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learning</title> <meta name="description" content="Search results for: transfer learning"> <meta name="keywords" content="transfer learning"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="transfer learning"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 9755</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: transfer learning</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9755</span> A Study of Transferable Strategies in Multilanguage Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zixi%20You">Zixi You</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the demand of multilingual speakers increasing in the job market, multi-language learning programs have become more and more popular among undergraduate students. A study on multi-language learning strategies is therefore highly demanded on both practical and theoretical levels. Based on previous classification of learning strategies in SLA, and an investigation of BA Modern Language program students (with post-A level L2 and ab initio L3 learning experience from year one), this study explores and compares different types of learning strategies used by multi-language speakers and learners, transferable learning strategies between L2 and L3, and factors affecting the transfer. The results indicate that all the 23 types of learning strategies of L2 are employed when learning L3 from ab initio level, yet with different tendencies. Learning strategy transfer from L2 to L3 (i.e., the learners attribute the applying of these L3 learning strategies to be a direct result of their L2 learning experience) are observed in all 23 types of learning strategies. Comparatively, six types of “cognitive strategies” have higher transfer tendency than others. With regard to the failure of the transfer of some particular L2 strategies and the development of independent L3 strategies of individual learners, factors such as language proficiency, language typology and learning environment have played important roles among others. The presentation of this study will provide audiences with detailed data, insightful analysis and discussion on both theoretical and practical aspects of multi-language learning that will benefit both students and educators. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=learning%20strategy" title="learning strategy">learning strategy</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-language%20acquisition" title=" multi-language acquisition"> multi-language acquisition</a>, <a href="https://publications.waset.org/abstracts/search?q=second%20language%20acquisition" title=" second language acquisition"> second language acquisition</a>, <a href="https://publications.waset.org/abstracts/search?q=strategy%20transfer" title=" strategy transfer"> strategy transfer</a> </p> <a href="https://publications.waset.org/abstracts/29430/a-study-of-transferable-strategies-in-multilanguage-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/29430.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">575</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9754</span> A Comprehensive Study of Camouflaged Object Detection Using Deep Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Khalak%20Bin%20Khair">Khalak Bin Khair</a>, <a href="https://publications.waset.org/abstracts/search?q=Saqib%20Jahir"> Saqib Jahir</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammed%20Ibrahim"> Mohammed Ibrahim</a>, <a href="https://publications.waset.org/abstracts/search?q=Fahad%20Bin"> Fahad Bin</a>, <a href="https://publications.waset.org/abstracts/search?q=Debajyoti%20Karmaker"> Debajyoti Karmaker</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Object detection is a computer technology that deals with searching through digital images and videos for occurrences of semantic elements of a particular class. It is associated with image processing and computer vision. On top of object detection, we detect camouflage objects within an image using Deep Learning techniques. Deep learning may be a subset of machine learning that's essentially a three-layer neural network Over 6500 images that possess camouflage properties are gathered from various internet sources and divided into 4 categories to compare the result. Those images are labeled and then trained and tested using vgg16 architecture on the jupyter notebook using the TensorFlow platform. The architecture is further customized using Transfer Learning. Methods for transferring information from one or more of these source tasks to increase learning in a related target task are created through transfer learning. The purpose of this transfer of learning methodologies is to aid in the evolution of machine learning to the point where it is as efficient as human learning. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title="deep learning">deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=transfer%20learning" title=" transfer learning"> transfer learning</a>, <a href="https://publications.waset.org/abstracts/search?q=TensorFlow" title=" TensorFlow"> TensorFlow</a>, <a href="https://publications.waset.org/abstracts/search?q=camouflage" title=" camouflage"> camouflage</a>, <a href="https://publications.waset.org/abstracts/search?q=object%20detection" title=" object detection"> object detection</a>, <a href="https://publications.waset.org/abstracts/search?q=architecture" title=" architecture"> architecture</a>, <a href="https://publications.waset.org/abstracts/search?q=accuracy" title=" accuracy"> accuracy</a>, <a href="https://publications.waset.org/abstracts/search?q=model" title=" model"> model</a>, <a href="https://publications.waset.org/abstracts/search?q=VGG16" title=" VGG16"> VGG16</a> </p> <a href="https://publications.waset.org/abstracts/152633/a-comprehensive-study-of-camouflaged-object-detection-using-deep-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/152633.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">149</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9753</span> Partial Knowledge Transfer Between the Source Problem and the Target Problem in Genetic Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Terence%20Soule">Terence Soule</a>, <a href="https://publications.waset.org/abstracts/search?q=Tami%20Al%20Ghamdi"> Tami Al Ghamdi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> To study how the partial knowledge transfer may affect the Genetic Algorithm (GA) performance, we model the Transfer Learning (TL) process using GA as the model solver. The objective of the TL is to transfer the knowledge from one problem to another related problem. This process imitates how humans think in their daily life. In this paper, we proposed to study a case where the knowledge transferred from the S problem has less information than what the T problem needs. We sampled the transferred population using different strategies of TL. The results showed transfer part of the knowledge is helpful and speeds the GA process of finding a solution to the problem. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=transfer%20learning" title="transfer learning">transfer learning</a>, <a href="https://publications.waset.org/abstracts/search?q=partial%20transfer" title=" partial transfer"> partial transfer</a>, <a href="https://publications.waset.org/abstracts/search?q=evolutionary%20computation" title=" evolutionary computation"> evolutionary computation</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a> </p> <a href="https://publications.waset.org/abstracts/147924/partial-knowledge-transfer-between-the-source-problem-and-the-target-problem-in-genetic-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/147924.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">132</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9752</span> Student-Created Videos to Foster Active Learning in Heat Transfer Course</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=W.Appamana">W.Appamana</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Jantasee"> S. Jantasee</a>, <a href="https://publications.waset.org/abstracts/search?q=P.