A Neural-CBR System for Real Property Valuation

In recent times, the application of artificial intelligence (AI) techniques for real property valuation has been on the increase. Some expert systems that leveraged on machine intelligence concepts include rule-based reasoning, case-based reasoning and artificial neural networks. These approaches have proved reliable thus far and in certain cases outperformed the use of statistical predictive models such as hedonic regression, logistic regression, and discriminant analysis. However, individual artificial intelligence approaches have their inherent limitations. These limitations hamper the quality of decision support they proffer when used alone for real property valuation. In this paper, we present a Neural-CBR system for real property valuation, which is based on a hybrid architecture that combines Artificial Neural Networks and Case- Based Reasoning techniques. An evaluation of the system was conducted and the experimental results revealed that the system has higher satisfactory level of performance when compared with individual Artificial Neural Network and Case- Based Reasoning systems

Crystal structure of 4,4-dimethyl-2-(trifluoromethyl)-4,5-dihydro-1H-imidazole, C6H9F3N2

C6H9F3N2, monoclinic, P21/n (no. 14), a = 10.6224(9) Å, b = 11.8639(9) Å, c = 13.3139(11) Å, β = 105.903(3)°, V = 1613.6(2) Å3, Z = 8, Rgt(F) = 0.0618, wRref(F2) = 0.1629, T = 102(2) K [1–3]

A fuzzy expert system (FES) tool for online personnel recruitments

The advent of the internet has facilitated greater access to the myriad of job opportunities available globally. Currently there exist many job application submission portals that are being used for online job recruitment purposes. However, the task of many of these job submission portals is limited to matching the professional and academic qualifications of applicants with the requirements of employers and several organisations and does not involve the ranking of applicants’ credentials according to their relative suitability for the jobs applied for. In this paper, we describe the implementation of fuzzy expert system (FES) tool for selection of qualified job applicants with the aim of minimising the rigour and subjectivity associated with the candidate selection process. A performance evaluation of the FES tool that was conducted confirmed the viability of a FES-based approach in handling the fuzziness that is associated with the problem of personnel recruitment

A Data Mining Process Framework for Churn Management in Mobile Telecommunication Industry

Churning which is a sudden defection of a subscriber to competitors is a disturbing problem in the global telecommunication industry. However, the effectiveness of existing churn control strategies can be improved if an integrated approach that incorporates several dimensions of the phenomenon of churning is adopted. In contrast to existing approaches, this paper proposes an integrated approach to churn management and control by using a data mining process framework that enables churn prediction, determination of reason(s) for churn, and recommendation of appropriate intervention strategy for customer retention. A datamining experiment that was undertaken using data from a major telecom operator in Nigeria to assess the viability of the approach yielded encouraging results

An Approach to Reaeration Coefficient Modeling in Local Surface Water Quality Monitoring

Reaeration coefficient (k2) for River Atuwara, Ogun State, Nigeria was calculated from dissolved oxygen and biochemical oxygen demand data collected over period of 3 months covering the two prevailing climatic seasons in the country. Both the Akaike and Bayesian information criteria were used in the selection and analysis of ten models to identify the most suitable reaeration coefficient (k2) model for Atuwara River. Models that passed the confidence limit were subjected to model evaluation using measures of agreement between observed and predicted data such as percent bias, Nash–Sutcliffe efficiency, and root mean square observation standard deviation ratio. The used approach yield better results than empirical models developed for local conditions while it is also useful in conserving scarce resources