Swarm intelligence
Artificial Neural Networks in Hydrology
Artificial Neural Networks in Hydrology
Real-Time Prediction of Water Stage with Artificial Neural Network Approach
AI '02 Proceedings of the 15th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
River stage forecasting with particle swarm optimization
IEA/AIE'2004 Proceedings of the 17th international conference on Innovations in applied artificial intelligence
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
A split-step PSO Algorithm in predicting construction litigation outcome
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Risk analysis for intellectual property litigation
Proceedings of the 13th International Conference on Artificial Intelligence and Law
Prediction of construction litigation outcome using a split-step PSO algorithm
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Prediction of construction litigation outcome – a case-based reasoning approach
IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
Identifying patent monetization entities
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Law
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Construction claims are normally affected by a large number of complex and interrelated factors. It is highly desirable for the parties to a dispute to know with some certainty how the case would be resolved if it were taken to court. The use of artificial neural networks can be a cost-effective technique to help to predict the outcome of construction claims, on the basis of characteristics of cases and the corresponding past court decisions. In this paper, a particle swarm optimization model is adopted to train perceptrons. The approach is demonstrated to be feasible and effective by predicting the outcome of construction claims in Hong Kong in the last 10 years. The results show faster and more accurate results than its counterparts of a benching back-propagation neural network and that the PSO-based network are able to give a successful prediction rate of up to 80%. With this, the parties would be more prudent in pursuing litigation and hence the number of disputes could be reduced significantly.