Government College

About: Government College is a based out in . It is known for research contribution in the topics: Population & Ring (chemistry). The organization has 4481 authors who have published 5986 publications receiving 57398 citations.

Development of Biodegradable Starch Microspheres for Intranasal Delivery

Abstract: Domperidone microspheres for intranasal administration were prepared by emulsification crosslinking technique. Starch a biodegradable polymer was used in preparation of microspheres using epichlorhydrine as cross-linking agent. The formulation variables were drug concentration and polymer concentration and batch of drug free microsphere was prepared for comparisons. All the formulations were evaluated for particle size, morphological characteristics, percentage drug encapsulation, equilibrium swelling degree, percentage mucoadhesion, bioadhesive strength, and in vitro diffusion study using nasal cell. Spherical microspheres were obtained in all batches with mean diameter in the range of above 22.8 to 102.63 μm. They showed good mucoadhesive property and swelling behaviour. The in vitro release was found in the range of 73.11% to 86.21%. Concentration of both polymer and drug affect in vitro release of drug.

Local and global characteristics-based kernel hybridization to increase optimal support vector machine performance for stock market prediction

Abstract: In this paper, a novel multi-kernel support vector machine (MKSVM) combining global and local characteristics of the input data is proposed. Along with, a parameter tuning approach is developed using the fruit fly optimization (FFO), which is applied to stock market movement direction prediction problem. At first, factor analysis is used for identifying reduced key features called as factor scores from the raw stock index data which when applied to the model contributes to improvement in prediction performance. Subsequently, a hybrid kernel method combining local and global characteristics of input data is proposed, where polynomial is used for global kernel and radial basis function is utilized for local kernel. Additionally, FFO-based parameter tuning scheme is proposed to enhance the prediction performance further. Lastly, the evolving MKSVM with best feature subset and optimal parameters is used to predict stock market movement direction based upon historical data series. For evaluation and illustration purposes, three significant stock databases, NYSE, DJI and S&P 500 are used as testing targets. The effectiveness of this proposed approach is proved by three different stock market datasets, which demonstrate that the proposed approach outperforms the MKSVM with default parameter, MKSVM with PSO, MKSVM with GA and other methods. In addition, our findings reveal that the optimization strategy proposed here may be used as a promising choice forecasting tool for better generalization ability higher forecasting accuracy.

Irrigation Planning Under Uncertainty—A Multi Objective Fuzzy Linear Programming Approach

Abstract: The problem of irrigation planning becomes more complex by considering an uncertainty. The uncertainties can be tackled by formulating the problem of irrigation planning as Fuzzy Linear Programming (FLP). FLP models can incorporate the scenario of real world problem. In the present study, Multi Objective Fuzzy Linear Programming (MOFLP) irrigation planning model is formulated for deriving the optimal cropping pattern plan for the case study of Jayakwadi project in the Godavari river sub basin in Maharashtra State, India. Four conflicting objectives are considered such as Net Benefits (NB), Crop/Yield Production (CP), Employment Generation/Labour Requirement (EG) and Manure Utilization (MU). Four different cases are considered to incorporate the uncertainty in MOFLP model. To include the uncertainty in irrigation planning problem only objectives are taken as fuzzy and constraints are crisp in nature in Case-I. To consider the uncertainty involved in availability of resources, in Case-II the stipulations are fuzzy. The technological coefficients are fuzzy in Case-III. The Case-IV includes both technological coefficients and stipulations fuzzy. The level of satisfaction (λ) works out to be 0.58, 0.50, 0.50 and 0.28 respectively for Case-I to IV. The results obtained in Case-IV are more realistic and promising as it involves the uncertainty in technological coefficients and stipulations simultaneously.