Deep-SDM: A Unified Computational Framework for Sequential Data Modeling Using Deep Learning Models
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’s primary design criteria. The platfo...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Software |
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| Online Access: | https://www.mdpi.com/2674-113X/3/1/3 |
| _version_ | 1797239290617397248 |
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| author | Nawa Raj Pokhrel Keshab Raj Dahal Ramchandra Rimal Hum Nath Bhandari Binod Rimal |
| author_facet | Nawa Raj Pokhrel Keshab Raj Dahal Ramchandra Rimal Hum Nath Bhandari Binod Rimal |
| author_sort | Nawa Raj Pokhrel |
| collection | DOAJ |
| description | 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’s 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. |
| first_indexed | 2024-04-24T17:49:11Z |
| format | Article |
| id | doaj.art-35bbe4e249bb4a5da553bd1dc2c55d7d |
| institution | Directory Open Access Journal |
| issn | 2674-113X |
| language | English |
| last_indexed | 2024-04-24T17:49:11Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Software |
| spelling | doaj.art-35bbe4e249bb4a5da553bd1dc2c55d7d2024-03-27T14:04:46ZengMDPI AGSoftware2674-113X2024-02-0131476110.3390/software3010003Deep-SDM: A Unified Computational Framework for Sequential Data Modeling Using Deep Learning ModelsNawa Raj Pokhrel0Keshab Raj Dahal1Ramchandra Rimal2Hum Nath Bhandari3Binod Rimal4Department of Physics and Computer Science, Xavier University of Louisiana, New Orleans, LA 70125, USADepartment of Mathematics, State University of New York Cortland, Cortland, NY 13045, USADepartment of Mathematical Sciences, Middle Tennessee State University, Murfreesboro, TN 37132, USADepartment of Mathematics and Physics, Roger Williams University, Bristol, RI 02809, USADepartment of Mathematics, The University of Tampa, Tampa, FL 33606, USADeep-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’s 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.https://www.mdpi.com/2674-113X/3/1/3deep learningsequential data modelingtime seriesLSTMGRUCNN |
| spellingShingle | Nawa Raj Pokhrel Keshab Raj Dahal Ramchandra Rimal Hum Nath Bhandari Binod Rimal Deep-SDM: A Unified Computational Framework for Sequential Data Modeling Using Deep Learning Models Software deep learning sequential data modeling time series LSTM GRU CNN |
| title | Deep-SDM: A Unified Computational Framework for Sequential Data Modeling Using Deep Learning Models |
| title_full | Deep-SDM: A Unified Computational Framework for Sequential Data Modeling Using Deep Learning Models |
| title_fullStr | Deep-SDM: A Unified Computational Framework for Sequential Data Modeling Using Deep Learning Models |
| title_full_unstemmed | Deep-SDM: A Unified Computational Framework for Sequential Data Modeling Using Deep Learning Models |
| title_short | Deep-SDM: A Unified Computational Framework for Sequential Data Modeling Using Deep Learning Models |
| title_sort | deep sdm a unified computational framework for sequential data modeling using deep learning models |
| topic | deep learning sequential data modeling time series LSTM GRU CNN |
| url | https://www.mdpi.com/2674-113X/3/1/3 |
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