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This paper aims to deal with application of artificial intelligence for solving real time control complication adhered with the controlled operation of a buck power converter. This type of converter finds application for power conversion at various levels for the direct current-direct current power industry to step down the input voltage.

Use of ANN-RL (Artificial Neural Networks- Reinforcement Learning)-based control algorithm to control buck power converter shows robustness against parameter and load variation. Because of non-linearity instigated by element used for switching, control of this converter becomes an arduous control predicament. All the classical control techniques are based on an approximate linear model of the step down converter and these techniques fail to handle actual non-linearity.

In this paper, a reinforcement learning-based algorithm has been used to handle and control buck power converter output voltage, without approximating the model of converter. The non-linearity instigated in converter is subjected to state of switch. Model of buck power converter is defined as a multi-step decision problem so that it can be solved using mathematical model of Markov decision process (MDP) and, in turn, reinforcement learning can be implemented. As MDP model is available for a discrete state system so model of converter has to be discretized and then value iteration is applied and output is analyzed. Load regulation and integral time absolute error analysis is done to show efficacy of this technique.

To mitigate the effect of discretization function approximation using neural network is applied. MATrix LABoratory has been used for implementation and result indicates an improvement in the overall response.

This paper aims to examine the design and control of a symmetric multilevel inverter (MLI) using grey wolf optimization and differential evolution algorithms.

The optimal modulation index along with the switching angles are calculated for an 11 level inverter. Harmonics are used to estimate the quality of output voltage and measuring the improvement of the power quality.

The simulation is carried out in MATLAB/Simulink for 11 levels of symmetric MLI and compared with the conventional inverter design. A solar photovoltaic array-based experimental setup is considered to provide the input for symmetric MLI. Field Programmable Gate Array (FPGA) based controller is used to provide the switching pulses for the inverter switches.

Attempted to develop a system with different optimization techniques.

Adequate resources for learning and training the data are an important constraint to develop an efficient classifier with outstanding performance. The data usually follows a biased distribution of classes that reflects an unequal distribution of classes within a dataset. This issue is known as the imbalance problem, which is one of the most common issues occurring in real-time applications. Learning of imbalanced datasets is a ubiquitous challenge in the field of data mining. Imbalanced data degrades the performance of the classifier by producing inaccurate results.

In the proposed work, a novel fuzzy-based Gaussian synthetic minority oversampling (FG-SMOTE) algorithm is proposed to process the imbalanced data. The mechanism of the Gaussian SMOTE technique is based on finding the nearest neighbour concept to balance the ratio between minority and majority class datasets. The ratio of the datasets belonging to the minority and majority class is balanced using a fuzzy-based Levenshtein distance measure technique.

The performance and the accuracy of the proposed algorithm is evaluated using the deep belief networks classifier and the results showed the efficiency of the fuzzy-based Gaussian SMOTE technique achieved an AUC: 93.7%. F1 Score Prediction: 94.2%, Geometric Mean Score: 93.6% predicted from confusion matrix.

The proposed research still retains some of the challenges that need to be focused such as application FG-SMOTE to multiclass imbalanced dataset and to evaluate dataset imbalance problem in a distributed environment.

The proposed algorithm fundamentally solves the data imbalance issues and challenges involved in handling the imbalanced data. FG-SMOTE has aided in balancing minority and majority class datasets.

This study aims to systematically review the existing literature and develop an understanding of work-life balance (WLB) and its relationship with other forms of work-related behavior and unearth research gaps to recommend future research possibilities and priorities.

The current study attempts to make a detailed survey of the research work done by the pioneers in the domain WLB and its related aspects. A total of 99 research work has been included in this systematic review. The research works have been classified based on the year of publication, geographical distribution, the methodology used and the sector. The various concepts and components that have made significant contributions, factors that influence WLB, importance and implications are discussed.

The paper points to the research gaps and scope for future research in the area of WLB.

The current study uncovered the research gaps regarding the systematic review and classifications based on demography, year of publication, the research method used and sector being studied.

