Journal articles: 'Online deep learning'

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Wu, Sheng, Ancong Wu, and Wei-Shi Zheng. "Online deep transferable dictionary learning." Pattern Recognition 118 (October 2021): 108007. http://dx.doi.org/10.1016/j.patcog.2021.108007.

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Murray‐Johnson, Kayon, Andrea Munro, and Racheal Popoola. "Immersive deep learning activities online." New Directions for Adult and Continuing Education 2021, no. 169 (March 2021): 35–49. http://dx.doi.org/10.1002/ace.20412.

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Lee, Hea-Jin, and Eun-ok Baek. "Facilitating Deep Learning in a Learning Community." International Journal of Technology and Human Interaction 8, no. 1 (January 2012): 1–13. http://dx.doi.org/10.4018/jthi.2012010101.

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The purpose of this study is to explore how the integration of online discussion into a mathematics methods course affected pre-service teachers’ learning. Students’ transcription of online discussion was analyzed using a mixed methods approach, combining computer-mediated discourse analysis and Chi-square test analysis. The data revealed that the online discussion helped pre-service teachers not only deepen their learning of mathematics methods, but also demonstrated their abilities to teach mathematics in different ways. It also indicated that the depth of their learning depended on the levels of threads and topics of discussion. Deep learning occurs 1) more often in the first level thread than subsequent level threads, and 2) in discussion topics, primarily those related to practice-based issues rather than theory-based topics.

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Xu, Jie, Yang Liu, Jinzhong Liu, and Zuguang Qu. "Effectiveness of English Online Learning Based on Deep Learning." Computational Intelligence and Neuroscience 2022 (April 13, 2022): 1–10. http://dx.doi.org/10.1155/2022/1310194.

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With the popularization of the Internet lifestyle and the innovation of learning methods, more and more online learning systems have emerged, allowing users to study in the system anytime and anywhere. While providing convenience to users, online learning systems also bring troubles to users, who cannot quickly find the resources they are interested in from the huge amount of learning resources. In this paper, we apply deep learning to an English online learning platform and analyze learners and learning contents by clustering algorithm and association rules. Based on this, a content organization system is developed using genetic algorithms, which is applied to the case of this paper to provide learners with personalized learning content. With the hope that the system can be extended to other online learning platforms in the future, three data mining techniques were selected to solve the problems found in the English online learning platform, and we designed how these techniques should be applied to the online learning platform. The first technique is the cluster mining technique, which is used to analyze learners’ profiles, classify learners in different categories, provide them with personalized learning content, and organize group learning. The second technique is association rules, which is used to analyze the correlation between learning contents. For the adaptive student-teacher knowledge migration strategy, the teacher model can guide the student model to track online and migrate the task-specific knowledge to the online tracking student model through the network parameters. Finally, a case study is selected and the above design is applied to this case study, and the results are analyzed in detail. The data mining technology is applied to the English online learning platform, and an innovative English learning content organization system is developed. It is hoped that the results of this study will have some practical value for promotion and provide an idea for the construction of the online learning platform, and it is also expected that the idea can improve the quality of online learning to a certain extent. Specifically, the online student model is adaptively updated by the teacher model parameters and the online student model parameters together.

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Zhang, Si-si, Jian-wei Liu, Xin Zuo, Run-kun Lu, and Si-ming Lian. "Online deep learning based on auto-encoder." Applied Intelligence 51, no. 8 (January 9, 2021): 5420–39. http://dx.doi.org/10.1007/s10489-020-02058-8.

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Du, Bingqian, Zhiyi Huang, and Chuan Wu. "Adversarial Deep Learning for Online Resource Allocation." ACM Transactions on Modeling and Performance Evaluation of Computing Systems 6, no. 4 (December 31, 2021): 1–25. http://dx.doi.org/10.1145/3494526.

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Online algorithms are an important branch in algorithm design. Designing online algorithms with a bounded competitive ratio (in terms of worst-case performance) can be hard and usually relies on problem-specific assumptions. Inspired by adversarial training from Generative Adversarial Net and the fact that the competitive ratio of an online algorithm is based on worst-case input, we adopt deep neural networks (NNs) to learn an online algorithm for a resource allocation and pricing problem from scratch, with the goal that the performance gap between offline optimum and the learned online algorithm can be minimized for worst-case input. Specifically, we leverage two NNs as the algorithm and the adversary, respectively, and let them play a zero sum game, with the adversary being responsible for generating worst-case input while the algorithm learns the best strategy based on the input provided by the adversary. To ensure better convergence of the algorithm network (to the desired online algorithm), we propose a novel per-round update method to handle sequential decision making to break complex dependency among different rounds so that update can be done for every possible action instead of only sampled actions. To the best of our knowledge, our work is the first using deep NNs to design an online algorithm from the perspective of worst-case performance guarantee. Empirical studies show that our updating methods ensure convergence to Nash equilibrium and the learned algorithm outperforms state-of-the-art online algorithms under various settings.

