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This paper presents a novel 'MapReduce Based Optimum Victim Item and Transaction Selection Approach (MR-OVnTSA)' that provides a feasible and intelligent solution for protecting sensitive frequent itemsets present in big data. The approach advocates to resolve the captious challenges, existing knowledge hiding algorithms are encountering. The proposed solution optimally minimizes the side effect of hiding process on non-sensitive information, and maintains a balance between knowledge and privacy as well as handles the exponential growth in data volume efficiently. The algorithm plugs the most optimum item and transaction as victim, by intelligently analyzing their coverage value i.e. it chooses one with maximal impact on sensitive knowledge but minimal on non-sensitive information. Further, the MapReduce version of the proposed scheme resolves the issue of non-feasibility by processing large-scale data (big data) in a parallel fashion. Experiments have been demonstrated over real and synthetically generated large-scale datasets. Results evince that the proffered scheme is much more efficient and maintains the balance between the privacy preservation, data quality maintenance, and CPU time, when dealing with large voluminous big datasets compared to existing knowledge hiding techniques. [ABSTRACT FROM AUTHOR]

Reservoir characterization is one of the most challenging tasks that help in modeling different lithological properties like porosity, permeability and fluid saturation using seismic readings like velocity profile, impedance, etc. Such a model is required for field development, placing new wells and prediction management. Seismic attributes are being progressively utilized for the tasks of model building, exploration and properties estimation from the data. However, these tasks become very complex due to the nonlinear and heterogeneous nature of subsurface properties. In this context, present work proposes a recurrent neural network-based learning framework to classify porosity using seismic attributes as predictor variables. The approach begins by calculating different seismic attributes from the data. From the initially calculated attribute set, features that are to be used for classification are selected by using two different strategies. Firstly, the seismic attributes having good correlation strength with reservoir porosity are extracted. Subsequently, generative topographic map is utilized to select the significant features. The final reduced features set obtained by the integrated result of above two strategies is then fed as an input to the empirical mode decomposition (EMD) algorithm. The denoised features resulting from the EMD algorithm are used to train the classification models. Further, a comparison is carried out between the proposed classification framework (E M D + R N N) and other supervised classifiers to show the performance of the proposed framework. [ABSTRACT FROM AUTHOR]

The article discusses an accurate and valid model developed for predicting the survivability of burn patients. Topics covered include the different classification algorithms used in developing the proposed prediction model, the methods and performance measures used in comparing the classification algorithms, and the analyzed experimental results obtained using real medical data from Swami Ramanand Tirth (SRT) Hospital in India.

Abstract: A wide range of computational methods and tools for data analysis are available. In this study we took advantage of those available technological advancements to develop prediction models for the prediction of a Type-2 Diabetic Patient. We aim to investigate how the diabetes incidents are affected by patients’ characteristics and measurements. Efficient predictive modeling is required for medical researchers and practitioners. This study proposes Hybrid Prediction Model (HPM) which uses Simple K-means clustering algorithm aimed at validating chosen class label of given data (incorrectly classified instances are removed, i.e. pattern extracted from original data) and subsequently applying the classification algorithm to the result set. C4.5 algorithm is used to build the final classifier model by using the k-fold cross-validation method. The Pima Indians diabetes data was obtained from the University of California at Irvine (UCI) machine learning repository datasets. A wide range of different classification methods have been applied previously by various researchers in order to find the best performing algorithm on this dataset. The accuracies achieved have been in the range of 59.4–84.05%. However the proposed HPM obtained a classification accuracy of 92.38%. In order to evaluate the performance of the proposed method, sensitivity and specificity performance measures that are used commonly in medical classification studies were used. [ABSTRACT FROM AUTHOR]