Aadeet Kumar

The present investigation was undertaken to investigate the acute toxicity of cadmium, a heavy metal widely detected in the aquatic environment due to natural effects and anthropogenic activities, in freshwater teleost, Channa punctata (Bloch). The experiments for the bioassay were performed in semi-static test condition according to the standard guidelines. The behavioural changes in the fish were observed for all tested concentrations of the metal. The data obtained for bioassay were analyzed for median lethal concentrations (LC ) of the metal by SPSS computer software. The LC 50 50 values, estimated by SPSS, with 95% confidence level were found to be:20.589 (18.326-23.221), 17.133 (14.840- 19.734), 11.815 (9.696 – 13.850) mg/L and 9.908 (7.635-12381) mg/L for cadmium chloride at 24, 48, 72 and 96 h exposure, respectively.

The paper presents the outlines of an ontology of plans and guidelines, which is then used as the basis for a framework for implementing guideline-based systems for the management of workflow in health care organizations. The framework has a number of special features, above all in that it enables us to represent in formal terms assignments of work-items both to individuals and to teams and to tailor guideline to specific contexts of application in health care organizations. It is designed also to enable implementations to do justice to the fact that the processes carried out in health care organizations may deviate in different ways from the norms set forth in corresponding guideline definitions. This means that implementations built in conformity with the framework will be marked by a type of flexibility that might make them more likely to be accepted by healthcare professionals than are standard guideline-based management systems.

This paper implements the continuous Hindi Automatic Speech Recognition (ASR) system using the proposed integrated features vector with Recurrent Neural Network (RNN) based Language Modeling (LM). The proposed system also implements the speaker adaptation using Maximum-Likelihood Linear Regression (MLLR) and Constrained Maximum likelihood Linear Regression (C-MLLR). This system is discriminatively trained by Maximum Mutual Information (MMI) and Minimum Phone Error (MPE) techniques with 256 Gaussian mixture per Hidden Markov Model(HMM) state. The training of the baseline system has been done using a phonetically rich Hindi dataset. The results show that discriminative training enhances the baseline system performance by up to 3%. Further improvement of ~7% has been recorded by applying RNN LM. The proposed Hindi ASR system shows significant performance improvement over other current state-of-the-art techniques.