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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.

This study aims to explore the thermal energy diffusion and flow features of a hybrid nanofluid in a thin film. In particular, the focus is to elicit the impact of shape factor in the backdrop of a magnetic field. The hybrid nanofluid is the amalgamation of various shaped nanoscale particles of copper and alumina in water.

The equations of motion and energy are modeled using the Tiwari–Das model. The differential equations governing the physics of the designed model have been obtained by the application of scaling analysis. To achieve quantitative outcomes, Runge–Kutta–Fehlberg numerical code along with shooting techniques is used. Validation of the derived outcomes with available data in literature reveals a greater accuracy of the numerical procedure used in this investigation.

The dynamics of the slender nano liquid film is explored eliciting the impact of various flow parameters. The rate of energy transport of the Cu-Al₂O₃/ water with blade-shaped nanoparticle, at a fixed Prandtl number (=2) is enhanced by 14.7% compared to that evaluated with spherical particles. The presence of hybrid nanoparticles has an affirmative impact in boosting the rate of heat transfer (RHT). The temperature and the rate of thermal diffusion of the hybrid nanofluid are more prominent than those of the Cu-H₂O case. The numerical outcomes of this investigation are collated with the already published works as a limiting case and are found to be in good agreement.

The adopted methodology helped to obtain the results of the present problem. To the best of authors’ knowledge, it can be shown that the originality of the work with the table of comparison. There is a good agreement between present outcomes with the existed results.

The aim of this study is to examine the relationship between the perceived firm ethicality and loyalty behaviour of consumers through the lens of corporate social responsibility (CSR), green brand innovativeness (GBI) and green marketing (GM).

The authors used questionnaires as a primary method of data collection. The authors selected four large malls in Jakarta, the capital of Indonesia and three large malls in Surabaya, the second-largest city in the country. These locations were chosen due to their high levels of consumer activity and the presence of numerous products from major companies.

The findings provide strong support for all proposed hypotheses, confirming that perceived firm ethicality plays a crucial role in significantly enhancing brand loyalty. This positive relationship however is mediated through distinct pathways, namely CSR, GBI and GM. Each of these factors contributes meaningfully to shaping consumer perceptions of a firm’s ethicality, which in turn drives greater loyalty to the brand. Collectively, these strategies synergise to amplify a firm’s perceived ethicality, further solidifying brand loyalty. Notably, CSR, perceived firm ethicality, GBI and GM together account for approximately 65.2% of the variance in customer brand loyalty.

The study offers important theoretical insights into attachment theory by extending it to consumer-brand relationships, demonstrating that perceived firm ethicality fosters emotional attachment, which enhances brand loyalty. Just as secure attachment in human relationships builds trust and loyalty, consumers develop emotional bonds with brands they perceive as ethical, supported by CSR practices, GBI and GM from the firms. These strategies reinforce consumer trust and loyalty, paralleling attachment theory’s secure base concept.

This study aims to investigate the influence of knowledge application, government policy and green market orientation (GMO) on organizational sustainable performance in Vietnam, with a particular focus on the mediating role of green innovation in these relationships.

Data were collected from 253 Vietnamese organizations using purposive random sampling and manager-administered questionnaires. The analysis was conducted using partial least squares structural equation modeling.

The findings underscore the critical role of government policy in facilitating green innovation and enhancing the sustainable performance of organizations. While knowledge application and GMO are important drivers of green innovation, their direct impact on sustainable performance is not significant. Furthermore, the study confirms the mediating role of green innovation in bridging these relationships.

The study offers critical insights for policymakers and organizational leaders in emerging markets like Vietnam, demonstrating that strategic investments in knowledge application, green innovation and alignment with GMO and government policies can substantially enhance sustainable performance.

This research extends the resource-based view and institutional theory by identifying key determinants of sustainable organizational performance. It underscores the pivotal role of knowledge application, GMO and government policy in promoting sustainable performance through the facilitation of green innovation.

