Combined toxic effects of oil and pesticides on selected marine invertebrates

In recent years, pollution in general and sea water pollution in particular, has become an important topic for national and international considerations. Because of its impact on society, marine pollution has attracted great attention from politicians, administrators, natural scientists and technologists all over the world. To save our environment from further deterioration, it is essential to have an assessment of this problem This thesis involves investigation of the lethal and sub lethal effects of four pesticides and two petroleum oil, individually and in combinations on two commercially important bivalves. Among the four pesticides used two are organophosphates and the other two are organochlorines. Synthetic Pesticides, especially organophosphates and organochlorines have become increasingly important additions to chemical wastes polluting natural aquatic Communities special attention is given in the present investigation to delineate the combined toxic effect of oil and pesticides. The results are presented under different sections to make the presentation meaningful.

COMBINED FEATURE EXTRACTION TECHNIQUES AND NAIVE BAYES CLASSIFIER FOR SPEECH RECOGNITION

Speech processing and consequent recognition are important areas of Digital Signal Processing since speech allows people to communicate more natu-rally and efficiently. In this work, a speech recognition system is developed for re-cognizing digits in Malayalam. For recognizing speech, features are to be ex-tracted from speech and hence feature extraction method plays an important role in speech recognition. Here, front end processing for extracting the features is per-formed using two wavelet based methods namely Discrete Wavelet Transforms (DWT) and Wavelet Packet Decomposition (WPD). Naive Bayes classifier is used for classification purpose. After classification using Naive Bayes classifier, DWT produced a recognition accuracy of 83.5% and WPD produced an accuracy of 80.7%. This paper is intended to devise a new feature extraction method which produces improvements in the recognition accuracy. So, a new method called Dis-crete Wavelet Packet Decomposition (DWPD) is introduced which utilizes the hy-brid features of both DWT and WPD. The performance of this new approach is evaluated and it produced an improved recognition accuracy of 86.2% along with Naive Bayes classifier.