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This paper aims to model a technique that categorizes the texts from huge documents. The progression in internet technologies has raised the count of document accessibility, and thus the documents available online become countless. The text documents comprise of research article, journal papers, newspaper, technical reports and blogs. These large documents are useful and valuable for processing real-time applications. Also, these massive documents are used in several retrieval methods. Text classification plays a vital role in information retrieval technologies and is considered as an active field for processing massive applications. The aim of text classification is to categorize the large-sized documents into different categories on the basis of its contents. There exist numerous methods for performing text-related tasks such as profiling users, sentiment analysis and identification of spams, which is considered as a supervised learning issue and is addressed with text classifier.

At first, the input documents are pre-processed using the stop word removal and stemming technique such that the input is made effective and capable for feature extraction. In the feature extraction process, the features are extracted using the vector space model (VSM) and then, the feature selection is done for selecting the highly relevant features to perform text categorization. Once the features are selected, the text categorization is progressed using the deep belief network (DBN). The training of the DBN is performed using the proposed grasshopper crow optimization algorithm (GCOA) that is the integration of the grasshopper optimization algorithm (GOA) and Crow search algorithm (CSA). Moreover, the hybrid weight bounding model is devised using the proposed GCOA and range degree. Thus, the proposed GCOA + DBN is used for classifying the text documents.

The performance of the proposed technique is evaluated using accuracy, precision and recall is compared with existing techniques such as naive bayes, k-nearest neighbors, support vector machine and deep convolutional neural network (DCNN) and Stochastic Gradient-CAViaR + DCNN. Here, the proposed GCOA + DBN has improved performance with the values of 0.959, 0.959 and 0.96 for precision, recall and accuracy, respectively.

This paper proposes a technique that categorizes the texts from massive sized documents. From the findings, it can be shown that the proposed GCOA-based DBN effectively classifies the text documents.

In this article, a novel hybrid composite patch consisting of unidirectional carbon fiber and glass fiber is considered for repair of the aircraft structure. The purpose of this paper is to assess the performance of hybrid composite patch repair of cracked structure and propose an optimized solution to a designer for selection of the appropriate level of a parameter to ensure effective repair solution.

Elastic properties of the hybrid composites are estimated by micromechanical modeling. Performance of hybrid composite patch repair is evaluated by numerical analysis of stress intensity factor (SIF), shear stress, and peel stress. Design of experiment is used to determine responses for a different combination of design parameters. The second-order mathematical model is suggested for SIF and peel stress. Adequacy of the model is checked by ANOVA and used as a fitness function. Multiobjective optimization is carried out with a genetic algorithm to arrive at the optimal solution.

The hybrid composite patch has maintained equilibrium between the SIF reduction and rise of the peel stress. The repair efficiency and repair durability can be ensured by selection of an optimum value of volume fraction of glass fiber, applied stress, and adhesive thickness.

The composite patch with varying stiffness is realized by hybridization with different volume fraction of fibers. Analysis and identification of optimum parameter to reduce the SIF and peel stress for hybrid composite patch repair are presented in this article.

The main theme of this paper is the effect of viscous dissipation Darcy–Forchheimer flow and heat transfer augmentation of a viscoelastic fluid over an incessant moving needle.

The governing partial differential equations of the current problem are diminished into a set of ordinary differential equations using requisite similarity transformations. Energy equation is extended by using Cattaneo–Christov heat flux model with variable thermal conductivity. By applying boundary layer approximation system of equations is framed.

Convective condition is also introduced in this analysis. Obtained set of similarity equations are then solved with the help of efficient numerical method four–fifth-order RKF-45.

The outcomes of various pertinent parameters on the velocity, temperature distributions are analysed by using portraits.