Many training algorithms (like gradient descent, for example) use random initial weights. These algorithms are rather sensitive to their starting position in the error space, which is represented by their initial weights. This paper shows that the training performance can be improved signiicantly by using a Particle Swarm Optimizer (PSO) to initialize the weights, rather than random initializat...

In models of Data Envelopment Analysis (DEA), an optimal set of input and output weights is generally as-sumed to represent the assessed Decision Making Unit (DMU) in the best light in comparison to all the other DMUs. These sets of weights are, typically, different for each of the participating DMUs. Thus, it is important to find a Common Set of Weights (CSW) across the set of DMUs. In this pa...

Background and Objectives: One of the main concerns of hospital managers is their ability in improving their organization's performance. The use of quality management and decision-making techniques facilitates managers to achieve this goal. In this research, the corrective activities to increase the quality of hospital services are determined and selected using an integration o...

OBJECTIVE To calculate the effect of using two different sets of disability weights for estimates of disability-adjusted life-years (DALYs) averted by interventions delivered in one hospital in India. METHODS DALYs averted by surgical and non-surgical interventions were estimated for 3445 patients who were admitted to a 106-bed private hospital in a semi-urban area of northern India in 2012-2...

a characteristic of data envelopment analysis (dea) is to allow individual decision making units (dmus) to select the factor weights which are the most advantageous for them in calculating their efficiency scores. this flexibility in selecting the weights, on the other hand, deters the comparison among dmus on a common base. for dealing with this difficulty and assessing all the dmus on the sam...