%20Siwarasak"> P. Siwarasak</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20Mueansichai"> T. Mueansichai</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20Kaewbuddee"> C. Kaewbuddee </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Heat transfer is important in chemical engineering field. We have to know how to predict rates of heat transfer in a variety of process situations. Therefore, heat transfer learning is one of the greatest challenges for undergraduate students in chemical engineering. To enhance student learning in classroom, active-learning method was proposed in a single classroom, using problems based on videos and creating video, think-pair-share and jigsaw technique. The result shows that active learning method can prevent copying of the solutions manual for students and improve average examination scores about 5% when comparing with students in traditional section. Overall, this project represents an effective type of class that motivates student-centric learning while enhancing self-motivation, creative thinking and critical analysis among students. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=active%20learning" title="active learning">active learning</a>, <a href="https://publications.waset.org/abstracts/search?q=student-created%20video" title=" student-created video"> student-created video</a>, <a href="https://publications.waset.org/abstracts/search?q=self-motivation" title=" self-motivation"> self-motivation</a>, <a href="https://publications.waset.org/abstracts/search?q=creative%20thinking" title=" creative thinking"> creative thinking</a> </p> <a href="https://publications.waset.org/abstracts/78158/student-created-videos-to-foster-active-learning-in-heat-transfer-course" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/78158.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">235</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9751</span> Efficient Deep Neural Networks for Real-Time Strawberry Freshness Monitoring: A Transfer Learning Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mst.%20Tuhin%20Akter">Mst. Tuhin Akter</a>, <a href="https://publications.waset.org/abstracts/search?q=Sharun%20Akter%20Khushbu"> Sharun Akter Khushbu</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20M.%20Shaqib"> S. M. Shaqib</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A real-time system architecture is highly effective for monitoring and detecting various damaged products or fruits that may deteriorate over time or become infected with diseases. Deep learning models have proven to be effective in building such architectures. However, building a deep learning model from scratch is a time-consuming and costly process. A more efficient solution is to utilize deep neural network (DNN) based transfer learning models in the real-time monitoring architecture. This study focuses on using a novel strawberry dataset to develop effective transfer learning models for the proposed real-time monitoring system architecture, specifically for evaluating and detecting strawberry freshness. Several state-of-the-art transfer learning models were employed, and the best performing model was found to be Xception, demonstrating higher performance across evaluation metrics such as accuracy, recall, precision, and F1-score. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=strawberry%20freshness%20evaluation" title="strawberry freshness evaluation">strawberry freshness evaluation</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20neural%20network" title=" deep neural network"> deep neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=transfer%20learning" title=" transfer learning"> transfer learning</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20augmentation" title=" image augmentation"> image augmentation</a> </p> <a href="https://publications.waset.org/abstracts/177872/efficient-deep-neural-networks-for-real-time-strawberry-freshness-monitoring-a-transfer-learning-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/177872.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">90</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9750</span> Transfer Knowledge From Multiple Source Problems to a Target Problem in Genetic Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Terence%20Soule">Terence Soule</a>, <a href="https://publications.waset.org/abstracts/search?q=Tami%20Al%20Ghamdi"> Tami Al Ghamdi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> To study how to transfer knowledge from multiple source problems to the target problem, we modeled the Transfer Learning (TL) process using Genetic Algorithms as the model solver. TL is the process that aims to transfer learned data from one problem to another problem. The TL process aims to help Machine Learning (ML) algorithms find a solution to the problems. The Genetic Algorithms (GA) give researchers access to information that we have about how the old problem is solved. In this paper, we have five different source problems, and we transfer the knowledge to the target problem. We studied different scenarios of the target problem. The results showed combined knowledge from multiple source problems improves the GA performance. Also, the process of combining knowledge from several problems results in promoting diversity of the transferred population. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=transfer%20learning" title="transfer learning">transfer learning</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=evolutionary%20computation" title=" evolutionary computation"> evolutionary computation</a>, <a href="https://publications.waset.org/abstracts/search?q=source%20and%20target" title=" source and target"> source and target</a> </p> <a href="https://publications.waset.org/abstracts/147927/transfer-knowledge-from-multiple-source-problems-to-a-target-problem-in-genetic-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/147927.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">140</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9749</span> Teaching for Knowledge Transfer: Best Practices from a Graduate-Level Educational Psychology Distance Learning Program</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bobby%20Hoffman">Bobby Hoffman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> One measure of effective instruction is the ability to solve authentic, real-world problems by effectively transferring and applying classroom and textbook knowledge. While many students can productively earn high grades and learn course content, they are not always able to apply the knowledge they gain. As such, this quasi-experimental study compared the comprehensive exit exam results of learners across instructional modalities who completed a prominent graduate-level educational psychology program. ANCOVA revealed superior knowledge transfer for blended-learning students compared to those who completed distance education and significantly greater transfer of declarative, procedural, and self-regulatory knowledge by the blended-learning students. This paper briefly summarizes the study results while highlighting evidence-based programmatic and course level modifications that were implemented to specifically address the transfer of learning and practical application of educational psychology knowledge. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=assessment" title="assessment">assessment</a>, <a href="https://publications.waset.org/abstracts/search?q=distance%20learning" title=" distance learning"> distance learning</a>, <a href="https://publications.waset.org/abstracts/search?q=educational%20psychology" title=" educational psychology"> educational psychology</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20transfer" title=" knowledge transfer"> knowledge transfer</a> </p> <a href="https://publications.waset.org/abstracts/136938/teaching-for-knowledge-transfer-best-practices-from-a-graduate-level-educational-psychology-distance-learning-program" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/136938.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">177</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9748</span> A Review of Machine Learning for Big Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Devatha%20Kalyan%20Kumar">Devatha Kalyan Kumar</a>, <a href="https://publications.waset.org/abstracts/search?q=Aravindraj%20D."> Aravindraj D.</a>, <a href="https://publications.waset.org/abstracts/search?q=Sadathulla%20A."> Sadathulla A.