Papers

Title: Low Power Anomaly Detection and Notification Systems using Deep Learning
Year of Publication: 2017
Publisher: International Journal of Computer Systems (IJCS)
ISSN: 2394-1065
Series: Volume 04, Number 8, August 2017
Authors: Sheetal Thakkar, Ashok Patel, Tanay Varshney, Farheen Kamal, Saloni Parekh

Citation:

Sheetal Thakkar, Ashok Patel, Tanay Varshney, Farheen Kamal, Saloni Parekh, "Low Power Anomaly Detection and Notification Systems using Deep Learning", In International Journal of Computer Systems (IJCS), pp: 168-171, Volume 4, Issue 11, November 2017. BibTeX

@article{key:article,
	author = {Sheetal Thakkar, Ashok Patel, Tanay Varshney, Farheen Kamal, Saloni Parekh},
	title = {Low Power Anomaly Detection and Notification Systems using Deep Learning},
	journal = {International Journal of Computer Systems (IJCS)},
	year = {2017},
	volume = {4},
	number = {11},
	pages = {168-171},
	month = {November}
	}


Abstract

Considering the advancements in computer vision and deep learning algorithms, with facilitations of open source libraries aiding various applications, a lot of research work is done keeping in mind various problems faced by the society. Such two important aspects are security surveillance and environment wellness. With the use of state of the art machine learning algorithms, such problems can be solved. TensorFlow is one such eminent open source library used for training, classification and identification purposes, deployed to train a model that is able to classify assault objects such as knife, rifle, handgun etc. for security surveillance and raise an immediate alarm on confident identification. Similarly, the same model has been used and deployed on a portable computation chip, in this case, Raspberry Pi, to be installed on a UAV that will take aerial shots of city streets and send geo-location of the area with anomalies such as potholes and unattended garbage heaps. The model trained, identifies the objects with appreciable confidence levels and can be readily scaled for many more such applications pertaining to security surveillance and environment wellness - the two pillars on which national programmes such as Digital India and Swachha Bharat Abhiyan stand.

References

[1] Alex Krixhevsky, Ilya Sutskever, Geoffrey E. Hinton, “ImageNet Classification with Deep Convolution Neural Networks”, Advances in Neural Informamtion Processing Systems 25 (NIPS 2012), Available at: http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf
[2] The 9 Deep Learning Papers you need to know – Article by Adesh Pande Available at: https://adeshpande3.github.io/adeshpande3.github.io/The-9-Deep-Learning-Papers-You-Need-To-Know-About.html
[3] Image Recognition, TensorFlow - https://www.tensorflow.org/tutorials/image_recognition
[4] Neural NetworkArchitectures, Eugenio Culurciello's blog, Available at: https://culurciello.github.io/tech/2016/06/04/nets.html
[5] CNN Benchmarks by jcjohnson, GitHub, Available at: https://github.com/jcjohnson/cnn-benchmarks
[6] Every Day Big Data Statistics, VCloud News -http://www.vcloudnews.com/every-day-big-data-statistics-2-5-quintillion-bytes-of-data-created-daily/
[7] Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning - https://arxiv.org/pdf/1602.07261.pdf
[8] ImageNet Database, Available at: http://image-net.org/download-imageurls
[9] What’s the difference betwwen Artificial Intelligence, Machine Learning and Deep Learning?, Available at https://blogs.nvidia.com/blog/2016/07/29/whats-difference-artificial-intelligence-machine-learning-deep-learning-ai/


Keywords

Deep Learning, CNN, TensorFlow, Inception, Machine Learning, Classification, ImageNet.