelling land use predictions is a complex matter that involves land use maps and driving forces. AI technology can support land use forecasting by interpreting land use data, analyzing drivers, and modeling. However, AI has limitations in terms of broad contextual understanding and algorithmic errors. To ant

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Akrim, Akrim. "Application of Learning Model Strategies to improve Islamic Learning Outcomes." Budapest International Research and Critics Institute (BIRCI-Journal): Humanities and Social Sciences 3, no. 2 (2020): 1157–66. http://dx.doi.org/10.33258/birci.v3i2.956.

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This study aims to analyze the learning process of Islamic religious education in junior high schools. This study uses qualitative research, with the phenomenological approach of Al-Ulum MedanJakarta Integrated Islamic Middle School. Data collection techniques used in this study were observation, interviews, focus group discussions and documentation and then analyzed descriptively with an interactive analysis model. The results obtained are modeling learning strategies that have a good impact on improving student learning outcomes, by making some characters become models who practice certain m

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