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A MANET is a type of infrastructure less wireless network that can change locations and configure itself instantaneously. MANETS being mobile, they use wireless connections to connect to different networks. In this paper we analyze the impact of Network size on the performance of OLSR routing protocol using random placement model in MANETs.

Music genre identification is crucial for the classification and recommendation of songs in music applications. Manually labelling songs takes up a significant amount of time. In this paper, we propose a deep learning model to automate the process of genre identification. The process mainly involves three steps: preprocessing the dataset to get a simplified version of each song, building a deep neural network, and training and using it to predict the genre of songs. Input to the model is Mel-frequency Cepstral Coefficient (MFCC) values of the audio files from the GTZAN dataset that consists of 10 different genres. After training, the model produced a result of 60% accuracy. Observing the actual and predicted values, the model seemed to exhibit overfitting. To overcome this, we used dropouts and regularization in the model, followed by early stopping, which gave a final accuracy of 67.5%.