Applications of neural networks
One of the areas that has gained attention is in cardiopulmonary diagnostics. The ways neural networks work in this area or other areas of medical diagnosis is by the comparison of many different models. A patient may have regular checkups in a particular area, increasing the possibility of detecting a disease or dysfunction.
The data may include heart rate, blood pressure, breathing rate, etc. to different models. The models may include variations for age, sex, and level of physical activity. Each individual's physiological data is compared to previous physiological data and/or data of the various generic models. The deviations from the norm are compared to the known causes of deviations for each medical condition. The neural network can learn by studying the different conditions and models, merging them to form a complete conceptual picture, and then diagnose a patient's condition based upon the models.
A list at the Pacific Northwest Laboratory has several different applications in the environment, medical, and food industries.
Environment: identification of toxic wastes, analysis of fuel mixtures (7-11 example), detection of oil leaks, identification of household odors, monitoring air quality, monitoring factory emission, and testing ground water for odors.
Medical: The idea of using these in the medical field is to examine odors from the body to identify and diagnose problems. Odors in the breath, infected wounds, and body fluids all can indicate problems. Artificial neural networks have even been used to detect tuberculosis.
Food: The food industry is perhaps the biggest practical market for electronic noses, assisting or replacing entirely humans. Inspection of food, grading quality of food, fish inspection, fermentation control, checking mayonnaise for rancidity, automated flavor control, monitoring cheese ripening, verifying if orange juice is natural, beverage container inspection, and grading whiskey.
Loans and credit cards
In fact, in some banks, the failure rate of loans approved using neural networks is lower than that of some of their best traditional methods. Some credit card companies are now beginning to use neural networks in deciding whether to grant an application.
The process works by analyzing past failures and making current decisions based upon past experience. Nonetheless, this creates its own problems. For example, the bank or credit company must justify their decision to the applicant. The reason "my neural network computer recommended against it" simply isn't enough for people to accept. The process of explaining how the network learned and on what characteristics the neural network made its decision is difficult. As we alluded to earlier in the history of neural networks, self-modifying code is very difficult to debug and thus difficult to trace. Recording the steps it went through isn't enough, as it might be using conventional computing, because even the individual steps the neural network went through have to be analyzed by human beings, or possibly the network itself, to determine that a particular piece of data was crucial in the decision-making process.