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Household chores are one of the most essential aspects of each individual's daily routine. The author has observed people from middle and upper socioeconomic backgrounds, outsourcing women domestic workers to perform these household tasks. Even though these women domestic workers make up a significant portion of the total working class, they remain a socially and financially vulnerable section of society. The job of working in other people's private spaces comes with little or no regulation, social protection and no guarantee of decent work standards. The major aim of this study is to find out the social wellbeing of part-time domestic workers of Pune.

For this purpose, the researchers have interviewed 167 women working in the Pune region of Maharashtra, India from the period of October 2020 to January 2021. Descriptive methods and factor analysis have been used to analyze the collected data, so that socioeconomic wellbeing correlated with the significant factors explored. Further, the factors identified that Exploratory Factor Analysis (EFAs) are further validated through reliability analysis (Cronbach’s alpha for economic wellbeing and social wellbeing index for social wellbeing).

With the help of this study, researchers have tried to explore the significant factors to the social and economic wellbeing of domestic workers. The qualitative facts collected during the interview time have substantiated the findings got in EFA.

The paper aims to provide ground-level insights to policymakers focusing on the domestic work sector, and the gaps identified in the research will help the policymakers to frame the guidelines for the betterment of these informal domestic workers.

Illustrate the typical organizational responsibility of a small, medium industry dealing with precision manufacturing products. Introduce a balanced scorecard (BSC) as a concept about the case in the context. Introduce the parameters specific to small and medium enterprise (SME) that could be considered to be part of the key performance indicators. Understand the advantages and disadvantages of using a BSC in SMEs in emerging economies.

Gopika Rani, the recently hired Executive Assistant along with Sanjana M, the Business Development Manager of SEP India Private Ltd. (SEPI), a small medium enterprise, were finalizing a proposal for the forthcoming “India Small Business Excellence Awards 2020.” The proposal was to be considered by the Board of Directors scheduled to meet next week for approvals. Sanjana apprises Gopika on CRISIL’s policy advisory role and its annual awards scheme for SMEs in India. She also details recent modifications announced by the Government of India that had impacted SEPI and was pertinent for filling the application. Gopika understood that SEPI was well-known for the precision and durability of its component, and was poised for growth. The business catered to global suppliers (Tier-1 companies) of the Indian automotive industry that accounted for over 75% and the balance contributed to exports. SEPI’s unique products such as Starter Motor Ignition or the Fuel Vending pump (Automotive) or the non-automotive products such as arrowheads and bowstrings (sports) or the heart-valves (medical) have all the quality certifications. For new product development, customer feedback played a crucial role at all stages of development from prototype to pilot tests. SEPI’s mission “be our customers’ preferred supplier and business partner” drove their personnel and organizational objectives. Also, SEPI could get multiple benefits and be in a strong market position because of this award recognition. Gopika was, however, unclear about SEPI’s business strategies and use of appropriate performance measurement tools. Gopika desired to address the Board of Directors next week on her idea of applying a BSC as a useful “strategic planning and management tool.” The BSC methodology can be used to monitor the performance of SME firms against strategic goals. It can be successfully implemented in smaller organizations because of their simpler set-ups and tendency to arrive at a consensus quickly. However, implementation of BSC within the Indian micro, small and medium enterprises has been scant. Several studies found that the lack of ownership, resistance to change, a scarcity of training and coordination between the departments and lack of funds were among the challenges. The firms also had to make numerous changes to their strategies as business environments evolved. Gopika was convinced that the tool could blend in all the “four perspectives – customer, financial, internal business and learning and growth” and grow. The tool could demonstrate meeting all the prerequisites, “needs to have an exemplary vision, demonstrate outstanding business acumen, use best practices and create a legacy for the others to follow,” that were prerequisites for receipt of this award. Her next project would be to seek approval for the implementation of BSC, a beneficial and apt tool for SEPI. Do you agree with Gopika Rani that BSC is a suitable tool for SEPI? If yes, why? If no, why?

This case study titled leveraging the BSC – a tool for SME advancement is intended for use in the graduate management program (MBA) in subject electives, namely, entrepreneurship, strategy formulation, human resource management or production management.

Teaching Notes are available for educators only.

CSS 3: Entrepreneurship.

