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Neural Networks

Neural Networks

The neural networks are an emulation of the neural system of the brain, where each element of the biological system is replaced by a mathematical equivalent. An artificial neural network can do similar tasks of the human brain, tasks that a regular computer is unable to perform as image recognition, speech recognition and making decisions .Today the use of neural networks are being implemented in software, emulating the parallel nature of a neural network to a linear system. The most common applications are voice recognition, character recognition (OCR), image reconstruction and more, but are also being implemented in hardware, where lineal structure of processors is changed to a neural structure. This takes advantage of the parallel nature of the neural networks. This new technology is called Neural Processors. Currently neural processors are being used in specific applications, such as robotics.

Beyond its role as an alternative computing mechanism, and in data mining, neural computing can be combined with other computer-based information systems to produce powerful hybrid systems. Neural computing is emerging as an effective technology in pattern recognition. Neural networks can analyze large quantities of data to establish patterns and characteristics in situations where the logic and rules are not known.

A New Approach to Stock Trading

Neural systems have been used increasingly in a variety of business applications, including projecting and marketing research solutions. The major fields where neural networks are finding use are financial operations, enterprise preparation, trading, organization analytics plus product maintenance. Neural networks is often applied productively by lots of traders, therefore, if you are a trader and you haven’t yet been introduced to neural networks, we’ll take you through this process of technical analysis and show you how to apply the idea towards your trading approach.

Most people have never heard about neural networks and, if they aren’t traders, they probably don’t need to know what they are. What is really surprising, nevertheless, is the truth that a huge number of those who could benefit highly from neural network systems haven’t even heard about it; accepting it for only a complex scientific idea .There are also those who pin their hopes on neural systems, acclaiming the nets after some positive experience with them and considering them as being a silver-bullet method to fix any kind of problem.  But, like any trading method, neural nets are no quick-fix that will permit you to strike it rich simply by clicking a key . In fact, the proper understanding of neural networks and their purpose is essential for its effective application. As far as trading is concerned, neural networks really are a new, unique approach to technical analysis, intended for people who take a thinking approach to their business and are also willing to invest some time and effort for making this method benefit them. Best of all, whenever applied correctly, neural networks is able to bring a nice gain on a regular basis

Iris Recognition

Nowadays, the most important ways of human verification are recognition via DNA, face, fingerprint, signature, speech, and iris. Among all, one of the recent, reliable, and technological methods is iris recognition which is practiced by some organizations today. The iris is a non identical organism made of colorful muscles including robots with shaped lines. These lines are the main causes of making everyone’s iris non identical. Even the irises of a pair of eyes of one person are completely different from one another. The precision of identification via iris is increased by using more and more details. It has been proven that iris patterns are never changed nearly from the time the child is one year old throughout all life.

Over the past few years there has been considerable interest in the development of neural networks based pattern recognition systems, because of their ability to classify data. The kind of neural network practiced by the researcher, Learning Vector Quantization, is a competitive network function in the field of classification of the patterns. The iris images prepared in a database, are in the form of PNG (portable network graphics) pattern, meanwhile they must be preprocessed through which the boundary of the iris is recognized and their features to be extracted.

Play significant role in medicine

Neural Network in Medicine has nicely summarized some of the areas that they have applied this to. They are:

  • Image recognition and interpretation
  • Generating alerts and reminders
  • Diagnostic Assistance
  • Therapy appraisal and planning
  • Agents for information retrieval

In a study during the late 1990s, researchers of the University Hospital, Sweden, ventured to include 1,120 ECG records of Heart Attack patients, and 10,452 records of normal patients. The neural networks were found to be able to use this input data, and establish a relationship and pattern. This leaning phase was internalized by the system, and started identifying patients with abnormal ECGs with a 10% better accuracy than most clinicians/cardiologists on staff.

The Neural Network is more a “teachable software” that absorbs and learns from data input. When properly computed, even at a fast pace with a tried and tested algorithm, it develops patterns within the input data, or a combination of multiple data dimensions or factors, to which a given situation can be compared to, and a prognosis declared.

Some researchers in Mayo Clinic, Florida studied 189 patients with device related Endocarditis diagnosed between 1991 and 2003. Endocarditis is an infection involving the valves and at times the chambers of the heart, that are often caused due to implanted devices in the heart. The mortality due to the infection could be as high as 60%. The researchers at Mayo, fed the data from the same 189 patients in the Artificial Neural Network and had it undergo three separate “trainings” to learn to evaluate these symptoms. Upon being tested with different sample populations (only known cases, and then a overall sample of a combination of both known and unknown cases), the best trained network was able to identify Endocarditis cases very effectively, thus eliminating the need for such an invasive procedure.

With modern day health becoming more and more data centric, access to relevant patient data is gradually becoming extremely convenient. Expert systems with its Neural Networks and computational algorithms, has tremendous opportunities to speed up diagnosis, and effect patient care with speed and more & more accuracy. 

Conclusion

One might think that the technology of Neural Networking will help society achieve a better standard of living, but we must also keep in mind, that inheriting our biological capabilities to a machine makes us more dependent on them, and that dependency, only inhibits our intellectual capacities. A networks without training is like a newborn child coming into the world, so scientists continue to develop various models of neural networks each with different abilities and different algorithms.

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