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Nowadays, the speech emotion recognition (SER) model has enhanced as the main research topic in various fields including human–computer interaction as well as speech processing. Generally, it focuses on utilizing the models of machine learning for predicting the exact emotional status from speech. The advanced SER applications go successful in affective computing and human–computer interaction, which is making as the main component of computer system's next generation. This is because the natural human machine interface could grant the automatic service provisions, which need a better appreciation of user's emotional states.

This paper implements a new SER model that incorporates both gender and emotion recognition. Certain features are extracted and subjected for classification of emotions. For this, this paper uses deep belief network DBN model.

Through the performance analysis, it is observed that the developed method attains high accuracy rate (for best case) when compared to other methods, and it is 1.02% superior to whale optimization algorithm (WOA), 0.32% better from firefly (FF), 23.45% superior to particle swarm optimization (PSO) and 23.41% superior to genetic algorithm (GA). In case of worst scenario, the mean update of particle swarm and whale optimization (MUPW) in terms of accuracy is 15.63, 15.98, 16.06% and 16.03% superior to WOA, FF, PSO and GA, respectively. Under the mean case, the performance of MUPW is high, and it is 16.67, 10.38, 22.30 and 22.47% better from existing methods like WOA, FF, PSO, as well as GA, respectively.

This paper presents a new model for SER that aids both gender and emotion recognition. For the classification purpose, DBN is used and the weight of DBN is used and this is the first work uses MUPW algorithm for finding the optimal weight of DBN model.

Sustainability involves ensuring that sufficient resources are available for current and future generations. Non-revenue water (NRW) creates a barrier to sustainability through energy and water loss. However, a comprehensive overview of NRW reduction strategies is lacking. This study reviews the existing literature to identify available strategies for reducing NRW and its components and discusses their merits.

A systematic literature review was conducted to identify and analyze different strategies for reducing NRW. The initial search identified 158 articles, with 41 of these deemed suitably relevant following further examination. Finally, 14 NRW reduction strategies were identified from the selected articles.

The identified NRW reduction strategies were grouped into strategies for reducing (1) apparent losses (AL), (2) real losses (RL) and (3) water losses, with the latter involving the combination of AL and RL. The strategies adopted most frequently are “prevent water leakage” and “control water pressure.” In addition, water distribution network (WDN) rehabilitation has additional benefits over other RL reduction strategies, including saving water and energy, increasing hydraulic performance and enhancing reliability. Finally, utilizing decision support systems is the only strategy capable of reducing multiple NRW categories.

This review provides insights into the overall NRW problem and the strategies best equipped to address it. Authorities can use these findings to develop case-specific NRW reduction action plans that save water and energy, while providing other economic benefits. In addition, NRW reduction can improve WDN reliability.

Recalling that the introductory chapter (Chapter 1) wanted to carry out similar types of analysis for the major states in India. Thus, the present chapter tries to examine the trends of a bank branch, deposit, credit, the credit–deposit ratio, sectoral shares of credit, magnitudes of banking transactions, credit concentration, etc., for the selected 15 states and Delhi as the only union territory for the period 1972–2019. The study period covers the pre-reform period from 1972 to 1992 and the post-reform period 1993–2019. The observations show that the branch, deposit and credit did not grow significantly during the post-reform period. As a result, the credit–deposit ratio did not increase significantly during the reform period. But, the magnitude of banking transactions increased in most of the states during the reform period. Regarding the sector-wise share of credit, AP, Maharashtra, UP and TN are the leading states in agricultural credit, WB, Gujarat and Maharashtra are in industrial credit and Kerala, Assam and Delhi are in the service sector. On the other hand, the study finds rising magnitudes credit concentrations of the states during the post-reform period in contrast to the declining concentration in the pre-reform period. Maharashtra is the state which holds around 25 per cent of all states’ credit throughout the entire period of 1972–2019. Hence, there are the notions of rising disparity and inequality in credit as well as incomes of the states and all India levels.

The study assesses the relationship between institutional credit access and farmer satisfaction using contextual mediating and moderating variables. This study identifies various socioeconomic, service features and service quality determinants impacting institutional credit access.

