Keywords can present the main concepts of the text without human intervention according to the model. Keywords are important vocabulary words that describe the text and play a very important role in accurate and fast understanding of the content. The purpose of extracting keywords is to identify the subject of the text and the main content of the text in the shortest time. Keyword extraction plays an important role in the fields of text summarization, document labeling, information retrieval, and subject extraction from text. For example, summarizing the contents of large texts into smaller texts is difficult, but having keywords in the text can make you aware of the topics in the text. Identifying keywords from the text with common methods is time-consuming and costly. Keyword extraction methods can be classified into two types with observer and without observer. In general, the process of extracting keywords can be explained in such a way that first the text is converted into smaller units called the word, then the redundant words are removed and the remaining words are weighted, then the keywords are selected from these words. Our proposed method in this paper for identifying keywords is a method with observer. In this paper, we first calculate the word correlation matrix per document using a feed forward neural network and word2vec algorithm. Then, using the correlation matrix and a limited initial list of keywords, we extract the closest words in terms of similarity in the form of the list of nearest neighbors. Next we sort the last list in descending format, and select different percentages of words from the beginning of the list, and repeat the process of learning the neural network 10 times for each percentage and creating a correlation matrix and extracting the list of closest neighbors. Finally, we calculate the average accuracy, recall, and F-measure. We continue to do this until we get the best results in the evaluation, the results show that for the largest selection of 40% of the words from the beginning of the list of closest neighbors, the acceptable results are obtained. The algorithm has been tested on corpus with 800 news items that have been manually extracted by keywords, and laboratory results show that the accuracy of the suggested method will be 78%.

With the growing number of Persian electronic documents and texts, the use of quick and inexpensive methods to access desired texts from the extensive collection of these documents becomes more important. One of the effective techniques to achieve this goal is the extraction of the keywords which represent the main concept of the text. For this purpose, the frequency of a word in the text can not be a proper indication of its significance and its crucial role. Also, most of the keyword extraction methods ignore the concept and semantic of the text. On the other hand, the unstructured nature of new texts in news and electronic documents makes it difficult to extract these words. In this paper, an automated, unsupervised method for keywords extraction in the Persian language that does not have a proper structure is proposed. This method not only takes into account the probability of occurrence of a word and its frequency in the text, but it also understands the concept and semantic of the text by learning word2vec model on the text. In the proposed method, which is a combination of statistical and machine learning methods, after learning word2vec on the text, the words that have the smallest distance with other words are extracted. Then, a statistical equation is proposed to calculate the score of each extracted word using co-occurence and frequency. Finally, words which have the highest scores are selected as the keywords. The evaluations indicate that the efficiency of the method by the F-measure is 53. 92% which is 11% superior to other methods.

Word embeddings (WE) have received much attention recently as word to numeric vectors architec-ture for all text processing approaches and has been a great asset for a large variety of NLP tasks. Most of text processing task tried to convert text components like sentences to numeric matrix to apply their processing algorithms. But the most important problems in all word vector-based text processing approaches are di erent sentences size and as a result, di erent dimension of sentences matrices. In this paper, we suggest an e cient but simple statistical method to convert text sen-tences into equal dimension and normalized matrices Proposed method aims to combines three most e cient methods (averaging based, most likely n-grams, and words mover distance) to use their advantages and reduce their constraints. The unique size resulting matrix does not depend on lan-guage, Subject and scope of the text and words semantic concepts. Our results demonstrate that normalized matrices capture complementary aspects of most text processing tasks such as coherence evaluation, text summarization, text classi cation, automatic essay scoring, and question answering.

This study focuses on the investigation of intelligent form-finding and vibration analysis of a triangular polyhedral tensegrity that is enclosed within a sphere and subjected to external loads. The nonlinear dynamic equations of the system are derived using the Lagrangian approach and the finite element method. The proposed form-finding approach, which is based on a basic genetic algorithm, can determine regular or irregular tensegrity shapes without dimensional constraints. Stable tensegrity structures are generated from random configurations and based on defined constraints (nodes located on the sphere, parallelism, and area of upper and lower surfaces), and shape finding is performed using the fitness function of the genetic algorithm and multi-objective optimization goals. The genetic algorithm's efficacy in determining the shape of structures with unpredictable configurations is evaluated in two distinct scenarios: one involving a known connection matrix and the other involving fixed or random member positions (struts and cables). The shapes obtained from the algorithm suggested in this study are validated using the force density approach, and their vibration characteristics are examined. The findings of the comparative study demonstrate the efficacy of the proposed methodology in determining the vibrational behavior of tensegrity structures through the utilization of intelligent shape seeking techniques.