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Zinovyeva, Elizaveta, Wolfgang Karl Härdle, and Stefan Lessmann. "Antisocial online behavior detection using deep learning." Decision Support Systems 138 (November 2020): 113362. http://dx.doi.org/10.1016/j.dss.2020.113362.

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Jain, Prisha, and Chaya Ravindra. "Classifying Emotional Engagement in Online Learning Via Deep Learning Architecture." International Journal of Advanced Engineering, Management and Science 10, no. 5 (2024): 063–70. http://dx.doi.org/10.22161/ijaems.105.2.

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The world has seen a phenomenal rise in online learning over the past decade, with universities shifting courses to online modes, MOOCs(Massive Open Online Course) emerging and laptop and tab-based initiatives being extensively promoted. However, educators face significant challenges in analyzing learning environments due to issues like lack of in-person cues, small video size, etc. To address these challenges, it is crucial to analyze the engagement levels of online classes. Out of the various subcategories of engagement, emotional engagement is one that is overlooked, but integral to analysis and deterministic in its approach. In response, we developed a deep learning architecture to analyze emotional engagement in online classes. Our method utilizes a ResNet50-based algorithm, refined through experimentation with various techniques such as transfer learning, optimizers, and pre-trained weights. The model adds a unique layer to the analysis of different algorithms used for engagement detection in academia while also achieving stellar rates of 81.34% validation accuracy and 81.04% training accuracy. Unlike other models, our approach employs high-quality image data for training, ensuring more reliable results. Moreover, we constructed a novel framework for applying emotional engagement to real-world scenarios, thus bridging the pre-existing gap between implementation and academia. The integration of this technology into online learning has immense potential, and can bring with it a shift in the quality of education. By fostering a safe and healthy learning space for every student, we can significantly enhance the effectiveness of online education systems.

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D, Swaroop Gowda, and Ravi Dandu. "Machine Learning and Deep Learning Algorithm for Online Bullying Identification." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (June 30, 2023): 2708–11. http://dx.doi.org/10.22214/ijraset.2023.53951.

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Abstract: The popularity of information technologies has led to bullying in cyberbullying and social media has become its main place compared to mobile phones, gaming platforms and messaging. Cyberbullying can take many forms, including sexual harassment, threats, hate mail, and posting false information about a person that millions of people can see and read. Compared to bullying, cyberbullying has a longer-term impact on victims, which can affect them physically, emotionally, psychologically or in combination. Suicides due to cyberbullying have been on the rise in recent years, and India is among the four countries with the highest incidence.

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Musa Yaagoup, Khalid Mohammed, and Mohamed Elhafiz Mustafa. "Online Arabic Handwriting Characters Recognition using Deep Learning." IJARCCE 9, no. 10 (October 30, 2020): 83–92. http://dx.doi.org/10.17148/ijarcce.2020.91014.

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Cerezci, Feyza, Serap Kazan, Muhammed Ali Oz, Cemil Oz, Tugrul Tasci, Selman Hizal, and Caglayan Altay. "Online metallic surface defect detection using deep learning." Emerging Materials Research 9, no. 4 (December 1, 2020): 1266–73. http://dx.doi.org/10.1680/jemmr.20.00197.

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Al-Adhaileh, Mosleh Hmoud, Theyazn H. H. Aldhyani, and Ans D. Alghamdi. "Online Troll Reviewer Detection Using Deep Learning Techniques." Applied Bionics and Biomechanics 2022 (June 8, 2022): 1–10. http://dx.doi.org/10.1155/2022/4637594.

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The concentration of this paper is on detecting trolls among reviewers and users in online discussions and link distribution on social news aggregators such as Reddit. Trolls, a subset of suspicious reviewers, have been the focus of our attention. A troll reviewer is distinguished from an ordinary reviewer by the use of sentiment analysis and deep learning techniques to identify the sentiment of their troll posts. Machine learning and lexicon-based approaches can also be used for sentiment analysis. The novelty of the proposed system is that it applies a convolutional neural network integrated with a bidirectional long short-term memory (CNN–BiLSTM) model to detect troll reviewers in online discussions using a standard troll online reviewer dataset collected from the Reddit social media platform. Two experiments were carried out in our work: the first one was based on text data (sentiment analysis), and the second one was based on numerical data (10 attributes) extracted from the dataset. The CNN-BiLSTM model achieved 97% accuracy using text data and 100% accuracy using numerical data. While analyzing the results of our model, we observed that it provided better results than the compared methods.

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Rosie, Anthony. "Online pedagogies and the promotion of “deep learning”." Information Services & Use 20, no. 2-3 (April 1, 2000): 109–16. http://dx.doi.org/10.3233/isu-2000-202-306.

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SAMATHA, JULURI, PRASHANTH REDDY VOLADRI, SAGAR DHANUNJAY, and SAI KOUNDINYA GATTU. "VERIFICATION FOR ONLINE SIGNATURE BIOMETRICS USING DEEP LEARNING." i-manager's Journal on Computer Science 8, no. 3 (2020): 12. http://dx.doi.org/10.26634/jcom.8.3.18074.

