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The purpose of this paper is to investigate the performance of chaotic gravitational search algorithm (CGSA) in solving mechanical engineering design frameworks including welded beam design (WBD), compression spring design (CSD) and pressure vessel design (PVD).

In this study, ten chaotic maps were combined with gravitational constant to increase the exploitation power of gravitational search algorithm (GSA). Also, CGSA has been used for maintaining the adaptive capability of gravitational constant. Furthermore, chaotic maps were used for overcoming premature convergence and stagnation in local minima problems of standard GSA.

The chaotic maps have shown efficient performance for WBD and PVD problems. Further, they have depicted competitive results for CSD framework. Moreover, the experimental results indicate that CGSA shows efficient performance in terms of convergence speed, cost function minimization, design variable optimization and successful constraint handling as compared to other participating algorithms.

The use of chaotic maps in standard GSA is a new beginning for research in GSA particularly convergence and time complexity analysis. Moreover, CGSA can be used for solving the infinite impulsive response (IIR) parameter tuning and economic load dispatch problems in electrical sciences.

The hybridization of chaotic maps and evolutionary algorithms for solving practical engineering problems is an emerging topic in metaheuristics. In the literature, it can be seen that researchers have used some chaotic maps such as a logistic map, Gauss map and a sinusoidal map more rigorously than other maps. However, this work uses ten different chaotic maps for engineering design optimization. In addition, non-parametric statistical test, namely, Wilcoxon rank-sum test, was carried out at 5% significance level to statistically validate the simulation results. Besides, 11 state-of-the-art metaheuristic algorithms were used for comparative analysis of the experimental results to further raise the authenticity of the experimental setup.

In this paper, a newly proposed hybridization algorithm namely constriction coefficient-based particle swarm optimization and gravitational search algorithm (CPSOGSA) has been employed for training MLP to overcome sensitivity to initialization, premature convergence, and stagnation in local optima problems of MLP.

In this study, the exploration of the search space is carried out by gravitational search algorithm (GSA) and optimization of candidate solutions, i.e. exploitation is performed by particle swarm optimization (PSO). For training the multi-layer perceptron (MLP), CPSOGSA uses sigmoid fitness function for finding the proper combination of connection weights and neural biases to minimize the error. Secondly, a matrix encoding strategy is utilized for providing one to one correspondence between weights and biases of MLP and agents of CPSOGSA.

The experimental findings convey that CPSOGSA is a better MLP trainer as compared to other stochastic algorithms because it provides superior results in terms of resolving stagnation in local optima and convergence speed problems. Besides, it gives the best results for breast cancer, heart, sine function and sigmoid function datasets as compared to other participating algorithms. Moreover, CPSOGSA also provides very competitive results for other datasets.

The CPSOGSA performed effectively in overcoming stagnation in local optima problem and increasing the overall convergence speed of MLP. Basically, CPSOGSA is a hybrid optimization algorithm which has powerful characteristics of global exploration capability and high local exploitation power. In the research literature, a little work is available where CPSO and GSA have been utilized for training MLP. The only related research paper was given by Mirjalili et al., in 2012. They have used standard PSO and GSA for training simple FNNs. However, the work employed only three datasets and used the MSE performance metric for evaluating the efficiency of the algorithms. In this paper, eight different standard datasets and five performance metrics have been utilized for investigating the efficiency of CPSOGSA in training MLPs. In addition, a non-parametric pair-wise statistical test namely the Wilcoxon rank-sum test has been carried out at a 5% significance level to statistically validate the simulation results. Besides, eight state-of-the-art meta-heuristic algorithms were employed for comparative analysis of the experimental results to further raise the authenticity of the experimental setup.

The purpose of the study is to examine three significant components of service brand equity – i.e. perceived service quality, brand loyalty, and brand image – and analyze relationships among the components of brand equity and also their relationship with brand equity, which is still to be theorized and developed in the healthcare literature.

Effective responses were received from 206 respondents, selected conveniently from the localities of Jammu city. After scale item analysis, the data were analyzed using factor analysis, correlations, t‐tests, multiple regression analysis and path modeling using SEM.

The findings of the study support that service brand equity in the healthcare sector is greatly influenced by brand loyalty and perceived quality. However, brand image has an indirect effect on service brand equity through brand loyalty (mediating variable).

The research can be criticized on the ground that data were selected conveniently from respondents residing in the city of Jammu, India. But at the same time the respondents were appropriate for the study as they have adequate knowledge about the hospitals, and were associated with the selected hospital for more than four years. Furthermore, the validity and reliability of the data are strong enough to take care of the limitations of the convenience sampling selection method.

The study has unique value addition to the service marketing vis‐à‐vis healthcare literature, from both theoretical and managerial perspectives. The study establishes a direct and significant relationship between service brand equity and its two components, i.e. perceived service quality and brand loyalty in the healthcare sector. It also provides directions to healthcare service providers in creating, enhancing, and maintaining service brand equity through service quality and brand loyalty, to sustain competitive advantage.

The present study aims to trace out the science research output of top Indian universities from 2015 to 2019, as reflected in the Web of Science (WOS) database.

The present study has selected the Science Citation Index (SCI) of WOS core collection for selecting top Indian universities in terms of total publications in the last five years (2015–2019). The University of Delhi (DU), Banaras Hindu University (BHU), Anna University (AU), Jadavpur University (JU) and Punjab University (PU) have been selected. The study identified the most prolific authors, collaborating countries, collaborating institutions and the impact of their output in terms of citations per paper (CPP) and relative citation impact (RCI). For visualizing purposes, VOSviewer was used. The study also identified frequently used keywords and channels used for communicating research results.

The authors retrieved 26,173 documents consisting of journal articles, review papers and proceeding papers. The consistent growth of science research output has been observed. The University of Delhi (DU) has the maximum science publications. The study reflects that multi-authored papers have more research impact in terms of citation received. The USA, South Korea and Germany are the most collaborating countries. The top Indian Universities have a major collaboration with Anna University, Indian Institute of Technology, Center for Scientific and Industrial Research (CSIR) of India.

The present study reveals how the science research output of top Indian universities has grown in the last few years. The findings of the study can be used for identifying specific science research areas where special attention can be given.

The purpose of this paper is to investigate brand trust and brand image effects on healthcare service users. Nowadays, managers and health activists are showing increased tendency to marketing and branding to attract and satisfy customers.

The current study’s design is based on a conceptual model examining brand trust and brand image effects on customer satisfaction. Data obtained from 240 questionnaires (310 respondents) were analyzed using path analysis.

Results revealed that the most effective items bearing the highest influence on customer satisfaction and on benefiting from healthcare services include brand image, staff sincerity to its patients, interactions with physicians and rapport.

This study needs to be conducted in different hospitals and with different patients, which would lead to the model’s expansion and its influence on the patient satisfaction.

Being the first study that simultaneously addresses brand trust and brand image effects on customer satisfaction, this research provides in-depth insights into healthcare marketing. Moreover, identifying significant components associated with healthcare branding helps managers and healthcare activists to create and protect their brands and, consequently, leading to an increased profitability resulting from the enhanced consumer satisfaction. Additionally, it would probably facilitate purchasing processes during the service selection.