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The finishing train of a hot strip mill has been modelled by using a constant volume element model. The accuracy of the model has been increased by using an Artificial Neural Network (ANN). A non-linear Rank Based Genetic Algorithm has been developed for the optimization of the work roll profiles in the finishing stands of the simulated hot strip mill. It has been compared with eight other experimental optimization algorithms: Random Walk, Hill Climbing, Simulated Annealing (SA) and five different Genetic Algorithms (GA). Finally, the work roll profiles have been optimized by the non-linear Rank Based Genetic Algorithm. The quality of the strip from the simulated mill was significantly improved. [ABSTRACT FROM AUTHOR]

The spatio-temporal modelling and forecasting of incidences of crimes have now become a routine part of crime prevention operations. However, obtaining reliable forecasts for cases of changing spatial boundaries using models which take the influences of exogenous factors on the spatial and temporal dynamics of crime into account remains a challenge. The proposed Dynamic Spatial Distribution Approach (DSDA) is a modelling approach that provides spatio-temporal forecasts that incorporate the influences of salient weather conditions that have a bearing on crime dynamics at both the temporal and spatial levels. The DSDA starts with modelling and forecasting the dataset of the entire area of interest as a whole. Given reliable forecasts, the DSDA spatially disaggregates them by extrapolating the spatial patterns identified from the historic data. This is carried out by firstly identifying the spatial pattern and then defining a set of weights, one for each of the clusters making up this pattern. A number of methods to determining these weights, including a benchmark, univariate and causal approaches are proposed. The DSDA was developed within a GIS and classical statistics environment. Applied to the daily number of criminal damage incidences in the City of Cardiff, UK, the DSDA yielded a city-wide forecast error of 3.51 crimes per day. The spatial disaggregation using weights identified using Least Squares estimates of expected crime percentages in each cluster yielded an average of 1.1240 crimes per day per cluster. The model used incorporated the influences of weekdays and ambient conditions on the spatial distribution of crime. [ABSTRACT FROM AUTHOR]

The issue of modelling and forecasting the share prices of the banking sector remains a challenge because of high volatilities in individual stock prices. Reliably forecasting the future values of shares is essential to minimize the risk for investors, but there is currently no standard forecasting procedure or technique that can be used in modelling and forecasting the share prices of the banking sector. This research is concerned with the development of a forecasting algorithm that can be applied in modelling and forecasting the share prices of the banking sector. It proposes six steps that, when followed, may lead to obtaining superior models. These steps are Data Selection, Data Preparation, Training the Model, Refining the Model, Testing the Model, and Forecast Production. These building steps were used to build a forecasting model for HSBC Bank Plc. and Lloyds TSB using the Back-propagation Neural Network. Empirical results show that superior models were obtained by applying the proposed algorithm compared to the financial forecasting models published in the literature. [ABSTRACT FROM AUTHOR]