The document details Yvonne K. Matos's project within the Chipy mentorship program, focusing on developing a TSA screening algorithm to reduce false alarm rates at airport checkpoints using deep learning techniques. It outlines the data science pipeline involved, including data cleaning and preprocessing, along with the deployment of convolutional neural networks for classification. The project anticipates challenges related to Python programming and cloud computing while highlighting the achieved model accuracies for different body zones.

1. Building a Better TSA Screening Algorithm:
a ChiPy Mentorship Project
Yvonne K. Matos
ChiPy Meeting, December 14, 2017
Photo: Benoit Tessier/Reuters

2. My Goals, ChiPy Mentorship Program
1. Get more experience with Python
2. Develop deep learning algorithms
3. Work with large datasets and cloud computing
4. Get hired as a data scientist!
Data Science Pipeline
Data science is 80% cleaning and preprocessing, 20% modeling
Business
Question
Data
Question
Data
Collection
Data
Loading
Data
Cleaning
PreprocessingModelingValidation
Data
Answer
Business
Answer
Exploratory
Analysis

3. TSA Passenger Algorithm Screening Challenge
Problem: High false alarm rates create bottlenecks at airport checkpoints.
Challenge: Create an algorithm with a lower false alarm rate using a dataset of scan
images with simulated threats
Business
Question
Data
Question
Data
Collection
Data
Loading
Data
Cleaning
PreprocessingModelingValidation
Data
Answer
Business
Answer
Exploratory
Analysis

4. Anticipated Challenges
• First Python project
• Huge files = long run times
• Enormous scope
– Full training in cloud = $$$$$
Plan of attack
• Begin small
• Scale up locally
• Run in the cloud

5. Exploration: Visualizing the Images
(n, 512, 660, 16)
Raw data for lowest res 10 MB image: 4D array
• 3D images
• 1,147 total
• 3TB dataset
(n, 512, 512, 660)
Higher res 330 MB image:
Samples
Dimensions
Channels

6. Start with two body zones
17 body zones * 1,147 images = 19,499 potential threats
Zone 17
• 1052 non-threat
• 95 threat (8.3%)
Zone 5
• 1041 non-threat
• 106 threat (9.2%)

7. Preprocessing:
3D to 2D Classification Problem
16 channels
(n, 512, 660, 16)
Raw data 10 MB image: 4D array
Zone 5 cropped
Zone 17 cropped
Crop images and save to PNG format

8. Developing Models: Convolutional Neural Network
classifier = Sequential()
classifier.add(Convolution2D(32, (3, 3), input_shape=(250, 250, 3), activation='relu'))
classifier.add(MaxPooling2D(pool_size=(2, 2)))
classifier.add(Convolution2D(32, (3, 3), activation='relu'))
classifier.add(MaxPooling2D(pool_size=(2, 2)))
classifier.add(Convolution2D(64, (3, 3), activation='relu'))
classifier.add(MaxPooling2D(pool_size=(2, 2)))
classifier.add(Flatten())
classifier.add(Dense(units=128, activation='relu'))
classifier.add(Dense(units=256, activation='relu'))
classifier.add(Dense(units=1, activation='sigmoid'))
classifier.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
from keras.preprocessing.image import ImageDataGenerator
train_datagen = ImageDataGenerator(rescale=1./255, shear_range=0.2,
zoom_range=0.2, horizontal_flip=True)
test_datagen = ImageDataGenerator(rescale=1./255)
training_set = train_datagen.flow_from_directory('C:PathZ17_training_png',
target_size=(250, 250), batch_size=32, class_mode='binary')
test_set = test_datagen.flow_from_directory('C:PathZ17_test_png',
target_size=(250, 250), batch_size=32, class_mode='binary')
classifier.fit_generator(training_set, steps_per_epoch=917, epochs=10,
validation_data=test_set, validation_steps=230)
classifier.save('C:PathZ17_classifier.h5')
Convolutional Layers
Fully Connected Layers
Image Augmentation

9. Preliminary Pipeline Zone 17 Model
Accuracy: 95%
Precision: 67%
Recall: 86%
Zone 5 Model
Accuracy: 78%
Precision: 27%
Recall: 77%
1 row = 1 body scan image
Body scan
image
Crop, PNG Model
Prediction
Prediction
If
Pred. threat
FLAG!!

10. Thanks!
• Thanks for coming tonight
• ChiPy mentorship program
• mHUB for hosting