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Journal articles on the topic 'Applied learning'

Author: Grafiati

Published: 4 June 2021

Last updated: 15 June 2025

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1

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

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M, Ms Jeevitha, Abinaya K, Sharmila R, Arthi S, and Karthika B. "DeepLeuko-(Deep learning applied to leukemia detection)." International Journal of Research Publication and Reviews 6, no. 4 (2025): 3463–70. https://doi.org/10.55248/gengpi.6.0425.1426.

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

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

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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 (2018): 379–81. http://dx.doi.org/10.1055/a-0642-9545.

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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) (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 (2017): 1630–35. http://dx.doi.org/10.22214/ijraset.2017.8231.

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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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Anees, Amir, Iqtadar Hussain, Umar M. Khokhar, Fawad Ahmed, and Sajjad Shaukat. "Machine Learning and Applied Cryptography." Security and Communication Networks 2022 (January 27, 2022): 1–3. http://dx.doi.org/10.1155/2022/9797604.

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Lipovetsky, Stan, and Jong-Min Kim. "Machine Learning in Applied Statistics." Model Assisted Statistics and Applications 12, no. 3 (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 (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 (2011): 576–78. http://dx.doi.org/10.1016/j.system.2011.10.005.

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Ibrahim, Naser Alajmi. "Applied English Linguistics and Learning through Translation." International Journal of Social Science and Humanities Research 13, no. 1 (2025): 110–12. https://doi.org/10.5281/zenodo.14800080.

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<strong>Abstract:</strong><em> </em>This paper discusses the theoretical backdrop and approaches of EAP and translation-mediated learning, and how they are synergistic in the field of teaching language. Applied English linguistics deals with the practical utility of linguistic theory for the enhancement of language teaching, language learning and language usage, and in translation-based learning, translation exercises are used to promote language teaching, vocabulary, and grammar learning, cultural awareness, and language understanding. Integrating these methods, language teachers can set up a holistic learning environment, taking responsibility for the technical and cultural sides of the language, so that it results in fluent language users who are effective in multilingual situations. This paper focuses on the relevance of applied English linguistics and translation-based learning, and examines its major methods, insights gained, and the combined effect in contemporary language teaching. By combining these approaches, language educators can develop innovative methods that enhance language teaching, improve language learners' outcomes, and foster effective communication in a globalized society. <strong>Keywords:</strong> English linguistics,<em> </em>translation-mediated learning, translation-based learning, globalized society. <strong>Title:</strong> Applied English Linguistics and Learning through Translation <strong>Author:</strong> Ibrahim Naser Alajmi <strong>International Journal of Social Science and Humanities Research  </strong> <strong>ISSN 2348-3156 (Print), ISSN 2348-3164 (online)</strong> <strong>Vol. 13, Issue 1, January 2025 - March 2025</strong> <strong>Page No: 110-112</strong> <strong>Research Publish Journals</strong> <strong>Website: www.researchpublish.com</strong> <strong>Published Date: 04-</strong><strong>February-2025</strong> <strong>DOI: https://doi.org/10.5281/zenodo.14800080</strong> <strong>Paper Download Link (Source)</strong> <strong>https://www.researchpublish.com/papers/applied-english-linguistics-and-learning-through-translation</strong>

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Zormanova, Lucie. "Learning Strategies Applied by University Students in Distance Learning." International Journal of Research in E-learning 7, no. 1 (2021): 1–20. http://dx.doi.org/10.31261/ijrel.2021.7.1.04.

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This review study aims to provide an overview of previous research on learning strategies used by students studying in a distance-learning form. The basic sources for research were the Web of Science, Research, Gate and Google Scholar databases. We also proceeded with a subsequent search based on frequent citations, using the Internet search engine Google.In accordance with the aim of this study, we sought answers to the following research questions in the analysis:1. Which learning strategies are most frequently used by students studying in a distance-learning form?2. In connection with which factors influencing distance-learning outcomes are learning strategies examined?3. Which research methods are used to study distance-learning strategies?We came to the following conclusions: In distance learning, it is necessary to help students develop their autonomous learning competence, knowledge of diverse learning strategies and reflective use of learning strategies, and increase the repertoire of reading strategies used by students. For some distance-learning students, however, the use of diverse learning strategies poses an issue; adult distance-learning students use only a fairly limited repertoire of learning strategies. Adult students use only those learning strategies which have worked for them in the past and are therefore convinced of their effectiveness. The most frequently used learning strategies for distance-learning students are retelling the text in their own words, with which students check whether they have understood the text sufficiently, compilation of an overview of information based on the read text, and the use of mnemonics.In connection with the factors influencing distance-learning outcomes, research has shown that there is a relationship between the degree of cognitive development and the use of a broader repertoire of learning strategies. It was found that the effectiveness of learning in self-directed learning and the use of a wider repertoire of learning strategies are closely related to students’ mood, selfconfidence, self-assurance, and perseverance. In this study of learning strategies, quantitative and qualitative research methods are applied. However, mixed method research design is the most commonly used approach.