The authors used the stratified random sampling method and selected 512 farmers from 40 villages in Maharashtra, India. Initially, the study employed probit regression analysis to identify the credit adoption determinants. Subsequently, the relationship between institutional credit and farmer satisfaction is identified through moderated-mediation analysis using the Statistical Package for the Social Sciences and Analysis of a Moment Structures (SPSS - AMOS model).

Probit model's results suggest that socioeconomic variables like education and bank distance; service quality variables like prompt service and employee behavior; and service characteristics variables like the interest rate, loan sanction time, repayment period, and documents for loan application significantly affect institutional credit adoption across the smallholders. Subsequently, the results of the moderating-mediation analysis show that working capital, perceived value and risk perception partially mediate the association between credit adoption and farmer satisfaction. The mediated effects are further moderated by farm advisory services and financial knowledge and skills.

The study is restricted in opportunity due to primary data, and it considers only farmers' perspectives to measure service quality and service features as constraints for institutional credit access.

The government, nongovernment organizations, civil societies and private institutions should provide sufficient financial knowledge and training to the farmers via extension services to utilize the borrowed capital effectively to bring economic welfare and mental satisfaction.

The existing literature rarely considered banking service quality and service features (demand side) variables as determinants of credit access. Further, the study brings novelty in examining how the capital management cognitive factors of the formal credit adopters influence the relationship between credit access and satisfaction.

The purchase of green products is not the finale of green consumer behaviour but the environmental concern is crucial in post-purchase behaviour. Studies on pro-environmental purchase behaviour are abundant and but studies on environmental concern in use, evaluation and disposal are scarce. This paper aims to examine the pro-environmental factors influencing post-purchase behaviour and their impact on green consumer behaviour.

Data for this study was collected from the respondents with the help of a structured questionnaire. Data is analysed using factor analysis to examine the important factors influencing post-purchase variables and green consumer behaviour and the multiple regression to understand the contribution of post-purchase variables to green consumer behaviour.

Eco-conscious, risk and comfort in user behaviour, satisfaction and eco-appraisal in evaluation behaviour and eco-conscience, disposal challenges and eco-responsible in disposal behaviour are the vital factors. Eco-conscious and comfort in use, satisfaction in evaluation and eco-conscience, disposal challenges and eco-responsible behaviour in disposal are positively related to green consumer behaviour and risk in use and eco-appraisals in the evaluation are insignificant.

Considering the very limited studies on green post-purchase behaviour, this study provides insights into the pro-environmental post-purchase behaviour and its contribution to green consumer behaviour.

Indian government initiated several skill development policies and different types of vocational education and training (VET). Yet the participation in skill education is low because of poor labour market outcomes. This paper aims to calculate returns to skill education to understand the type of training that will have better labour market outcomes.

In this paper nationally representative data from the periodic labour force survey (PLFS), collected by the national sample survey office for 2017–2018, are used to estimate the returns to formal and non-formal VET obtained (after different levels of general education) with the help of Heckman's two-stage method.

Nearly 8% of the working-age population has received some form of VET (mostly non-formal), generating poor returns. For the overall population, formal on-job training (OJT) and full-time VET influence wage positively and significantly. Full-time VET obtained after secondary and below levels of education generates positive returns, whereas part-time VET is profitable only to those without formal education. At the graduate level, technical education obtained along with VET is associated with better wages.

In India where a considerable proportion of the workforce is employed in the informal sector, different types of skill training like full-time, part-time and OJT influence labour market outcomes. This finding has policy implication for countries with large informal sector and calls for further research in such countries.

Today’s competitive business world presents unanticipated challenges to enterprises worldwide. So, the well-being of the employees may be a sustained competitive edge for corporations in improving employee performance. Positive psychology served as the foundation for this study, investigating the interplay between employee well-being and task performance by incorporating organizational-specific factors like organizational virtuousness (OV) and individual-specific factors such as Psychological Capital (PsyCap).

In total, 639 dyadic responses were gathered from the banking sector, encompassing employees in both private and public banks in India, along with their immediate supervisors. The hypotheses were subsequently examined by applying Structural Equation Modeling (SEM).

OV and PsyCap are considerably associated with the well-being of employees and task performance, according to the findings. Employee well-being mediates the relationships between the perceptions of Organizational Virtuousness (OV) and task performance, as well as between PsyCap and task performance.