Enrolling in an academic discipline or selecting the college major choice is a dynamic process. Very few studies examine this aspect in India. This paper makes a humble attempt to fill this gap using NSSO 71st round data on social consumption on education. The purpose of this paper is to use multinomial regression model to study the different factors that influence course choice in higher education. The different factors (given the availability of information) considered relate to ability, gender, cost of higher education, socio-economic and geographical location. The results indicate that gender polarization is apparent between humanities and engineering. The predicated probabilities bring out the dichotomy between the choice of courses and levels of living expressed through consumption expenditures in terms of professional and non-professional courses. Predicted probabilities of course choices bring in a clear distinction between south and west regions preferring engineering and other professional courses, whereas north, east and NES prefer humanities.

The present paper follows the same approach as that of Turner and Bowen (1999). The Multinomial regression is specified as P ( M i = j ) = ( exp ( β j × X i ) / ∑ j − 1 5 exp ( β j × X i ) ) , where P (M_i=j) denotes the probability of choosing outcome j, the particular course/major choice that categorizes different disciplines. This response variable is specified with five categories: such as medicine, engineering, other professional courses, science and humanities. The authors’ primary interest is to determine the factors governing an individual’s decision to choose a particular subject field as compared to humanities. In other words, to make the system identifiable in the MLR, humanities is treated as a reference category. The vector X_i includes the set of explanatory variables and β_j refers to the corresponding coefficients for each of the outcome j. From an aggregate perspective, the distribution of course choices is an important input to the skill (technical skills) composition of future workforce. In that sense, except humanities, the rest of the courses are technical-intensive courses; hence, humanities is treated as a reference category.

The results indicate that gender polarization is apparent between humanities and engineering. The predicated probabilities bring out the dichotomy between the choice of courses and levels of living expressed through consumption expenditures in terms of professional and non-professional courses. Predicted probabilities of course choices bring in a clear distinction between south and west regions preferring engineering and other professional courses, whereas north, east and NES prefer humanities.

Predicted probabilities of course choices bring in a clear distinction between south and west regions preferring engineering and other professional courses, whereas north, east and NES prefer humanities. This course and regional imbalance need to be worked with multi-pronged strategies of providing both access to education and employment opportunities in other states. But the predicted probabilities of medicine and science remain similar across the board. Very few research studies on the determinants of field choice in higher education prevail in India. Research studies on returns to education by field or course choices hardly exist in India. These evidences are particularly important to know which course choices can support student loans, which can be the future area of work.

The research evidence is particularly important to know which course choices can support student loans, which can be the future area of work, as well as how to address the gender bias in the course choices.

The paper has social implications in terms of giving insights into the course choices of students. These findings bring in implications for practice in their ability to predict the demand for course choices and their share of demand, not only in the labor market but also across regions. India has 36 states/UTs and each state/UT has a huge population size and large geographical areas. The choice of course has state-specific influence because of nature of state economy, society, culture and inherent education systems. Further, within the states, rural and urban variation has also a serious influence on the choice of courses.

The present study is a value addition on three counts. First, the choice of courses includes the recent trends in the preference over market-oriented/technical courses such as medicine, engineering and other professional courses (chartered accountancy and similar courses, courses from Industrial Training Institute, recognized vocational training institute, etc.). The choice of market-oriented courses has been examined in relation to the choice of conventional subjects. Second, the socio-economic background of students plays a significant role in the choice of courses. Third, the present paper uses the latest data on Social Consumption on Education.

This article attempts to highlight the defining role that community media engagements play during times of the pandemic. It is argued that the outbreak of COVID-19 pandemic forced community news media houses to reinvent their news reporting practices to cover issues pertaining to the marginalized and underprivileged sections of the society. It explores the role of community media in engaging and empowering the citizens during the COVID-19 pandemic.

Central to our study is the analysis of the news model of “Video Volunteers” (henceforth VV), an independent community-based online news platform based in India. To understand the level of citizen participation and engagement in the making and dissemination of news during the pandemic, the authors conducted 13 interviews with different stakeholders of VV, including founders and news audiences.

It seeks to reveal that when the mainstream media have failed to represent the issues of a local community, it is the independent media platforms like VV which function as a veritable source of information and sharing of knowledge. Most importantly, this paper emphasizes that the communicative model of independent community-based online platforms has been most successful in the coverage of the pandemic and the level of engagement with the citizenry.

The study contributes to the aspects of reciprocity and collaborative journalism in community news media and its potential impacts on news creation and dissemination.

Introduction: Artificial intelligence (AI) assists recruiters in effectively and efficiently nominating applicants precisely and accurately. It helps in the screening of resumes without biasness. This chapter will identify different AI technology and various organisations using it fully or partially.

