In this paper, we aim to propose a charging scheduling algorithm for electric vehicles on highways. While the number of electric vehicles has been increasing recently, charging stations are not becoming widespread com...
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A metaheuristic chain based memetic algorithm namely MCMA is proposed for the classification of metabolomics data. MCMA regards both global evolution and local search as equivalent elemental metaheuristics,and searche...
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A metaheuristic chain based memetic algorithm namely MCMA is proposed for the classification of metabolomics data. MCMA regards both global evolution and local search as equivalent elemental metaheuristics,and searches with a chain of metaheuristics performed alternatively on the target problem. A hidden Markov model based scheduling mechanism is employed in MCMA for the selection of metaheuristics. By using MCMA for optimizing the linkage weight vector, a feature weighting algorithm for metabolomics data is formed to identify relevant metabolite features and reveal their exact relationships with the given physiological states. An extreme learning machine based classifier is utilized in predicting the physiological states according to the weighted metabolite features. Experimental results on real metabolomics data of clinical liver transplantation demonstrate that the proposed feature weighting and classification method obtains better performance than the other compared algorithms.
Decision trees are one of the most transparent methods probably most often used for solving classification, tasks. In the process of decision tree induction a lot of input parameters have to be fine-tuned in order to ...
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Decision trees are one of the most transparent methods probably most often used for solving classification, tasks. In the process of decision tree induction a lot of input parameters have to be fine-tuned in order to obtain good results. However the most important and probably most influential parameter in top-down induction method is a splitting criterion, which can vary from one classification task to another. In this paper we empirically tested four of them, based on well known purity measures, on 54 UCI databases. Although none of the proposed splitting criterion in the literature has proved to be universally better than the rest, they are not always suitable for solving any classification task. Therefore we propose two new splitting criteria: hybrid linear combination of purity measures and voting of purity measures, which obtain at least comparable results to other well-known criteria used in greedy decision tree induction method, while their structure makes them ideal for non-expert users. Boosting was also applied as one of the most successful ensemble methods, which once again proved the ability to significantly improve the classification accuracy on many databases.
An algorithm for finding the unequal error protection (UEP) capability of a q-ary image of a low-rate q(m)-ary cyclic code is presented by combining its concatenated structure with the UEP capability of concatenated c...
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An algorithm for finding the unequal error protection (UEP) capability of a q-ary image of a low-rate q(m)-ary cyclic code is presented by combining its concatenated structure with the UEP capability of concatenated codes. The results are independent of a choice of a basis to be used for expanding an element over GF (q(m)) into GF (q). A table of the UEP capability of binary images of low-rate Reed-Solomon codes over GF(2(6)) is given. It is shown that the encoding and decoding algorithms for superimposed concatenated codes can be applied to a q-ary image as a linear UEP code.
Elliptic curve cryptography (ECC) has shorter key length than other asymmetric cryptography algorithms such as RSA with the same security level. Existing faults in cryptographic computations can cause faulty results. ...
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It is shown that five optimal and one quasi-optimal binary codes with respect to the Griesmer bound can be obtained from cyclic codes over GF(2(m)). An [m(2(em)-1), em, 2(em-1)m] code, a [3(2(2e)-1), 2e, 3 . 2(2e-1)] ...
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It is shown that five optimal and one quasi-optimal binary codes with respect to the Griesmer bound can be obtained from cyclic codes over GF(2(m)). An [m(2(em)-1), em, 2(em-1)m] code, a [3(2(2e)-1), 2e, 3 . 2(2e-1)] code, a [2(2(2e)-1), 2, (2(2e+2)-4)/3] code, a [3(2(2e)-1), 2, 2(2e+1)-2] code, and a [3(2(2e)-1), 2(e+1), 3 . 2(2e-1)-2] code are optimal and a [2(2(2e)-1)2(e+1), 2(2e)-2] code is quasi-optimal.
We present the results of a data mining research on the use of GPS positions and speed of travel of vehicles in urban areas to determine optimal travel routes for emergency vehicles. A new approach using predictive pr...
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ISBN:
(纸本)9781424448784
We present the results of a data mining research on the use of GPS positions and speed of travel of vehicles in urban areas to determine optimal travel routes for emergency vehicles. A new approach using predictive programming is presented. The approach focuses on using data mining of existing data as the key factor in the application logic where various decisions are to be made. The main appeal of such an approach is that the application logic evolves over time according to the application data. As the data changes the decisions soon follow. This gives the impression of intelligent behavior of the application. Copy;2009 IEEE.
Games have been used extensively to study human behavior. Researchers in the field of human-robot interaction (HRI) are becoming more aware of the importance of designing compelling and playful games to study interrel...
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Machine learning (ML) methods are used to extract new knowledge from existing datasets. Ensemble methods (EM) were introduced to improve the ML performance. While EM's offer performance improvements, they limit th...
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ISBN:
(纸本)1601322178
Machine learning (ML) methods are used to extract new knowledge from existing datasets. Ensemble methods (EM) were introduced to improve the ML performance. While EM's offer performance improvements, they limit the amount of control and general understanding of how the final result was achieved. We are researching the possibility to verify ML results with formalization of research results accessible in natural language. We propose to use the extracted information from scientific papers for the verification of ML results. In order to be able to extract the information, relevant to the ML results, an approach to extract specific information rich segments from scientific papers is needed. We present an approach to automated selection of relevant segments from large repositories.
To compute multidimensional data, tensor algebra is a powerful tool and it has many applications in machine learning and data analytics. This multidimensional data which is represented with higher-order tensors for mu...
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