Teaching Notes are available for educators only.

Health insurance in India has shown little development. It has not been able to evoke enthusiasm among Indian insurers. Consequently, several reports on Indian health care insurance have been produced. The purpose of this paper is to offer a review of this matter.

Critical review of related published and grey literature.

Almost 79 per cent of health expenditure is borne by private bodies and the rest by the public. Authors argue that to stimulate private health insurance growth, the Indian government should recognize health insurance as a separate line of business and distinguish it from other non‐life insurance. Particular emphasis is placed on the present health care scenario in India and international field generally. A global comparison of selected Asian countries, regarding their national incomes and health expenditure in public and private sectors, generates insights. Third party administrators (TPAs) facilitate a cashless health services for their customers and offer back‐up services to the insurance companies. Desired strategies and ways of furthering the role of the Insurance Regulatory and Development Authority in acting as a regulator for the purpose of ensuring the industry's smooth functioning is an issue for India's health services.

Information about the present complexities in the health insurance market has been gathered from various sources and summarized.

Fueled by the rapid growth of internet, steganography has emerged as one of the promising techniques in the communication system to obscure the data. Steganography is defined as the process of concealing the data or message within media files without affecting the perception of the image. Media files, like audio, video, image, etc., are utilized to embed the message. Nowadays, steganography is also used to transmit the medical information or diagnostic reports. The paper aims to discuss these issues.

In this paper, the novel wavelet transform-based steganographic method is proposed for secure data communication using OFDM system. The embedding and extraction process in the proposed steganography method exploits the wavelet transform. Initially, the cost matrix is estimated by the following three aspects: pixel intensity, edge transformation and wavelet transform. The cost estimation matrix provides the location of the cover image where the message is to be entrenched. Then, the wavelet transform is utilized to embed the message into the cover image according to the cost value. Subsequently, in the extraction process, the wavelet transform is applied to the embedded image to retrieve the message efficiently. Finally, in order to transfer the secret information over the channel, the newly developed wavelet-based steganographic method is employed for the OFDM system.

The experimental results are evaluated and performance is analyzed using PSNR and MSE parameters and then compared with existing systems. Thus, the outcome of our wavelet transform steganographic method achieves the PSNR of 71.5 dB which ensures the high imperceptibility of the image. Then, the outcome of the OFDM-based proposed steganographic method attains the higher PSNR of 71.07 dB that proves the confidentiality of the message.

In the authors’ previous work, the embedding and extraction process was done based on the cost estimation matrix. To enhance the security throughout the communication system, the novel wavelet-based embedding and extraction process is applied to the OFDM system in this paper. The idea behind this method is to attain a higher imperceptibility and robustness of the image.

This paper aims to develop the Hybrid feature descriptor and probabilistic neuro-fuzzy system for attaining the high accuracy in face recognition system. In recent days, facial recognition (FR) systems play a vital part in several applications such as surveillance, access control and image understanding. Accordingly, various face recognition methods have been developed in the literature, but the applicability of these algorithms is restricted because of unsatisfied accuracy. So, the improvement of face recognition is significantly important for the current trend.

This paper proposes a face recognition system through feature extraction and classification. The proposed model extracts the local and the global feature of the image. The local features of the image are extracted using the kernel based scale invariant feature transform (K-SIFT) model and the global features are extracted using the proposed m-Co-HOG model. (Co-HOG: co-occurrence histograms of oriented gradients) The proposed m-Co-HOG model has the properties of the Co-HOG algorithm. The feature vector database contains combined local and the global feature vectors derived using the K-SIFT model and the proposed m-Co-HOG algorithm. This paper proposes a probabilistic neuro-fuzzy classifier system for the finding the identity of the person from the extracted feature vector database.

The face images required for the simulation of the proposed work are taken from the CVL database. The simulation considers a total of 114 persons form the CVL database. From the results, it is evident that the proposed model has outperformed the existing models with an improved accuracy of 0.98. The false acceptance rate (FAR) and false rejection rate (FRR) values of the proposed model have a low value of 0.01.

