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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...
FCM is sensitive to initialization and tends to result in local minimum in iterations. This paper studies the crossover and mutation probability of genetic algorithm and presents a new crossover and mutation probability. The proposed clustering scheme based on genetic algorithm and fuzzy c-means takes full advantage of the global optimization of genetic algorithm and the local search ability of FCM...
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...
This paper shows the effect of the application of aspiration level (AL) on the solution accuracy of Tabu Search (TS) in optimal operation planning problem for residential PEFC (Polymer Electrolyte Fuel Cell)-CGS (Co-Generation System). Residential PEFC -CGS has been spreading as one of new distributed power sources that have high total efficiency. Therefore, it becomes essential work to study operation...
Previous studies never uses Gompertz model integrated with genetic algorithms to express the evolutions of capital flows although Gompertz model is suitable in the field of statistics, management science, information technology, product innovation, technological forecasting and finance to express the growth diffusions. This work utilizes Gompertz diffusion model integrated with genetic algorithm to...
Expert systems for classification tasks in medical diagnosis systems require two properties. The true positives should be very high, as well as the true negatives, i.e. the system should correctly catch those who are ill, and correctly dismiss those who are healthy. The multi-modal evolutionary classifier uses a genetic algorithm to learn a reference vector for each class, and classification is done...
Eliminating all stop words from the feature space is a standard practice of preprocessing in text mining, regardless of the domain which it is applied to. However, this may result in loss of important information, which adversely affects the accuracy of the text mining algorithm. Therefore, this paper proposes a novel methodology for selecting the optimal set of domain specific stop words for improved...
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...
The sequential covering strategy has been and still is a very common way to develop rule learning algorithms. This strategy follows a greedy procedure to learn rules, where, after each step one rule is obtained. Recently, we proposed a new sequential covering strategy that allowed the review of previously learned knowledge during the learning process itself. This review of knowledge allowed the algorithm...
Kernel methods for classification is a well-studied area in which data are implicitly mapped from a lower-dimensional space to a higher-dimensional space to improve classification accuracy. However, for most kernel methods, one must still choose a kernel to use for the problem. Since there is, in general, no way of knowing which kernel is the best, multiple kernel learning (MKL) is a technique used...
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,...
Intrusion detection systems (IDS) are important to protect our systems and networks from attacks and malicious behaviors. In this paper, we propose a new hybrid intrusion detection system by using accelerated genetic algorithm and rough set theory (AGAAR) for data feature reduction, and genetic programming with local search (GPLS) for data classification. The AGAAR method is used to select the most...
The paper presents the comparison of two genetic methods that can be used for feature selection, NSGA (Nondominated Sorting Genetic Algorithm) and GAAM (genetic algorithm with aggressive mutation). While the first method is very popular for optimizing multi-objective functions, the second one is a new method that was introduced just two years ago. The comparison was made with a benchmark file from...
We introduce SCALER, a two-pronged strategy utilizing digital resources for refining intrinsic evolution of analog computational circuits. A Self-Scaling Genetic Algorithm is proposed to adapt solutions to computationally-tractable ranges in hardware-constrained analog reconfigurable fabrics. Differential Digital Correction is developed utilizing an error metric computed from the evolved analog circuit...
This paper presents an application of cognitive networking paradigm to the problem of inter-cell interference coordination (ICIC) in Long-Term Evolution-Uplink (LTE-UL). We describe state-of-the-art, research challenges involved, and a novel random neural network (RNN) based power controller and interference management framework. The RNN based cognitive engine (CE) learns how the electromagnetic environment...
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...
Data mining has been an active area of research for the past couple of decades. Classification is an important data mining technique that consists of assigning a data instance to one of the several predefined categories. Various successful methods have already been suggested and tested to solve the problems of the classification. In this paper, author proposed a new hybrid classifier by combining...
In the foreign currency exchange (FOREX), a technical data analysis system for predicting currency movements is needed to help traders in decision making. Thus, this study proposes a system of technical data analysis to movement prediction of Euro to USD using Genetic Algorithm-Neural Network (GANN). To generate a predicted value, Genetic Algorithm searching for the best value of Feed Forward Neural...
It is well-known that using floating-point numbers may inevitably result in inaccurate results and sometimes even cause serious software failures. Safety-critical software often has strict requirements on the upper bound of inaccuracy, and a crucial task in testing is to check whether significant inaccuracies may be produced. The main existing approach to the floating-point inaccuracy problem is...
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