Search results

This chapter discussed the role of business intelligence (BI) in healthcare twofold strategic decision making of the organization and the stakeholders. The visualization techniques of data mining are applied for the early and correct diagnosis of the disease, patient’s satisfaction quotient and also helpful for the hospital to know their best commanders.

In this chapter, the usefulness of BI is shown at two levels: at doctor level and at hospital level. As a case study, a hospital is taken which deals with three different kinds of diseases: Breast Cancer, Diabetes, and Liver disorder. BI can be applied for taking better strategic decisions in the context of hospital and its department’s growth. At the doctor level, on the basis of various symptoms of the disease, the doctor can advise the suitable treatment to the patients. At the hospital level, the best department among all can be identified. Also, a patient’s type of admission, continued their treatments with the hospital, patient’s satisfaction quotient, etc., can be calculated. The authors have used different methods like Correlation matrix, decision tree, mosaic plots, etc., to conduct this analysis.

The utterly un-nostalgic person is probably a non-existent being. At both a personal and collective level, we explore how nostalgia is experienced and in demand during times of transition, disjuncture, conflict and uncertainty. This chapter explores the emotion of nostalgia and connects it specifically to the current corona pandemic – the challenges of lockdowns and social distancing measures on interaction, feelings of loneliness and a generalised sense of uncertainty and despair, and also a rise of nostalgia as a possible response to these challenges. The predominant view of nostalgia put forth in this chapter is that nostalgia has the capacity to provide a great deal of benefit (meaning, hope, direction and purpose) to individuals, groups, institutions and societies at large. Indeed, nostalgia can be a tranquil feeling in a fearful world. We relate nostalgia to studies and experiences from the pandemic period and speculate on how the so-called ‘corona crisis’ may impact feelings of nostalgia in the post-pandemic world – perhaps even a nostalgia and longing for the pandemic period itself.

If the corona pandemic has in fact sparked a new (or renewed) interest in nostalgia in contemporary society due to the corona pandemic, it may indeed prove to be a positive thing, particularly if it makes it easier for people to deal with current feelings of adversity and anxiety. We suggest the nostalgia mood that is generated and perpetuated by the continuing twists and turns of the corona pandemic may – in the short and long run – prove useful in coping with and giving meaning to the problems and perplexing circumstances of life, rather than being a regressive phenomenon. Perhaps, something good may, in the end, grow from something bad?

Although corporate social responsibility (CSR) is typically conceptualized as a discretionary pursuit that firms voluntarily engage in, state intervention in CSR — which we call legislated CSR — has been increasing globally. The nature and scope of CSR legislations, however, vary among countries. This chapter provides a broad overview of legislated CSR but it also presents a detailed analysis of a specific CSR legislation, the CSR law of India, in order to closely examine how and why CSR legislations emerge and what could be their implications for CSR.

Marketing concept is being widely used in all disciplines including library and Information Science domain for disseminating the services, resources and products to the target audiences. Thus, this study aims to define the marketing concept and how libraries can do marketing of their services, resources and products using marketing techniques and tools, such as 7Ps of the marketing mix. This study further pointed out the important internet resources available freely on the marketing of library services on the Internet which can be used by library and Information Science professionals with regard to getting the ideas how to market the library services and resources to the targeted users.

Breast cancer (BC) is one of the leading cancer in the world, BC risk has been there for women of the middle age also, it is the malignant tumor. However, identifying BC in the early stage will save most of the women’s life. As there is an advancement in the technology research used Machine Learning (ML) algorithm Random Forest for ranking the feature, Support Vector Machine (SVM), and Naïve Bayes (NB) supervised classifiers for selection of best optimized features and prediction of BC accuracy. The estimation of prediction accuracy has been done by using the dataset Wisconsin Breast Cancer Data from University of California Irvine (UCI) ML repository. To perform all these operation, Anaconda one of the open source distribution of Python has been used. The proposed work resulted in extemporize improvement in the NB and SVM classifier accuracy. The performance evaluation of the proposed model is estimated by using classification accuracy, confusion matrix, mean, standard deviation, variance, and root mean-squared error.

The experimental results shows that 70-30 data split will result in best accuracy. SVM acts as a feature optimizer of 12 best features with the result of 97.66% accuracy and improvement of 1.17% after feature reduction. NB results with feature optimizer 17 of best features with the result of 96.49% accuracy and improvement of 1.17% after feature reduction.

The study shows that proposal model works very effectively as compare to the existing models with respect to accuracy measures.