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The harmonious appearance in multilevel inverter output voltage is more for the case of unequal DC sources. In this paper, a hybrid technique incorporating fuzzy inference system (FIS) and artificial bee's colony (ABC) algorithm is proposed. FIS is a rulebased artificial intelligent technique which is used for generating the data set in terms of switching angle, harmonic voltage and harmonic distortion. The data set is generated as per the behaviour of the multilevel inverter without using any harmonic elimination technique. In the generated data set, the switching angle and the harmonic voltage are categorised as SMALL, MEDIUM and LARGE. Then, the ABC algorithm is used to optimise the selection of switching angles from the training data set. The performance of the proposed hybrid technique is tested on a 7-level cascade H-bridge inverter for different voltage levels of unequal DC sources using MATLAB/SIMULINK platform. The effectiveness and superiority of the proposed technique is evaluated by comparing the reduction capacity of total harmonic distortion for different voltage levels of unequal DC sources with particle swarm optimisation (PSO) algorithm and fuzzy-PSO algorithm.

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In the booming era of Internet, web search is inevitable to everyone. In web search, mining frequent pattern is a challenging one, particularly when handling tera byte size databases. Finding solution for these issues have primarily started attracting the key researchers. Due to high the demand in finding the best search methods, it is very important and interesting to predict the user's next request. The number of frequent item sets and the database scanning time should be reduced for fast generating frequent pattern mining. It fulfills user's accurate need in a magic of time and offers a customized navigation. Association Rule mining plays key role in discovering associated web pages and many researchers are using Apriori algorithm with binary representation in this area. But it does not provide best solution for finding navigation order of web pages. To overcome this, weighted Apriori was introduced. But still, it is difficult to produce most favorable results especially in large databases. In the effort of finding best solution, the authors have proposed a novel approach which combines weighted Apriori and dynamic programming. The conducted experiments so far, shows' better tracking of maintaining navigation order and gives the confidence of making the best possible results. The proposed approach enriches the web site effectiveness, raises the knowledge in surfing, ensures prediction accuracies and achieves less complexity in computing with very large databases.