Purpose: This chapter aims to get insights about various AI tools that assist human recruiters, save time and cost, and provide modern experiences. It will help identify various applications that are currently in use and their features. It also helps in finding out the benefits and the challenges faced by the recruiters and the applicants while assimilating those applications in hiring.

Need for the Study: The study will be helpful to all those recruiting firms who are presently using AI or not using it to understand the benefits and challenges they might face.

Methodology: The chapter will be based on reviews and industry reports. This chapter will include a study related to human resource (HR) functions where AI is used. To give more insights into AI technology, this study mentions various applications like Mya, Brazen, etc., and their usefulness in recruitment. Also, special emphasis would be given to the recruitment functions as most companies use AI. Some companies like Deloitte and Oracle are using AI fully or partially will also be incorporated.

Findings: The study finds out that although many companies have started to use AI tools for recruitment, they have not explored all the algorithms that can be used to complete the whole recruitment and selection process. Companies like Loreal use AI for candidate applications and recruiter screening, but human recruiters stand strong for assessments and interviews. AI’s widespread use presents human resource management (HRM) practitioners with both opportunities and challenges.

Practical implications: The basic idea of the study is to scrutinise the related literature and find out the features, advantages and limitations/challenges of using AI which would be helpful for recruiters in better understanding of the technology-driven recruitment.

Intimate partner violence (IPV) is a prevalent public health issue impacting women’s physical and mental health and psychosocial walks of life across cultures and societies. Despite this, many women continue to stay in such relationships. This study aims to examine, from a constructionist perspective, why women stay in abusive marriages and what factors prevent them from taking appropriate actions. Also, women’s experiences of surviving IPV were explored.

Using a descriptive qualitative research method, the authors recruited and interviewed 17 women from northern India. The data were analysed thematically.

The underlying themes that emerged in response to the research questions were the lack of family support, societal ideals, the culture of normalizing violence, fear, love and hope and emotional turmoil. The reason for women not to leave abusive marriages corresponds to the broader social constructions of marriage and women’s perceived positions in family and society.

Research on women’s decision to stay in abusive relationships is limited, especially from the Global South. This study generates fresh evidence on the subject matter, specifically from the Indian context. The study result contributes uniquely by approaching the problem of staying in an abusive relationship from a social constructionist perspective. This study has implications for policy and psychosocial interventions to bring progressive changes in the lives of women experiencing IPV.

This chapter aims to contribute to the literature on sustainability in the public sector by discussing how human resource and human resource management can help to integrate environmental management into organizations and improve environmental performance. Public sector scholars have neglected the study of Green Human Resource Management (GHRM) until now. Nevertheless, implementing such practices could lead to positive outcomes regarding awareness of environmental issues, organizational reputation and attractiveness, job satisfaction and organizational performance. The authors discuss the relevance and the necessity of developing a field of research on GHRM in public organizations. Starting from a conceptual review of the main literature on GHRM, this chapter provided some directions for future research.

This paper aims to identify the variables responsible for classifying the investors into risk takers (RT) and risk avoiders (RA) across their economic perspectives.

The research offers a novel and unobtrusive measure of classifying investors into RT and RA based on a set of financial risk tolerance (FRT) questions. The authors have investigated the causes of discrimination across economic perspectives over a sample of 552 investors exposed to market risk.

The authors identify that out of the total of 11 risk assessment variables, only three are responsible for classifying investors into RA and RT. The variables are risk return trade-off, comfort level dealing with risk, and understanding short-term volatility. Financial literacy is considered as an emerging cause of discrimination. Further, the authors highlight the most striking finding to be the discriminating factors across wealth and source of income of the investors.

Existing research on FRT can be loosely segregated into three groups: the relationship between an individual's financial and non-FRT, estimation of FRT score (FRTS), and perceived self-assessed FRTS. The current research roughly falls into the third category of study where the authors have not only studied the self-assessed risk tolerance but also evaluated the predictors. Most of the studies have focussed on estimating self-assessed FRT with the help of one direct question to the respondent. However, the uniqueness of this study is that the researchers have used an instrument comprising a series of direct and indirect questions that can easily estimate the self-assessed risk perception and also discriminate the role of the economic factors that have any impact on self-assessed FRTS.