A movie poster image is one of the important media in the filmmaking process, providing valuable information about the movie, such as movie titles, characters, and genres. Identifying a movie genre from a poster can be a daunting task, as it can relate to multiple genres. To solve this problem, this paper uses a deep feedforward neural network to classify movie genres from movie poster images. In this regard, we used and trained a state-of-the-art InceptionV3 deep neural network. The network is trained on our dataset consisting of 36,423 movie poster images taken from the IMDB website, which is categorized into 28 genres. The model predicts the top three classes with the highest probability of a particular movie poster.

Evaluation of flow time and service process of a queueing systems is a very special and powerful concept to analyze the flow time of any arriving data packet at any point of the system. In this paper, we construct and demonstrate the flow process and service process transition diagram to determine the flow time of a data packet in an early arrival finite capacity discrete-time queueing system where arriving data packets are hypogeometrically distributed. We compute the probabilities of all starting states where a data packet can possibly enters and its flow process begins. We validated the obtained analytical results, for probability mass function of each starting state and total probability mass function with simulation results.