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This study investigates the evaluation of machine learning models based on multiple criteria. The criteria included are: predictive model accuracy, model complexity, and algorithmic complexity (related to the learning/adaptation algorithm and prediction delivery) captured by monitoring the execution time. Furthermore, it compares the models generated from optimising the criteria using two approaches...
Prediction of protein structural class has been a new area of research in the scientific community in the last decade. Various approaches has been adopted and analysed. However representing the raw amino acid sequence to preserve the property of proteins has posed a great challenge. Chou's pseudo amino acid composition feature representation method has fetched wide attention in this regard. In Chou's...
A vehicle-mounted GPS receiver used for localization can suffer from signal blockage. To remedy to this problem, GPS/ INS integration can be considered as a solution in some cases. However, in the case of urban areas where there are severe multipath conditions, the performance degrades considerably. The last decade have seen many proposals of techniques aiming at improving the accuracy of GPS positions...
When trying to solve classification or time-series prediction problem statements by the application of Artificial Neural Networks (ANNs), commonly applied structures like feed forward or recurrent Multi-Layer Perceptrons (MLP) characteristically tend to come up with bad performance and accuracy. This is especially the case when dealing with manifold datasets containing numerous input (predictors)...
A method of classifying the P2P traffic based on the Probabilistic Neural Network is proposed. Firstly, we utilize "WinPcap" to capture the network packets and then the core characteristics of the obtained data are analyzed with the help of the tool. Based on the above work, the P2P traffic classification system was realized. It makes full use of the advantage of the PNN which has a high...
Several adaptation approaches, such as policy-based and reinforcement learning, have been devised to ensure end-to-end quality-of-service (QoS) for enterprise distributed systems in dynamic operating environments. Not all approaches are applicable for distributed real-time and embedded (DRE) systems, however, which have stringent accuracy, timeliness, and development complexity requirements. Supervised...
We show that the learning sample complexity of a sigmoidal neural network constructed by Sontag (1992) required to achieve a given misclassification error under a fixed purely atomic distribution can grow arbitrarily fast: for any prescribed rate of growth there is an input distribution having this rate as the sample complexity, and the bound is asymptotically tight. The rate can be super exponential,...
This paper provides performance analysis of advanced frequency estimators that has been recently proposed in the literature for signals in the additive white Gaussian noise. The optimal maximum likelihood (ML) solution is too complicated for practical implementation. Many suboptimal techniques have been presented, which are mainly classified as correlation-based or periodogram-based estimators. The...
A low complexity weighted two-bit transforms (2BT) based multiple candidates motion estimation algorithm is proposed in this paper. By exploiting almost the identical operations in two different matching error criteria, we can efficiently determine two best motion vectors according to the respective matching criteria and can enhance the overall motion estimation accuracy. Experimental results show...
What makes a combinatorial optimization problem hard? The concept of phase transition was introduced in combinatorial decision problems to explain that not all NP-Complete problems are hard, and that there exists a phase transition from solvable to unsolvable problems, within which hard problems exist. Phase transition has been studied using randomly generated problems in which variables have uniform...
In order to optimally set up and configure an analog neural network in system-level, fundamental issues such as accuracy, robustness, function smoothness and minimality has to be considered. This paper focuses on choosing optimal Continuous Valued Number System (CVNS) neural networks, and shows using system-level analysis that how CVNS networks can be used to implement large size networks. The network...
Electrograms (EGM) stored in Implantable Cardioverter Defibrillator (ICD) during ventricular tachycardia episodes have recently been shown to convey valuable information for the identification of the anatomical origin of the arrhythmia and subsequent ablation therapy. We developed an automatic procedure for estimating the focal origin of the arrhythmia by analyzing the EGM waveforms. A clinical protocol...
Variable Block Size Motion Estimation (VBSME) is one of the most important features of state-of-theart video encoders. In the H.264/AVC encoder, the computational complexity of integer motion estimation is about 75%. Therefore, reducing this complexity is one of the key points to provide low power video encoding. In this paper, a reconfigurable bit plane matching based VBSME method and a runtime reconfigurable...
Nonnegative Matrix Factorization (NMF) has been widely used in computer vision and pattern recognition. It aims to find two nonnegative matrices whose product can well approximate the original matrix, which naturally leads to parts-based representation. In this paper, we propose a Two Dimensional Nonnegative Matrix Factorization (2DNMF), specifically for a sequence of matrices. In contrast to NMF...
Nowadays practical solutions of engineering problems involve model-integrated computing. Due to their flexibility, robustness, and easy interpretability, the application of soft computing based models, may have an exceptional role. Despite of their advantages, the usage is still limited by their exponentially increasing computational complexity. Although, combining soft computing and anytime techniques...
In this paper, the establishment of the neural network model of forecasting short-term power load in an electric power grid is studied. Basing on the model, the BP algorithm for power load is explored. The research on BP network model includes determining the hidden layer number, hidden layer nodes number, training frequency and accuracy of learning rate. In this paper, we focus on that how to give...
Abstract-This paper presents a new approach to recognize and predict succedent epileptic seizures by using single-channel electroencephalogram (EEG) analysis. Eight channels of EEG from each patient of the seven consenting patients with generalized epilepsy were collected in Epilepsy Center of Xijing Hospital. The raw EEGs were decomposed by the algorithm of empirical mode decomposition (EMD), the...
Credit scoring has attracted lots of research interests in the literature. The credit scoring manager often evaluates the consumer's credit with intuitive experience. However, with the support of the credit classification model, the manager can accurately evaluate the applicant's credit score. Support Vector Machine (SVM) classification is currently an active research area and successfully solves...
Meteorological conditions are crucial for the agricultural production. Rainfall, in particular, can be cited as the most influential by having direct relation with hydric balance. Meteorological satellites that cover the whole earth have been extensively used for the development of statistical and artificial intelligence models for rainfall estimation. However, some of these techniques have flaws...
Distributed fusion algorithm and its model (DFM) are discussed for oil forecast in this paper. DFM comprises a global fusion center (GFC) and several local fusion units (LFU) tightly connecting with each other. LFU performs fusion through two steps: the feature-level fusion that analyzes qualitative data through classifying analysis method and extracts quantitative data through BP neural network method;...
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