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In this research paper, a comparative analysis of various image enhancements in the spatial domain techniques was performed based on three-dimensional image quality statistics such as mean squared error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity for measuring the image quality (SSIM). The conditions for choosing the best method are low mean square error (MSE) and high peak signal-to-noise ratio as well as the structural similarity for measuring the image quality. The pre-processed image was subjected to a classification task using a stepwise version of pre-trained deep convolutional neural networks. The problem of driver drowsiness caused by fatigue, lack of sleep, medication, etc. will have to be solved by improving the efficiency of driver drowsiness detection. Many traffic accidents are largely caused by drowsy drivers. Driver fatigue and distraction can cause a lack of alertness, which can lead to driver inattention. In this paper, we proposed a new deep-learning technique for driver drowsiness detection. Two methods using convolutional deep neural network and GoogleNet transfer learning are compared to achieve a better accuracy of 99%.

Physically, information carriers are encountered in two occurrences, either in native form as physical structures, or in arbitrarily coded, symbolic form such as signal systems or sequences of signs. The symbolic form may rigorously be associated with the existence of life. In contrast, structural information may be present in various physical processes or structures independent of life. The self-organised emergence of symbolic information from structural information may be called ritualisation. A century ago, Julian Huxley had introduced this term in behavioural biology. Subsequently, this evolutionary key process of the emergence of animal and social communication was studied in depth by Konrad Lorenz, Günter Tembrock and other ethologists. Ritualisation exhibits typical features of kinetic phase transitions of the 2^nd kind. From a more general viewpoint, the origin of life, the appearance of human languages and the emergence of human social categories such as money can also be understood as ritualisation transitions. Occurring at some stage of evolutionary history, these transitions have in common that after the crossover, arbitrary symbols are issued and recognised by information-processing devices, by transmitters and receivers in the sense of Shannon’s information theory. In this paper, general properties of the ritualisation transition and the related code symmetry are described. These properties are demonstrated by tutorial examples of very different such transitions in natural, social and technical evolution, reviewed from the perspective of the emergence of symbolic information and its structural historicity.