Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Sreedevi, S.; Rajashekar; Ayesha, Samreen; Basha, Md Shaik Amzad; Prathibha, L. Title A name given to the resource User Behavior Prediction with Deep Learning: An Evaluation of CNN, LSTM, RNN, and Hybrid Models Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICICACS65178.2025.10967848" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICICACS65178.2025.10967848</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105004738556?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105004738556?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Sreedevi S., Sri Venkateswara College of Engineering, Department of Management Studies, Tirupati, India; Rajashekar, Rajeev Gandhi Memorial College of Engineering and Technology, Department of Management studies, Nandyal, India; Ayesha S., Christ University, Department of Professional Studies, Bangalore, India; Basha M.S.A., Gitam School of Business, Gitam (Deemed to be University), Hyderabad, India; Prathibha L., Ashoka Women Engineering College (Autonomous), Kurnool, India Description An account of the resource In this work, with a novel Hybrid CNN LSTM model, we present an in depth comparison and evaluation of various deep learning models (CNN, LSTM, RNN) on predicting user behaviour. To compare our proposed models we conducted detailed experimentation and rigorous performance comparisons using aforementioned metrics: accuracy, precision, recall, F1-score. The findings show that our RNN model performed incredibly well, reporting an accuracy of 99.86%. These results illustrate the power of the model in generalizing, and in capturing sequential dependencies in the user behaviour. On the other hand, the CNN model also performed very robustly, achieving 97.86% accuracy showing that it has powerful ability to extract spatial features. However, the LSTM model got decent results, with an accuracy of 87.14%. However, the Hybrid CNN-LSTM model's accuracy was for 80.29% which was up to the Hybrid CNN as mentioned above, and hence had lagged behind and has scope for improvement. Our proposed approach has provable advantages over existing methods, most notably, by using the RNN model outperforming the existing method. The RNN is able to detect sequential patterns more effectively than traditional techniques and show its ability to learn deeply to better predict user behaviour. Finally, we also hope that this investigation highlights two important directions for future RNN research: first, the promise of RNNs, and second, promising avenues for future research, such as refining hybrid architectures to yield improved performance. 2025 IEEE. Subject The topic of the resource convolutional neural network (CNN); deep learning; hybrid CNN-LSTM model; long short-term memory (LSTM); recurrent neural network (RNN); user behavior prediction Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833150845-6; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online