Comparative Analysis: Heart Diagnosis Classification using BP-LVQ Neural Network Models For Analog and Digital Data

Decades onwards companies are creating massive data warehouses to store the collected resources. Even though the stored resources are available, only few companies have been able to know that the actual value stored in the database. Procedure used to extract those values is known as data mining. We use so-many technologies to apply this data-mining technique, artificial neural network(ANN) also includes in this data-mining techniques ,ANN is the information processing units which are similar to biological nervous systems. Backpropagation is one of the techniques that used for classification and LVQ (learning Vector Quantization) can be plotted under the competitive learning scheme which is also used for classification. This paper elaborates artificial neural networks, its characteristics and working of backpropagation and LVQ algorithms. In this paper we show the intriguing comparisons between backpropagation and LVQ (Learning Vector Quantization) for both analog and digital data. It also attempts to explain the results between back-propagation and LV

An Extensive Investigation on Coronory Heart Disease using Various Neuro Computational Models

The diagnosis of heart disease at the early time is important to save the life of people as it is absolutely annoying process which requires extent knowledge and rich experience. By and large the expectation of heart infections in conventional method for inspecting reports, for example, Electrocardiogram-ECG, Magnetic Resonance Imaging- MRI, Blood Pressure-BP, Stress tests by medicinal professionals. Presently a-days a huge volume of therapeutic information is accessible in restorative industry in all maladies and these truths goes about as an incredible source in foreseeing the coronary illness by the professionals took after by appropriate ensuing treatment at an early stage can bring about noteworthy life sparing. There are numerous systems in ANN ideas which are likewise contributing themselves in yielding most elevated expectation precision over medical information. As of late, a few programming devices and different techniques have been proposed by analysts for creating powerful decision supportive systems. More over many new tools and algorithms are continued to develop and representing the old ones day by day. This paper aims the study of such different methods by researchers with high accuracy in predicting the heart diseases and more study should go on to improve the accuracy over predictions of heart diseases using Neuro Computing

Machine Learning Techniques on Multidimensional Curve Fitting Data Based on R- Square and Chi-Square Methods

Curve fitting is one of the procedures in data analysis and is helpful for prediction analysis showing graphically how the data points are related to one another whether it is in linear or non-linear model. Usually, the curve fit will find the concentrates along the curve or it will just use to smooth the data and upgrade the presence of the plot. Curve fitting checks the relationship between independent variables and dependent variables with the objective of characterizing a good fit model. Curve fitting finds mathematical equation that best fits given information. In this paper, 150 unorganized data points of environmental variables are used to develop Linear and non-linear data modelling which are evaluated by utilizing 3 dimensional ‘Sftool’ and ‘Labfit’ machine learning techniques. In Linear model, the best estimations of the coefficients are realized by the estimation of R- square turns in to one and in Non-Linear models with least Chi-square are the criteria.

An Optimistic Approach for Clustering Multi-version XML Documents Using Compressed Delta

Today with Standardization of XML as an information exchange over web, huge amount of information is formatted in the XML document. XML documents are huge in size. The amount of information that has to be transmitted, processed, stored, and queried is often larger than that of other data formats. Also in real world applications XML documents are dynamic in nature. The versatile applicability of XML documents in different fields of information maintenance and management is increasing the demand to store different versions of XML documents with time. However, storage of all versions of an XML document may introduce the redundancy. Self describing nature of XML creates the problem of verbosity,in result documents are in huge size. This paper proposes optimistic approach to Re-cluster multi-version XML documents which change in time by reassessing distance between them by using knowledge from initial clustering solution and changes stored in compressed delta. Evolving size of XML document is reduced by applying homomorphic compression before clustering them which retains its original structure. Compressed delta stores the changes responsible for document versions, without decompressing them. Test results shows that our approach performs much better than using full pair-wise document comparison

Analytical Review and Study on Various Vertical Handover Management Technologies in 5G Heterogeneous Network

In recent mobile networks, due to the huge number of subscribers, the traffic may occur rapidly; therefore, it is complex to guarantee the accurate operation of the network. On the other hand, the Fifth generation (5G) network plays a vital role in the handover mechanism. Handover management is a prominent issue in 5G heterogeneous networks. Therefore, the Handover approach relocates the connection between the user equipment and the consequent terminal from one network to another. Furthermore, the handover approaches manage each active connection for the user equipment. This survey offers an extensive analysis of 50 research papers based on existing handover approaches in the 5G heterogeneous network. Finally, existing methods considering conventional vertical handover management strategies are elaborated to improve devising effective vertical handover management strategies. Moreover, the possible future research directions in attaining efficient vertical handover management in a 5G heterogeneous network are elaborated

Essential oil composition of petiole of Cinnamomum verum Bercht. & Presl.

Essential oil isolated from the petiole of  Cinnamomum verum was analysed by gaschromatography and gas chromatography-mass spectrometry. Twenty five compoundsaccounting for 87.31% of the total essential oil were identified. (E)-Cinnamaldehyde (33.04%)followed by eugenol (17.32%), linalool (16.85%) and (E)-cinnamyl acetate (11.78%) were themain components of the essential oil. This is the first report on the composition of essentialoil of petiole of C. verum. &nbsp

Effect of short and long-term storage on essential oil content and composition of cinnamon (Cinnamomum verum Bercht. & Presl.) leaves

The effect of duration of storage of cinnamon (Cinnamomum verum) leaves on the content andchemical composition of essential oil was studied. The results revealed that neither the es-sential oil content (1.9%-2.2%), nor the chemical composition of essential oil (eugenol 87.1%-90.7%; eugenyl acetate 2.9%-5.5%; linalool 0.8%-1.2%; benzyl benzoate 0.3%-0.6%) wasaffected during the storage of leaves for up to 15 months. &nbsp

Finitely generated ideals in rings of analytic functions

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/46214/1/208_2005_Article_BF02052394.pd