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Filius, Renée Marianne, Renske A. M. de Kleijn, Sabine G. Uijl, Frans J. Prins, Harold V. M. Rijen, and Diederick E. Grobbee. "Promoting deep learning through online feedback in SPOCs." Frontline Learning Research 6, no. 2 (November 12, 2018): 92–113. http://dx.doi.org/10.14786/flr.v6i2.350.

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Chowanda, Andry, Nadia Nadia, and Lie Maximilianus Maria Kolbe. "Identifying clickbait in online news using deep learning." Bulletin of Electrical Engineering and Informatics 12, no. 3 (June 1, 2023): 1755–61. http://dx.doi.org/10.11591/eei.v12i3.4444.

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Several industries use clickbait techniques as their strategy to increase the number of readers for their news. Some news companies implement catchy headlines and images in their news article links, with the expectation that the readers will be interested in reading the news and click the provided link. The majority of the news is not hoax news. However, the content might not be as grand as the catchy headlines and images provided to the readers. This research aims to explore the classification model using machine learning to identify if the headlines are classified as clickbait in online news. This research explores several machine learning techniques to classify clickbait in online news and comprehensively explain the results. Several popular machine learning techniques were implemented and explored in this research. The results demonstrate that the model trained with fast large margin provides the best accuracy and classification error (90% and 10%, respectively). Moreover, to improve the performance, bidirectional encoder representations from transformers architecture was used to model clickbait in online news. The best BERT model achieved 98.86% in the test accuracy. BERT model requires more time to train (0.9 hour) compared to machine learning (0.4 hour).

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Wu, Yun. "Online course resource recommendation based on deep learning." Procedia Computer Science 228 (2023): 638–46. http://dx.doi.org/10.1016/j.procs.2023.11.074.

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Wu, Hankang. "Deep learning in multiplayer online battle arena games." Applied and Computational Engineering 38, no. 1 (January 22, 2024): 80–85. http://dx.doi.org/10.54254/2755-2721/38/20230533.

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Multiplayer Online Battle Arena games, known as MOBA in abbreviation, are developing rapidly, and more and more new players are growing interests to it. But some parts of these games are quite complicate for those beginners, such as how to pick appropriate champions, how to choose suitable items for purchasing, what is the win rate for current game session and how to make correct strategy decisions. This paper summarized some works, that can help players to solve those complicate parts and understand the game well, using machine learning and deep learning models. These works have all proved their feasibility according to either their result comparing with other baseline methods, or simulating some game sessions played by or against AI using their champion picking, item purchasing and strategy making suggestions. There are also some limitations of these works and some improvements of using machine learning and deep learning in MOBA game industry mentioned in this paper.

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Shani, Lior, Tom Zahavy, and Shie Mannor. "Online Apprenticeship Learning." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 8 (June 28, 2022): 8240–48. http://dx.doi.org/10.1609/aaai.v36i8.20798.

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In Apprenticeship Learning (AL), we are given a Markov Decision Process (MDP) without access to the cost function. Instead, we observe trajectories sampled by an expert that acts according to some policy. The goal is to find a policy that matches the expert's performance on some predefined set of cost functions. We introduce an online variant of AL (Online Apprenticeship Learning; OAL), where the agent is expected to perform comparably to the expert while interacting with the environment. We show that the OAL problem can be effectively solved by combining two mirror descent based no-regret algorithms: one for policy optimization and another for learning the worst case cost. By employing optimistic exploration, we derive a convergent algorithm with O(sqrt(K)) regret, where K is the number of interactions with the MDP, and an additional linear error term that depends on the amount of expert trajectories available. Importantly, our algorithm avoids the need to solve an MDP at each iteration, making it more practical compared to prior AL methods. Finally, we implement a deep variant of our algorithm which shares some similarities to GAIL, but where the discriminator is replaced with the costs learned by OAL. Our simulations suggest that OAL performs well in high dimensional control problems.

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Krishna, Dr N. Gopala. "FACIAL EXPRESSION DETECTION USING DEEP LEARNING." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (April 28, 2024): 1–5. http://dx.doi.org/10.55041/ijsrem32013.

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The use of machines to perform different tasks is constantly increasing in society. Providing machines with perception can lead them to perform a great variety of tasks, even very complex ones such as elderly care. Machine requires that machines understand about their environment and interlocutor’s intention. Recognizing facial emotions might help in this regard. During the development of this work, deep learning techniques have been used over images displaying the following facial emotions : happiness, sadness, anger, surprise, disgust, and fear. As results, such method best resolves issues of lighting variations and different orientation of object in the image and thus achieves a higher accuracy.In the field of education online learning plays a vital role.. The fundamental problem facing in the online learning environment is the low engagement of Listener to the Preceptor. The educational institutions and Preceptors are responsible to guarantee best learning environment with maximum engagement in educational activities for online learners. Key Words: Environment, interlocutor, happiness, sadness, anger, surprise, listener, educational.

