Nandini Khatod
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Nandini Khatod has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Alejandro Arteaga
Khamai Foundation
1 shared publication - Anika Patel
Data Science Institute, Saint Peter’s University, Jersey City, NJ 07306, USA
1 shared publication - Irina Matijosaitiene
Data Science Institute, Saint Peter’s University, Jersey City, NJ 07306, USA
1 shared publication - Joseph W. Gilkey Jr.
Data Science Institute, Saint Peter’s University, Jersey City, NJ 07306, USA
1 shared publication - Lisa Cheung
Data Science Institute, Saint Peter’s University, Jersey City, NJ 07306, USA
1 shared publication
Latest Publications
- Open Access
Revealing the Unknown: Real-Time Recognition of Galápagos Snake Species Using Deep Learning
Real-time identification of wildlife is an upcoming and promising tool for the preservation of wildlife. In this research project, we aimed to use object detection and image classification for the racer snakes of the Galápagos Islands, Ecuador. The final target of this project was to build an artificial intelligence (AI) platform, in terms of a web or mobile application, which would serve as a real-time decision making and supporting mechanism for the visitors and park rangers of the Galápagos Islands, to correctly identify a snake species from the user’s uploaded image. Using the deep learning and machine learning algorithms and libraries, we modified and successfully implemented four region-based convolutional neural network (R-CNN) architectures (models for image classification): Inception V2, ResNet, MobileNet, and VGG16. Inception V2, ResNet and VGG16 reached an overall accuracy of 75%.
Open AccessRevealing the Unknown: Real-Time Recognition of Galápagos Snake Species Using Deep Learning
Real-time identification of wildlife is an upcoming and promising tool for the preservation of wildlife. In this research project, we aimed to use object detection and image classification for the racer snakes of the Galápagos Islands, Ecuador. The final target of this project was to build an artificial intelligence (AI) platform, in terms of a web or mobile application, which would serve as a real-time decision making and supporting mechanism for the visitors and park rangers of the Galápagos Islands, to correctly identify a snake species from the user’s uploaded image. Using the deep learning and machine learning algorithms and libraries, we modified and successfully implemented four region-based convolutional neural network (R-CNN) architectures (models for image classification): Inception V2, ResNet, MobileNet, and VGG16. Inception V2, ResNet and VGG16 reached an overall accuracy of 75%.