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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 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.

With suitable optimization criteria, hybrid models have proven to be efficient for preparing portfolios in capital markets of developed countries. This study adapts and investigates these methods for a developing country, thus providing a novel approach to the application of banking and finance. Our specific objectives are to employ a stochastic dominance criterion to evaluate the performances of over-the-counter (OTC) companies in a developing country and to analyze them with a hybrid model involving particle swarm optimization and artificial neural networks.

In order to achieve these aims, the authors conduct a case study of OTC companies in Iran. Weekly and daily returns of 36 companies listed in this market are calculated for one year during 2014–2015. The hybrid model is particularly interesting, and the results of the study identify first-, second- and third-order stochastic dominances among these companies. The study’s chosen model uses the best performing combination of activation functions in our analysis, corresponding to TPT, where T represents hyperbolic tangent transfers and P represents linear transfers.

Our portfolios are based on the shares of companies ranked with respect to the stochastic dominance criterion. Considering the minimum and maximum numbers of shares to be 2 and 10 for each portfolio, an eight-share portfolio is determined to be optimal. Compared with the index of Iran OTC during the research period of this study, our selected portfolio achieves a significantly better performance. Moreover, the methods used in this analysis are shown to be as efficient as they were in the capital markets of developed countries.

The problem of optimizing investment portfolios has to allow for correlations among returns from the financial maintenance period under consideration if an asymmetric distribution of returns exists (Babaei et al., 2015). Therefore, it is desirable to select an appropriate criterion in order to prepare an optimal portfolio and prioritize investment options. Although a back propagation technique is very popular in artificial neural (ANN) training, it is time-consuming to train a network in this way, and other methods such as particle swarm optimization (PSO) should be considered instead. In the hybrid combination of PSO and ANN, it is not the structure of a neural network that changes. Rather, the weighting method and the training technique chosen for the network are the important aspects, and these relate to PSO, so the only role ANN plays in this process is to reduce the errors.

The hybrid model combining ANN and PSO is seen to be considerably successful for generating optimal results and appropriate activation functions. These results are consistent with the theoretical findings of Das et al. (2013) and an application of the simple PSO in a study conducted by Pederson and Chipperfield (2010). Our research results also confirm the efficiency of stochastic dominance criteria as noted in the studies conducted by Roman et al. (2013), ANN as in a study carried out by Kristijanpoller et al. (2014) and PSO as in studies conducted by Liu et al. (2015) and Deng et al. (2012). These studies were carried out in the capital markets of developed countries, whereas the authors’ analysis relates to a developing country.

The authors deduce that the tools and methods whose efficiency was proven in the capital markets of developed countries also apply to, and demonstrate efficiency in, two novel applications of portfolio optimization within developing countries. The first of these is gaining familiarity with the theory and practice of these research tools and the methods that enrich financial knowledge of investors in developing countries. The second of these is the application of tools and methods identified by investors in the capital markets of developing countries, which enables optimal allocation of financial resources and growth of the markets. The authors expect that these findings will contribute to improving the economies of developing countries and thus help with economic development and facilitation of improving trends.

This paper attempts to examine the transmission of exchange rate changes into the domestic prices together with other important determinants of later, in case of a developing country, namely, India.

In an open economy Philips curve framework, a symmetric model developed by Pesaran et al. (2001) together with a complete asymmetric model developed by Shin et al. (2014) has been applied to assess the transmission of exchange rate changes into the domestic prices (inflation) of India. In addition, non-linear cumulative dynamic multipliers are used to portray the route between disequilibrium position of short run and new long-run equilibrium of the system. The multipliers highlight the asymmetric adjustment paths and/or duration of disequilibrium and therefore add valuable information to the long and short-run asymmetry.

In symmetric framework, exchange rate pass-through is reported to be incomplete and short-run pass through is found to be lower than the long-run pass through. A contractionary monetary policy stance is observed to decrease inflation in the long-run only and in the short-run, a case for price puzzle is observed, although the coefficient is statistically insignificant. Similarly, the impact of output growth is positive in both the short and long-run and both the coefficients are statically significant. Finally, the oil price inflation is also found to escalate the domestic inflationary pressures in both the short and long run, although the pass-through transmission is lower in the short-run than in the long-run. In case of an asymmetric setting, evidence in favour of directional asymmetry is reported whereby long-run impact of currency appreciation is found to be higher than depreciation. Similarly, a contractionary monetary policy action lowers the inflation, the easy one increases it; however, the impact of both the positive and negative changes in interest rate is found to be symmetric. An increase in GR is found to increase the inflation by a relatively appreciable magnitude than is observed when the fall in GR is reported. The possible reason for this asymmetric response of inflation may be explained in terms of asymmetric behaviour of demand conditions during economic upturns and downturns and downward inflexibility of prices. Finally, the transmission of oil price inflation to domestic inflation is also found to be asymmetric. An increase in oil price inflation leads to an increase in domestic inflation by a higher magnitude. whereas a decrease in it lowers inflation only marginally.

