This book offers an elaborate and empirical look at service quality of hospitals in the emerging market of India. The poor quality of service is a major issue in a large number of hospitals (particularly in government hospitals), which forces patients to opt for private hospitals that are generally much more expensive than government hospitals. This book provides a comprehensive understanding of service quality antecedents in Indian hospitals. It focuses on patient satisfaction and includes valuable insights and implications for hospital management and government. The book is theoretically grounded in SERVQUAL literature and uses appropriate and sophisticated techniques and tools to analyse data. It highlights causal model development with Structural Equation Modelling (SEM) and introduces a classification model, developed using Artificial Neural Networks (ANNs), in order to benchmark specialty cardiac care. The book also deals with Support Vector Machines (SVMs) and compares the error rates between SVM and ANN to find the best classification technique among the two. Overall, this book is a timely and relevant work that contributes to the theory, practice and policy of service quality in hospitals.

Resource Allocation (RA) involves the distribution and utilization of available resources in the system. Because resource availability is usually scarce and expensive, it becomes important to find optimal solutions to such problems. Thus RA problems represent an important class of problems faced by mathematical programmers. This book focuses on development of models and heuristics for six new and complex sub-classes of RA problems in Supply Chain (SC) networks, focusing on bi-objectives, dynamic input data, and multiple performance measures based allocation and integrated allocation, and routing with complex constraints. It considers six set of variants of the RA problems normally encountered in practice but have not yet been studied. These variants of the classical RA are complex and pertaining to both manufacturing and service industry.

Stock market manipulation is detrimental to traders and corporations, causes unnecessary price fluctuations, and only benefits financial criminals. The research presented here determines an appropriate model to help identify stocks witnessing activities that are indicative of potential manipulation through three separate but related studies. In Developing an Effective Model for Detecting Trade-Based Market Manipulation, classifiers based on three different techniques namely discriminant analysis, a composite classifier based on Artificial Neural Network and Genetic Algorithm and support Vector Machines is proposed. The proposed models help investigators, with varying degree of accuracy, to arrive at a shortlist of securities which could be subject to further detailed investigation to detect the type and nature of the manipulation, if any. Following a fluid outline, Developing an Effective Model for Detecting Trade-Based Market Manipulation, introduces the topic, explores the aims and scopes of the research, before delving into the data and modelling to explore their application to the stock market to detect price manipulation.