This paper proposes a face recognition system with proposed m-Co-HOG vector and the hybrid neuro-fuzzy classifier. Feature extraction was based on the proposed m-Co-HOG vector for extracting the global features and the existing K-SIFT model for extracting the local features from the face images. The proposed m-Co-HOG vector utilizes the existing Co-HOG model for feature extraction, along with a new color gradient decomposition method. The major advantage of the proposed m-Co-HOG vector is that it utilizes the color features of the image along with other features during the histogram operation.

This study aims to examine the influence of five sports celebrity personality (SCP) attributes – attractiveness, expertise level, credibility, trustworthiness and character – on consumers' purchase intentions (CPI). It identifies celebrity brand congruence (CBC), endorsed brand celebrity (EBC) and transfer of brand image (TBI) as antecedents of CPI.

The purposive sampling technique was used to collect the data from 838 respondents. This study developed a multidimensional construct for SCP. The covariance-based structural equation modeling (SEM) technique was used to examine the relationship between SCP and the endorsed brand. The study used CBC as a mediator and EBC and TBI as partial mediators. The direct and indirect effect of SCP on CPI was investigated using CBC, EBC and TBI as mediators.

This study supports the importance of three antecedents (i.e. CBC, EBC and TBI) on CPI. It finds congruence across SCP and CBC variables, and a positive impact of SCP on EBC and TBI variables. Also, it exhibits a significant direct effect of CBC on EBC and TBI, whereas the direct effect of CBC on CPI is not substantial. The indirect effect of CBC through mediating variables EBC and TBI found to be significant.

This study concludes that sports celebrity endorsement is essential to transfer the positive celebrity image to the endorsed brand image. However, it is not merely sufficient to influence the buyers' purchase conduct; the brand credibility additionally assumes to take a role in changing their behavioral intentions.

This study contributes to the sports marketing literature by its novelty in analyzing the sports celebrity personality at a multidimensional level. It uses SCP's different attributes as one construct and studies its impact on CPI by taking CBC, EBC and TBI as mediators. The results of this study equip sports management professionals with the knowledge to build better long-term relationships with consumers.

Brain tumor segmentation and classification is the interesting area for differentiating the tumorous and the non-tumorous cells in the brain and classifies the tumorous cells for identifying its level. The methods developed so far lack the automatic classification, consuming considerable time for the classification. In this work, a novel brain tumor classification approach, namely, harmony cuckoo search-based deep belief network (HCS-DBN) has been proposed. Here, the images present in the database are segmented based on the newly developed hybrid active contour (HAC) segmentation model, which is the integration of the Bayesian fuzzy clustering (BFC) and the active contour model. The proposed HCS-DBN algorithm is trained with the features obtained from the segmented images. Finally, the classifier provides the information about the tumor class in each slice available in the database. Experimentation of the proposed HAC and the HCS-DBN algorithm is done using the MRI image available in the BRATS database, and results are observed. The simulation results prove that the proposed HAC and the HCS-DBN algorithm have an overall better performance with the values of 0.945, 0.9695 and 0.99348 for accuracy, sensitivity and specificity, respectively.

The proposed HAC segmentation approach integrates the properties of the AC model and BFC. Initially, the brain image with different modalities is subjected to segmentation with the BFC and AC models. Then, the Laplacian correction is applied to fuse the segmented outputs from each model. Finally, the proposed HAC segmentation provides the error-free segments of the brain tumor regions prevailing in the MRI image. The next step is to extract the useful features, based on scattering transform, wavelet transform and local Gabor binary pattern, from the segmented brain image. Finally, the extracted features from each segment are provided to the DBN for the training, and the HCS algorithm chooses the optimal weights for DBN training.

The experimentation of the proposed HAC with the HCS-DBN algorithm is analyzed with the standard BRATS database, and its performance is evaluated based on metrics such as accuracy, sensitivity and specificity. The simulation results of the proposed HAC with the HCS-DBN algorithm are compared against existing works such as k-NN, NN, multi-SVM and multi-SVNN. The results achieved by the proposed HAC with the HCS-DBN algorithm are eventually higher than the existing works with the values of 0.945, 0.9695 and 0.99348 for accuracy, sensitivity and specificity, respectively.

This work presents the brain tumor segmentation and the classification scheme by introducing the HAC-based segmentation model. The proposed HAC model combines the BFC and the active contour model through a fusion process, using the Laplacian correction probability for segmenting the slices in the database.