Soft computing applications in business

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Soft computing techniques are integral to various business applications. This collection features significant papers exploring these techniques, which include Bayesian networks, biclustering methods, case-based reasoning, data mining, Dempster-Shafer theory, ensemble learning, evolutionary programming, fuzzy decision trees, hidden Markov models, intelligent agents, k-means clustering, maximum likelihood Hebbian learning, neural networks, opportunistic scheduling, probability distributions with Monte Carlo methods, rough sets, self-organizing maps, support vector machines, uncertain reasoning, and other statistical and machine learning methods, often in combination. The business challenges addressed encompass a wide range of topics such as analyzing correlations between currency exchange rates, evaluating USA banks and Moody’s financial strength ratings, managing arrears, identifying business risks, assessing company audit fees, dental treatments, internal controls, intelligent tutoring systems, modeling agent behavior, the motor insurance industry, personal loan defaults, pricing strategies for market share growth, supply chain management pricing, probabilistic sales forecasting, user relevance feedback in online retrieval, and forecasting world crude oil spot prices.