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Sugden, Nicole, Robyn Brunton, Jasmine MacDonald, Michelle Yeo, and Ben Hicks. "Evaluating student engagement and deep learning in interactive online psychology learning activities." Australasian Journal of Educational Technology 37, no. 2 (May 10, 2021): 45–65. http://dx.doi.org/10.14742/ajet.6632.

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There is growing demand for online learning activities that offer flexibility for students to study anywhere, anytime, as online students fit study around work and family commitments. We designed a series of online activities and evaluated how, where, and with what devices students used the activities, as well as their levels of engagement and deep learning with the activities. A mixed-methods design was used to explore students’ interactions with the online activities. This method integrated learning analytics data with responses from 63 survey, nine interview, and 16 focus group participants. We found that students used a combination of mobile devices to access the online learning activities across a variety of locations during opportunistic study sessions in order to fit study into their daily routines. The online activities were perceived positively, facilitating affective, cognitive, and behavioural engagement as well as stimulating deep learning. Activities that were authentic, promoted problem-solving, applied theory to real-life scenarios, and increased students’ feelings of being supported were perceived as most beneficial to learning. These findings have implications for the future design of online activities, where activities need to accommodate students’ need for flexibility as students’ study habits become more mobile.

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Naidu, B. Ramesh, Naresh Tangudu, Chandra Sekhar, K. Kavitha, B. V. Ramana, P. Venkateswarlu Reddy, Jayavardhanarao Sahukaru, and Raj Ganesh Lopinti. "Toxic Comment Classification using Deep Learning." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 7 (September 1, 2023): 93–104. http://dx.doi.org/10.17762/ijritcc.v11i7.7834.

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Online Conversation media serves as a means for individuals to engage, cooperate, and exchange ideas; however, it is also considered a platform that facilitates the spread of hateful and offensive comments, which could significantly impact one's emotional and mental health. The rapid growth of online communication makes it impractical to manually identify and filter out hateful tweets. Consequently, there is a pressing need for a method or strategy to eliminate toxic and abusive comments and ensure the safety and cleanliness of social media platforms. Utilizing LSTM, Character-level CNN, Word-level CNN, and Hybrid model (LSTM + CNN) in this toxicity analysis is to classify comments and identify the different types of toxic classes by means of a comparative analysis of various models. The neural network models utilized for this analysis take in comments extracted from online platforms, including both toxic and non-toxic comments. The results of this study can contribute towards the development of a web interface that enables the identification of toxic and hateful comments within a given sentence or phrase, and categorizes them into their respective toxicity classes.

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Li, Pingyang, and Juan Zhang. "Online mobile learning resource recommendation method based on deep reinforcement learning." International Journal of Innovation and Sustainable Development 1, no. 1 (2023): 1—thisLastPage. http://dx.doi.org/10.1504/ijisd.2023.10056670.

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Ding, Hui, Yajun Chen, and Linling Wang. "College English Online Teaching Model Based on Deep Learning." Security and Communication Networks 2021 (December 21, 2021): 1–11. http://dx.doi.org/10.1155/2021/8919320.

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In today’s era, online teaching plays an important part in the college English teaching. Deep learning, famous for its ability of imitating the learning process of human brains and obtaining the internal essential features or rules of voice, videos, images, and other data, can be applied to assist and improve the college English online teaching which involves a wide use of those data. Based on the combination of the multilayer neural network model and the k-means clustering algorithm, this paper designs a kind of deep learning method that can be used to assist and improve the college English online teaching. Experiments were designed to test the reliability of this deep learning method. The results show that the optimization algorithm designed in this paper, which can adjust the learning rate, will improve the common probability gradient descent algorithm. Besides, it is proved that the deep learning’s efficiency of the CNN model is significantly higher than that of the MLP model. With the help of this deep learning method, it becomes feasible to apply the technologies related to the artificial intelligence to help teachers deeply analyze and diagnose students’ English learning behavior, replace the teachers in part to answer students’ questions in time, and automatically grade assignments in the process of the college English online teaching. Surveys and exams were then conducted to evaluate the effect of the application of the college English online teaching model based on deep learning on the students’ learning cognition and their academic performance. The results show that the college English online teaching model based on deep learning can stimulate students’ learning motivation and improve their academic performance.

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Wang, Chunyan. "Emotion Recognition of College Students’ Online Learning Engagement Based on Deep Learning." International Journal of Emerging Technologies in Learning (iJET) 17, no. 06 (March 29, 2022): 110–22. http://dx.doi.org/10.3991/ijet.v17i06.30019.

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In actual learning scenarios, learners have more and more personalized needs. The traditional measuring tools of emotional engagement can no longer meet the personalized needs of online learning. To solve the problem, this paper explores the emotion recognition of college students' online learning engagement based on deep learning. Firstly, the features were extracted from the texts related to online learning reviews and interactive behaviors of college students, and the texts were vectorized by the multi-head attention mechanism. Based on the multi-head attention mechanism, a bidirectional long short-term memory (BLSTM) emotion classification model was established, which describes the emotional attitude of learners towards learning engagement more clearly and more accurately. Through experiments, the proposed model was proved effective in emotion recognition of college students’ online learning engagement.

