Search and Classification of "Interesting" Business Applications in the World Wide Web using a Neural Network Approach

Using NeuroSolutions, this study searched through a database of WWW businesses and classified them as “interesting” and “not interesting” for determining future business ventures. With generalized models, the new data sets were classified at 84.75% on average correctly.

Classification With Artificial Neural Networks and Supprt Vector Machines: Application to Oil Fluorescence Spectra

This paper reports on oil classification with fluorescence spectroscopy. Using NeuroSolutions, the objective is to classify the oil fluorescence spectra based on a laboratory dataset of fluorescence spectra of several oil classes (sludge, crude and heavy oil). The classification was carried out using the following three methods: channel relationship method (CRM), artificial neural networks (ANNs), and support vector machines (SVMs).

Trader's account has increased by over 90% in three months!

Mr. Honorowski is trading the New Zealand Dollar/U.S. Dollar with a 5x leverage and his four most recent trades alone have yielded 4.46%, 5.31%, 13.28% and 20.60% returns with an overall 77% wins since April '06! *
Mr. Honorowski said he "Absolutely loves the software (TradingSolutions)" and "Would recommend it to anyone and everyone". His favorite features are the Optimal Signal technology along with the neural network modeling. He likes that TradingSolutions was "non-blackbox" with all of the settings and parameters he had control over and said that it was "hard to beat TradingSolutions optimization results".

Trader uses TradingSolutions to achieve a 31% return in 6 months

Mr. Cuni began using TradingSolutions in June '06. He actively traded the Solution Service and models from the Sample Performance section of www.tradingsolutions.com while learning how to create his own profitable models in TradingSolutions. In March '07 he began trading his own models and has earned a 31% annualized return in the past 6 months alone! Mr. Cuni wanted to emphasize four aspects of his model development process that he attributes to his success with TradingSolutions:

Neural Networks in Mobile Robot Motion

This paper deals with a path planning and intelligent control of an autonomous robot which should move safely in partially structured environment. This environment may involve any number of obstacles of arbitrary shape and size; some of them are allowed to move. We describe our approach to solving the motion-planning problem in mobile robot control using neural networks-based technique. Our method of the construction of a collision-free path for moving robot among obstacles is based on two neural networks. The first neural network is used to determine the “free” space using ultrasound range finder data. The second neural network “finds” a safe direction for the next robot section of the path in the workspace while avoiding the nearest obstacles. Simulation examples of generated path with proposed techniques will be presented

Using Satellite Imagery for Mapping Forest Types or Changes

Using remote sensing satellite imagery, multi-spectral sensor data is used for applications to map forest types or changes in a forest or to extract statistical information from the images together with available maps. In the case of mapping of forest types and changes, the information is essential for forestry companies in order to keep their databases up-to-date for planning and forest management. In addition, the imagery has been used to do wall-to-wall mapping of land use and vegetation from space as well as measuring the depth in the sea water, mapping along the coastlines and measuring the vegetation at the bottom of the sea. The primary benefit of using NeuroSolutions over other methods is not having to set up a mathematical model for the classification and regression applications. Common classification methods used in remote sensing is based on the assumption of normal distributed classes. However, in Mr. Rosengren's applications, the classes often have multimodal distributions. Using NeuroSolutions rather than other classification and regression methods, often save as much as 25% of the time and cost of the project!

Seabed Recognition Using Neural Networks

Side Scan Sonar (SSS) imaging is one of the advanced methods for data acquisition about the sea floor. The possibilities of intelligence-based approaches in the analysis of sonar images and classification of seabed material have been explored in this study. The only available type of measurement for classification SSS images is the grey level of the pixels corresponding to the acoustic reflectance. It is difficult to recognize and classify objects based on a single feature. However, the spatial order of the grey level transitions gives ‘texture’ characteristics to the image and it is these that act as an important aid in human interpretation. Image texture can be characterized by the Spatial Grey Level Dependence Method (SGLDM) based on the cooccurrence matrix of pairs of grey levels.

Seabed Recognition Using Neural Networks

Side Scan Sonar (SSS) imaging is one of the advanced methods for data acquisition about the sea floor. The possibilities of intelligence-based approaches in the analysis of sonar images and classification of seabed material have been explored in this study. The only available type of measurement for classification SSS images is the grey level of the pixels corresponding to the acoustic reflectance. It is difficult to recognize and classify objects based on a single feature. However, the spatial order of the grey level transitions gives ‘texture’ characteristics to the image and it is these that act as an important aid in human interpretation. Image texture can be characterized by the Spatial Grey Level Dependence Method (SGLDM) based on the cooccurrence matrix of pairs of grey levels.