The issue of brain magnetic resonance image exploration together with classification receives a significant awareness in recent years. Indeed, various computer-aided-diagnosis solutions were suggested to support radiologist in decision-making. In this circumstance, adequate image classification is extremely required as it is the most common critical brain tumors which often develop from subdural hematoma cells, which might be common type in adults. In healthcare milieu, brain MRIs are intended for identification of tumor. In this regard, various computerized diagnosis systems were suggested to help medical professionals in clinical decision-making. As per recent problems, Neuroendoscopy is the gold standard intended for discovering brain tumors; nevertheless, typical Neuroendoscopy can certainly overlook ripped growths. Neuroendoscopy is a minimally-invasive surgical procedure in which the neurosurgeon removes the tumor through small holes in the skull or through the mouth or nose. Neuroendoscopy enables neurosurgeons to access areas of the brain that cannot be reached with traditional surgery to remove the tumor without cutting or harming other parts of the skull. We focused on finding out whether or not visual images of tumor ripped lesions ended up being much better by auto fluorescence image resolution as well as narrow-band image resolution graphic evaluation jointly with the latest neuroendoscopy technique. Also, within the last several years, pathology labs began to proceed in the direction of an entirely digital workflow, using the electronic slides currently being the key element of this technique. Besides lots of benefits regarding storage as well as exploring capabilities with the image information, among the benefits of electronic slides is that they can help the application of image analysis approaches which seek to develop quantitative attributes to assist pathologists in their work. However, systems also have some difficulties in execution and handling. Hence, such conventional method needs automation. We developed and employed to look for the targeted importance along with uncovering the best-focused graphic position by way of aliasing search method incorporated with new Neuroendoscopy Adapter Module (NAM) technique.

The continuous wavelet transform (CWT) is an effective tool when the emphasis is on the analysis of non-stationary signals and on localization and characterization of singularities in signals. We have used the B-spline based CWT, the Lipschitz Exponent (LE) and measures derived from it to detect and quantify the singularity characteristics of biomedical signals. In this article, a real-time implementation of a B-spline based CWT on a digital signal processor is presented, with the aim of providing quantitative information about the signal to a clinician as it is being recorded. A recursive algorithm implementation was shown to be too slow for real-time implementation so a parallel algorithm was considered. The use of a parallel algorithm involves redundancy in calculations at the boundary points. An optimization of numerical computation to remove redundancy in calculation was carried out. A formula has been derived to give an exact operation count for any integer scale m and any B-spline of order n (for the case where n is odd) to calculate the CWT for both the original and the optimized parallel methods. Experimental results show that the optimized method is 20-28% faster than the original method. As an example of applying this optimized method, a real-time implementation of the CWT with LE postprocessing has been achieved for an EMG Interference Pattern signal sampled at 50 kHz.

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Incremental Learning on non stationary distribution has been shown to be a very challenging problem in machine learning and data mining, because the joint probability distribution between the data and classes changes over time. Many real time problems suffer concept drift as they changes with time. For example, an advertisement recommendation system, in which customer's behavior may change depending on the season of the year, on the inflation and on new products made available. An extra challenge arises when the classes to be learned are not represented equally in the training data i.e. classes are imbalanced, as most machine learning algorithms work well only when the training data is balanced. The objective of this paper is to develop an ensemble based classification algorithm for non-stationary data stream (ENSDS) with focus on two-class problems. In addition, we are presenting here an exhaustive comparison of purposed algorithms with state-of-the-art classification approaches using different evaluation measures like recall, fmeasure and g-mean.

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Abstract-Learning on non-stationary distribution has been shown to be a very challenging problem in machine learning and data mining, because the joint probability distribution between the data and classes changes over time. Many real time problems suffer concept drift as they changes with time. For example, in stock market, the customer's behavior may change depending on the season of the year and on the inflation. Concept drift can occurs in the stock market for a number of reasons for example, trader's preference for stocks change over time, increases in a stock's value may be followed by decreases. The objective of this paper is to develop an ensemble based classification algorithm for non-stationary data stream which would consider misclassified instances during learning process. In addition, we are presenting here an exhaustive comparison of proposed algorithms with state-of-the-art classification approaches using different evaluation measures like recall, f-measure and g-mean.