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Schlagenhauf, Tobias, Nicholas Ammann, and Jürgen Fleischer. "Online Learning für die präventive Verschleißdetektion/Online Learning for preventive wear detection – Online Retraining of Deep Learning models for unknown wear patterns." wt Werkstattstechnik online 111, no. 07-08 (2021): 475–80. http://dx.doi.org/10.37544/1436-4980-2021-07-08-7.

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Die industrielle Zustandsüberwachung mithilfe von Techniken des Maschinellen Lernens (ML) wird für die Wettbewerbsfähigkeit von Herstellern immer wichtiger [1]. In diesem Beitrag wird eine Methode vorgestellt, ML-Modelle zur präventiven Verschleißerkennung von Kugelgewindetrieben auf Umgebungsveränderungen im Betrieb (online) nachzutrainieren. Damit lässt sich Domänenwissen graduell im Modell implementieren, um die Klassifikationsgüte auch für neuartige Verschleißmuster stabil zu halten.   Industrial condition monitoring using machine learning (ML) techniques is becoming increasingly important for manufacturers‘ competitiveness [1]. This paper presents a method to retrain ML models for preventive wear detection of ball screw drives in Process (online) to environmental changes and thus gradually implement domain knowledge in the model to keep the classification quality stable even for novel wear patterns.

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Kankevičiūtė, Eglė, Milita Songailaitė, Bohdan Zhyhun, and Justina Mandravickaitė. "LITHUANIAN HATE SPEECH CLASSIFICATION USING DEEP LEARNING METHODS." Automation of technological and business processes 15, no. 3 (September 12, 2023): 20–29. http://dx.doi.org/10.15673/atbp.v15i3.2621.

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The ever-increasing amount of online content and the opportunity for everyone to express their opinions online leads to frequent encounters with social problems: bullying, insults, and hate speech. Some online portals are taking steps to stop this, such as no longer allowing user-generated comments to be made anonymously, removing the possibility to comment under the articles, and some portals employ moderators who identify and eliminate hate speech. However, given the large number of comments, an appropriately large number of people are required to do this work. The rapid development of artificial intelligence in the language technology area may be the solution to this problem. Automated hate speech detection would allow to manage the ever-increasing amount of online content, therefore we report hate speech classification for Lithuanian language by application of deep learning.

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Ma, XiaoRui. "Influence Study of Learners’ Independent Learning Ability on Learning Performance in Online Learning." International Journal of Emerging Technologies in Learning (iJET) 17, no. 09 (May 10, 2022): 201–13. http://dx.doi.org/10.3991/ijet.v17i09.30925.

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In this paper, questionnaires regarding the influence of learners’ autonomous learning ability on learning performance in online learning were designed, and the mediating role played by deep learning orientation in the effect of autonomous learning ability on learning performance was analyzed. The results revealed that the overall Cronbach’s α coefficient of the questionnaire was 0.884, the KMO value was 0.817, and the corresponding P value was 0.000, manifesting the good reliability and validity of this questionnaire. The learning performance could be obviously influenced by four aspects of autonomous learning ability: preparation of technology and target plan, utilization of materials in learning contents, regulation of learning process, and evaluation of learning effect. Deep learning orientation played a complete mediating role in the promoting effect of autonomous learning ability on learning performance. Learning frequency exerted a significant (0.01) influence on learning performance. Results are of considerable importance to enriching the literature on online learning environments and the development of autonomous learning ability, helping learners to cultivate autonomous learning ability, and enhancing the effectiveness of guidance provided by teachers to learners in online learning environments.

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Chae, Byungjoo, Jinsun Park, Tae-Hyun Kim, and Donghyeon Cho. "Online Learning for Reference-Based Super-Resolution." Electronics 11, no. 7 (March 28, 2022): 1064. http://dx.doi.org/10.3390/electronics11071064.

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Online learning is a method for exploiting input data to update deep networks in the test stage to derive potential performance improvement. Existing online learning methods for single-image super-resolution (SISR) utilize an input low-resolution (LR) image for the online adaptation of deep networks. Unlike SISR approaches, reference-based super-resolution (RefSR) algorithms benefit from an additional high-resolution (HR) reference image containing plenty of useful features for enhancing the input LR image. Therefore, we introduce a new online learning algorithm, using several reference images, which is applicable to not only RefSR but also SISR networks. Experimental results show that our online learning method is seamlessly applicable to many existing RefSR and SISR models, and that improves performance. We further present the robustness of our method to non-bicubic degradation kernels with in-depth analyses.

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Fahid, Fahmid Morshed, Jonathan Rowe, Yeojin Kim, Shashank Srivastava, and James Lester. "Online Reinforcement Learning-Based Pedagogical Planning for Narrative-Centered Learning Environments." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 21 (March 24, 2024): 23191–99. http://dx.doi.org/10.1609/aaai.v38i21.30365.

