VIDEO ASSIGNMENT INTELLIGENT SYSTEM GROUP27

By s65989 | Updated: Jan. 5, 2025, 3:17 p.m.

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Discover the transformative potential of deep learning in wildlife conservation! This video presents the Wildlife Monitoring System Using Deep Neural Networks, which automates animal detection and species identification using advanced CNN architectures like Lite AlexNet, VGG-16, and ResNet-50. Learn how this system enhances efficiency, improves accuracy, reduces costs, and supports citizen science efforts for large-scale conservation. Perfect for those passionate about ecological research and AI innovation.

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VIDEO ASSIGNMENT:
WILDLIFE MONITORING SYSTEM USING DEEP NEURAL NETWORKS
AQILAH HUSNA BINTI ABDUL HANNAN
S65989
SITI ASIAH BINTI MOHD ASMADI
S67624
NURJEHA BINTI NOOR AZIZAN
S65415

Hello!
Wildlife Monitoring 
System Using Deep 
Neural Networks
This video is about 
Wildlife monitoring is crucial for conservation but it relies more on manual processes that are time-consuming.
Camera traps have improved this, but there are still challenges in analyzing large datasets.
This system leverages deep learning, specifically Convolutional Neural Networks (CNNs), 
to automate animal detection and species identification, enabling faster and more accurate analysis.
Methodolody 
The system perform two tasks:
Wildlife Identification
Wildlife Detection
Models Used:
Lite AlexNet: Efficient for simple tasks.
ResNet-50: Handles deeper networks with high accuracy.
VGG-16: Effective for classification.
Images are resized to 224×224 pixels and normalized.
Augmentation like shearing and zooming diversifies training data.
Processing:
Training:
Models are trained using Keras and TensorFlow, with ImageNet pre-trained models fine-tuned for better performance.
Efficiency: Processes thousands of images faster than manual methods.

Accuracy: Identifies species reliably, even in poor-quality images.
Benefits:
Cost-Effective: Reduces labor and equipment costs.
Citizen Science: Attract volunteers or researches, enabling large-scale data collection.
Benefits:
THANK YOU FOR WATCHING!

VIDEO ASSIGNMENT INTELLIGENT SYSTEM GROUP27

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