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Predicting price has now become an important task in the operation of electrical power system. Day-ahead prediction provides forecast prices for a day ahead that is useful for daily operation and decision-making. The main challenge for day ahead price forecasting is the accuracy and efficiency. Lower accuracy is produced due to the nature of electricity price that is highly volatile compared to load...
This paper suggested a technique based on MFCC analysis for audio signals with speech classification application. The proposed work used multi-resolution (wavelet) analysis and spectral analysis based features for feature extraction. The proposed approach uses a no. of features like Mel Frequency Cepstral Coefficient (MFCC), and FFT Coefficients combined with wavelet based features. In addition, accuracy...
We present electromagnetic optimizations by heuristic algorithms supported by approximate forms of the multilevel fast multipole algorithm (MLFMA). Optimizations of complex structures, such as antennas, are performed by considering each trial as an electromagnetic problem that can be analyzed via MLFMA and its approximate forms. A dynamic accuracy control is utilized in order to increase the efficiency...
This paper propose a new spherical parallel robot for celestial orientation, and rehabilitation applications (TV satellite dish, tracking systems, solar panels, cameras, telescopes, table of the machine tools, ankle, shoulder, wrist and etc.). The proposed robot can completely rotate about an axis. After describing the robot and its inverse position analysis, using the genetic algorithm, the dimensional...
Attribute reduction approach is proposed in this paper based on a modified version of the flower pollination algorithm optimization (FPA). Flower pollination algorithm (FPA) is one of recently evolutionary computation technique, inspired by the pollination process of flowers. The modified FPA algorithm adaptively balance the exploration and exploitation to quickly find the optimal solution through...
With multiple channels, Polarimetric SAR (PolSAR) contains abundant target information and anti-jamming ability, which can improve the ability of target discrimination and image interpretation. The classification problem of PolSAR has become one of the most urgent problems to be solved in PolSAR application with the improvement of PolSAR technology. Due to the complexity of multiple-dimensional classification,...
The presence of a large number of irrelevant features degrades the classifier accuracy, reduces the understanding of data, and increases the overall time needed for training and classification. Hence, Feature selection is a critical step in the machine learning process. The role of feature selection is to select a subset of size ‘d’ (d<n) from the given set of ‘n’ features that leads to the smallest...
A new fuzzy c-means clustering with non-extensive entropy regularization is proposed in this paper. The purpose of entropy regularization is to form approximate solutions of singular problems in the maximum entropy framework. The non-extensive entropy with Gaussian gain is generally used for identifying non-uniform probability densities as in regular texture patterns. It is thus well suited for regularizing...
Feature selection is an effective technique for dimensionality reduction and an essential step in successful data mining applications. It is a process of selecting a subset of features from the candidate set of features according to certain criteria. The main goal of supervised learning is finding feature subset that produces higher classification accuracy. The proposed method is to select an optimal...
Normally, statistical methods are used to generate rankings for genes in terms of their ability to distinguish between normal and malignant tumors from a gene expression dataset. However, different statistical methods yield different ranks for same gene and there is no universally accepted method for ranking. Therefore rank aggregation is required to find the overall ranking of the set of genes. There...
Learning rate and momentum coefficient are critical parameters on back propagation algorithm because of their effect on learning speed and deviation ratio from global minimum. Hidden neuron number has an effect on classification accuracy, and excessive number of hidden neuron causes to increase the operation load. Because these parameters are selected randomly, finding the accurate values requires...
We present an evolutionary multi-objective optimization method for sensor deployment applied to an indoor positioning system with range-difference measurements. Stationary sensors at known locations are used to obtain the position of a moving emitter. Coverage and accuracy of the positioning system depend on the number and location of the sensors for a given indoor space (floor plan) and on the properties...
Intermediate results of two state-of-the-art wrapper feature selection approaches (GA and SFFS) applied to hyperspectral data sets were used to derive information about band importance for specific land cover classification problems. Several feature selection performance scores (classification accuracies, Bhattacharyya separability) were tested. The impact of the number of selected bands on classification...
A force fields-based multi-scale docking method is proposed in this paper. Molecular docking problem has been divided into three sub problems: rigid-rigid phase, flexible-flexible phase and flexible-rigid phase. Residue groups of protein have been adopted to describe the conformation of protein. K-mean clustering algorithm and genetic algorithm have been developed to solve the optimization problem...
Approximate nearest neighbor (ANN) search provides computationally viable option for retrieval from large document collection. Hashing based techniques are widely regarded as most efficient methods for ANN based retrieval. It has been established that by combination of multiple features in a multiple kernel learning setup can significantly improve the effectiveness of hash codes. The paper presents...
The evolution of industrial clusters is a critical factor in the strategic development of locations for high-tech industries. Most previous studies have seldom quantified the location selections of Taiwanese IC design firms engaging in foreign direct investments in China because their access to crucial data may have been limited. This work developed a novel diffusion model to illustrate the extension...
A new optimization algorithm, namely the Forest Algorithm (FA), is introduced for the first time. This algorithm simulates trees' growth, reproduction and death in a forest to perform optimization. In the algorithm, trees and branches represent a collection of trial solutions and parameters needed to be optimized respectively, and three mechanisms, i.e. Growth, proliferation and death, are employed...
Hybridization has become one of the current focuses of new research areas of the evolutionary algorithms over the past few years. Hybridization offers better speed of convergence to the evolutionary approach and better accuracy of the final solutions. This paper presents a hybrid non-dominated sorting genetic algorithm-II (NSGA-II) to optimize Three-Term Backpropagation (TBP) network in terms of two...
The classification performance of Support Vector Machine (SVM) is heavily influenced by its kernel parameter g and penalty factor c. in this paper, Cross-validation (CV) based grid-search optimization, CV-based genetic algorithm (GA) and CV-based particle swarm optimization (PSO) are respectively used for parameters optimization in SVM for fault classification of inverters in traction converter. Simulation...
The parameters of Support Vector Machine (SVM) are optimized using heuristic genetic algorithm and then to detect the network intrusion behavior. The heuristic real-coded genetic algorithm is used to optimize the best parameters of SVM with Gauss kernel aimed at the classification accuracy of the model. The classification accuracy is largely improved. Experimental results show that this method has...
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