From a policy perspective, it is certainly important for the central banks to monitor the exchange rate changes so as to design the appropriate policy actions to resist any inflationary pressures resulting from the external sector. More importantly, a gauge on the factors that lead to destabilizing exchange rate movements or large currency price fluctuations is highly warranted. The results also highlight the relevance of proper domestic demand management and lowering dependence on oil imports to avoid the unnecessary inflation pressures in the economy.

While some studies have explored the possibilities of asymmetric interactions in the case of India, however, these studies have considered only the partial asymmetric model specifications and have not included a well-established theoretical base to include the other potential determinants of inflation as well. In this regard, the authors applied a complete asymmetric model specification developed by Shin et al. (2014) in an open economy Philips curve framework to assess the transmission of exchange rate changes into the domestic prices (inflation) of India. This paper will enrich the existing literature from a viewpoint of a comprehensive analysis of exchange rate pass-through by taking note of potential asymmetries coupled with other important determinants of inflation.

The purpose of this paper is to review the nutritional and phytochemical value of Moringa oleifera L., along with health benefits. Moringa oleifera, a highly valued plant grown throughout the world and all parts of tree used in different food formulations, possess industrial and therapeutic uses. This plant is gaining popularity because of its nutrient-rich root, leaves, flowers and fruits, having immense traditional medicinal uses and proved pharmacological properties.

Major well-known bibliometric information sources such as Web of Science, Scopus, Mendeley and Google Scholar were searched with keywords such as nutrition value of Moringa oleifera, bioactive compounds, health benefits, processing and safety were chosen to obtain a database of 1,386 papers. A final database of 70 scientific sources was made after sorting and classifying them according to different criteria based on topic relevance, country of origin and year of publication.

The literature reflects that Moringa contains all necessary macro, micro-nutrients and bioactive compounds (terpenoids, polyphenols, flavonoids, glucosinolates, alkaloids, glycosides and carotenoids). Scientific studies illustrate that M. oleifera and its bioactive constituents could play a vital role in the prevention of several chronic and degenerative diseases associated with oxidation stress. The recent upsurge in consumer interest for health foods has opened up new vistas for plant products containing bioactive compounds in different food formulations.

This paper highlights phytochemicals, pharmacological properties, bio-accessibility, food and industrial applications of Moringa. Moringa pods are traditionally preferred for enlarged liver and spleen, intestinal worms, weakness, neurological disorders and skin disease. A seed is natural and an inexpensive coagulant used to remove organic particles from water.

The objective of this study is to examine the drivers of retail apps satisfaction and continuance intention. An integrative theoretical framework was developed based on the IS success model, E-S-QUAL and expectancy and disconfirmation model to explain retail apps users’ satisfaction and continuance intention.

A total of 359 useable data were collected from the targeted Malaysian respondents who had experience in using retail apps services. Data were analysed using the partial least squares technique.

The results indicate that system quality and e-service quality positively influence retail apps usage satisfaction and have positive direct and indirect effects through satisfaction on continuance intention. The price level has a negative effect on retail apps usage satisfaction. Even though price level has no direct effect on continuance intention to use retail apps, it has an indirect effect on continuance intention through satisfaction.

Although the success of a marketing channel mainly depends on its continuance usage rather than first-time usage, few studies have paid attention to retail apps services. This study contributes to the advancement of knowledge on retail apps by explaining the roles of system quality, e-service quality and price level on retail apps satisfaction and continuance intention. Interestingly, the findings of multi-group analysis imply that female Gen Y app users are more satisfied than males while such differences do not impact their continuance intention to use the retail apps. The findings also suggested that frequency of using apps has no relevance to retail apps user satisfaction, but highly relevant to their continuance intention to use retail Apps services.