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Pedagogical planners can provide adaptive support to students in narrative-centered learning environments by dynamically scaffolding student learning and tailoring problem scenarios. Reinforcement learning (RL) is frequently used for pedagogical planning in narrative-centered learning environments. However, RL-based pedagogical planning raises significant challenges due to the scarcity of data for training RL policies. Most prior work has relied on limited-size datasets and offline RL techniques for policy learning. Unfortunately, offline RL techniques do not support on-demand exploration and evaluation, which can adversely impact the quality of induced policies. To address the limitation of data scarcity and offline RL, we propose INSIGHT, an online RL framework for training data-driven pedagogical policies that optimize student learning in narrative-centered learning environments. The INSIGHT framework consists of three components: a narrative-centered learning environment simulator, a simulated student agent, and an RL-based pedagogical planner agent, which uses a reward metric that is associated with effective student learning processes. The framework enables the generation of synthetic data for on-demand exploration and evaluation of RL-based pedagogical planning. We have implemented INSIGHT with OpenAI Gym for a narrative-centered learning environment testbed with rule-based simulated student agents and a deep Q-learning-based pedagogical planner. Our results show that online deep RL algorithms can induce near-optimal pedagogical policies in the INSIGHT framework, while offline deep RL algorithms only find suboptimal policies even with large amounts of data.

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Goud, Sai Pavan. "Cyber Bullying Detection Using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (May 31, 2024): 3911–15. http://dx.doi.org/10.22214/ijraset.2024.62270.

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Abstract: Cyber bullying detection leveraging deep learning techniques. By harnessing the power of deep neural networks, specifically convolutional neural networks (CNNs) and recurrent neural networks (RNNs), we aim to develop a robust and efficient model capable of accurately identifying instances of cyberbullying in textual and multimedia content. Through extensive experimentation on diverse datasets, we demonstrate the effectiveness of our proposed method in detecting subtle forms of online harassment with high precision and recall. This paper presents an approach for cyber bullying detection through keyword analysis. With the proliferation of online platforms, identifying instances of cyberbullying has become a pressing concern. Our method leverages a predefined set of keywords associated with bullying behavior to flag potentially harmful content. Through a combination of keyword matching and contextual analysis, we demonstrate the efficacy of our approachin accurately detecting cyberbullying instances across various digital communication channels. This keyword-based detection system offers a simple yet effective means of identifying and addressing cyberbullying.

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Makris, Christos, and Michael Angelos Simos. "OTNEL: A Distributed Online Deep Learning Semantic Annotation Methodology." Big Data and Cognitive Computing 4, no. 4 (October 29, 2020): 31. http://dx.doi.org/10.3390/bdcc4040031.

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Semantic representation of unstructured text is crucial in modern artificial intelligence and information retrieval applications. The semantic information extraction process from an unstructured text fragment to a corresponding representation from a concept ontology is known as named entity disambiguation. In this work, we introduce a distributed, supervised deep learning methodology employing a long short-term memory-based deep learning architecture model for entity linking with Wikipedia. In the context of a frequently changing online world, we introduce and study the domain of online training named entity disambiguation, featuring on-the-fly adaptation to underlying knowledge changes. Our novel methodology evaluates polysemous anchor mentions with sense compatibility based on thematic segmentation of the Wikipedia knowledge graph representation. We aim at both robust performance and high entity-linking accuracy results. The introduced modeling process efficiently addresses conceptualization, formalization, and computational challenges for the online training entity-linking task. The novel online training concept can be exploited for wider adoption, as it is considerably beneficial for targeted topic, online global context consensus for entity disambiguation.

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Hasugian, Leshivan Savenjer, and Suharjito Suharjito. "Fraud Detection for Online Interbank Transaction Using Deep Learning." Syntax Literate ; Jurnal Ilmiah Indonesia 8, no. 6 (June 20, 2023): 4263–75. http://dx.doi.org/10.36418/syntax-literate.v8i6.12627.

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The World Banking with its various financial services is an easy target for fraudsters to carry out their actions. Various kinds of fraud that occurred such as credit card fraud, online payment fraud, debit card fraud, online transaction fraud, e-commerce fraud and other services including interbank online transactions. Fast and reliable fraud detection is important because many financial losses have occurred due to fraud. The objective of this study is obtaining a more effective deep learning model for fraud detection in the interbank online transaction system compared to similar models. This study using CNN, LSTM and hybrid model CNN-LSTM models are used to build an interbank online transaction system. The proposed model CNN consist of three convolution layer, one maxpooling layer, one dropout layer and one fully connected layer. The proposed model LSTM built by double layer LSTM with each layer consist 32 cell LSTM, dropout layer and one fully connected layer. The proposed model CNN-LSTM built by combination three convolution layer, 1 maxpooling layer, dropout layer, 1 LSTM layer with 64 LSTM cell and one fully connected layer. The Dataset taken from an interbank online transaction in March 2021 from one of the switching company in Indonesia. SMOTE is use to overcome the imbalance Dataset in training and validation Dataset. The Dataset contains 279513 transactions with 2374 transactions categorized as fraud. The results showed that the CNN model scored an F1-score value at 93,09%, followed by the LSTM model at 86,25% and the CNN-LSTM hybrid model at 69,22%. Based on these results, the proposed CNN model can be accurate for fraud detection in interbank online transaction systems compared to similar models.

