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0. References

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E Burg edited this page Jan 18, 2023 · 1 revision
  1. _Stanford CS230: Deep Learning | Autumn 2018 _

_Introduction to Convolutional Neural Networks for Visual Recognition _ I used the following lectures to familiarize myself with deep learning, and to get an idea on how to create a convolutional neural network.

  1. MIT

I referred to various websites of MIT throughout writing my project report

  1. Towards Datascience

For information on deep learning, particularly on theory.

  1. Gereziher Adhane et al, A Deep Convolutional Neural Network for Classification of Aedes Albopictus Mosquitoes _Detecting Aedes Aegypti Mosquitoes through Audio Classification with Convolutional Neural Networks _

Here I analysed the previous research of various computer scientists, to continue on with vector identification of various genera of insects

  1. Vector-borne diseases (who.int) Centers for Disease Control and Prevention (cdc.gov) _Lymedisease.org _ Lancet Commission on Diagnostics

These websites helped me gain an understanding of vector-borne diseases, as well as their carriers.

  1. ImageNet

For possible application of the dataset and pretrained weights to identify excess objects in the computer

  1. _Scrapy _

I used the scrapy python library and documentation to data mine images to be used to train the neural network

  1. Tensorflow

This is the library I used for writing the code for the neural network in Python. I also used the documentation to understand how the library operates.

  1. Python 3.10.0 documentation

I referred to the official documentation throughout the writing of my code.

  1. _National Geographic _

I researched various insects here, as well as how the population interacts and effects them, and the reason for their rapid decline

  1. Algorithms used a) Early stopping b) ResNet c) Adam optimiser

  2. _Images_

The following image credits are due:

  • who.int
  • cdc.gov
  • istockphoto.org
  • icatcare.org
  • hozuyut.ru
  • ncbi.nlm.nih.gov
  • machinelearningmastery.org
  1. _Image Augmentation Libraries_

The following libraries and documentation of said libraries were referred to throughout the project:

  • os – system management
  • random
  • skipy – image editing
  • PIL – image editing
  • Glob – file recursion
  1. Data miner Libraries

The following libraries and documentation of said libraries were referred to throughout the project:

  • Numpy
  • Scrapy
  • random
  1. Deep Learning Libraries

The following libraries and documentation of said libraries were referred to throughout the project:

  • os
  • sys
  • cv2
  • numpy
  • copy
  • PIL
  • Keras
  • Tensorflow
  • random
  • datetime
  • shutil

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