Object and goal

• This is our first work utilizing a transformation method that converts numerical data into images. In this research, we focused on exploring the feasibility and forecasting efficiency of NISE using YOLO. The results are presented in our published paper. Performance speed was not a primary consideration in this initial version, as our main goal was to demonstrate the concept. The processing speed depends largely on the YOLO model used, as noted in our discussion. We are currently working on improvements for future versions to enhance efficiency.
• In the specific case of a more efficient and faster prediction implementation that we have not reported in this paper, after you run strip_pre.py you can go to our implementation on Google, which uses fewer epochs and is faster only for all types of numeric forecasting. In the case of text or other features that are not the first one, we will develop it in the future.

Project Resources

Download Links

• Paper Available:
  Read our published paper on Springer
  If you find this work helpful, please consider citing our paper.
• View Source Code on GitHub The source code is now available on GitHub. The project was developed and tested on Windows, but with small modifications, it should also be possible to run it on Linux.
  How to Run:
  Start by running strip_pre.py. The code contains step-by-step comments (steps 0 to 3) explaining how to create the NISE.
  Note: The requirements file (python setup package > requirements.txt) is not yet prepared. However, all libraries used are standard and can be installed via pip.
  We apologize for any inconvenience.
  Required Libraries (We use 2 files to generate the NISE image: 1.strip_pre.py and 2.lib.py):

  • At file: strip_pre.py
  • os
  • json
  • At file: lib.py
  • statistics
  • cv2 (pip install opencv-python)
  • numpy
  • PIL (Pillow)
  • sklearn (decomposition, preprocessing, metrics)
  • shutil
  • pathlib
  • glob
  • collections.Counter
  • math
  • pandas
  • matplotlib

  strip_pre.py will generate 5 k-fold image sets in the folders for 3 algorithms: output_k_fold, output_k_fold2, output_k_fold3 which produce ready to use for training, validating and testing for YOLOv5 format.
• Download Weights and Training Files from Google Drive
  The trained weights, image k-folds, datasets, and related experimental files for every dataset and the 3 algorithms following the paper are available in this Google Drive folder.
• Download Dataset from Google Drive
  The datasets are from UCI, including wine, iris, student performance, and breast cancer, as cited in our paper.
• Future Work
  • We plan to use MobileNet as our base model for improved efficiency and performance.
  • We are currently testing NISE on Google Teachable Machine using the MobileNet architecture which we think in our case it be better performace. It is also look possible and also can be useable in actual work.
  
• References
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