Week 4 Progress -Applications - Neural Networks in Python

Summary/Overview:

During this week I continued to learn more about neural network algorithms and coding using previously found resources and attempted to understand them better. During this time I also read some resources on several uses of neural networks in various fields including in Geographic Information Science (GIS) applications, healthcare and image recognition. Additionally I created a template for my final product slideshow and began planning out the sections that I would go over in it.


Healthcare Applications:

There are many different applications of neural networks in healthcare fields I explored several from a general perspective. Some of these applications include the creation of diagnostic systems to identify issues, recreating neural systems as shown in the image below, analyzing bichemical samples, analyzing drug trial data to better develop them and the analysis of visual data captured from a wide variety of medical devices.

RevolverDeBlake. (2019). "Study shows that artificial neural networks can be used to drive brain activity". [Image]. Retreived from https://revolverdeblake.com/health-problems/study-shows-that-artificial-neural-networks-can-be-used-to-drive-brain-activity/


GIS Applications:

Neural Networks have a variety of applications in GIS I explored several while researching this week, the first was in general GIS data classification, using neural networks to classify GIS data of various types into different categories such as geological and remotely sensed data. The second GIS application was on using neural networks for land use classifications, this could be very useful as speaking from experience in creating land use classification systems they can be extremely complex and time consuming to create requiring extensive research, testing and revisions that could all be handled by a neural network to a potentially even greater degree of accuracy. The third application was using neural networks to intake geographic data in order to predict earthquake damage and potentially mitigate such damage by preparing based on the output. One of the neural network classified outputs is shown below.

Neural network classifiers for GIS data: improved search ...

GeoComputation99. (1999). "Neural network classifiers for GIS data: improved search strategies". [Map]. Retrieved from http://www.geocomputation.org/1999/093/gc_093.htm


Image Recognition Applications:

One of the most widely explored applications of neural networks is in the field of image recognition, the rough overview of the process is shown below. Based on large datasets of images on whatever the subject is the neural network learns to determine what the image is of and is able to classify these images into categories with a high degree of accuracy. In the example below the neural network is input a picture of a cat and a dog and determines the one picture is a dog and the other is not based on learned characteristics of dogs but this concept has a wide range of further application from recognition of healthcare images as discussed above or the recognition of features in remotely sensed GIS data as mentioned above as well. This technology can also be applied to real world navigation when input as a live feed of images the network sees.

https://lh5.googleusercontent.com/lIrTeaAJnd3hx43lqzNVobN48eDzdm24BRXs9u_OH4LfJqkKDVaDsUcqwI8-CYjdzdmJlvbLhJGjYLLgPf1oCAT3LjlYz-MWjbqyRW5dEJav2x2QQzegqOJCbGPZk3_fTKMMLObx 

Altexsoft. (2019). "Image Recognition with Deep Neural Networks and its Use Cases" [GIF]. Retrieved from https://www.altexsoft.com/blog/image-recognition-neural-networks-use-cases/

 

Template:

As mentioned above I also began working on my final product slideshow by creating a template and planning out the various sections that I will fill in in future weeks as I consolidate my final product.


Next Steps / Future steps: 

-I will consolidate my knowledge and understanding of python neural networks

-I will attempt to approach my understanding of neural networks processes from a teaching perspective so that I can explain it better in my final product.

-I will continue to work on my final product filling in the information, finding diagrams and citing sources, etc.

 

Resources:

 

Reused Resources:

-https://www.askpython.com/python/examples/neural-networks

-https://towardsdatascience.com/inroduction-to-neural-networks-in-python-7e0b422e6c24

-https://helloml.org/a-simple-guide-to-build-a-neural-network-from-scratch-in-python/

-https://towardsdatascience.com/how-to-build-your-own-neural-network-from-scratch-in-python-68998a08e4f6

-https://realpython.com/python-ai-neural-network/

 

 Application Resources:

-https://revolverdeblake.com/health-problems/study-shows-that-artificial-neural-networks-can-be-used-to-drive-brain-activity/

-http://www.geocomputation.org/1999/093/gc_093.htm

-https://www.sciencedirect.com/science/article/pii/S0198971501000151

-https://ieeexplore.ieee.org/document/5523070

-https://royaljay.com/healthcare/neural-networks-in-healthcare/

-https://www.altexsoft.com/blog/image-recognition-neural-networks-use-cases/


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