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.

A Comparative Study of Wavelet Based Feature Extraction Techniques in Recognizing Isolated Spoken Words

Speech is a natural mode of communication for people and speech recognition is an intensive area of research due to its versatile applications. This paper presents a comparative study of various feature extraction methods based on wavelets for recognizing isolated spoken words. Isolated words from Malayalam, one of the four major Dravidian languages of southern India are chosen for recognition. This work includes two speech recognition methods. First one is a hybrid approach with Discrete Wavelet Transforms and Artificial Neural Networks and the second method uses a combination of Wavelet Packet Decomposition and Artificial Neural Networks. Features are extracted by using Discrete Wavelet Transforms (DWT) and Wavelet Packet Decomposition (WPD). Training, testing and pattern recognition are performed using Artificial Neural Networks (ANN). The proposed method is implemented for 50 speakers uttering 20 isolated words each. The experimental results obtained show the efficiency of these techniques in recognizing speech

Antibiotic resistance of Aeromonas hydrophila isolated from marketed fish and prawn of South India

A total of 319 strains of Aeromonas hydrophila were isolated from 536 fish and 278 prawns for a 2-year period. All the strains were tested for resistance to 15 antibiotics and 100% of the strains was resistant to methicillin and rifampicin followed by bacitracin and novobiocin (99%). Only 3% of the strains exhibited resistance against chloramphenicol. The multiple antibiotic resistance (MAR) indexing of A. hydrophila strains showed that all of them originated from high-risk sources

Development of Techniques for the Automatic Extraction and Grade Detection of Glioma Tumors from Conventional Brain Magnetic Resonant Images

Cerebral glioma is the most prevalent primary brain tumor, which are classified broadly into low and high grades according to the degree of malignancy. High grade gliomas are highly malignant which possess a poor prognosis, and the patients survive less than eighteen months after diagnosis. Low grade gliomas are slow growing, least malignant and has better response to therapy. To date, histological grading is used as the standard technique for diagnosis, treatment planning and survival prediction. The main objective of this thesis is to propose novel methods for automatic extraction of low and high grade glioma and other brain tissues, grade detection techniques for glioma using conventional magnetic resonance imaging (MRI) modalities and 3D modelling of glioma from segmented tumor slices in order to assess the growth rate of tumors. Two new methods are developed for extracting tumor regions, of which the second method, named as Adaptive Gray level Algebraic set Segmentation Algorithm (AGASA) can also extract white matter and grey matter from T1 FLAIR an T2 weighted images. The methods were validated with manual Ground truth images, which showed promising results. The developed methods were compared with widely used Fuzzy c-means clustering technique and the robustness of the algorithm with respect to noise is also checked for different noise levels. Image texture can provide significant information on the (ab)normality of tissue, and this thesis expands this idea to tumour texture grading and detection. Based on the thresholds of discriminant first order and gray level cooccurrence matrix based second order statistical features three feature sets were formulated and a decision system was developed for grade detection of glioma from conventional T2 weighted MRI modality.The quantitative performance analysis using ROC curve showed 99.03% accuracy for distinguishing between advanced (aggressive) and early stage (non-aggressive) malignant glioma. The developed brain texture analysis techniques can improve the physician’s ability to detect and analyse pathologies leading to a more reliable diagnosis and treatment of disease. The segmented tumors were also used for volumetric modelling of tumors which can provide an idea of the growth rate of tumor; this can be used for assessing response to therapy and patient prognosis.

Investigations on Longline Fishing in Lakshadweep Sea with special reference to Bycatch Issues Bait and Hook Selectivity

The main objectives of the present study have been studies on the operational performance of tuna longline in Lakshadweep Sea studies on the efficiency of hooks in the longline operation studies on the efficiency of baits in the longline operation studies on bycatch in longline operation studies on predation on the longline catch and the hook loss encountered during the fishing operation

A marine bacterium, Micrococcus MCCB 104, antagonistic to vibrios in prawn larval rearing systems

marine bacterium, Micrococcus MCCB 104, isolated from hatchery water, demonstrated extracellular antagonistic properties against Vibrio alginolyticus, V. parahaemolyticus, V. vulnificus, V. fluviallis, V. nereis, V. proteolyticus, V. mediterranei, V. cholerae and Aeromonas sp., bacteria associated with Macrobrachium rosenbergii larval rearing systems. The isolate inhibited the growth of V. alginolyticus during co-culture. The antagonistic component of the extracellular product was heat-stable and insensitive to proteases, lipase, catalase and α-amylase. Micrococcus MCCB 104 was demonstrated to be non-pathogenic to M. rosenbergii larvae

Antimicrobial activity of chitosan against vibrios from freshwater prawn Macrobrachium rosenbergii larval rearing systems

Chitosan is a biocompatible and biodegradable natural polymer with established antimicrobial properties against specific microorganisms. The present study demonstrates its antibacterial activity against 48 isolates of Vibrio species from prawn larval rearing systems. The antibacterial activity had a positive correlation with the concentration of chitosan. This work opens up avenues for using chitosan as a prophylactic biopolymer for protecting prawn larvae from vibriosis.