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Big data are now rapidly expanding in all engineering and science and many other domains. The potential of large or massive data is undoubtedly significant, make sense to require new ways of thinking and learning techniques to address the various big data challenges. Machine learning is continuously unleashing its power in a wide range of applications. In this paper, the latest advances and advancements in the researches on machine learning for big data processing. First, the machine learning techniques methods in recent studies, such as deep learning, representation learning, transfer learning, active learning and distributed and parallel learning. Then focus on the challenges and possible solutions of machine learning for big data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=active%20learning" title="active learning">active learning</a>, <a href="https://publications.waset.org/abstracts/search?q=big%20data" title=" big data"> big data</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a> </p> <a href="https://publications.waset.org/abstracts/72161/a-review-of-machine-learning-for-big-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72161.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">445</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9747</span> Enhancing Fall Detection Accuracy with a Transfer Learning-Aided Transformer Model Using Computer Vision</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sheldon%20McCall">Sheldon McCall</a>, <a href="https://publications.waset.org/abstracts/search?q=Miao%20Yu"> Miao Yu</a>, <a href="https://publications.waset.org/abstracts/search?q=Liyun%20Gong"> Liyun Gong</a>, <a href="https://publications.waset.org/abstracts/search?q=Shigang%20Yue"> Shigang Yue</a>, <a href="https://publications.waset.org/abstracts/search?q=Stefanos%20Kollias"> Stefanos Kollias</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Falls are a significant health concern for older adults globally, and prompt identification is critical to providing necessary healthcare support. Our study proposes a new fall detection method using computer vision based on modern deep learning techniques. Our approach involves training a trans- former model on a large 2D pose dataset for general action recognition, followed by transfer learning. Specifically, we freeze the first few layers of the trained transformer model and train only the last two layers for fall detection. Our experimental results demonstrate that our proposed method outperforms both classical machine learning and deep learning approaches in fall/non-fall classification. Overall, our study suggests that our proposed methodology could be a valuable tool for identifying falls. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=healthcare" title="healthcare">healthcare</a>, <a href="https://publications.waset.org/abstracts/search?q=fall%20detection" title=" fall detection"> fall detection</a>, <a href="https://publications.waset.org/abstracts/search?q=transformer" title=" transformer"> transformer</a>, <a href="https://publications.waset.org/abstracts/search?q=transfer%20learning" title=" transfer learning"> transfer learning</a> </p> <a href="https://publications.waset.org/abstracts/168230/enhancing-fall-detection-accuracy-with-a-transfer-learning-aided-transformer-model-using-computer-vision" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/168230.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">146</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9746</span> Sustaining Language Learning: A Case Study of Multilingual Writers' ePortfolios</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amy%20Hodges">Amy Hodges</a>, <a href="https://publications.waset.org/abstracts/search?q=Deanna%20Rasmussen"> Deanna Rasmussen</a>, <a href="https://publications.waset.org/abstracts/search?q=Sherry%20Ward"> Sherry Ward</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper examines the use of ePortfolios in a two-course sequence for ESL (English as a Second Language) students at an international branch campus in Doha, Qatar. ePortfolios support the transfer of language learning, but few have examined the sustainability of that transfer across an ESL program. Drawing upon surveys and interviews with students, we analyze three case studies that complicate previous research on metacognition, language learning, and ePortfolios. Our findings have implications for those involved in ESL programs and assessment of student writing. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=TESOL" title="TESOL">TESOL</a>, <a href="https://publications.waset.org/abstracts/search?q=electronic%20portfolios" title=" electronic portfolios"> electronic portfolios</a>, <a href="https://publications.waset.org/abstracts/search?q=assessment" title=" assessment"> assessment</a>, <a href="https://publications.waset.org/abstracts/search?q=technology" title=" technology"> technology</a> </p> <a href="https://publications.waset.org/abstracts/72564/sustaining-language-learning-a-case-study-of-multilingual-writers-eportfolios" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72564.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">261</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9745</span> A Study of Learning Achievement for Heat Transfer by Using Experimental Sets of Convection with the Predict-Observe-Explain Teaching Technique</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wanlapa%20Boonsod">Wanlapa Boonsod</a>, <a href="https://publications.waset.org/abstracts/search?q=Nisachon%20Yangprasong"> Nisachon Yangprasong</a>, <a href="https://publications.waset.org/abstracts/search?q=Udomsak%20Kitthawee"> Udomsak Kitthawee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Thermal physics education is a complicated and challenging topic to discuss in any classroom. As a result, most students tend to be uninterested in learning this topic. In the current study, a convection experiment set was devised to show how heat can be transferred by a convection system to a thermoelectric plate until a LED flashes. This research aimed to 1) create a natural convection experimental set, 2) study learning achievement on the convection experimental set with the predict-observe-explain (POE) technique, and 3) study satisfaction for the convection experimental set with the predict-observe-explain (POE) technique. The samples were chosen by purposive sampling and comprised 28 students in grade 11 at Patumkongka School in Bangkok, Thailand. The primary research instrument was the plan for predict-observe-explain (POE) technique on heat transfer using a convection experimental set. Heat transfer experimental set by convection. The instruments used to collect data included a heat transfer achievement model by convection, a Satisfaction Questionnaire after the learning activity, and the predict-observe-explain (POE) technique for heat transfer using a convection experimental set. The research format comprised a one-group pretest-posttest design. The data was analyzed by GeoGebra program. The statistics used in the research were mean, standard deviation and t-test for dependent samples. The results of the research showed that achievement on heat transfer using convection experimental set was composed of thermo-electrics on the top side attached to the heat sink and another side attached to a stainless plate. Electrical current was displayed by the flashing of a 5v LED. The entire set of thermo-electrics was set up on the top of the box and heated by an alcohol burner. The achievement of learning was measured with the predict-observe-explain (POE) technique, with the natural convection experimental set statistically higher than before learning at a 0.01 level. Satisfaction with POE for physics learning of heat transfer by using convection experimental set was at a high level (4.83 from 5.00). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=convection" title="convection">convection</a>, <a href="https://publications.waset.org/abstracts/search?q=heat%20transfer" title=" heat transfer"> heat transfer</a>, <a href="https://publications.waset.org/abstracts/search?q=physics%20education" title=" physics education"> physics education</a>, <a href="https://publications.waset.org/abstracts/search?q=POE" title=" POE"> POE</a> </p> <a href="https://publications.waset.org/abstracts/93014/a-study-of-learning-achievement-for-heat-transfer-by-using-experimental-sets-of-convection-with-the-predict-observe-explain-teaching-technique" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/93014.