This study examines the impact of green promotions and green packaging on the green purchase behavior through the mediating role of environmental knowledge and green satisfaction in the context of fast-food chains.

The study follows a quantitative approach for data collection. The data were collected from 400 consumers of fast food chains operating in Pakistan through questionnaires.

The results showed that green promotions and green packaging had a strong positive impact on consumers’ green satisfaction, environmental knowledge and green purchase behavior. Green satisfaction and environmental knowledge mediated the relationship between green promotions and green purchase behavior and green packaging and green purchase behavior.

The study is the first of its kind to determine the mediating roles of green satisfaction and environmental knowledge in the context of green marketing. The research offers significant theoretical and managerial insights.

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.

This paper examines the effect of cryptocurrencies on the portfolio risk-adjusted returns of traditional and alternative investments within an emerging market economy.

The paper employs daily arithmetic returns from August 2015 to October 2018 of traditional assets (stocks, bonds, currencies), alternative assets (commodities, real estate) and cryptocurrencies. Using the mean-variance analysis, the Sharpe ratio, the conditional value-at-risk and the mean-variance spanning tests.

The paper documents evidence to support the diversification benefits of cryptocurrencies by utilising the mean-variance tests, improving the efficient frontier and the risk-adjusted returns of the emerging market economy portfolio of investments.

This paper firmly broadens the Modern Portfolio Theory by authenticating cryptocurrencies as assets with diversification benefits in an emerging market economy investment portfolio.

As far as the authors are concerned, this paper presents the first evidence of the effect of diversification benefits of cryptocurrencies on emerging market asset portfolios constructed using traditional and alternative assets.

The mischievous nodes that defy the standard corrupt the exhibition of good nodes considerably. Therefore, an intrusion discovery mechanism should be included to the mobile ad-hoc network (MANET). In this paper, worm-hole and other destructive malignant attacks are propelled in MANET.

A wireless ad-hoc network also called as mobile ad-hoc network (MANET) is a gathering of hubs that utilizes a wireless channel to exchange information and coordinate together to establish information exchange among any pair of hubs, without any centralized structure. The security issue is a major difficulty while employing MANETs.

Consequently, the attacks due to the malicious node activity are detected using Hybrid Reactive Search and Bat (HRSB) mechanism to prevent the mischievous nodes from entering the network beneath the untruthful information. Moreover, the attack detection rate and node energy are predicted for determining the lifetime of the node.

The simulation outcomes of the proposed HRSB technique are evaluated with the prevailing methods. The comparison studies have proven the efficacy of the current research model by attaining high attack detection rate and achieving more network lifetime.

The purpose of this study is to determine the optimal shape of a one-sided elliptical composite material patch of an adhesively bonded repair of cracked metal plates under biaxial stress.

The approach consists on determining the patch topology and adhesive thickness that minimize the stress intensity factor and the bending moment caused by the asymmetry of the repair by applying a differential evolution algorithm with a selection phase using the Deb’s rules.

The results demonstrate that an elliptical patch of major axis length equal to the plate width, and minor axis length equal to the crack length, with a thin adhesive thickness, provides the highest stress intensity factor and bending moment reduction, maximizing the fatigue life of the repair.

The results are limited to linear elastic behavior of the cracked plate and a fully rigid bond between the cracked plate and the patch. The effectiveness of the repair was verified by theoretical calculation of the fatigue life, thus experimental validation is still needed.

The results of this work can be applied to experimental validations of the effectiveness of the elliptical one-side composite bonded repairs, avoiding and extensive number of experiments, and also, encourage maintainers to explore on this technique that is more economical and easier to apply, in comparison to other repair techniques. By following the patch geometry recommendations proposed herein, it is analytically predicted that the fatigue life may increase by as much as 27 times that of the unpatched plate.

Currently, there are no detailed studies that assess one-side patch repair procedures, which require consideration of the bending moment and biaxial stress state, and therefore, the optimal patch geometry and adhesive thickness are unknown.

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.