کمک در انجام پروژه درسی راجب Chaotic ANN

شرح پروژه

انجام کل پروژه مورد نظر نیست. فقط توضیح روش رسیدن به اهداف پروژه کافیست. اهداف پروژه به شرح زیر است.
•Objective 1: demonstrate the failure of a standard, fully connected deep Artificial Neural Network (such as a Multilayer Perceptron with 3 hidden layers of 32 neurons: {4096, 32, 32, 32, 1}) on the detection of wildfire smoke plumes.
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•Dataset: The High Performance Wireless Research and Education Network (HPWREN) provides FIgLib, which is the gold standard for early-stage, long-distance wildfire smoke detection. It contains sequential images from mountaintop cameras in Southern California, capturing wildfires in their initial minutes.
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•How to structure the dataset to break the 3x32 ANN:
–Data Prep: Extract small 64x64 pixel crops from the high-resolution images. Create a binary classification dataset: Patches with early smoke vs. Patches with atmospheric distractors (fog, low clouds, haze, dust).
–The Input Bottleneck: To feed a 64x64 grayscale image patch into your ANN, you must flatten it into a 1D vector of 4,096 inputs.

•Objective 2: Determine an algorithm and schedule for applying chaotic inputs to one of the injection points (see next slide) of the deep learning ANN.
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•Determine how to inject chaotic values into an ANN using Matlab or Python (your choice).
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•Run several experiments to determine if the chaotic injection provides a better classification rate than that obtained by the {4096, 32, 32, 32, 1} DL ANN?

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