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">218</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9744</span> Self-Supervised Pretraining on Sequences of Functional Magnetic Resonance Imaging Data for Transfer Learning to Brain Decoding Tasks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sean%20Paulsen">Sean Paulsen</a>, <a href="https://publications.waset.org/abstracts/search?q=Michael%20Casey"> Michael Casey</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this work we present a self-supervised pretraining framework for transformers on functional Magnetic Resonance Imaging (fMRI) data. First, we pretrain our architecture on two self-supervised tasks simultaneously to teach the model a general understanding of the temporal and spatial dynamics of human auditory cortex during music listening. Our pretraining results are the first to suggest a synergistic effect of multitask training on fMRI data. Second, we finetune the pretrained models and train additional fresh models on a supervised fMRI classification task. We observe significantly improved accuracy on held-out runs with the finetuned models, which demonstrates the ability of our pretraining tasks to facilitate transfer learning. This work contributes to the growing body of literature on transformer architectures for pretraining and transfer learning with fMRI data, and serves as a proof of concept for our pretraining tasks and multitask pretraining on fMRI data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=transfer%20learning" title="transfer learning">transfer learning</a>, <a href="https://publications.waset.org/abstracts/search?q=fMRI" title=" fMRI"> fMRI</a>, <a href="https://publications.waset.org/abstracts/search?q=self-supervised" title=" self-supervised"> self-supervised</a>, <a href="https://publications.waset.org/abstracts/search?q=brain%20decoding" title=" brain decoding"> brain decoding</a>, <a href="https://publications.waset.org/abstracts/search?q=transformer" title=" transformer"> transformer</a>, <a href="https://publications.waset.org/abstracts/search?q=multitask%20training" title=" multitask training"> multitask training</a> </p> <a href="https://publications.waset.org/abstracts/165380/self-supervised-pretraining-on-sequences-of-functional-magnetic-resonance-imaging-data-for-transfer-learning-to-brain-decoding-tasks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/165380.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">90</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9743</span> Factors Affecting General Practitioners’ Transfer of Specialized Self-Care Knowledge to Patients</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Weidong%20Xia">Weidong Xia</a>, <a href="https://publications.waset.org/abstracts/search?q=Malgorzata%20Kolotylo"> Malgorzata Kolotylo</a>, <a href="https://publications.waset.org/abstracts/search?q=Xuan%20Tan"> Xuan Tan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study examines the key factors that influence general practitioners’ learning and transfer of specialized arthritis knowledge and self-care techniques to patients during normal patient visits. Drawing on the theory of planed behavior and using matched survey data collected from general practitioners before and after training sessions provided by specialized orthopedic physicians, the study suggests that the general practitioner’s intention to use and transfer learned knowledge was influenced mainly by intrinsic motivation, organizational learning culture and absorptive capacity, but was not influenced by extrinsic motivation. The results provide both theoretical and practical implications. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=empirical%20study" title="empirical study">empirical study</a>, <a href="https://publications.waset.org/abstracts/search?q=healthcare%20knowledge%20management" title=" healthcare knowledge management"> healthcare knowledge management</a>, <a href="https://publications.waset.org/abstracts/search?q=patient%20self-care" title=" patient self-care"> patient self-care</a>, <a href="https://publications.waset.org/abstracts/search?q=physician%20knowledge%20transfer" title=" physician knowledge transfer"> physician knowledge transfer</a> </p> <a href="https://publications.waset.org/abstracts/50458/factors-affecting-general-practitioners-transfer-of-specialized-self-care-knowledge-to-patients" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/50458.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">299</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9742</span> Lung Disease Detection from the Chest X Ray Images Using Various Transfer Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aicha%20Akrout">Aicha Akrout</a>, <a href="https://publications.waset.org/abstracts/search?q=Amira%20Echtioui"> Amira Echtioui</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Ghorbel"> Mohamed Ghorbel</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Pneumonia remains a significant global health concern, posing a substantial threat to human lives due to its contagious nature and potentially fatal respiratory complications caused by bacteria, fungi, or viruses. The reliance on chest X-rays for diagnosis, although common, often necessitates expert interpretation, leading to delays and potential inaccuracies in treatment. This study addresses these challenges by employing transfer learning techniques to automate the detection of lung diseases, with a focus on pneumonia. Leveraging three pre-trained models, VGG-16, ResNet50V2, and MobileNetV2, we conducted comprehensive experiments to evaluate their performance. Our findings reveal that the proposed model based on VGG-16 demonstrates superior accuracy, precision, recall, and F1 score, achieving impressive results with an accuracy of 93.75%, precision of 94.50%, recall of 94.00%, and an F1 score of 93.50%. This research underscores the potential of transfer learning in enhancing pneumonia diagnosis and treatment outcomes, offering a promising avenue for improving healthcare delivery and reducing mortality rates associated with this debilitating respiratory condition. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=chest%20x-ray" title="chest x-ray">chest x-ray</a>, <a href="https://publications.waset.org/abstracts/search?q=lung%20diseases" title=" lung diseases"> lung diseases</a>, <a href="https://publications.waset.org/abstracts/search?q=transfer%20learning" title=" transfer learning"> transfer learning</a>, <a href="https://publications.waset.org/abstracts/search?q=pneumonia%20detection" title=" pneumonia detection"> pneumonia detection</a> </p> <a href="https://publications.waset.org/abstracts/187213/lung-disease-detection-from-the-chest-x-ray-images-using-various-transfer-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/187213.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">42</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9741</span> A Comparison of Methods for Neural Network Aggregation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=John%20Pomerat">John Pomerat</a>, <a href="https://publications.waset.org/abstracts/search?q=Aviv%20Segev"> Aviv Segev</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recently, deep learning has had many theoretical breakthroughs. For deep learning to be successful in the industry, however, there need to be practical algorithms capable of handling many real-world hiccups preventing the immediate application of a learning algorithm. Although AI promises to revolutionize the healthcare industry, getting access to patient data in order to train learning algorithms has not been easy. One proposed solution to this is data- sharing. In this paper, we propose an alternative protocol, based on multi-party computation, to train deep learning models while maintaining both the privacy and security of training data. We examine three methods of training neural networks in this way: Transfer learning, average ensemble learning, and series network learning. We compare these methods to the equivalent model obtained through data-sharing across two different experiments. Additionally, we address the security concerns of this protocol. While the motivating example is healthcare, our findings regarding multi-party computation of neural network training are purely theoretical and have use-cases outside the domain of healthcare. