Learn More
ACKNOWLEDGEMENT The timely and successful completion of the book could hardly be possible without the helps and supports from a lot of individuals. I will take this opportunity to thank all of them who helped me either directly or indirectly during this important work. First of all I wish to express my sincere gratitude and due respect to my supervisor Dr.(More)
In pursuit of better CRTh 2 receptor antagonist agents, 3D-QSAR studies were performed on a series of 2,4-disubstituted-phenoxy acetic acid derivatives. In this paper we report a novel three-dimensional QSAR approach, kNN-MFA, developed based on principles of the k-nearest neighbor method combined with various variable selection procedures. The kNN-MFA(More)
Quantitative Structure-Activity Relationship (QSAR) is based on the hypothesis that changes in molecular structure reflect changes in the observed response or biological activity. The success of any quantitative structure–activity relationship model depends on the accuracy of the input data, selection of appropriate descriptors, statistical tools and the(More)
The metabotropic glutamate (mGluRs) receptors are a distinct class of G-protein-coupled receptors that act through activation of phospholipase C and/or inhibition of adenylate cyclase. They encompass seven-transmembrane domain proteins, comprehensively expressed in neuronal and glial cells within the brain, spinal cord and periphery and are involved in(More)
Properly comprehending and modeling the dynamics of financial data has indispensable practical importance. The prime goal of a financial time series model is to provide reliable future forecasts which are crucial for investment planning, fiscal risk hedging, governmental policy making, etc. These time series often exhibit notoriously haphazard movements(More)
Recently, the Particle Swarm Optimization (PSO) technique has gained much attention in the field of time series forecasting. Although PSO trained Artificial Neural Networks (ANNs) performed reasonably well in stationary time series forecasting, their effectiveness in tracking the structure of non-stationary data (especially those which contain trends or(More)
Improvement of time series forecasting accuracy is an active research area having significant importance in many practical domains. Extensive works in literature suggest that substantial enhancement in accuracies can be achieved by combining forecasts from different models. However, forecasts combination is a difficult as well as a challenging task due to(More)