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Build-Operate-Transfer (BOT) contracts have been widely implemented in developing countries facing budget constraints. Analysing the expected variability in project viability requires extensive risk analysis. An objective analysis of various risk variables and their influence on a BOT project evaluation requires study and integration of many sce­narios into the concession terms, which is complicated and time-consuming. If the process of negotiating the financial parameters and uncertainties of a BOT project could be automated, this would be a milestone in objective decision-mak­ing from various stakeholders' points of view. A soft computing model would let the user incorporate as many scenarios as could be provided. Extensive risk analysis could then be easily performed, leading to more accurate and dependable results. In this research, an artificial neural network model with correlation coefficient of 0.9064 has been used to model the relationship between important project parameters and risk variables. This information was extracted from sensitiv­ity analysis and Monte Carlo simulation results obtained from conventional spreadsheet data. The resulting consensus would yield to fair contractual agreements for both the government and the concession company. First published online:01 Jul 2016

This book covers the emerging applications of different computational and optimization techniques in order to achieve a sustainable agriculture. A sustainable agricultural management requires tools in providing integrated, area-specific, and interpreted prediction or forecasting and guidance in every aspect in agriculture.

This book proposes complex hierarchical deep architectures (HDA) for predicting bankruptcy, a topical issue for business and corporate institutions that in the past has been tackled using statistical, market-based and machine-intelligence prediction models. The HDA are formed through fuzzy rough tensor deep staking networks (FRTDSN) with structured, hierarchical rough Bayesian (HRB) models. FRTDSN is formalized through TDSN and fuzzy rough sets, and HRB is formed by incorporating probabilistic rough sets in structured hierarchical Bayesian model. Then FRTDSN is integrated with HRB to form the compound FRTDSN-HRB model. HRB enhances the prediction accuracy of FRTDSN-HRB model. The experimental datasets are adopted from Korean construction companies and American and European non-financial companies, and the research presented focuses on the impact of choice of cut-off points, sampling procedures and business cycle on the accuracy of bankruptcy prediction models. The book also highlights the fact that misclassification can result in erroneous predictions leading to prohibitive costs to investors and the economy, and shows that choice of cut-off point and sampling procedures affect rankings of various models. It also suggests that empirical cut-off points estimated from training samples result in the lowest misclassification costs for all the models. The book confirms that FRTDSN-HRB achieves superior performance compared to other statistical and soft-computing models. The experimental results are given in terms of several important statistical parameters revolving different business cycles and sub-cycles for the datasets considered and are of immense benefit to researchers working in this area.

Intro -- Preface -- Contents -- Analyses of Aspects of Economic, Social and Technological Development -- Ranking of Innovation and Sustainability of Tourist Destinations in Sinaloa: An Analysis with the Ordered Weighted Average Operator -- 1 Introduction -- 2 Theoretical Framework -- 2.1 A Literature Review of the Innovation and Sustainability of Tourist Destinations -- 2.2 Innovation and Sustainability in the Tourist Destinations of Mexico -- 3 The Ordered Weighted Average Operator -- 4 Measurement of the Innovation and Sustainability in the Tourist Destinations in Sinaloa with the OWA Operator -- 5 Conclusions -- References -- Dimensional Analysis Under Pythagorean Fuzzy Set with Hesitant Linguists Term Entropy Information -- 1 Introduction -- 2 Preliminaries -- 2.1 Pythagorean Fuzzy Sets -- 2.2 Dimensional Analysis -- 2.3 Entropy with Unknown Weights in Hesitant Fuzzy Linguistic Term Setting -- 3 DA-PFS with Hesitant Entropy -- 3.1 Dimensional Analysis Under Pythagorean Fuzzy Set (DA-PFS) -- 3.2 Algorithm for DA-PFS with Hesitant Entropy -- 4 Application -- 4.1 Numerical Example -- 4.2 Hesitant Entropy Weight -- 4.3 Sensitivity Analysis -- 5 Conclusion -- References -- Wages Returns in Mexico: A Comparison Between Parametric and Nonparametric Approaches -- 1 Introduction -- 2 Preliminaries -- 2.1 Mincer Equation Analysis for Mexico -- 2.2 Mincer with Decision Trees for Mexico -- 2.3 Outliers -- 2.4 Discriminant Analysis -- 3 Conclusions -- References -- Study of the Geographical Marginality in a Mexican Region Using the MR-Sort Method -- 1 Introduction -- 2 Previous Work of Marginalization in Mexico -- 3 The MR-Sort for Ordered Classification -- 4 Results -- 4.1 Data Marginalization in Mexico -- 4.2 Preference Information -- 4.3 Result Analysis -- 5 Conclusions -- References.

Artificial Intelligence (AI) is a part of computer science concerned with designing intelligent computer systems that exhibit the characteristics used to associate with intelligence in human behavior. Basically, it define as a field that study and design of intelligent agents. Traditional AI approach deals with cognitive and biological models that imitate and describe human information processing skills. This processing skills help to perceive and interact with their environment. But in modern era developers can build system that assemble superior information processing needs of government and industry by choosing from large areas of mature technologies. Soft Computing (SC) is an added area of AI. It focused on the design of intelligent systems that process uncertain, imprecise and incomplete information. It applied in real world problems frequently to offer more robust, tractable and less costly solutions than those obtained by more conventional mathematical techniques. This paper reviews correlation of artificial intelligence techniques with soft computing in various areas.

Stock markets are affected by many uncertainties and interrelated economic and political factors at both local and global levels; determining the set of relevant factors for making accurate predictions is a complicated task. This paper analyzes relevant literature on the Stock Exchange of Thailand (SET), according to the categories of techniques used. The research proposes an approach of soft computing on the SET forecasting and exposes the main driving indicators, from the literature, including Dow Jones, Nikkei index, Hang Seng index, Minimum Loan Rate, the value of the Thai baht and the gold price.

ABSTRACTNew dynamic benchmarks are developed as performance metrics for operational activities in the ingot mill of an aluminium smelter. This new type of high‐level performance measure enables both a systematic and a holistic appraisal of operations performance that is not possible at present. The benchmarks also allow changes in performance of each distinct resource group to be clearly identified. The method for computing such measures is based on a wide range of shop‐floor data, and possibility theory is used to model the soft characteristics of much of the input. Fuzzy performance estimates are dynamically updated with special attention to combining the different forms of uncertainty that exist in the input information. Although these procedures are site specific, the methodology is applicable to other types of manufacturing systems.