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Cals, Bram, Yingqian Zhang, Remco Dijkman, and Claudy van Dorst. "Solving the online batching problem using deep reinforcement learning." Computers & Industrial Engineering 156 (June 2021): 107221. http://dx.doi.org/10.1016/j.cie.2021.107221.

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Lu, Ange, Ruixue Guo, Qiucheng Ma, Lingzhi Ma, Yunsheng Cao, and Jun Liu. "Online sorting of drilled lotus seeds using deep learning." Biosystems Engineering 221 (September 2022): 118–37. http://dx.doi.org/10.1016/j.biosystemseng.2022.06.015.

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Zhao, Hang, Qijin She, Chenyang Zhu, Yin Yang, and Kai Xu. "Online 3D Bin Packing with Constrained Deep Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 1 (May 18, 2021): 741–49. http://dx.doi.org/10.1609/aaai.v35i1.16155.

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We solve a challenging yet practically useful variant of 3D Bin Packing Problem (3D-BPP). In our problem, the agent has limited information about the items to be packed into a single bin, and an item must be packed immediately after its arrival without buffering or readjusting. The item's placement also subjects to the constraints of order dependence and physical stability. We formulate this online 3D-BPP as a constrained Markov decision process (CMDP). To solve the problem, we propose an effective and easy-to-implement constrained deep reinforcement learning (DRL) method under the actor-critic framework. In particular, we introduce a prediction-and-projection scheme: The agent first predicts a feasibility mask for the placement actions as an auxiliary task and then uses the mask to modulate the action probabilities output by the actor during training. Such supervision and projection facilitate the agent to learn feasible policies very efficiently. Our method can be easily extended to handle lookahead items, multi-bin packing, and item re-orienting. We have conducted extensive evaluation showing that the learned policy significantly outperforms the state-of-the-art methods. A preliminary user study even suggests that our method might attain a human-level performance.

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Du, Jianxia, Byron Havard, and Heng Li. "Dynamic online discussion: task‐oriented interaction for deep learning." Educational Media International 42, no. 3 (September 2005): 207–18. http://dx.doi.org/10.1080/09523980500161221.

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Fan, Zipei, Xuan Song, Tianqi Xia, Renhe Jiang, Ryosuke Shibasaki, and Ritsu Sakuramachi. "Online Deep Ensemble Learning for Predicting Citywide Human Mobility." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2, no. 3 (September 18, 2018): 1–21. http://dx.doi.org/10.1145/3264915.

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Ke, Fengfeng, and Kui Xie. "Toward deep learning for adult students in online courses." Internet and Higher Education 12, no. 3-4 (December 2009): 136–45. http://dx.doi.org/10.1016/j.iheduc.2009.08.001.

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Austin, Daniel, Ashutosh Sanzgiri, Kannan Sankaran, Ryan Woodard, Amit Lissack, and Samuel Seljan. "Classifying sensitive content in online advertisements with deep learning." International Journal of Data Science and Analytics 10, no. 3 (March 20, 2020): 265–76. http://dx.doi.org/10.1007/s41060-020-00212-6.

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Hacker, Douglas J., and Dale S. Niederhauser. "Promoting Deep and Durable Learning in the Online Classroom." New Directions for Teaching and Learning 2000, no. 84 (2000): 53–63. http://dx.doi.org/10.1002/tl.848.

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and Biomechanics, Applied Bionics. "Retracted: Online Troll Reviewer Detection Using Deep Learning Techniques." Applied Bionics and Biomechanics 2023 (August 16, 2023): 1. http://dx.doi.org/10.1155/2023/9757592.

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Zhong, Yuan, Jing Zhou, Ping Li, and Jie Gong. "Dynamically evolving deep neural networks with continuous online learning." Information Sciences 646 (October 2023): 119411. http://dx.doi.org/10.1016/j.ins.2023.119411.

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Shiva, Sreenivasan, Minghui Hu, and Ponnuthurai Nagaratnam Suganthan. "Online learning using deep random vector functional link network." Engineering Applications of Artificial Intelligence 125 (October 2023): 106676. http://dx.doi.org/10.1016/j.engappai.2023.106676.

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Lin, Wei, Lei Shu, Weibo Zhong, Wei Lu, Daoyi Ma, and Yizhen Meng. "Online classification of soybean seeds based on deep learning." Engineering Applications of Artificial Intelligence 123 (August 2023): 106434. http://dx.doi.org/10.1016/j.engappai.2023.106434.

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全, 洁. "Development of Online Deep Learning Questionnaire for College Students." Advances in Psychology 14, no. 05 (2024): 543–48. http://dx.doi.org/10.12677/ap.2024.145346.

