Deep learning (DL) plays a significant role in several tasks, especially classification and prediction. Classification tasks can be efficiently achieved via convolutional neural networks (CNN) with a huge dataset, while recurrent neural networks (RNN) can perform prediction tasks due to their ability to remember time series data. In this paper, three models have been proposed to certify the evaluation track for classification and prediction tasks associated with four datasets (two for each task). These models are CNN and RNN, which include two models (Long Short Term Memory (LSTM)) and GRU (Gated Recurrent Unit). Each model is employed to work consequently over the two mentioned tasks to draw a road map of deep learning models for a variety of tasks under the control of a unified architecture for each proposed model.
Abstract:
Viral marketing has become one of the modern strategies adopted by organizations in the marketing of products and services. The idea of viral marketing focuses on the social relations between individuals and groups. As a result of the technological development, most organizations have resorted to using the Internet and its applications and social media to market and promote their products. To reach the largest number of consumers to display their products and services in many ways, including text, audio, visual or video and thus affect the behavior of the consumer.
The problem of the study was the following question (do viral marketing technologies have an impact on consumer behavior?)
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