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Blind source separation (BSS) using independent component based analysis (e.g., probabilistic ICA and infomax ICA) have been studied in-depth to extract common hemodynamic sources for a group of functional magnetic resonance images (fMRI). The inherent assumption here is that the sources must be non-Gaussian. For most of the real world data, the decomposition is non-unique. Furthermore, there is no...
This paper describes a technique to estimate the plucking point and magnetic pickup location along the strings of an electric guitar from a recording of an isolated guitar tone. The estimated values are calculated by minimising the difference between the magnitude spectrum of the recorded tone and that of an electric guitar model based on an ideal string. The recorded tones that are used for the experiment...
Process mining research discipline offers a spectrum of techniques for analysing event logs. Event logs represent the history of process execution. This information can be used for monitoring, analysing and improving the operational processes. The currently available methods in process mining emphasise on constructing the static process model. These models depict various dimensions of the process...
Stack Overflow is one of the most popular question-and-answer sites for programmers. However, there are a great number of duplicate questions that are expected to be detected automatically in a short time. In this paper, we introduce two approaches to improve the detection accuracy: splitting body into different types of data and using word-embedding to treat word ambiguities that are not contained...
Classification is one of the important tasks in Data Mining or Knowledge Discovery with prolific applications. Satisfactory classification depends on characteristics of the dataset too. Numerical and nominal attributes are commonly occurred in the dataset. However, classification performance may be aided by discretization of numerical attributes. At present, several discretization methods and numerous...
Parallelizing compilers are a promising solution to tackle key challenges ofMPSoC programming. One fundamental aspect for a profitable parallelization is to estimate the performance of the applications on the target platforms. There is a wide range of state-of-theart performance estimation techniques, such as, simulation-based, measurement-based, among others. They provide performance estimates typically...
This paper proposes a classification algorithm based on ensemble neural networks. In the training phase, the proposed algorithm uses a random number of training data to develop multiple random artificial neural network (ANN) models until those ANN models converge. Those models with lower accuracy than the threshold are filtered out. The remaining highly accurate models will be used to predict the...
Healthcare sector today generate a large amount of complex data about death cause, disease diagnosis, electronic patient records, etc. In this paper prediction and the causes of death event is done using web mining techniques. In the proposed approach, large-scale digital histories are captured for duration of 18 years from news reports of Queensland Government archive to make real-time predictions...
In this paper, a low-cost data acquisition approach with novel forward modeling is proposed for model extraction of digital predistortion (DPD) of RF power amplifiers (PA). Compared to the existing 1-bit DPD solution, the proposed approach employs a new loss function using only 1-bit resolution error signal to extract the forward model and the coefficients of DPD model can be extracted afterwards...
In this paper presented research methods of intrusion detection in the cloud systems. Described such methods of data mining as support vector machines (SVM) and restricted Boltzmann machines (RBM). The proposed a hybrid intrusion detection method based on a combination of the above two methods using hyperbolic Ricci flows to the Poincare disk. To test used the database KDD-99, described four classes...
In recent years, online social networks have gained tremendous popularity because of the massive number of online users, the fast spread of information, and strong inter-personal influence. However, due to the high complexity of the user interaction and the real-time changing of the online social networks, it is still a big challenge to model the spreading process of the information delicately and,...
This paper focused on discrete time Geo/G/1 queue with Bernoulli gated service. We proposed a new algorithm to calculate stock's money flow by the probability generating function (P.G.F.) of stationary waiting time and stationary queue length. We did program by data mining of a variety of stock trading. The new algorithm could get any stock's money flow. We have established a series of quantitative...
In order to simulate this feature and detect the salient region rapidly, we propose the Spatial-Temporal Feature in Compress Domain (STFCD) model. By respectively using H.264 residual coding length and motion vector coding length, we simulate the salient stimulus intensity and then get video saliency features. Finally, we use the linear weighted fusion algorithm to get the final video saliency maps...
Data mining is an emerging field of research in Information Technology as well as in agriculture. The present study focus on the applications of data mining techniques in tea plantations in the face of climatic change to help the farmer in taking decision for farming and achieving the expected economic return. This paper presents an analysis using data mining techniques for estimating the future yield...
Business Intelligence proves to be extremely useful to a vendor in order to raise the sales and product performance of products. It is an essential aspect to take business conclusions into account. There is massive data on social media that can be exploited to give us useful information. The present paper deals with a system created to exhibit intelligence. This system speculates the sales performance...
In this paper, we present a novel hardware implementation of a watermarking system applied on the digital image. The proposed hardware system is based on the DWT (Haar discrete wavelet transform) on the first level of the decomposition. The watermark is hidden in the LH0 (mean frequency sub-band) in goal to get a maximum of the compromise between the visibility and the robustness factors against several...
Machine learning is the science of getting computers to learn from data without being unambiguously programmed. Instigated two supervised classification problem (Heart disease prediction and Springleaf Marketing Response) using machine learning algorithms. In heart disease prediction, the objective is to envisage the status of heart disease which can be 0, 1, 2, 3 and 4. These 5 classes are not cleared...
Risk refers to a set of events that lead to loss but risk from the tax perspective refers to the taxpayers' behaviors that may lead to negligence from the public property by the taxpayers due to tax evasion. Such actions cause unusual volatilities in the amounts envisaged in the government budgeting. The fiscal and financial transactions outside the scope of the precautionary bound and failure to...
Earthquake prediction has been long considered as impossible phenomenon but recent research studies show some progress in this field by considering it as a data mining problem. There are numerous challenges in earthquake prediction, which includes highly non-linear behavior of seismic activity and non-availability of reliable seismic precursors. This work focuses on earthquake prediction in Hindukush...
This paper presents a predictive model which to predict the trends of stock prices using Data Mining techniques. This research will allow the investor to make a more informed decision to buy and sell stocks, and in the most appropriate period. The predictive concept in this work implies learning historical price patterns, indicators, and behavior; and then predicting the future trends in one, five,...
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