Face Expression Classification Using Neural Network and PCA algorithm

The purpose of this article is to shed light on the mechanism and the procedures of a program that classifies an input face into any of the six basic facial expressions, which are Anger, Disgust, Fear, Happiness, Sadness and Surprise, in addition to normal face. This program works by apply PCA- principal component analysis algorithm, which is applied of one side of the face, and depends, on contrast to the traditional studies which rely on the whole face, on three components: Eyebrows, Eyes and Mouth. Those out-value are used to determine the facial feature array as an input to the neural network, and the neural network is trained by using the back-propagation algorithm. Note that the faces used in this study belong to people from different ages and races.

Simulating the Behavior of an Integrate-and-Fire Neuron

Video image data can be analyzed and processed in many ways. This research explores the extent at which spiking neurons, which are designed along the Hodgkin-Huxley model, are suitable for this task. The simulations reported in this research consider integrate-and-fire neurons constant and alternating input currents, as well as pixel-intensity driven inputs. Currently, the simulation software employs 64 independently operating spiking neurons that process image data taken every 25 ms. In order to define the response of these neurons, the experiments were done on 100 digital images which include different illuminations, contrast, and saturation situations. The results show that the integrate-and-fire-neuron is highly sensitive to the changes in the intensity of pixels if its parameters are properly set. So in many applications, such as "Saliency Maps", which highly depend on the intensity values of a set of pixels, a neural network made of this neuron will perfectly fit.

Developingan adaptive controller based on cerebellar model

In this study we developed an adaptive model inspired by internal models in the cerebellum and this approach called Feedback Error Learning (FEL). FEL is the origin of Learning Feed-Forward Control (LFFC). It depends on Feedback Controller and Feed-Forward Controller which is a Neural Network, and this Neural Network uses feedback controller output as training signal. We developed this approach to control a robot arm, and to balance inverted pendulum and to control bus suspension system. We developed this approach by adding a second Neural Network, and this new Neural Network uses FEL controller output as training signal. We simulate these systems by using Matlab and Simulink, and we find that this development improves control performance.

Automatic Facial Expression Classification Using Image Processing Technique ( Fear – disgust – sadness – Surprise – Anger – Happiness – Natural )

This paper presents an algorithm for designing a system that classifies standard human facial expressions which are fear, disgust, sad , surprise, anger, happiness, and the normal expression . The facial expression that is presented in the input image of the system can be classified depending on extracting appearance features then, it is entered into neural network to complete the classification process using Matlab as a programming language. Multiple stages completed the work, which are, (collection images, pre-processing of the images, feature extraction, training neural network, classification and testing). Our system has been able to achieve the highest rating when the expression of anger reached 100 %, while the lowest rating was at the expression of sad by 30%.

Analysis study about (MFCC and Endpoint) algorithms and the extent of their impact in voice recognition rates

Voice recognition includes two basic parts: speech and speaker recognition. These recognition processes consider as the most important processes of modern technologies, many systems has been developed that differ in the methods used to extract features and classification ways to support recognition systems of this type. The study was conducted in this research on the previous subject, where the system is designed to recognize the speaker and his voice orders and focus on several complementary algorithms to carry out the research. we conducted an analytical study on MFCC algorithm used in the extraction of features, and it has been studying two parameters the number of filters in the filters bank and the number of features that taken from each frame and the impact of these two parameters in the recognition rate and the relationship of these two parameters on each other. It was the use of feed forwarding back propagation neural networks performance analysis as characteristics and we analyze the performance of the network to gain access to the best features and components to the process of achieving recognition. And it has been studying Endpoint algorithm that used to remove periods of silence and its impact on voice recognition rates.

Anti-Money Laundering using Data Mining techniques

This research presents literature review on using Artificial intelligence and Data Mining techniques in Anti Money Laundering systems. We compare many methodologies used in different research papers with the purpose of shedding some light on real life applications using Artificial intelligence

Using artificial neural networks for short term electrical load forecasting in Tartous province

A reliable and continuous supply of electrical energy is necessary for the functioning of today’s complex society. Because of the increasing consumption and the extension of existing electrical transmission networks and these power systems are operated closer and closer to their limits accordingly the possibilities of overloading, equipment failures and blackout are also increasing, furthermore, we have an additional obstacle which is that electrical energy cannot be stored efficiently, so, electrical energy should be generated only when it's needed. Due to the fact that world is facing a lack of oil reserves and the difficulties related to have alternative sources to generate electrical power, then, electrical load forecasting is considered as a crucial factor in electrical power system either from economical or technical point of view on both planning and operating levels. This research introduces a short term electrical load forecasting system by using artificial neural networks with a simulation in Matlab environment in addition to an interface for the system and all that is depending on previous load data and weather parameters in Tartous province.