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

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Ana, Esteso, Peidro David, Mula Josefa, and Díaz-Madroñero Manuel. "Reinforcement learning applied to production planning and control." International Journal of Production Research, no. 2022 (August 6, 2022): 1–18. https://doi.org/10.1080/00207543.2022.2104180.

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The objective of this paper is to examine the use and applications of reinforcement learning (RL) techniques in the production planning and control (PPC) field addressing the following PPC areas: facility resource planning, capacity planning, purchase and supply management, production scheduling and inventory management. The main RL characteristics, such as method, context, states, actions, reward and highlights, were analysed. The considered number of agents, applications and RL software tools, specifically, programming language, platforms, application programming interfaces and RL frameworks, among others, were identified, and 181 articles were sreviewed. The results showed that RL was applied mainly to production scheduling problems, followed by purchase and supply management. The most revised RL algorithms were model-free and single-agent and were applied to simplified PPC environments. Nevertheless, their results seem to be promising compared to traditional mathematical programming and heuristics/metaheuristics solution methods, and even more so when they incorporate uncertainty or non-linear properties. Finally, RL value-based approaches are the most widely used, specifically Q-learning and its variants and for deep RL, deep Q-networks. In recent years however, the most widely used approach has been the actor-critic method, such as the advantage actor critic, proximal policy optimisation, deep deterministic policy gradient and trust region policy optimisation.

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Barone, T., and Beth Ritter. "Applied Anthropology and Community Service-Learning." Practicing Anthropology 32, no. 2 (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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19

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 (2020): 76. http://dx.doi.org/10.12962/j24068535.v18i2.a940.

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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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Akiyama, T. S., J. Marcato Junior, W. N. Gonçalves, et al. "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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Rézio, Sofia, Marina Pedro Andrade, and Maria Filomena Teodoro. "Problem-Based Learning and Applied Mathematics." Mathematics 10, no. 16 (2022): 2862. http://dx.doi.org/10.3390/math10162862.

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Problem-based learning (PBL) is a teaching method that appeared in the early 1960s and is widely applied in distinct areas nowadays. In the presented manuscript, we describe a PBL methodology use restricted to applied mathematics for problem solving among a group of engineering students in a Portuguese university. In the pandemic context, it was a huge challenge both for the students and for the teacher. Supported by the available literature, the experiment was defined. As it is well known, teachers are not only knowledge transmitters but also designers of teaching initiatives. Thus, teachers and students both have a large role in PBL methodologies, where collaboration, reflection and concepts discussion are essential. In the presented pedagogical challenge, students were devoted to integrating the previous knowledge acquired and the one acquired during the project. This process improved their new competences—both personal and team work. Despite being a recent pedagogical method, PBL is revealed to be an important teaching tool.

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Sailer, Maria, Florian Schiller, Thorsten Falk, et al. "Applied machine learning for liver surgery." Current Directions in Biomedical Engineering 7, no. 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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Dudek, Grzegorz. "Special Issue on Applied Machine Learning." Applied Sciences 12, no. 4 (2022): 2039. http://dx.doi.org/10.3390/app12042039.

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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 (2021): 321–38. http://dx.doi.org/10.1121/10.0005545.

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Lleo, Sébastien. "Machine Learning: An Applied Mathematics Introduction." Quantitative Finance 20, no. 3 (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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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 (2002): 69–83. http://dx.doi.org/10.1080/j003v15n01_08.

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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 (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 (2016): 665–68. http://dx.doi.org/10.1148/radiol.2016162151.

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Pauschenwein, Jutta, and Anni Koubek. "Networked Learning in Applied Science Education." Proceedings of the International Conference on Networked Learning 2 (April 17, 2000): 280–85. https://doi.org/10.54337/nlc.v2.9818.