The intense competition and series of scandals in Indian banks urge the introduction of some behavioral precautionary measures. Banks need to understand and intervene in positive organizational behavior and help the employees build strong PsyCap to enhance their well-being and task performance to gain a competitive edge.

The present study integrated Positive Organizational Behavior (POB) and Positive Organizational Scholarship (POS) to enhance work performance.

The aim of the study is to attempt to analyze the trends and patterns of institutional credit flow, deployed by the CBs, SCBs and RRBs, for production and investment purposes in agriculture and allied activities in India in the light of banking sector reforms initiated in the early 1990s.

The study is based on secondary data collected from the Handbook of Statistics of Indian Economy, 2009‐2010 published by the Reserve Bank of India, Agricultural Statistics at a Glance, Economic Survey of India, etc. The data relating to institutional credit at the all India level were collected for 1971‐1972 to 2007‐2008. The period from 1971‐1972 to 1980‐1981 is considered as the beginning of multi‐agency approach and bank branch expansion, from 1981‐1982 to 1990‐1991 is regarded as the pre‐reform period, from 1991‐1992 to 2007‐2008, as the post‐reform period. In order to examine the extent of institutional credit flow for development of agriculture and allied activities, the indictors such as the average institutional credit per hectare cultivated area and as percentage of agricultural GDP were estimated, besides the CAGR during different periods.

The study found that the annual growth rate of total institutional credit for agriculture and allied activities was much higher during the reform period as compared to that of pre‐reform period. The average institutional credit per hectare and as a percentage of agricultural GDP has gone up significantly during the reform regime. The RRBs followed by the SCBs registered highest growth rates of production credit as compared to that of CBs during the entire period; it was higher during the reform than the pre‐reform period. The growth rate of investment credit was highest for SCBs followed by the RRBs as against the CBs during the reform period. It has been observed that the CBs have lost their historical prime position in provision of agricultural credit. The growth pattern of production as well as investment credit constituted what can be described as the “U‐shaped” curve. This implies that the bulk of the increase in institutional credit for agriculture and allied activities during the reform period was attributed to the banking sector reforms initiated in the early 1990s.

The data on institutional credit provided by the SCARDBs and PCARDBs were not included under the co‐operative sector prior to 1999‐2000, and it covered credit by only PACs. Hence, the temporal comparability of data on institutional credit under the co‐operative sector for the period 1998‐1999 to 2007‐2008 with that of earlier periods may be erroneous.

Adequate and timely inflow of both production and investment credit for development of agriculture and allied activities through further reforms in the banking sector would go a long way in sustained growth of agriculture and food security for a great majority of the rural masses in India.

The study establishes the “U‐shaped” curve for the growth pattern of institutional credit for development of agriculture and allied activities in India. This follows that the increase in the growth rates of institutional credit during 1991‐1992 to 2007‐2008 was largely due to the banking sector reforms.

In modern technology, the wireless sensor networks (WSNs) are generally most promising solutions for better reliability, object tracking, remote monitoring and more, which is directly related to the sensor nodes. Received signal strength indication (RSSI) is main challenges in sensor networks, which is fully depends on distance measurement. The learning algorithm based traditional models are involved in error correction, distance measurement and improve the accuracy of effectiveness. But, most of the existing models are not able to protect the user’s data from the unknown or malicious data during the signal transmission. The simulation outcomes indicate that proposed methodology may reach more constant and accurate position states of the unknown nodes and the target node in WSNs domain than the existing methods.

This paper present a deep convolutional neural network (DCNN) from the adaptation of machine learning to identify the problems on deep ranging sensor networks and overthrow the problems of unknown sensor nodes localization in WSN networks by using instance parameters of elephant herding optimization (EHO) technique and which is used to optimize the localization problem.

In this proposed method, the signal propagation properties can be extracted automatically because of this image data and RSSI data values. Rest of this manuscript shows that the ECO can find the better performance analysis of distance estimation accuracy, localized nodes and its transmission range than those traditional algorithms. ECO has been proposed as one of the main tools to promote a transformation from unsustainable development to one of sustainable development. It will reduce the material intensity of goods and services.

The proposed technique is compared to existing systems to show the proposed method efficiency. The simulation results indicate that this proposed methodology can achieve more constant and accurate position states of the unknown nodes and the target node in WSNs domain than the existing methods.