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Neural networks are applicable in virtually every situation in which a relationship between the predictor variables (independents, inputs) and predicted variables (dependents, outputs) exists, even when that relationship is very complex and not easy to articulate in the usual terms of “correlations” or “differences between groups”. A few representative examples of problems to which neural network analysis has been applied successfully are:
Detection of medical phenomena. A variety of health-related indices (e.g., a combination of heart rate, levels of various substances in the blood, respiration rate) can be monitored. The onset of a particular medical condition could be associated with a very complex (e.g., nonlinear and interactive) combination of changes on a subset of the variables being monitored. Neural networks have been used to recognize this predictive pattern so that the appropriate treatment can be prescribed.
Stock market prediction. Fluctuations of stock prices and stock indices are another example of a complex, multidimensional, but in some circumstances at least partially-deterministic phenomenon. Neural networks are being used by many technical analysts to make predictions about stock prices based upon a large number of factors such as past performance of other stocks and various economic indicators.
Credit assignment. A variety of pieces of information are usually known about an applicant for a loan. For instance, the applicant’s age, education, occupation, and many other facts may be available. After training a neural network on historical data, neural network analysis can identify the most relevant characteristics and use those to classify applicants as good or bad credit risks.
Engine management. Neural networks have been used to analyze the input of sensors from an engine. The neural network controls the various parameters within which the engine functions, in order to achieve a particular goal, such as minimizing fuel consumption.
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The Recycling Guide
How to Recycle Different Materials
Computers
Electronic rubbish, and computer equipment in particular, is a rapidly expanding stream of UK waste. Low prices allow consumers to replace “gadgets” often, and rapid technological change means there are always newer, better, more powerful products on the market. The result is a burgeoning computer waste mountain. Up to 20 million “obsolete” PCs are discarded annually in the USA alone.
Why is it important to recycle computer equipment?
Also known as e-waste, discarded computer equipment comprises monitors, printers, hard drives and circuit boards. Such items should on no account be thrown out with your household rubbish because they contain toxic substances, and are effectively hazardous waste. E-waste often ends up in the developing world, and the UN’s Environment Programme is alarmed by the amount of electronic goods which is improperly disposed of overseas. There is increasing concern about the pollution caused hazardous chemicals and heavy metals in Africa, Asia and South America.
Manufacturer disposal
Increasingly, manufacturers of electronic goods incorporate e-waste management into their environmental policies and operate consumer recycling schemes. Dell, for example, covers the cost of home pick-up, shipping to the recycling centre, and recycling of any obsolete equipment. The goods are “de-manufactured” and sorted according to type or material. Materials like steel and aluminum are then re-cycled to make new products, from car parts to plastic toys. Meanwhile non-reusable substances are disposed of in an environmentally sound manner. Another big brand, Hewlett Packard, recycled over 74 million kilograms of electronics in 2005. Since beginning the program 20 years ago, HP has expanded recycling operations to more than 40 world regions. These schemes help to:
· reduce of the volume of waste which ends up in landfill sites
· cut down on the amount of raw materials needed for the manufacture of new products
· make recycling convenient for the consumer
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