Category Intelligent Software>Neural Network Systems/Tools

Abstract EasyNN-plus is a neural network development system. It can be used for forward planning, investment, insurance, valuation, travel, clinical testing, results prediction, climate modeling, weather forecasting and much more.

EasyNN-plus grows multi-layer neural networks from the data in a grid. The neural network input and output layers are created to match the grid input and output columns. Hidden layers connecting the input and output layers can then be grown to hold the optimum number of nodes. Each node contains a neuron and its connection addresses. The whole process is automatic.

The grid is produced by importing data from spreadsheet files, tab separated plain text files, and comma separated files, bitmap files or binary files. The grid can also be produced manually using the EasyNN- plus editing facilities. Numeric, text, image or combinations of the data types in the grid can be used to grow the neural networks.

The neural networks learn the training data in the grid and they can use the validating data in the grid to self validate at the same time. When training finishes the neural networks can be tested using the querying data in the grid, using the interactive query facilities or using querying data in separate files.

The steps that are required to produce neural networks are automated in EasyNN-plus.

EasyNN-plus produces the simplest neural network that will learn the training data. The graphical editor can be used to produce complex networks.

Inside EasyNN-plus --

A neural network produced by EasyNN-plus has two (2) components. The components are the Node and the Connection. The components are duplicated to make the neural network. A Node consists of a Neuron with positioning and connecting information. A Connection consists of a Weight with node addressing information.

The Grid used by EasyNN-plus also has two (2) components. These are the Example row and the Input/Output column. These are replicated to make the Grid.

All the component parts of EasyNN-plus are implemented as C++ reusable classes to simplify future development.

Case Studies and Projects of EasyNN-plus:

Wound healing treatments -- A medical physicist developed a network that was trained using the treatment and medical history of hundreds of patients to determine which treatments produced the optimum healing time.

R.J.Taylor et al: Using an artificial neural network to predict healing times and risk factors for venous leg ulcers; Journal of Wound Care Vol. 1, No.3 2002.

Stroke rehabilitation progress -- This network was developed by a stroke rehabilitation specialist to help find the combination of therapies that produced the best results. (Marc van Gestel)

Drug interactions -- The side effects reported by patients involved in a stage of drug testing were used to train a network. The reported symptoms and the dosage of all the drugs being taken were used in the training. The trained network could then indicate possible drug interactions that would need further investigation.

Multiple sclerosis (MS) symptoms and treatments -- This project uses a network that establishes which MS symptoms are related to treatments. The inputs to the network are the treatments and the outputs are the symptom scores over the following eight (8) days. A second network uses a much longer period. In both networks, the importance of the treatments is determined after training. Querying is used to investigate if the importance is due to a positive or a negative change in symptoms.

Identifying liver cancer -- The distinction of hepatocellular carcinoma (HCC) from chronic liver disease (CLD) is a significant clinical problem. In this project a network has been used to help identify tumor-specific proteomic features, and to estimate the values of the tumor-specific proteomic features in the diagnosis of HCC.

Poon et al.: Comprehensive Proteomic Profiling and HCC Detection; Clinical Chemistry 49, No. 5, 2003

Chemical analysis -- An application in gas chromatography which predicts retention indices (the position when a chemical compound appears in a chromatogram/plot compared to other components) on the base of topological descriptors, which describe the structure and/or properties of a chemical.

Chronic Nephropathies -- Dimitrov BD, Ruggenenti P, Stefanov R, Perna A, Remuzzi G. Chronic Nephropathies: Individual Risk for Progression to End-Stage Renal Failure as Predicted by an Integrated Probabilistic Model. Nephron Clin Pract 2003; 95:c47-c59.

System Requirements

Operating Systems: Windows® 9x 2000 XP 2003 Vista


Manufacturer Web Site EasyNN-plus

Price Single user license: $79.00 USD

G6G Abstract Number 20173

G6G Manufacturer Number 101865