An application of data mining and knowledge discovery process in the field of natural gas exploration

Chevron;EMC2;et al.;itkz;MIKRO Information Handling and Distribution FZE;Thomson Reuters8th IEEE International Conference on Application of Information and Communication Technologies, AICT 2014 -- 15 October 2014 through 17 October 2014 -- -- 112596Natural gas exploration has been shifting significantly in the last decades with the progression of new ingenious technologies. However, such technologies generates large amount of data sets and handling of them create problems such as interpretation of data. To solve such problems Data Mining techniques could be used. This work includes the application procedure of Data Mining and Knowledge Discovery to Natural Gas Well Log data using a set of algorithms and a decent Data Mining tool. And the success of each algorithm in terms of the amount of useful rules is compared

A Drift-Reduced Hierarchical Wavelet Coding Scheme for Scalable Video Transmissions

Abstract—Scalable video coding allows for the capability of (partially) decoding a video bitstream when faced with communication deficiencies such as low bandwidth or loss of data resulting in lower video quality. As the encoding is usually based on perfectly reconstructed frames, such deficiencies result in differently decoded frames at the decoder than the ones used in the encoder and, therefore, leading to errors being accumulated in the decoder. This is commonly referred to as the drift error. Drift-free scalable video coding methods also suffer from the low performance problem as they do not combine the residue encoding scheme of the current standards such as MPEG-4 and H.264 with scalability characteristics. We propose a scalable video coding method which is based on the motion compensation and residue encoding methods found in current video standards combined with the scalability property of discrete wavelet transform. Our proposed method aims to reduce the drift error while preserving the compression efficiency. Our results show that the drift error has been greatly reduced when a hierarchical structure for frame encoding is introduced

ILA-2: An Inductive Learning Algorithm over uncertain data

In this paper we describe the ILA-2 rule induction algorithm from the machine learning domain. ILA2 is the improved version of a novel inductive learning algorithm, namely ILA. We first describe the basic algorithm ILA, then present how the algorithm was improved. We also compare ILA-2 to a range of induction algorithms, including ILA. According to the empirical comparisons, ILA-2 appears to be comparable to CN2 and C4.5 algorithms in terms of output classifiers' accuracy and size

A new framework using deep auto-encoder and energy spectral density for medical waveform data classification and processing

WOS: 000462350100012This paper proposes a new framework for medical data processing which is essentially designed based on deep autoencoder and energy spectral density (ESD) concepts. The main novelty of this framework is to incorporate ESD function as feature extractor into a unique deep sparse auto-encoders (DSAEs) architecture. This allows the proposed architecture to extract more qualified features in a shorter computational time compared with the conventional frameworks. In order to validate the performance of the proposed framework, it has been tested with a number of comprehensive medical waveform datasets with varying dimensionality, namely, Epilepsy Serious Detection, SPECTF Classification and Diagnosis of Cardiac Arrhythmias. Overall, the ESD function speeds up the deep auto-encoder processing time and increases the overall accuracy of the results which are compared to several studies in the literature and a promising agreement is achieved. (C) 2018 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved

A New Generalized Deep Learning Framework Combining Sparse Autoencoder and Taguchi Method for Novel Data Classification and Processing

Deep autoencoder neural networks have been widely used in several image classification and recognition problems, including hand-writing recognition, medical imaging, and face recognition. The overall performance of deep autoencoder neural networks mainly depends on the number of parameters used, structure of neural networks, and the compatibility of the transfer functions. However, an inappropriate structure design can cause a reduction in the performance of deep autoencoder neural networks. A novel framework, which primarily integrates the Taguchi Method to a deep autoencoder based system without considering to modify the overall structure of the network, is presented. Several experiments are performed using various data sets from different fields, i.e., network security and medicine. The results show that the proposed method is more robust than some of the well-known methods in the literature as most of the time our method performed better. Therefore, the results are quite encouraging and verified the overall performance of the proposed framework