Abstract
Deep learning is a popular machine learning technique, used in variety of applications like autonomous vehicles, aerospace and medical research. Wireless Integrated Network Sensors (WINS) is an architecture that provide continuous monitoring and control of an environment with high accuracy and low power consumption. The amalgamation of deep learning and WINS can provide an effective system to monitor the remote environment. In this paper a system is designed using WINS and Improved Convolution Neural Network (ICNN). This can be used to create a virtual wall across the border of the county. This virtual wall is made up of sensors and cameras that are placed at regular intervals. The images of the suspected intruders are captured and are classified by ICNN. The processed image is stored in the fire base cloud which in turn will alert the country border security authorities by sending a message to them using MQTT protocol. This system is effective and can easily identify the intruders instantaneously. The experimental results shows that the improved convolution algorithm performs image classification better and faster than the traditional convolution algorithms.
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Kanagaraj, K., Swamynathan, S., Karthikeyan, A. (2019). Cloud Enabled Intrusion Detector and Alerter Using Improved Deep Learning Technique. In: Akoglu, L., Ferrara, E., Deivamani, M., Baeza-Yates, R., Yogesh, P. (eds) Advances in Data Science. ICIIT 2018. Communications in Computer and Information Science, vol 941. Springer, Singapore. https://doi.org/10.1007/978-981-13-3582-2_2
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DOI: https://doi.org/10.1007/978-981-13-3582-2_2
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