Despite the tremendous advancement of information technology worldwide, a few attempts have been made to uncover the effectiveness of paid internet advertising (PIA) on consumers buying decisions for retail enterprises in emerging markets. The purpose of this paper is to examine the structural relationships between consumer self-confidence, perceived intrusiveness, continuance search intention, attitude towards PIA and frequency of apps-buying decisions.

A total number of 515 online questionnaires were obtained from a sample of experienced apps shoppers who were disposed to PIA, in order to evaluate the theoretical model. VB-SEM, which is a variance-based structural equation modelling technique, was applied to assess the measurement scales and structural relationship between exogenous and endogenous constructs.

Overall, the structural results imply that the proposed model explains 0.738 per cent of variances in consumer’s continuance search intention and 0.756 per cent of the frequency of apps-buying decisions. The results support that consumer self-confidence positively influences attitude towards PIA, continuance search intention and frequency of apps-buying decisions; however, consumer self-confidence is negatively related to perceived intrusiveness. Similarly, attitude towards PIA is positively related to continuance search intention and frequency of apps-buying decision. Besides, perceived intrusiveness negatively influences attitude towards PIA, continuance search intention and frequency of apps-buying decisions. Finally, continuance search intention and the frequency of apps-buying decisions are positively related.

Despite the fact that PIA and its utilisation has been a recent growing trend in managing retail enterprises worldwide, a few studies have been conducted on possible end results of PIA, including consumer continuous apps search intention and purchase decisions.

This paper aims to explore the contextual problems and priorities that create tensions in the implementation of activity-based learning reforms such as learning enhancement through active pedagogy in Jammu and Kashmir (J&K). By doing so, it aims to understand the relevance of activity-based learning (ABL) in diverse contexts as perceived by stakeholders.

Using qualitative data drawn from semi-structured interviews with 29 teachers, this study uses an anthropological approach to policy implementation to unleash factors creating a disjunction between policy text and policy practice.

Narratives from teachers reveal how critical awareness about context raises tensions and shapes beliefs about ABL. Their beliefs about ABL can be summed as “pedagogy for its own sake”, reflecting the perceived ineptness of ABL in bringing about considerable improvements in students learning outcomes. Reference to “outcomes” was invoked as a crucial recontextualising discourse in teacher’s pedagogic orientation which resulted primarily from the poor learning attainments of rural children in the prevalent outcome-focussed educational setup.

The study is the first of its kind conducted in J&K and amongst very few around the world elucidating tension around the implementation of pedagogic reforms in underserved areas. This is the only study providing a critical analysis of pedagogic reforms in the school education system in J&K.

The study has significant implications in terms of helping immediate functionaries such as headmasters, teacher trainers and administrators and policy planners in understanding the dilemmas faced by teachers in enacting pedagogic reforms. It also adds to the field of policy implementation in terms of understanding the tensions of policy implementation at peripheries.

The study is the first of its kind conducted in J&K and amongst very few around the world elucidating tension around the implementation of pedagogic reforms in underserved areas. This is the only study providing a critical analysis of the school education system in J&K.

This study aims to estimate the impact of refugee inflows on host countries’ entrepreneurial rates. The refugee crisis led to an increased scientific and public policy interest in the impact of refugee inflows on host countries. One important perspective of such an impact, which is still underexplored, is the impact of refugee inflows on host countries entrepreneurial rates. Given the high number of refugees that flow to some countries, it would be valuable to assess the extent to which such countries are likely to reap the benefits from increasing refugee inflows in terms of (native and non-native) entrepreneurial talent enhancement.

Resorting to dynamic (two-step system generalized method of moments) panel data estimations, based on 186 countries over the period between 2000 and 2019, this study estimates the impact of refugee inflows on host countries’ entrepreneurial rates, measured by the total early-stage entrepreneurial activity (TEA) rate and the self-employment rate.

In general, higher refugee inflows are associated with lower host countries’ TEA rates. However, refugee inflows significantly foster self-employment rates of “medium-high” and “high” income host countries and host countries located in Africa. These results suggest that refugee inflows tend to enhance “necessity” related new ventures and/ or new ventures (from native and non-native population) operating in low value-added, low profit sectors.

This study constitutes a novel empirical contribution by providing a macroeconomic, quantitative assessment of the impact of refugee from distinct nationalities on a diverse set of host countries' entrepreneurship rates in the past two decades resorting to dynamic panel data models, which enable to address the heterogeneity of the countries and deal with the endogeneity of the variables of the model.