Deep Learning Model for Identifying Snakes by Using Snakes’ Bite Marks
Objective
Identifying snakes by using their bite marks may help the doctors to diagnose the victim with proper anti venoms for saving patients. Hence a study was done on processing images to classify them as different families of snakes using CNN (Convolution Neural Network) model in Deep Learning techniques.
Abstract
In this work, we will identify the snake either it is a venomous or non-venomous and also particular species that which belongs to, is performed using Deep Learning technique CNN (Convolution Neural Network) model. With the help of different snakes and their bite mark images CNN classify will them as venomous or non-venomous, by processing venomous snake bite marks images it can find the venomous snakes family.
This helps doctors to diagnose the victim in a lesser time span with proper anti venoms after a snake bite.
Keywords: Snake bites, Deep Learning, CNN Model, Snakes Classification,
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
Software & Hardware Requirements:
Software: Matlab 2018a or above
Hardware:
Operating Systems:
- Windows 10
- Windows 7 Service Pack 1
- Windows Server 2019
- Windows Server 2016
Processors:
Minimum: Any Intel or AMD x86-64 processor
Recommended: Any Intel or AMD x86-64 processor with four logical cores and AVX2 instruction set support
Disk:
Minimum: 2.9 GB of HDD space for MATLAB only, 5-8 GB for a typical installation
Recommended: An SSD is recommended a full installation of all Math Works products may take up to 29 GB of disk space
RAM:
Minimum: 4 GB
Recommended: 8 GB
Learning Outcomes
- Introduction to Matlab
- What is EISPACK & LINPACK
- How to start with MATLAB
- About Matlab language
- Matlab coding skills
- About tools & libraries
- Application Program Interface in Matlab
- About Matlab desktop
- How to use Matlab editor to create M-Files
- Features of Matlab
- Basics on Matlab
- What is an Image/pixel?
- About image formats
- Introduction to Image Processing
- How digital image is formed
- Importing the image via image acquisition tools
- Analyzing and manipulation of image.
- Phases of image processing:
- Acquisition
- Image enhancement
- Image restoration
- Color image processing
- Image compression
- Morphological processing
- Segmentation etc.,
- About Artificial Intelligence (AI)
- About Machine Learning
- About Deep Learning
- About layers in AI (input, hidden and output layers)
- Building AI (ANN/CNN) architecture using Matlab
- We will be able to know what’s the term “Training” means in Artificial Intelligence
- About requirements that can influence the AI training process:
- Data
- Training data
- Validation data
- Testing data
- Hardware requirements to train network
- How to identify the object using AI
- How to extend our work to another real time applications
- Project development Skills:
- Problem analyzing skills
- Problem-solving skills
- Creativity and imaginary skills
- Programming skills
- Deployment
- Testing skills
- Debugging skills
- Project presentation skills
- Thesis writing skills
Paper Publishing
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