In an effort to provide a more efficient representation of the speech signal, the application of the wavelet analysis is considered. This research presents an effective and robust method for extracting features for speech processing. Based on the time-frequency multi-resolution property of wavelet transform, the input speech signal is decomposed into various frequency channels. The major issues concerning the design of this Wavelet based speech recognition system are choosing optimal wavelets for speech signals, decomposition level in the DWT, selecting the feature vectors from the wavelet coefficients. More specifically automatic classification of various speech signals using the DWT is described and compared using different wavelets. Finally, wavelet based feature extraction system and its performance on an isolated word recognition problem are investigated. For the classification of the words, three layered feed forward network is used. General TermsDynamic Time Warping (DTW) Algorithm, Wavelet Transform (WT).

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The prevalence of DR was low. Long duration of diabetes, poor control of blood sugar, presence of nephropathy, and hypertension were associated with DR. Good vision could mislead about the severity of DR.

IntroductionAdrenal incidentalomas are defined as masses picked up on imaging studies that were done for apparently different reasons. With frequent use of imaging modalities, incidental adrenal masses are commonly encountered in clinical practice. Guidelines are currently available for the diagnosis and management of adrenal incidentalomas, but the appropriateness of initial work-up and subsequent follow-up of incidental adrenal masses in the community hospital setting is unknown.ObjectiveWe studied the appropriateness of initial work-up and follow-up of incidental adrenal masses discovered on abdominal computerized tomography (CT).MethodsIn our retrospective study, we reviewed sequential CT scans of the abdomen performed in the month of January 2010 at a community hospital. Once patients with one or more adrenal masses were identified, outpatient charts for initial biochemical testing and follow-up imaging were obtained either through directly accessing the electronic medical records or through contacting primary care physician's offices. Patient charts were reviewed to assess the data for the next 2 years following the discovery of an adrenal abnormality.ResultsTwenty adrenal masses were incidentally discovered on 723 abdominal CTs performed within the month of January 2010 resulting in an overall incidence of 2.76%. Of the patients with incidentally discovered adrenal masses, appropriate biochemical and follow-up imaging were only performed in patients referred to an endocrinologist (2 of 20 patients). Thirty percent of patients with incidental masses received a repeat CT scan for non-adrenal reasons, and no change in the mass size was noted.ConclusionDespite published guidelines, the initial work-up and follow-up of patients with an incidentally discovered adrenal mass is unsatisfactory. There is a desperate need for education of providers regarding appropriate work-up of incidental adrenal masses.

Data mining techniques have increasingly been studied specifically in their application in real-world databases. One typical problem is that databases tend to be very large, and these techniques often repeatedly scan the entire set. Sampling has been used for a long time, but subtle differences among sets of objects become less evident. This paper aims to bring attention to some of the fundamental challenging questions faced in applying data mining with the hope that future research aims to resolve these issues. This paper is organized as follows: Section 2 briefly discusses the KDDM process models and basic steps proposed for applying data mining. Section 3 discusses the fundamental questions faced during data mining application process. Section 4 concludes the paper.

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