Computational Intelligence in Data Mining - Volume 1: by Lakhmi C. Jain, Himansu Sekhar Behera, Jyotsna Kumar Mandal,

By Lakhmi C. Jain, Himansu Sekhar Behera, Jyotsna Kumar Mandal, Durga Prasad Mohapatra

The contributed quantity goals to explicate and tackle the problems and demanding situations for the seamless integration of 2 center disciplines of computing device technological know-how, i.e., computational intelligence and knowledge mining. info Mining goals on the computerized discovery of underlying non-trivial wisdom from datasets by means of utilising clever research innovations. The curiosity during this examine zone has skilled a substantial development within the final years because of key components: (a) wisdom hidden in companies’ databases could be exploited to enhance strategic and managerial decision-making; (b) the massive quantity of knowledge controlled via agencies makes it most unlikely to hold out a guide research. The booklet addresses diverse tools and methods of integration for reinforcing the final target of knowledge mining. The publication is helping to disseminate the data approximately a few cutting edge, energetic examine instructions within the box of knowledge mining, desktop and computational intelligence, besides a few present concerns and purposes of similar topics.

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Extra info for Computational Intelligence in Data Mining - Volume 1: Proceedings of the International Conference on CIDM, 20-21 December 2014

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366–369, Ramanathapuram, India (2012) 10. : Comparison of pole placement and LQR applied to single link flexible manipulator. In: International Conference on Communication Systems and Network Technologies (2012) Hybrid Synchronous Discrete Distance Time Model for Traffic Signal Optimization Sudip Kumar Sahana and Kundan Kumar Abstract This paper proposes a novel solution to the traffic signal optimization problem by reducing the wait time of individual vehicle users at intersections within the urban transportation system.

Behera Abstract In this work, a hybrid training algorithm for fuzzy MLP, called Fuzzy MLP-GSPSO, has been proposed by combining two meta-heuristics: gravitational search (GS) and particle swarm optimization (PSO). The result model has been applied for classification of medical data. Five medical datasets from UCI machine learning repository are used as benchmark datasets for evaluating the performance of the proposed ‘Fuzzy MLP-GSPSO’ model. The experimental results show that Fuzzy MLP-GSPSO model outperforms Fuzzy MLP-GS and Fuzzy MLP-PSO for all the five datasets in terms of classification accuracy, and therefore can reduce overheads in medical diagnosis.

Randomly initialize population(t) determine fitness of population(t) repeat select parents from population(t) perform crossover on parents creating population(t+1) perform mutation of population(t+1) determine fitness of population(t+1) until best individual is good enough End-procedure The first step of GA is to identify the genes which will form chromosomes (feasible solutions). In this problem, the genes are considered as the set of signal values of all intersections in the network for the given phase (also see Fig.

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