@article{MAKHILLJEAS201914817659,
    title = {Burglar Detection using Deep Learning Techniques},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {14},
    number = {8},
    pages = {2672-2686},
    year = {2019},
    issn = {1816-949x},
    doi = {jeasci.2019.2672.2686},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2019.2672.2686},
    author = {Se,Rabia,Sanam,Ayesha and},
    keywords = {Burglar detection,security,intrusion detection,convolution neural networks,deep neural
networks,human intrusion detection,object detection},
    abstract = {Burglar detection security systems have become a necessity in this age because of the increasing
break-in cases in urban cities thus making these systems essential for residential as well as office usage. This
study investigated how to model an intrusion detection system based on deep learning. Two deep learning
approaches named generic Deep Neural Networks (DNN) and Convolution Neural Networks (CNN) are used
for the training of the dataset. The experimental results showed that CNN approach is more suitable for burglar
detection as it gives high accuracy with a superior performance as compared to the generic DNN approach.
CNN provides a new research method with the improved accuracy of human intrusion detection. Experimental
results found that CNN is compatible to solve classification problems and significantly faster and precise as
compared to traditional object detection methods.}
    }