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=neural%20network%20aggregation" title="neural network aggregation">neural network aggregation</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-party%20computation" title=" multi-party computation"> multi-party computation</a>, <a href="https://publications.waset.org/abstracts/search?q=transfer%20learning" title=" transfer learning"> transfer learning</a>, <a href="https://publications.waset.org/abstracts/search?q=average%20ensemble%20learning" title=" average ensemble learning"> average ensemble learning</a> </p> <a href="https://publications.waset.org/abstracts/128037/a-comparison-of-methods-for-neural-network-aggregation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/128037.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">162</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9740</span> Comparison of Machine Learning and Deep Learning Algorithms for Automatic Classification of 80 Different Pollen Species</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Endrick%20Barnacin">Endrick Barnacin</a>, <a href="https://publications.waset.org/abstracts/search?q=Jean-Luc%20Henry"> Jean-Luc Henry</a>, <a href="https://publications.waset.org/abstracts/search?q=Jimmy%20Nagau"> Jimmy Nagau</a>, <a href="https://publications.waset.org/abstracts/search?q=Jack%20Molinie"> Jack Molinie</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Palynology is a field of interest in many disciplines due to its multiple applications: chronological dating, climatology, allergy treatment, and honey characterization. Unfortunately, the analysis of a pollen slide is a complicated and time consuming task that requires the intervention of experts in the field, which are becoming increasingly rare due to economic and social conditions. That is why the need for automation of this task is urgent. A lot of studies have investigated the subject using different standard image processing descriptors and sometimes hand-crafted ones.In this work, we make a comparative study between classical feature extraction methods (Shape, GLCM, LBP, and others) and Deep Learning (CNN, Autoencoders, Transfer Learning) to perform a recognition task over 80 regional pollen species. It has been found that the use of Transfer Learning seems to be more precise than the other approaches <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=pollens%20identification" title="pollens identification">pollens identification</a>, <a href="https://publications.waset.org/abstracts/search?q=features%20extraction" title=" features extraction"> features extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=pollens%20classification" title=" pollens classification"> pollens classification</a>, <a href="https://publications.waset.org/abstracts/search?q=automated%20palynology" title=" automated palynology"> automated palynology</a> </p> <a href="https://publications.waset.org/abstracts/148945/comparison-of-machine-learning-and-deep-learning-algorithms-for-automatic-classification-of-80-different-pollen-species" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/148945.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">136</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9739</span> Uncertainty Estimation in Neural Networks through Transfer Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ashish%20James">Ashish James</a>, <a href="https://publications.waset.org/abstracts/search?q=Anusha%20James"> Anusha James</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The impressive predictive performance of deep learning techniques on a wide range of tasks has led to its widespread use. Estimating the confidence of these predictions is paramount for improving the safety and reliability of such systems. However, the uncertainty estimates provided by neural networks (NNs) tend to be overconfident and unreasonable. Ensemble of NNs typically produce good predictions but uncertainty estimates tend to be inconsistent. Inspired by these, this paper presents a framework that can quantitatively estimate the uncertainties by leveraging the advances in transfer learning through slight modification to the existing training pipelines. This promising algorithm is developed with an intention of deployment in real world problems which already boast a good predictive performance by reusing those pretrained models. The idea is to capture the behavior of the trained NNs for the base task by augmenting it with the uncertainty estimates from a supplementary network. A series of experiments with known and unknown distributions show that the proposed approach produces well calibrated uncertainty estimates with high quality predictions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=uncertainty%20estimation" title="uncertainty estimation">uncertainty estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title=" neural networks"> neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=transfer%20learning" title=" transfer learning"> transfer learning</a>, <a href="https://publications.waset.org/abstracts/search?q=regression" title=" regression"> regression</a> </p> <a href="https://publications.waset.org/abstracts/153501/uncertainty-estimation-in-neural-networks-through-transfer-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/153501.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">135</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9738</span> Neural Style Transfer Using Deep Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shaik%20Jilani%20Basha">Shaik Jilani Basha</a>, <a href="https://publications.waset.org/abstracts/search?q=Inavolu%20Avinash"> Inavolu Avinash</a>, <a href="https://publications.waset.org/abstracts/search?q=Alla%20Venu%20Sai%20Reddy"> Alla Venu Sai Reddy</a>, <a href="https://publications.waset.org/abstracts/search?q=Bitragunta%20Taraka%20Ramu"> Bitragunta Taraka Ramu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We can use the neural style transfer technique to build a picture with the same "content" as the beginning image but the "style" of the picture we've chosen. Neural style transfer is a technique for merging the style of one image into another while retaining its original information. The only change is how the image is formatted to give it an additional artistic sense. The content image depicts the plan or drawing, as well as the colors of the drawing or paintings used to portray the style. It is a computer vision programme that learns and processes images through deep convolutional neural networks. To implement software, we used to train deep learning models with the train data, and whenever a user takes an image and a styled image, the output will be as the style gets transferred to the original image, and it will be shown as the output. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title="neural networks">neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=computer%20vision" title=" computer vision"> computer vision</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20networks" title=" convolutional neural networks"> convolutional neural networks</a> </p> <a href="https://publications.waset.org/abstracts/167224/neural-style-transfer-using-deep-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/167224.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">95</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9737</span> Variable Refrigerant Flow (VRF) Zonal Load Prediction Using a Transfer Learning-Based Framework</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Junyu%20Chen">Junyu Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Peng%20Xu"> Peng Xu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the context of global efforts to enhance building energy efficiency, accurate thermal load forecasting is crucial for both device sizing and predictive control. Variable Refrigerant Flow (VRF) systems are widely used in buildings around the world, yet VRF zonal load prediction has received limited attention. Due to differences between VRF zones in building-level prediction methods, zone-level load forecasting could significantly enhance accuracy. Given that modern VRF systems generate high-quality data, this paper introduces transfer learning to leverage this data and further improve prediction performance. This framework also addresses the challenge of predicting load for building zones with no historical data, offering greater accuracy and usability compared to pure white-box models. The study first establishes an initial variable set of VRF zonal building loads and generates a foundational white-box database using EnergyPlus. Key variables for VRF zonal loads are identified using methods including SRRC, PRCC, and Random Forest. XGBoost and LSTM are employed to generate pre-trained black-box models based on the white-box database. Finally, real-world data is incorporated into the pre-trained model using transfer learning to enhance its performance in operational buildings. In this paper, zone-level load prediction was integrated with transfer learning, and a framework was proposed to improve the accuracy and applicability of VRF zonal load prediction. