Translating the timing of brain developmental events across mammalian species using suitable models has provided unprecedented insights into neural development and evolution. More importantly, these models can prove to be useful abstractions and predict unknown events across species from known empirical event timing data retrieved from published literature. Such predictions can be especially us...

This paper presents a novel edge detector based on Feed-Forward Neural Networks (FFNNs). The FFNN computing architecture has two stages, which is a feature enhancement stage as well as a structural boundary extraction stage. The first stage is a traditional supervised BP network, and the second one is manually designed without training. Experiments based on both synthetic and natural images sho...

The Neural Network (NN) modelling and application to system identification, prediction and control was discussed for many authors [15]. Mainly, two types of NN models are used: Feedforward (FFNN) and Recurrent (RNN). The main problem here is the use of different NN mathematical descriptions and control schemes, according to the structure of the object model. For example, N a r e n d r a and P ...

Rainfall prediction is very complex hydrologic process and is important as it holds the key to any countries’ economy. Proposed model presents a new approach for yearly rainfall prediction of 30 Indian subdivisions. Yearly rainfall data of the Indian subdivision is available from IITM, Pune. The combination of Fast Fourier Transform (FFT) and Feed Forward Neural Network (FFNN) is applied for ne...

t is becoming increasingly difficult to have data security nowadays. There have been used various cryptography methods in literature, but recent developments in computational area have heightened the need of new methods. In this study the feed-forward artificial neural network (FFNN) was used with a different perspective by using the structure of artificial neural network as a key as a solution...

A feed forward neural network (FFNN) has been trained to discriminate between power transformer magnetizing inrush and fault currents. The training algorithm used was back-propagation, assuming initially a sigmoid transfer function for the network’s processing units (“neurons”). Once the network was trained the units’ transfer function was changed to hard limiters with thresholds equal to the b...

This letter describes the first derivatives estimation of nonlinear parameters through an embedded identifier in the hybrid system by using a feed-forward neural network (FFNN). The hybrid systems are modelled by the differential-algebraic-impulsive-switched (DAIS) structure. The FFNN is used to identify the full dynamics of the hybrid system. Moreover, the partial derivatives of an objective f...

The use of Artificial Intelligence methods is becoming increasingly common in the modeling and forecasting of hydrological and water resource processes. In this study, applicability of Adaptive Neuro Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) methods, Generalized Regression Neural Networks (GRNN) and Feed 5 Forward Neural Networks (FFNN), for forecasting of daily river f...

Perkembangan teknologi informasi yang pesat belakangan ini telah memasuki hampir semua kehidupan, hal ditandai dengan banyaknya pengguna komputer, baik untuk kepentingan perusahaan atau bisnis hingga hal-hal bersifat, hiburan, pendidikan, dan kesehatan. Permintaan layanan dilakukan dalam jumlah banyak tentu akan menjadi sebuah masalah.maka diterapkan bantuan asisten virtual biasa disebut chatbo...

This article presents an approach for data association in single camera, multi-object tracking scenarios using feed-forward neural networks (FFNN). The challenges of data association are object occlusions and changing features which are used to describe objects during the process. The presented algorithm within this article can be applied to any kind of object which has to be tracked, e.g. pers...