{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T01:15:04Z","timestamp":1773278104248,"version":"3.50.1"},"reference-count":76,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2024,2,28]],"date-time":"2024-02-28T00:00:00Z","timestamp":1709078400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Software"],"abstract":"<jats:p>Deep-SDM is a unified layer framework built on TensorFlow\/Keras and written in Python 3.12. The framework aligns with the modular engineering principles for the design and development strategy. Transparency, reproducibility, and recombinability are the framework\u2019s primary design criteria. The platform can extract valuable insights from numerical and text data and utilize them to predict future values by implementing long short-term memory (LSTM), gated recurrent unit (GRU), and convolution neural network (CNN). Its end-to-end machine learning pipeline involves a sequence of tasks, including data exploration, input preparation, model construction, hyperparameter tuning, performance evaluations, visualization of results, and statistical analysis. The complete process is systematic and carefully organized, from data import to model selection, encapsulating it into a unified whole. The multiple subroutines work together to provide a user-friendly and conducive pipeline that is easy to use. We utilized the Deep-SDM framework to predict the Nepal Stock Exchange (NEPSE) index to validate its reproducibility and robustness and observed impressive results.<\/jats:p>","DOI":"10.3390\/software3010003","type":"journal-article","created":{"date-parts":[[2024,2,28]],"date-time":"2024-02-28T07:56:02Z","timestamp":1709106962000},"page":"47-61","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Deep-SDM: A Unified Computational Framework for Sequential Data Modeling Using Deep Learning Models"],"prefix":"10.3390","volume":"3","author":[{"given":"Nawa Raj","family":"Pokhrel","sequence":"first","affiliation":[{"name":"Department of Physics and Computer Science, Xavier University of Louisiana, New Orleans, LA 70125, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3227-5039","authenticated-orcid":false,"given":"Keshab Raj","family":"Dahal","sequence":"additional","affiliation":[{"name":"Department of Mathematics, State University of New York Cortland, Cortland, NY 13045, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8182-0593","authenticated-orcid":false,"given":"Ramchandra","family":"Rimal","sequence":"additional","affiliation":[{"name":"Department of Mathematical Sciences, Middle Tennessee State University, Murfreesboro, TN 37132, USA"}]},{"given":"Hum Nath","family":"Bhandari","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Physics, Roger Williams University, Bristol, RI 02809, USA"}]},{"given":"Binod","family":"Rimal","sequence":"additional","affiliation":[{"name":"Department of Mathematics, The University of Tampa, Tampa, FL 33606, USA"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Alkhatib, K., Khazaleh, H., Alkhazaleh, H.A., Alsoud, A.R., and Abualigah, L. 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