The Artificial Neuron
Neural Network Header
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.

Electronic Noses
Electronic nose
An actual electronic "nose"
Image courtesy Pacific Northwest Laboratory
The idea of a chemical nose may seem a bit absurd, but it has several real-world applications. The electronic nose is composed of a chemical sensing system (such as a spectrometer) and an artificial neural network, which recognizes certain patterns of chemicals. An odor is passed over the chemical sensor array, these chemicals are then translated into a format that the computer can understand, and the artificial neural network identifies the chemical.

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.

One program that has already been started is the CATCH program. CATCH, an acronymn for Computer Aided Tracking and Characterization of Homicides. It learns about an existing crime, the location of the crime, and the particular characteristics of the offense. The program is subdivided into different tools, each of which place an emphasis on a certain characteristic or group of characteristics. This allows the user to remove certain characteristics which humans determine are unrelated.

Loans and credit cards
Loan granting is one area in which neural networks can aid humans, as it is an area not based on a predetermined and preweighted criteria, but answers are instead nebulous. Banks want to make as much money as they can, and one way to do this is to lower the failure rate by using neural networks to decide whether the bank should approve the loan. Neural networks are particularly useful in this area since no process will guarantee 100% accuracy. Even an 85-90% accuracy would be an improvement over the methods humans use.

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.

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