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Abstract-In recent years the workings which requires human-machine interaction such as speech recognition, emotion recognition from speech recognition is increasing. Not only the speech recognition also the features during the conversation is studied like melody, emotion, pitch, emphasis. It has been proven with the research that it can be reached meaningful results using prosodic features of speech. In this paper we performed pre-processing necessary for emotion recognition from speech data. We extract features from speech signal. To recognize emotion it has been extracted Mel Frequency Cepstral Coefficients (MFCC) from the signals. And we classified with k-NN algorithm.

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Abstract-Recently a new evolutionary algorithm CuckooOptimization Algorithm (COA), which is inspired by the life of Cuckoo has been proposed. Like other nature-inspired algorithms, COA is also a population-based method and uses a population of solutions to proceed to the global solution. In this study, Modified Cuckoo Optimization Algorithm (MCOA) has been presented. This algorithm has been applied to two constrained continuous engineering optimization problems. The results indicate that the MCOA is a powerful optimization technique that may yield better solutions to engineering problems.Index Terms-Cuckoo optimization algorithm (COA), evolutionary algorithms, constraint optimization.

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In this study a total of 464 carrots were taken from Kaşınhanı, where the most carrots in Turkey are produced. The radiuses with an interval of 5 cm, length and volume of each carrot were measured and recorded. Three different Artificial Neural Network models: BP, LM and PUNN were designed for predicting the carrot volume. To assess the success of the system, statistical measures such as Root Mean Squared Error, Mean Absolute Error and R 2 were used. The results showed that all three methods are successful in this problem, while LM and PUNN seems bit better.