Manual segregation of a playlist and annotation of songs, in accordance with the current emotional state of a user, is labor intensive and time consuming. Numerous algorithms have been proposed to automate this process. However the existing algorithms are slow, increase the overall cost of the system by using additional hardware (e.g. EEG systems and sensors) and have less accuracy. This paper presents an algorithm that automates the process of generating an audio playlist, based on the facial expressions of a user, for rendering salvage of time and labor, invested in performing the process manually. The algorithm proposed in this paper aspires to reduce the overall computational time and the cost of the designed system. It also aims at increasing the accuracy of the designed system. The facial expression recognition module of the proposed algorithm is validated by testing the system against user dependent and user independent dataset. Experimental results indicate that the user dependent results give 100% accuracy, while user independent results for joy and surprise are 100 %, but for sad, anger and fear are 84.3 %, 80 % and is 66% respectively. The overall accuracy of the emotion recognition algorithm, for user independent dataset is 86%. In audio, 100 % recognition rates are obtained for sad, sad-anger and joy-anger but for joy and anger, recognition rates obtained are 95.4% and 90 % respectively. The overall accuracy of the audio emotion recognition algorithm is 98%. Implementation and testing of the proposed algorithm is carried out using an inbuilt camera. Hence, the proposed algorithm reduces the overall cost of the system successfully. Also, on average, the proposed algorithm takes 1.10 sec to generate a playlist based on facial expression. Thus, it yields better performance, in terms of computational time, as compared to the algorithms in the existing literature.

The work presented in this paper describes a generic genetic algorithm called DUREHA's (Dominance, Universal stochastic sampling and Rank-based Emulation of a Heuristic Algorithm) Algorithm for cryptanalysis of classical ciphers. The underlying objective of this paper is to automate the process of cryptanalysis in order to render salvage of time, and resources available, preserve population diversity, minimize the convergence rate and control mutation rates. While numerous algorithms have been proposed to automate this process for variegated ciphers, these approaches are yet isolated from each other. The existence of a generic algorithm to cryptanalyze any type of cipher is yet not true. The algorithm proposed in this paper aspires to address such issues. The implementation and experimentation of the proposed algorithm is accomplished using three types of classical ciphers namely monosubstitution, poly-substitution and columnar transposition. The theoretical validation and experimental results indicate that the proposed algorithm is able to decrypt the ciphers by reclaiming80.71% ,87.31%and 77.66% of letters in correct position in Mono-substitution, Columnar Transposition and Vignere cipher respectively. It is also able to distinguish between the three types of ciphers correctly and is able to correctly control the mutation andconvergence rates and preserve population diversity.