Increasing population and food demand, disproportionate cultivation and annual production of various agricultural products with market needs and low productivity of the agricultural sector and the loss of water and soil resources have made it necessary to determine and implement the country's optimal cropping pattern. In this study, due to the limitations and problems of classical methods in order to reduce processing time and improve the quality of solutions, the Multi-Objective Chaotic Particle Swarm Optimization was used to determine the optimal cultivation pattern of Sistan plain in optimal conditions and deficit irrigation. The results of the Multi-Objective Chaotic Particle Swarm Optimization for the dominant cultures in the region showed that the current cropping pattern of the region is not optimal and with the implementation of the proposed model, the profit per unit area under cultivation will increase. The results of application of deficit irrigation during different growing periods of wheat, barley, alfalfa, sorghum, watermelon and grapes showed that applying deficit irrigation in this plain is not a good strategy and therefore only a full irrigation strategy is recommended. The results of sensitivity analysis of the model showed that at low prices, farmers reaction is less and at higher prices more reaction to price changes and with increasing prices, the program efficiency is lower.

One of the basic topics in hydrological and river engineering studies is flood routing.Flood flooding is common in multi-tributary rivers and rivers without intermediate basin statistics. Therefore, to achieve the determination of slopes and cross-sections in all sections of the river, the Muskingum hydrological model is a useful method that helps to save information on the depth and flow of the flood at any time by saving time and money. To specify. In this study, the nonlinear parameters of the new Muskingum model are optimized based on the fly algorithm (MA). In this non-linear model of Muskingum, which has eight parameters, the recovery coefficient γ is used, which has more or less values ​​than the number of peaks discharged in the output hydrograph.To evaluate the performance of Muskingum's new nonlinear model with the new MA algorithm, the Wilson and Weisman-Lewis case study has been used by many previous researchers for validation.The results of the MA algorithm for Wilson and Weissman-Lewis rivers show the minimization of the residual squares (SSQ) as the objective function, which is 3.21 for the Wilson River and 68722 for the Weissman River. The results of this study showed that the proposed model has high accuracy in estimating the output discharge values.

Discourse coherence modeling evaluation becomes a critical but challenging task for all content analysis tasks in Natural Language Processing subfields, such as text summarization, question answering, text generation and machine translation. Existing methods like entitybased and graph-based models are engaging in semantic and linguistic concepts of a text. It means that the problem cannot be solved very well and these methods are only very limited to available word co-occurrence information in the sequential sentences within a short part of a text. One of the greatest challenges of the above methods is their limitation in long documents coherence evaluation and being suitable for documents with low number of sentences. Our proposed method focuses on both local and global coherence. It can also assess the local topic integrity of text at the paragraph level regardless of word meaning and handcrafted rules. The global coherence in the proposed method is evaluated by sequence paragraph dependency. According to the derived results in word embeddings, by applying statistical approaches, the presented method incorporates the external word correlation knowledge into short and long stories to assess both local and global coherence, simultaneously. Using the effect of combined word2vec vectors and most likely n-grams, we show that our proposed method is independent of the language and its semantic concepts. The derived results indicate that the proposed method offers the higher accuracy with respect to the other algorithms, in long documents with a high number of sentences.

This article investigates the problem of simultaneous attitude and vibration control of a flexible spacecraft to perform high precision attitude maneuvers and reduce vibrations caused by the flexible panel excitations in the presence of external disturbances, system uncertainties, and actuator faults. Adaptive integral sliding mode control is used in conjunction with an attitude actuator fault iterative learning observer (based on sliding mode) to develop an active fault tolerant algorithm considering rigid-flexible body dynamic interactions. The discontinuous structure of fault-tolerant control led to discontinuous commands in the control signal, resulting in chattering. This issue was resolved by introducing an adaptive rule for the sliding surface. Furthermore, the utilization of the sign function in the iterative learning observer for estimating actuator faults has not only enhanced its robustness to external disturbances through a straightforward design, but has also led to a decrease in computing workload. The strain rate feedback control algorithm has been employed with the use of piezoelectric sensor/actuator patches to minimize residual vibrations caused by rigid-flexible body dynamic interactions and the effect of attitude actuator faults. Lyapunov's law ensures finite-time overall system stability even with fully coupled rigid-flexible nonlinear dynamics. Numerical simulations demonstrate the performance and advantages of the proposed system compared to other conventional approaches.