Word prediction methodologies depend heavily on the statistical approach that uses the unigram, bigram, and the trigram of words. However, the construction of the N-gram model requires a very large size of memory, which is beyond the capability of many existing computers. Beside this, the approximation reduces the accuracy of word prediction. In this paper, we suggest to use a cluster of comput...

This study presents a method to optimize the sampling design of environmental monitoring networks in a multi-objective setting. We optimize the permanent network of radiation monitoring stations in the Netherlands and parts of Germany as an example. The optimization method proposed combines minimization of prediction error under routine conditions with maximizing calamity detection capability i...

Arctic change and reductions in sea ice are impacting Arctic communities and are leading to increased commercial activity in the Arctic. Improved forecasts will be needed at a variety of timescales to support Arctic operations and infrastructure decisions. Increased resolution and ensemble forecasts will require significant computational capability. At the same time, high performance computing ...

Leading market analysts such as Gartner, Forrester Research and IDC expect again for 2012 an increase of global information technology (IT) spending between 3.7% and 6.9% compared to 2011. This trend emphasises the fact that ensuring the effectiveness and efficiency of IT or IT evaluation is a substantial task of strategic IT management. Weighing costs and benefits is already a real challenge f...

The high social costs associated with bankruptcy have spurred searches for better theoretical understanding and prediction capability. In this paper, we investigate a hybrid approach to bankruptcy prediction, using a genetic programming algorithm to construct a bankruptcy prediction model with variables from a rough sets model derived in prior research. Both studies used data from 291 US public...

Financial distress prediction (FDP) has always been an important issue in the business and financial management. This research proposed a novel multiple classifier ensemble model based on firm life cycle and Choquet integral for FDP, named MCELCCh-FDP, as a new approach to tackle with financial distress. Empirical study based on Chinese listed companies’ real data is conducted, and the results ...

Vibration signals acquired during robot traversal provide enough information to yield a reliable prediction of the current terrain type. In a recent approach, we combined a history of terrain class estimates into a final prediction. We therefore adopted a Bayes filter taking the posterior probability of each prediction into account. Posterior probability estimates, however, were derived from su...

Forecasting of wave height is necessary in a large number of ocean coastal activities. Recently, neural networks are used for prediction and approximation of wave heights in sea and ocean due to their great convergence rate. In this paper a cascade correlation neural network is used for prediction of wave heights at given times due to the useful capability of this network for prediction and app...

In mobile radio, Rayleigh fading poses the main threat to accurate data transmission. To solve this problem, an adaptive prediction algorithm of the Rayleigh fading channel is investigated here. We implement the adaptive method based on an autoregressive model of the past fading measurements in the sense of LMSE and predict the future fading multiplicative gain ahead. To mitigate the influence ...

This chapter proposes a learning and prediction based paradigm for designing smart home environments. The foundation of this paradigm lies in information theory as it manages uncertainties in inhabitants’ contexts (e.g., location or mobility, and activities) in daily lives. The underlying idea is to intelligently build compressed dictionaries of context profiles collected from sensor data, effi...