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In addition to traditional universities. within the last 5 years a number of applied science tertiary education programs ("Fachhochschulen") have been developed in Austria. The reason for this development was to install programs which are targeted co specific industrial needs and provide shorter, more targeted education. The courses are industry-oriented, compact and integrate theoretical knowledge with practical know-how. The didactical framework differs quite substantially from traditional university courses. The cooperation with industry accompanies the student through the whole study course. The majority of lecturers are working in industry and teach only part time. Projects in cooperation with industry, often including several subjects, arc posed in a group oriented approach, starting from the very first year. The complexity and professionality of such projects increases during the study time. Also each student works at least half a year with a company. This practical work is an integrated part of the courses and followed up by the teaching staff. Currently the FH Joanneum offers 9 study courses, all in very specialised, technologically oriented fields, such as automotive engineering, industrial design or construction planing and management.

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Schaeffer, Marion, Matthias Sesboüé, Jean-Philippe Kotowicz, Nicolas Delestre, and Cecilia Zanni-Merk. "OLAF: An Ontology Learning Applied Framework." Procedia Computer Science 225 (2023): 2106–15. http://dx.doi.org/10.1016/j.procs.2023.10.201.

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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 (2002): 69–83. http://dx.doi.org/10.1300/j003v15n01_08.

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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 (2021): 17–18. http://dx.doi.org/10.1016/j.cjca.2020.03.035.

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Mullainathan, Sendhil, and Jann Spiess. "Machine Learning: An Applied Econometric Approach." Journal of Economic Perspectives 31, no. 2 (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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Ibrahim, Rania, and Akila Sarirete. "Constructivist Approach applied to Leadership Learning." International Journal of Knowledge Society Research 4, no. 4 (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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Cox, E. Sam. "A Practical Aid for Applied Learning." Review of Communication 9, no. 2 (2009): 182–84. http://dx.doi.org/10.1080/15358590802650248.

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Hang, Weiqiang, and Timothy Banks. "Machine learning applied to pack classification." International Journal of Market Research 61, no. 6 (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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Loes, Chad N. "Applied Learning through Collaborative Educational Experiences." New Directions for Higher Education 2019, no. 188 (2019): 13–21. http://dx.doi.org/10.1002/he.20341.

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KRACL, CARRIE, and PHU VU. "Applied Learning Through Action Research Projects." Journal of Applied Learning in Higher Education 09, no. 2023 (2023): 47–54. http://dx.doi.org/10.57186/jalhe_2023_v9a3p47-54.

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This article explores the transformation of traditional comprehensive exams in a graduate master's program into real-world action research projects at a Midwest public university. It highlights the limitations of exams in translating theory into practice and introduces action research as a solution, aligning with recent trends in teacher action research. The article details the structure of the action research course and provides preliminary data on its outcomes, showcasing increased teacher confidence, impactful interventions, and successful academic journal publications. Ultimately, this innovative approach bridges the theory-practice gap, fosters professional growth, and positively impacts both graduate students and their classrooms, offering a practical alternative to traditional exams.

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

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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 (2014): 197–206. http://dx.doi.org/10.12785/ijcds/030303.

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Ani, Destri, Dewi Purnama Sari, and Rini Puspita Sari. "Behavioral Learning Theory Applied In PAI Learning At Sdit Juara." Al-Riwayah : Jurnal Kependidikan 15, no. 1 (2023): 41–48. http://dx.doi.org/10.47945/al-riwayah.v15i1.786.

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Behavioristic theory, which places a strong emphasis on improving personal conduct, is ideally suited for use in educational settings, particularly in the study of Islam. It is evident that subject teachers have used behavioristic theory extensively to promote positive student behavior since its inception. regardless of the learning of Islamic Education (PAI) itself, discussing associated behavior. Since PAI learning is a subject that all schools must provide. Students' behavioural modifications can be realized by giving them stimuli that will elicit positive responses. This study aims to comprehend the significance of using behavioristic theories in the study of Islamic Religious Education.The author's method is descriptive qualitative, and it includes methods for gathering data such interviews, observation, and documentation.Based on the findings of the author's research, it was determined that SDIT Juara did a good job of applying behaviorist learning theory to PAI learning. Positive behavioral changes in the students, such as increased drive for learning, interaction, memory improvement, and tolerance, are what define it. It is well known that SDIT Juara uses the behavioristic learning theory in its teaching of Islamic religious education. This theory is thought to be the best source of information to use when learning Islamic Religious Education, and it is hoped that by using this theory, it will be possible to see improvements in student conduct.