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=zonal%20load%20prediction" title="zonal load prediction">zonal load prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=variable%20refrigerant%20flow%20%28VRF%29%20system" title=" variable refrigerant flow (VRF) system"> variable refrigerant flow (VRF) system</a>, <a href="https://publications.waset.org/abstracts/search?q=transfer%20learning" title=" transfer learning"> transfer learning</a>, <a href="https://publications.waset.org/abstracts/search?q=energyplus" title=" energyplus"> energyplus</a> </p> <a href="https://publications.waset.org/abstracts/191023/variable-refrigerant-flow-vrf-zonal-load-prediction-using-a-transfer-learning-based-framework" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/191023.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">28</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9736</span> Competences for Learning beyond the Academic Context</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Cristina%20Galv%C3%A1n-Fern%C3%A1ndez">Cristina Galván-Fernández </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Students differentiate the different contexts of their lives as well as employment, hobbies or studies. In higher education is needed to transfer the experiential knowledge to theory and viceversa. However, is difficult to achieve than students use their personal experiences and social readings for get the learning evidences. In an experience with 178 education students from Chile and Spain we have used an e-portfolio system and a methodology for 4 years with the aims of help them to: 1) self-regulate their learning process and 2) use social networks and professional experiences for make the learning evidences. These two objectives have been controlled by interviews to the same students in different moments and two questionnaires. The results of this study show that students recognize the ownership of their learning and progress in planning and reflection of their own learning. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=competences" title="competences">competences</a>, <a href="https://publications.waset.org/abstracts/search?q=e-portfolio" title=" e-portfolio"> e-portfolio</a>, <a href="https://publications.waset.org/abstracts/search?q=higher%20education" title=" higher education"> higher education</a>, <a href="https://publications.waset.org/abstracts/search?q=self-regulation" title=" self-regulation"> self-regulation</a> </p> <a href="https://publications.waset.org/abstracts/59108/competences-for-learning-beyond-the-academic-context" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/59108.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">299</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9735</span> Bidirectional Encoder Representations from Transformers Sentiment Analysis Applied to Three Presidential Pre-Candidates in Costa Rica</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=F%C3%A9lix%20David%20Su%C3%A1rez%20Bonilla">Félix David Suárez Bonilla</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A sentiment analysis service to detect polarity (positive, neural, and negative), based on transfer learning, was built using a Spanish version of BERT and applied to tweets written in Spanish. The dataset that was used consisted of 11975 reviews, which were extracted from Google Play using the google-play-scrapper package. The BETO trained model used: the AdamW optimizer, a batch size of 16, a learning rate of 2x10⁻⁵ and 10 epochs. The system was tested using tweets of three presidential pre-candidates from Costa Rica. The system was finally validated using human labeled examples, achieving an accuracy of 83.3%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=NLP" title="NLP">NLP</a>, <a href="https://publications.waset.org/abstracts/search?q=transfer%20learning" title=" transfer learning"> transfer learning</a>, <a href="https://publications.waset.org/abstracts/search?q=BERT" title=" BERT"> BERT</a>, <a href="https://publications.waset.org/abstracts/search?q=sentiment%20analysis" title=" sentiment analysis"> sentiment analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20media" title=" social media"> social media</a>, <a href="https://publications.waset.org/abstracts/search?q=opinion%20mining" title=" opinion mining"> opinion mining</a> </p> <a href="https://publications.waset.org/abstracts/144475/bidirectional-encoder-representations-from-transformers-sentiment-analysis-applied-to-three-presidential-pre-candidates-in-costa-rica" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/144475.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">174</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9734</span> Violence Detection and Tracking on Moving Surveillance Video Using Machine Learning Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abe%20Degale%20D.">Abe Degale D.</a>, <a href="https://publications.waset.org/abstracts/search?q=Cheng%20Jian"> Cheng Jian</a> </p> <p class="card-text"><strong>Abstract:</strong></p> When creating automated video surveillance systems, violent action recognition is crucial. In recent years, hand-crafted feature detectors have been the primary method for achieving violence detection, such as the recognition of fighting activity. Researchers have also looked into learning-based representational models. On benchmark datasets created especially for the detection of violent sequences in sports and movies, these methods produced good accuracy results. The Hockey dataset's videos with surveillance camera motion present challenges for these algorithms for learning discriminating features. Image recognition and human activity detection challenges have shown success with deep representation-based methods. For the purpose of detecting violent images and identifying aggressive human behaviours, this research suggested a deep representation-based model using the transfer learning idea. The results show that the suggested approach outperforms state-of-the-art accuracy levels by learning the most discriminating features, attaining 99.34% and 99.98% accuracy levels on the Hockey and Movies datasets, respectively. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=violence%20detection" title="violence detection">violence detection</a>, <a href="https://publications.waset.org/abstracts/search?q=faster%20RCNN" title=" faster RCNN"> faster RCNN</a>, <a href="https://publications.waset.org/abstracts/search?q=transfer%20learning%20and" title=" transfer learning and"> transfer learning and</a>, <a href="https://publications.waset.org/abstracts/search?q=surveillance%20video" title=" surveillance video"> surveillance video</a> </p> <a href="https://publications.waset.org/abstracts/171296/violence-detection-and-tracking-on-moving-surveillance-video-using-machine-learning-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/171296.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">106</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9733</span> Deliberate Learning and Practice: Enhancing Situated Learning Approach in Professional Communication Course</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Susan%20Lee">Susan Lee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Situated learning principles are adopted in the design of the module, professional communication, in its iteration of tasks and assignments to create a learning environment that simulates workplace reality. The success of situated learning is met when students are able to transfer and apply their skills beyond the classroom, in their personal life, and workplace. The learning process should help students recognize the relevance and opportunities for application. In the module’s learning component on negotiation, cases are created based on scenarios inspired by industry practices. The cases simulate scenarios that students on the course may encounter when they enter the workforce when they take on executive roles in the real estate sector. Engaging in the cases has enhanced students’ learning experience as they apply interpersonal communication skills in negotiation contexts of executives. Through the process of case analysis, role-playing, and peer feedback, students are placed in an experiential learning space to think and act in a deliberate manner not only as students but as professionals they will graduate to be. The immersive skills practices enable students to continuously apply a range of verbal and non-verbal communication skills purposefully as they stage their negotiations. The theme in students' feedback resonates with their awareness of the authentic and workplace experiences offered through visceral role-playing. Students also note relevant opportunities for the future transfer of the skills acquired. This indicates that students recognize the possibility of encountering similar negotiation episodes in the real world and realize they possess the negotiation tools and communication skills to deliberately apply them when these opportunities arise outside the classroom. