This research investigates the way in which Neural Networks (NNs)
can be used to forecast insolvency. The research aimed at forecasting, oiie for three years in advance, a clear disclosure of tlie legal condition of insolvency in a jointstock company based in Italy and operating in the textile industry, throngh the analysis of the official balance sheet. The results refer to experiments which have been carried out for three years, concerning a sample of about 500 conipanies in the textile sector, their balance sheets for a three-year period (1990-1992) and the state of the art in the following two years (1993-1994). The research has pointed out how the problem of insolvency can be dealt with by using NNs in a satisfactory way and has proved useful to show how important the method of collecting data for a further analysis throngh NNs can be.
This research investigates the way in which Neural Networks (NNs)
can be used to forecast insolvency. The research aimed at forecasting, one for three years in advance, a clear disclosure of tlie legal condition of insolvency in a jointstock company based in Italy and operating in the textile industry, through the analysis of the official balance sheet. The results refer to experiments which have been carried out for three years, concerning a sample of abont 500 coinpanies” in the textile sector, their balance sheets for a three-year period (1990-1992) and the State of the art in the following two years (1993-1994). The research has pointed out how the problem of insolvency can be dealt with by nsing NNs in a satisfactory way and has proved useful to show how important the method of collecting data for a further analysis throngh NNs can be.
We present a neural network application to the diagnosis of vocal and voice disorders. These disorders normally cause changes in the voice signal, so we use acoustic parameters extracted from the voice as inputs for the neural network. The selected neural network structure is Multilayer Feedforward. In this paper, we focus our application on the classification between pathologic and non-pathologic voices. The performance of the neural network is very good, 100 % correct in the test. Furthermore, having used neural network techniques to reduce the initial nuniber of inputs (35), we conclude that only two acoustic parameters are needed for the classification between normal and pathological voices. The application can be a very useful diagnostic tool because it is non-invasive, makes it possible to develop an automatic computer-based diagnosis system, reduces the cost and tirne of the diagnosis, is objective and can also be useful for evaluation of surgical, pharmacological and rehabilitation processes. Finally, we discuss the lirnitation of our work and possible future research.