Research Paper on Basic of Artificial Neural Network
In this paper we discussed about the Artificial neural network, working of ANN. Also training phases of an ANN. There are various advantages of ANN over conventional approaches. Depending on the nature of the application and the strength of the internal data patterns you can generally expect a network to train quite well. This applies to problems where the relationships may be quite dynamic or non-linear. ANNs provide an analytical alternative to conventional techniques which are often limited by strict assumptions of normality, linearity, variable independence etc. Because an ANN can capture many kinds of relationships it allows the user to quickly and relatively easily model phenomena which otherwise may have been very difficult or imposible to explain otherwise. Today, neural networks discussions are occurring everywhere. Their promise seems very bright as nature itself is the proof that this kind of thing works. Yet, its future, indeed the very key to the whole technology, lies in hardware development. Currently most neural network development is simply proving that the principal works.
The Artificial Neural Networks for Cancer Research in Prediction & Survival (ANNCRIPS)
Prostate cancer is the most common form of cancer among men according to statistical study results.
Screening is a very rough estimate for cancer risk
Further staging systems such as A, B, C, D,TNM lack required efficiency.
Solution: Our proposed ANN model is far more efficient in predicting prostate cancer risk and reducing the no. of false positive test results.
Our model is developed to aid or substitute the current diagnosis and prognosis methods.
Our software test runs have shown that highly accurate ANNs based on extending the MLP model can detect prostate cancer early & reduce unnecessary tissue samplings as compared to current methods such as fPSA and tPSA.
ใครเก่งอังกฤษช่วยผมแปลหน่อยนะครับ ผมลองแปลดูแล้วมันไม่ค่อยคืออ่ะครับ
In this paper we discussed about the Artificial neural network, working of ANN. Also training phases of an ANN. There are various advantages of ANN over conventional approaches. Depending on the nature of the application and the strength of the internal data patterns you can generally expect a network to train quite well. This applies to problems where the relationships may be quite dynamic or non-linear. ANNs provide an analytical alternative to conventional techniques which are often limited by strict assumptions of normality, linearity, variable independence etc. Because an ANN can capture many kinds of relationships it allows the user to quickly and relatively easily model phenomena which otherwise may have been very difficult or imposible to explain otherwise. Today, neural networks discussions are occurring everywhere. Their promise seems very bright as nature itself is the proof that this kind of thing works. Yet, its future, indeed the very key to the whole technology, lies in hardware development. Currently most neural network development is simply proving that the principal works.
The Artificial Neural Networks for Cancer Research in Prediction & Survival (ANNCRIPS)
Prostate cancer is the most common form of cancer among men according to statistical study results.
Screening is a very rough estimate for cancer risk
Further staging systems such as A, B, C, D,TNM lack required efficiency.
Solution: Our proposed ANN model is far more efficient in predicting prostate cancer risk and reducing the no. of false positive test results.
Our model is developed to aid or substitute the current diagnosis and prognosis methods.
Our software test runs have shown that highly accurate ANNs based on extending the MLP model can detect prostate cancer early & reduce unnecessary tissue samplings as compared to current methods such as fPSA and tPSA.