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Cotrina-Teatino, Marco A., Alvaro I. Riquelme Sandoval, Jose A. Guartan Medina, and Jairo J. Marquina Araujo. "Machine Learning aplicado a la exploración minera usando matriz de confusión." Revista Ciencia y Tecnología 21, no. 1 (2025): 63–74. https://doi.org/10.17268/rev.cyt.2025.01.06.

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The objective of this study was to determine how to spatially validate geological datasets obtained from the Huancayo region in Peru, as well as to identify the most common methods for evaluating the performance of classification models. The methodology employed involved the use of machine learning algorithms applied to a confusion matrix, with results represented through a ROC curve. The findings indicated that the accuracy parameter reached a maximum percentage of 65.4%, precision was 80.4%, recall was 96.2%, and the F1-score achieved a value of 96.1%. It is concluded that when using classification models, it is essential to consider additional metrics beyond accuracy to obtain a more reliable estimation.

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Chanlin, Lih-Juan, and Kung-Chi Chan. "Augmented Reality Applied in Dietary Monitoring." Libri 68, no. 2 (2018): 137–47. http://dx.doi.org/10.1515/libri-2017-0024.

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Abstract Recent advances in augmented reality (AR) have attracted much attention in nutrition and healthcare education. In the context of obtaining dietary knowledge, a mobile AR system was developed for learning and exploration, which allowed students to scan food images, receive information about nutrient content and record as well as accumulate daily nutrient intake. Students could also access learning materials provided in the system to achieve the learning objectives. Their learning outcomes were gathered at the close, with reactions to the learning experiences also assessed via a set of questionnaire items (using a 5 point Likert scale) in various criteria. Both qualitative and quantitative data were gathered, showing that most students achieved the learning objectives and were positive about exploring with AR. The results of this study indicated that students with high levels of monitoring had better learning outcomes (p<0.05) and were more positive about the varied criteria in their responses, including: visual interest and learning enjoyment, functional use, personal relevance and learning of knowledge (p<0.05). The effect of the self-participatory aspect of AR on learning is emphasized.

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Cordeiro-Costas, Moisés, Daniel Villanueva, Pablo Eguía-Oller, and Enrique Granada-Álvarez. "Machine Learning and Deep Learning Models Applied to Photovoltaic Production Forecasting." Applied Sciences 12, no. 17 (2022): 8769. http://dx.doi.org/10.3390/app12178769.

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The increasing trend in energy demand is higher than the one from renewable generation, in the coming years. One of the greatest sources of consumption are buildings. The energy management of a building by means of the production of photovoltaic energy in situ is a common alternative to improve sustainability in this sector. An efficient trade-off of the photovoltaic source in the fields of Zero Energy Buildings (ZEB), nearly Zero Energy Buildings (nZEB) or MicroGrids (MG) requires an accurate forecast of photovoltaic production. These systems constantly generate data that are not used. Artificial Intelligence methods can take advantage of this missing information and provide accurate forecasts in real time. Thus, in this manuscript a comparative analysis is carried out to determine the most appropriate Artificial Intelligence methods to forecast photovoltaic production in buildings. On the one hand, the Machine Learning methods considered are Random Forest (RF), Extreme Gradient Boost (XGBoost), and Support Vector Regressor (SVR). On the other hand, Deep Learning techniques used are Standard Neural Network (SNN), Recurrent Neural Network (RNN), and Convolutional Neural Network (CNN). The models are checked with data from a real building. The models are validated using normalized Mean Bias Error (nMBE), normalized Root Mean Squared Error (nRMSE), and the coefficient of variation (R2). Standard deviation is also used in conjunction with these metrics. The results show that the models forecast the test set with errors of less than 2.00% (nMBE) and 7.50% (nRMSE) in the case of considering nights, and 4.00% (nMBE) and 11.50% (nRMSE) if nights are not considered. In both situations, the R2 is greater than 0.85 in all models.

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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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Blake, Damian. "Exploring the Challenge of Applied Learning Reform." International Journal of Pedagogies and Learning 3, no. 3 (2007): 58–76. http://dx.doi.org/10.5172/ijpl.3.3.58.

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