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Steganography is one of the oldest methods for securely sending and transferring secret information between two people without raising suspicion. Recently, the use of Artificial Intelligence (AI) has become simpler and more widely used. Since the emergence of natural language processing (NLP), building language models using deep learning has become more. Furthermore, because of the importance of concealing secret information in delivered messages, Artificial Intelligence theories along with Natural Language Processing algorithms were employed to conceal secret information within the text cover. The Arabic language was used because of its large number of words, vocabulary, and linguistic meanings, and its most significant feature is Arabic poetry. This study discovered a new way to hide secret data inside newly formulated Arabic poetry based on previous Arabic poetic texts and a database of a number of Arab poets from the ancient and modern eras using Artificial Intelligence and Long Short-Term Memory (LSTM) theories to increase storage capacity by 45 percent. The linguistic accuracy and volume of secret data hidden within the formulated poetry were increased using a Baudot Code algorithm, where the secret data is hidden at the level of letters rather than words, and the linguistic accuracy and volume of secret data hidden within the formulated poetry were increased to eliminate the drawbacks found in previous studies. 7. Hybrid Algorithms: In this section, two or more meth-341 ods of the previous six methods are combined or mixed 342 together to devise a new method to hide the data inside 343 the cover text. Any new method is just the consequence 344 of combining two or more of the six procedures listed 345 above, which are regarded as the foundation for the 346 concealment process in the Persian and Arabic lan-347 guages [30].348 7.1. In this study, by converting the secret text into a 349 binary code and dividing it into an odd matrix and 350 an even matrix, theIr is paired with the kashida 351 to extract a difficult-to-detect concealment path. 352 The odd matrix elements are hidden by using 353 the decoration. The elements of the even matrix 354 are hidden through the kashida. When the odd 355 matrix elements are hiddI decor Fatha ( ) is added 356 if the bit to be hidden is 1, and it is canceled 357 from the letter if the bit to be hidden is 0. As for 358 the even matrix, one kashida is added when the 359 bit to be hidden carries the value 1 and remains 360 without change when the bit carries the value 0. 361 As shown in tables (12), the data in the odd matrix 362 is concealed first, followed by the data in the even 363 matrix [31], [32].364 7.2. Some researchers have used pairing styles in con-365 cealment methods to find the best way to protect 366 hidden cover texts. They combined the Pseudo 367 Connection letter and letter dots to generate a text 368 cover that is well camouflaged. After converting369the secret text to the binary code, the bit to be 370 hidden is tested. If it carries the value 1, a pseudo...

Metaheuristic optimization algorithms are a relatively new class of optimization algorithms that are widely used for difficult optimization problems in which classic methods cannot be applied and are considered as known and very broad methods for crucial optimization problems. In this study, a new metaheuristic optimization algorithm is presented, the main idea of which is inspired by models in kinematics. This algorithm obtains better results compared to other optimization algorithms in this field and is able to explore new paths in its search for desirable points. Hence, after introducing the projectiles optimization (PRO) algorithm, in the first experiment, it is evaluated by the determined test functions of the IEEE congress on evolutionary computation (CEC) and compared with the known and powerful algorithms of this field. In the second try out, the performance of the PRO algorithm is measured in two practical applications, one for the training of the multi-layer perceptron (MLP) neural networks and the other for pattern recognition by Gaussian mixture modeling (GMM). The results of these comparisons are presented in various tables and figures. Based on the presented results, the accuracy and performance of the PRO algorithm are much higher than other existing methods.

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