Efficacy of fermented prawn shell waste as a feed ingredient for Indian white prawn, Fenneropenaeus indicus

Prawn shell waste collected from shrimp-processing plants in Cochin, India, was subjected to fermentation using 20 chitinoclastic and proteolytic/non-proteolytic bacterial strains. The products generated were analysed for protein, lipid, total sugars, N-acetyl glucosamine, free amino acids and ash. Shrimp diets were prepared using these 20 fermented products and a control diet using raw prawn shell waste. Feeding experiment was conducted with postlarvae (PL21) of Indian white prawn, Fenneropenaeus indicus for a period of 21 days. Biogrowth parameters such as mean weight gain, feed conversion ratio, specific growth rate and protein efficiency ratio were estimated and the animals were challenged with white spot virus orally via diet. Enhanced growth could be observed in prawns fed F134 and F124, incorporated with the fermentation products generated using Bacillus spp., C134 and C124 respectively. The percentage survival of prawns after 7 days of challenge was found to be highest for groups fed diet F111 incorporated with fermentation product generated using Bacillus sp. These products of bacterial fermentation hold promise as growth enhancers and immunostimulants in aquaculture. KEY WORDS: biogrowth parameters, feed

Bioremediation In Prawn Grow Out Systems

Winter School on 'RECENT ADVANCES IN DIAGNOSIS AND MANAGEMENT OF DISEASES IN MARICULTURE' 7-27 November, 2002 Course Manual,CMFRI

Applying Data mining using Statistical Techniques for Career Selection

For years, choosing the right career by monitoring the trends and scope for different career paths have been a requirement for all youngsters all over the world. In this paper we provide a scientific, data mining based method for job absorption rate prediction and predicting the waiting time needed for 100% placement, for different engineering courses in India. This will help the students in India in a great deal in deciding the right discipline for them for a bright future. Information about passed out students are obtained from the NTMIS ( National technical manpower information system ) NODAL center in Kochi, India residing in Cochin University of science and technology

Detection of Flock Movement in Spatio-Temporal Database using Clustering Techniques - an Experience

In this paper, moving flock patterns are mined from spatio- temporal datasets by incorporating a clustering algorithm. A flock is defined as the set of data that move together for a certain continuous amount of time. Finding out moving flock patterns using clustering algorithms is a potential method to find out frequent patterns of movement in large trajectory datasets. In this approach, SPatial clusteRing algoRithm thrOugh sWarm intelligence (SPARROW) is the clustering algorithm used. The advantage of using SPARROW algorithm is that it can effectively discover clusters of widely varying sizes and shapes from large databases. Variations of the proposed method are addressed and also the experimental results show that the problem of scalability and duplicate pattern formation is addressed. This method also reduces the number of patterns produced

Comparison of Statistical and Semantic Similarity Techniques for Paraphrase Identification

This paper compares statistical technique of paraphrase identification to semantic technique of paraphrase identification. The statistical techniques used for comparison are word set and word-order based methods where as the semantic technique used is the WordNet similarity matrix method described by Stevenson and Fernando in [3].

Mini-trawls for Estuarine Fishing Kasargod District

Mi ni -trawls are operated by the artisanal fishermen from small wooden non-motorised canoes. Shrimp, fish and crab trawls wi th head rope length rang ing from 3.5-8 m, made of Po lyethy lene mon ofila ment (PE) twisted and Polyamide mullifilament (PA) rigged to 6-7 kll fla t rectangular wooden otter boards are common in the lower reaches of Kariango de and Chandrag iri rive rs. Since the trawling speed is less, ca tch is do minated by crus taceans. Less scope ratio also may be affecting the catching efficiency of the gear. This pape r deals with the design, operation and economics of mini traw ling carried out by a group of fisherme n in the above rivers of Kasargod district Kerala state.

Techniques to Improve the word alignments in Statistical Machine Translation from English to Malayalam

In Statistical Machine Translation from English to Malayalam, an unseen English sentence is translated into its equivalent Malayalam translation using statistical models like translation model, language model and a decoder. A parallel corpus of English-Malayalam is used in the training phase. Word to word alignments has to be set up among the sentence pairs of the source and target language before subjecting them for training. This paper is deals with the techniques which can be adopted for improving the alignment model of SMT. Incorporating the parts of speech information into the bilingual corpus has eliminated many of the insignificant alignments. Also identifying the name entities and cognates present in the sentence pairs has proved to be advantageous while setting up the alignments. Moreover, reduction of the unwanted alignments has brought in better training results. Experiments conducted on a sample corpus have generated reasonably good Malayalam translations and the results are verified with F measure, BLEU and WER evaluation metrics

Significance Of Classification Techniques In Prediction Of Learning Disabilities

The aim of this study is to show the importance of two classification techniques, viz. decision tree and clustering, in prediction of learning disabilities (LD) of school-age children. LDs affect about 10 percent of all children enrolled in schools. The problems of children with specific learning disabilities have been a cause of concern to parents and teachers for some time. Decision trees and clustering are powerful and popular tools used for classification and prediction in Data mining. Different rules extracted from the decision tree are used for prediction of learning disabilities. Clustering is the assignment of a set of observations into subsets, called clusters, which are useful in finding the different signs and symptoms (attributes) present in the LD affected child. In this paper, J48 algorithm is used for constructing the decision tree and K-means algorithm is used for creating the clusters. By applying these classification techniques, LD in any child can be identified