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deliberate%20practice" title="deliberate practice">deliberate practice</a>, <a href="https://publications.waset.org/abstracts/search?q=interpersonal%20communication%20skills" title=" interpersonal communication skills"> interpersonal communication skills</a>, <a href="https://publications.waset.org/abstracts/search?q=role-play" title=" role-play"> role-play</a>, <a href="https://publications.waset.org/abstracts/search?q=situated%20learning" title=" situated learning"> situated learning</a> </p> <a href="https://publications.waset.org/abstracts/138338/deliberate-learning-and-practice-enhancing-situated-learning-approach-in-professional-communication-course" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/138338.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">214</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9732</span> University-Industry Technology Transfer and Technology Transfer Offices in Emerging Economies</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jos%C3%A9%20Carlos%20Rodr%C3%ADguez">José Carlos Rodríguez</a>, <a href="https://publications.waset.org/abstracts/search?q=Mario%20G%C3%B3mez"> Mario Gómez</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The aim of this paper is to get insight on the nature of university-industry technology transfer (UITT) and technology transfer offices (TTOs) activity at universities in the case of emerging economies. In relation to the process of transferring knowledge/technology in the case of emerging economies, knowledge/technology transfer in these economies are more reactive than in developed economies due to differences in maturity of technologies. It is assumed in this paper that knowledge/technology transfer is a complex phenomenon, and thus the paper contributes to get insight on the nature of UITT and TTOs creation in the case of emerging economies by using a system dynamics model of knowledge/technology transfer in these countries. The paper recognizes the differences between industrialized countries and emerging economies on these phenomena. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=university-industry%20technology%20transfer" title="university-industry technology transfer">university-industry technology transfer</a>, <a href="https://publications.waset.org/abstracts/search?q=technology%20transfer%20offices" title=" technology transfer offices"> technology transfer offices</a>, <a href="https://publications.waset.org/abstracts/search?q=technology%20transfer%20models" title=" technology transfer models"> technology transfer models</a>, <a href="https://publications.waset.org/abstracts/search?q=emerging%20economies" title=" emerging economies"> emerging economies</a> </p> <a href="https://publications.waset.org/abstracts/88464/university-industry-technology-transfer-and-technology-transfer-offices-in-emerging-economies" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/88464.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">250</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9731</span> Jet Impingement Heat Transfer on a Rib-Roughened Flat Plate</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20H.%20Alenezi">A. H. Alenezi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Cooling by impingement jet is known to have a significant high local and average heat transfer coefficient which make it widely used in industrial cooling systems. The heat transfer characteristics of an impinging jet on rib-roughened flat plate has been investigated numerically. This paper was set out to investigate the effect of rib height on the heat transfer rate. Since the flow needs to have enough spacing after passing the rib to allow reattachment especially for high Reynolds numbers, this study focuses on finding the optimum rib height which would be the best to maximize the heat transfer rate downstream the plate. This investigation employs a round nozzle with hydraulic diameter (Dh) of 13.5 mm, Jet-to-target distance of (H/D) of 4, rib location=1.5D and and finally jet angels of 45˚ and 90˚ under the influence of Re =10,000. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=jet%20impingement" title="jet impingement">jet impingement</a>, <a href="https://publications.waset.org/abstracts/search?q=CFD" title=" CFD"> CFD</a>, <a href="https://publications.waset.org/abstracts/search?q=turbulence%20model" title=" turbulence model"> turbulence model</a>, <a href="https://publications.waset.org/abstracts/search?q=heat%20transfer" title=" heat transfer"> heat transfer</a> </p> <a href="https://publications.waset.org/abstracts/57530/jet-impingement-heat-transfer-on-a-rib-roughened-flat-plate" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/57530.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">351</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9730</span> Experimental Study of Heat Transfer Enhancement Using Protruded Rectangular Fin</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tarique%20Jamil%20Khan">Tarique Jamil Khan</a>, <a href="https://publications.waset.org/abstracts/search?q=Swapnil%20Pande"> Swapnil Pande</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The investigation deals with the study of heat transfer enhancement using protruded square fin. This study is enough to determine whether protrusion in forced convection is enough to enhance the rate of heat transfer. It includes the results after performing experiments by using a plane rectangular fin of aluminum material and the same dimension rectangular fin of the same material but having protruded circular shape extended normally. The fins made by a sand casting method. The results clearly mentioned that the protruded surface is effective enough to enhance the rate of heat transfer. This research investigates a modern fin topologies heat transfer characteristics that will clearly outdated the conventional fin to increase the rate of heat transfer. Protruded fins improve the rate of heat transfer compared to solid fin by varying shape of the protrusion in diameter and height. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=heat%20transfer%20enhancement" title="heat transfer enhancement">heat transfer enhancement</a>, <a href="https://publications.waset.org/abstracts/search?q=forced%20convection" title=" forced convection"> forced convection</a>, <a href="https://publications.waset.org/abstracts/search?q=protruted%20fin" title=" protruted fin"> protruted fin</a>, <a href="https://publications.waset.org/abstracts/search?q=rectangular%20fin" title=" rectangular fin"> rectangular fin</a> </p> <a href="https://publications.waset.org/abstracts/56370/experimental-study-of-heat-transfer-enhancement-using-protruded-rectangular-fin" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/56370.