Text mining aims to understand texts correctly by utilising several phases to collect those features of Arabic words which are valuable and important to the applications mentioned above in making a correct decision. The technology then builds a strong system that relies on AI techniques, such as neural networks, to collect words in accordance with those features. An ANN is a collection of connected nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons. An artificial neuron is one that receives a signal then processes it and can signal to neurons connected to it. The current study is concerned with building a system for analysing words in the Arabic language. This system can be included in any application to address the Arabic language, becoming part of it. The system generates strings for all names and pronouns appearing in the entered text and depends mainly on the automatic assembly of a set features by using neural networks. We implemented the system, with its two phases, on the documents in succession. The results were encouraging, ranging between 83% and 96%.
AI-based treatments have shown promise in a variety of fields, particularly those directly connected to human health. Some AI processors are used to categorize and distinguish groupings and patterns, while others are used to forecast future values based on data from previous study and the environment in which that data was employed. An artificial neural network that employs radial basis functions as activation functions is known as a radial basis function network. The radial basis functions input and the neural parameters are combined linearly to produce the network output. There are several applications for radial-based functional networks, such as function approximation, classification, time series prediction, and system control. In this paper, the RBF network will be used in two phases: the data training phase, where the data is trained with the inputs and outputs to obtain new values for the outputs and compare them with the original outputs, and the testing phase, where only the inputs are entered without the outputs and the outputs are evaluated using the RMSE calculation, where it reached a performance of RMSE of 0.018. In the training phase of utilizing the system, the mistake rate was 0.04 and the success rate was 96%; in the testing phase, the error rate was 0.05 and the success rate was 95%.
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