Human face is a clear indicator of the fatigue and tiredness experienced by an individual. There may be many cues that can be derived through the analysis of facial parameters which clearly indicate the tiredness. Most of us feel the fatigue and tiredness but at the same time ignore it due to want of time to complete a task or necessity to complete an important work. However there can be instances when this fatigue may turn fatal. Hence an automated system that can easily predict the fatigue becomes the need of the hour. This work is focussed towards developing an automated application that can detect fatigue by analysing various facial parameters. This work uses Computer Vision and has been implemented using Python programming. The result shows good prediction accuracy when it comes to fatigue prediction using facial parameters.

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Abstract-In current day scenario, the need to protect information has become very important and hence the need for cryptographic algorithms is high. Here, we extend the parallel key encryption algorithm and bring out its full potential by implementing the various cryptographic modes such as cipher block chaining and interleaved cipher block chaining where commendable increase in efficiency and reduction in encryption and decryption time can be seen. We have also considerably increased the key size by using 1024 bit and 2048 bit keys for the algorithmic implementation and in CBC and interleaved CBC execution. Our practical analysis has brought to front the salient features of the parallel key encryption algorithm and its ability to provide enhanced data protection when using a larger key size along with its randomness property. In theoretical analysis, it can be shown that remarkable reduction in encryption and decryption time of cryptographic systems is achieved and an enhanced strength to the system against brute force attacks is achieved. Furthermore, PCA can be extended for different cryptographic modes, using varying key size and number of keys used in the process.