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">362</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9729</span> Learner Autonomy Transfer from Teacher Education Program to the Classroom: Teacher Training is not Enough</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ira%20Slabodar">Ira Slabodar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Autonomous learning in English as a Foreign Language (EFL) refers to the use of target language, learner collaboration and students’ responsibility for their learning. Teachers play a vital role of mediators and facilitators in self-regulated method. Thus, their perception of self-guided practices dictates their implementation of this approach. While research has predominantly focused on inadequate administration of autonomous learning in school mostly due to lack of appropriate teacher training, this study examined whether novice teachers who were exposed to extensive autonomous practices were likely to implement this method in their teaching. Twelve novice teachers were interviewed to examine their perception of learner autonomy and their administration of this method. It was found that three-thirds of the respondents experienced a gap between familiarity with autonomous learning and a favorable attitude to this approach and their deficient integration of self-directed learning. Although learner-related and institution-oriented factors played a role in this gap, it was mostly caused by the respondents’ not being genuinely autonomous. This may be due to indirect exposure rather than explicit introduction of the learner autonomy approach. The insights of this research may assist curriculum designers and heads of teacher training programs to rethink course composition to guarantee the transfer of methodologies into EFL classes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=learner%20autonomy" title="learner autonomy">learner autonomy</a>, <a href="https://publications.waset.org/abstracts/search?q=teacher%20training" title=" teacher training"> teacher training</a>, <a href="https://publications.waset.org/abstracts/search?q=english%20as%20a%20foreign%20language%20%28efl%29" title=" english as a foreign language (efl)"> english as a foreign language (efl)</a>, <a href="https://publications.waset.org/abstracts/search?q=genuinely%20autonomous%20teachers" title=" genuinely autonomous teachers"> genuinely autonomous teachers</a>, <a href="https://publications.waset.org/abstracts/search?q=explicit%20instruction" title=" explicit instruction"> explicit instruction</a>, <a href="https://publications.waset.org/abstracts/search?q=self-determination%20theory" title=" self-determination theory"> self-determination theory</a> </p> <a href="https://publications.waset.org/abstracts/178787/learner-autonomy-transfer-from-teacher-education-program-to-the-classroom-teacher-training-is-not-enough" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/178787.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">58</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9728</span> Attention Multiple Instance Learning for Cancer Tissue Classification in Digital Histopathology Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Afaf%20Alharbi">Afaf Alharbi</a>, <a href="https://publications.waset.org/abstracts/search?q=Qianni%20Zhang"> Qianni Zhang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The identification of malignant tissue in histopathological slides holds significant importance in both clinical settings and pathology research. This paper introduces a methodology aimed at automatically categorizing cancerous tissue through the utilization of a multiple-instance learning framework. This framework is specifically developed to acquire knowledge of the Bernoulli distribution of the bag label probability by employing neural networks. Furthermore, we put forward a neural network based permutation-invariant aggregation operator, equivalent to attention mechanisms, which is applied to the multi-instance learning network. Through empirical evaluation of an openly available colon cancer histopathology dataset, we provide evidence that our approach surpasses various conventional deep learning methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=attention%20multiple%20instance%20learning" title="attention multiple instance learning">attention multiple instance learning</a>, <a href="https://publications.waset.org/abstracts/search?q=MIL%20and%20transfer%20learning" title=" MIL and transfer learning"> MIL and transfer learning</a>, <a href="https://publications.waset.org/abstracts/search?q=histopathological%20slides" title=" histopathological slides"> histopathological slides</a>, <a href="https://publications.waset.org/abstracts/search?q=cancer%20tissue%20classification" title=" cancer tissue classification"> cancer tissue classification</a> </p> <a href="https://publications.waset.org/abstracts/167708/attention-multiple-instance-learning-for-cancer-tissue-classification-in-digital-histopathology-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/167708.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">110</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9727</span> Exergy Losses Relation with Driving Forces in Heat Transfer Process</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Ali%20Ashrafizadeh">S. Ali Ashrafizadeh</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Amidpour"> M. Amidpour</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20Hedayat"> N. Hedayat </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Driving forces along with transfer coefficient affect on heat transfer rate, on the other hand, with regard to the relation of these forces with irriversibilities they are effective on exergy losses. Therefore, the driving forces can be used as a relation between heat transfer rate, transfer coefficients and exergy losses. In this paper, first, the relation of the exergetic efficiency and resistant forces is obtained, next the relation between exergy efficiency, relative driving force, heat transfer rate and heat resistances is considered. In all cases, results are argued graphically. Finally, a case study inspected by obtained results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=heat%20transfer" title="heat transfer">heat transfer</a>, <a href="https://publications.waset.org/abstracts/search?q=exergy%20losses" title=" exergy losses"> exergy losses</a>, <a href="https://publications.waset.org/abstracts/search?q=exergetic%20efficiency" title=" exergetic efficiency"> exergetic efficiency</a>, <a href="https://publications.waset.org/abstracts/search?q=driving%20forces" title=" driving forces"> driving forces</a> </p> <a href="https://publications.waset.org/abstracts/30134/exergy-losses-relation-with-driving-forces-in-heat-transfer-process" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/30134.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">604</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9726</span> Forster Energy Transfer and Optoelectronic Properties of (PFO/TiO2)/Fluorol 7GA Hybrid Thin Films</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bandar%20Ali%20Al-Asbahi">Bandar Ali Al-Asbahi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Hafizuddin%20Haji%20Jumali"> Mohammad Hafizuddin Haji Jumali</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Forster energy transfer between poly (9,9'-di-n-octylfluorenyl-2,7-diyl) (PFO)/TiO2 nanoparticles (NPs) as a donor and Fluorol 7GA as an acceptor has been studied. The energy transfer parameters were calculated by using mathematical models. The dominant mechanism responsible for the energy transfer between the donor and acceptor molecules was Forster-type, as evidenced by large values of quenching rate constant, energy transfer rate constant and critical distance of energy transfer. Moreover, these composites which were used as an emissive layer in organic light emitting diodes, were investigated in terms of current density–voltage and electroluminescence spectra. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=energy%20transfer%20parameters" title="energy transfer parameters">energy transfer parameters</a>, <a href="https://publications.waset.org/abstracts/search?q=forster-type" title=" forster-type"> forster-type</a>, <a href="https://publications.waset.org/abstracts/search?q=electroluminescence" title=" electroluminescence"> electroluminescence</a>, <a href="https://publications.waset.org/abstracts/search?q=organic%20light%20emitting%20diodes" title=" organic light emitting diodes "> organic light emitting diodes </a> </p> <a href="https://publications.waset.org/abstracts/1635/forster-energy-transfer-and-optoelectronic-properties-of-pfotio2fluorol-7ga-hybrid-thin-films" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/1635.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">426</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=transfer%20learning&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=transfer%20learning&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=transfer%20learning&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=transfer%20learning&page=5">5</a></li> <li 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