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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Published on: November 30, 2022

Time Sequence Deep Learning Model for Ubiquitous Tabular Data with Unique 3D Tensors Manipulation.

Adaleta Gicic¹, Dženana Đonko¹, Abdulhamit Subasi^2,3

¹Faculty of Electrical Engineering, University of Sarajevo, 71000 Sarajevo, Bosnia and Herzegovina.

Entropy (Basel, Switzerland)

|September 27, 2024

Summary

This summary is machine-generated.

This study introduces a novel deep learning (DL) method using Stacked Bidirectional Long Short-Term Memory (LSTM) networks and 3D tensor modeling for tabular data. The approach achieves competitive performance, even with small datasets, offering fast training for large ones.

Keywords:

Stacked Bidirectional LSTM deep learning deep neural network architectures prediction with tabular data tabular datasets time sequence forecasting algorithms

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Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Deep learning (DL) models show limitations with tabular data compared to traditional methods.
Tabular data's size, structure, and context challenge conventional DL applications.
Existing DL algorithms often underperform on tabular datasets.

Purpose of the Study:

To propose a novel deep learning method for tabular data analysis.
To leverage Stacked Bidirectional Long Short-Term Memory (LSTM) networks for pattern discovery.
To integrate customized 3D tensor modeling for enhanced neural network input.

Main Methods:

Development of a deep learning model utilizing Stacked Bidirectional LSTM.
Implementation of customized 3D tensor modeling for tabular data representation.
Empirical validation on six diverse, publicly available datasets.

Main Results:

The proposed DL model demonstrates satisfactory performance on tabular data.
The model effectively competes with traditional machine learning algorithms for tabular data.
Fast model training is achieved, even for large datasets.
Exceptional predictive results are obtained even with very small datasets.

Conclusions:

Deep learning, specifically Stacked Bidirectional LSTM with 3D tensor modeling, is effective for tabular data.
The proposed method overcomes limitations of DL on tabular datasets.
This approach offers a simple, fast, and high-performing solution for tabular data modeling.