Energy performance analysis in buildings is becoming more and highlighted, due to the increasing trend of energy consumption building sector. Many studies have declared great potential soft computing for this analysis. A particular methodology sense employing hybrid machine learning that copes with drawbacks single methods. In work, an optimized version a popular model, namely feed-forward neur...

Automatic license plate recognition system is an image processing technology used to identify vehicles by their license plates. Such systems require the recognition of characters from the plate image. Artificial neural networks are commonly used to perform character recognition due to their high noise tolerance. Feed-Forward Neural Network (FFNN) can be used to recognize the characters from ima...

Feed Forward Neural Networks (FFNNs) are computational techniques inspired by the physiology of the brain and used in the approximation of general mappings from one nite dimensional space to another. They present a practical application of the theoretical resolution of Hilbert's 13 th problem by Kolmogorov and Lorenz, and have been used with success in a variety of applications. However, as the...

At present, polyhydroxyalkanoates (PHAs) have been considered as a promising alternative to conventional plastics due to their diverse variability in structure and rapid biodegradation. To ensure cost competitiveness in the market, thermoseparating aqueous two-phase extraction (ATPE) with the advantages of being mild and environmental-friendly was suggested as the primary isolation and purifica...

A breast neoplasia is often marked by the presence of microcalcifications and massive lesions in the mammogram: hence the need for tools able to recognize such lesions at an early stage. Our collaboration, among italian physicists and radiologists, has built a large distributed database of digitized mammographic images and has developed a Computer Aided Detection (CADe) system for the automatic...

In most of previous works on neural network based language models (NNLMs), the words are represented as 1-of-N encoded feature vectors. In this paper we investigate an alternative encoding of the word history, known as bag-of-words (BOW) representation of a word sequence, and use it as an additional input feature to the NNLM. Both the feedforward neural network (FFNN) and the long short-term me...

CONTEXT is vital in formulating intelligent classifications and responses, especially under uncertainty. In a standard feed-forward neural network (FFNN), context comes in the form of information encoded in the input vector and trained in weight parameters. However, useful information can also be present in the temporal nature of the input vectors, or from past internal states of a network. Fut...

In this paper, we have designed and developed a technique for burning area identification using Intensity Hue Saturation (IHS) transformation and image segmentation. The process of identifying the burnt area in proposed technique consists of four steps such as: IHS transformation, object segmentation, identification of smoke area using Feed-Forward Neural Network (FFNN) and discovering burning ...

Rainfall-runoff modeling in ungauged basins continues to be a great hydrological research challenge. A novel approach is the Long-Short-Term-Memory neural network (LSTM) from Deep Learning toolbox, which few works have addressed its use for rainfall-runoff regionalization. This work aims discuss application of LSTM as regional method against traditional (FFNN) and conceptual models practical fr...