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Gratz, Erin, Bettyjo Bouchey, Megan Kohler, Monica L. Simonsen, and Jessica L. Knott. "Creating Authentic Learning Through Online Personal Learning Networks." International Journal of Online Pedagogy and Course Design 11, no. 2 (April 2021): 31–47. http://dx.doi.org/10.4018/ijopcd.2021040103.

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As educators face challenges in creating and cultivating authentic learning experiences in online education, a new paradigm for peer-to-peer learning has emerged: personal learning networks (PLNs). This article outlines autoethnographic research conducted in summer 2019, in which six participants from distinct virtual PLNs reflected on the benefits of PLNs as a model of peer-to-peer learning, how their experiences within PLNs aligned with Rule's themes of authentic learning and ways PLNs can be incorporated into online programming to create deep, authentic learning environments. The study findings align with the core principles of authentic learning: (a) real-world scenarios, (b) inquiry and thinking skills, (c) discourse with the community, and (d) empowerment. The study makes a strong case for the incorporation of PLNs into traditional online programming as a means to create unique and authentic learning experiences.

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Tzeng, Jian-Wei, Chia-An Lee, Nen-Fu Huang, Hao-Hsuan Huang, and Chin-Feng Lai. "MOOC Evaluation System Based on Deep Learning." International Review of Research in Open and Distributed Learning 23, no. 1 (February 1, 2022): 21–40. http://dx.doi.org/10.19173/irrodl.v22i4.5417.

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Massive open online courses (MOOCs) are open access, Web-based courses that enroll thousands of students. MOOCs deliver content through recorded video lectures, online readings, assessments, and both student–student and student–instructor interactions. Course designers have attempted to evaluate the experiences of MOOC participants, though due to large class sizes, have had difficulty tracking and analyzing the online actions and interactions of students. Within the broader context of the discourse surrounding big data, educational providers are increasingly collecting, analyzing, and utilizing student information. Additionally, big data and artificial intelligence (AI) technology have been applied to better understand students’ learning processes. Questionnaire response rates are also too low for MOOCs to be credibly evaluated. This study explored the use of deep learning techniques to assess MOOC student experiences. We analyzed students’ learning behavior and constructed a deep learning model that predicted student course satisfaction scores. The results indicated that this approach yielded reliable predictions. In conclusion, our system can accurately predict student satisfaction even when questionnaire response rates are low. Accordingly, teachers could use this system to better understand student satisfaction both during and after the course.

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Febria, Nyka Dwi, and Lulu Hasnadianti Putri. "Tutorial and Lecture Activities on Deep Online Learning in Dentistry." Insisiva Dental Journal: Majalah Kedokteran Gigi Insisiva 13, no. 1 (May 21, 2024): 32–40. http://dx.doi.org/10.18196/di.v13i1.21843.

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Online learning has become a new normal in the era of the COVID-19 pandemic. However, this learning mode has brought many challenges that can impact the learning experience. The study aims to evaluate student feedback on the online learning model and identify any challenges that require attention to enhance the quality of Dentistry education at Muhammadiyah University of Yogyakarta. This research is a descriptive observational study that utilized a cross-sectional approach. A modified questionnaire comprising 27 questions was distributed to collect the data. It was found that most of the 277 surveyed students agreed that implementing online learning in tutorial activities had a positive impact. Notably, 47.3% of the students found the online tutorial activities to be an excellent way to learn the material, while an equal number of students, 47.3% believed that they developed their tolerance during the online tutorial group sessions. Furthermore, the student responses to the online lecture activities were also positive, with 40.8% of the students finding comfort in the recorded classes, while 37.2% better understood the material through the online lectures. As for student satisfaction, the majority of the surveyed students expressed their satisfaction with the online learning experience regarding the material 52%, learning strategy 40,4%, availability of e-resources 49,1%, and assistance provided 42,6%. Students responded positively to online learning in tutorials and lectures and reported overall satisfaction with online learning during the COVID-19 pandemic.

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Yao, Yanyan. "A Study on the Factors Influencing College Students' Autonomous Learning Ability from the Perspective of Online Learning." Journal of Intelligence and Knowledge Engineering 1, no. 3 (September 2023): 39–47. http://dx.doi.org/10.62517/jike.202304306.

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Online learning has become one of the directions for major universities to achieve educational reform, and the online learning ability of college students is the key to the effectiveness of online education. Based on the Biggs3P theory, an online learning strength table was constructed, and factor analysis was used to construct college students' online learning ability. Regression analysis was conducted to identify the influencing factors of online learning ability and determine the mediating role of deep learning orientation. The empirical results indicate that multiple factors in the online learning environment, including but not limited to learning resources, student interaction, teacher support, and learning evaluation, all have varying degrees of positive effects on college students' deep learning orientation. Among them, the learning evaluation and teacher support of online learning have a strong influence, while the technical usability of online learning platforms has the weakest influence. In response, suggestions are proposed to enrich the construction of online learning resources, enhance the interactive experience of online learning students, strengthen the diversified development guided by online teachers, and build a multi-dimensional online learning evaluation mechanism.

Journal articles on the topic 'Online deep learning'

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