Myoelectric prostheses are a viable solution for people with amputations. The challenge in implementing a usable myoelectric prosthesis lies in accurately recognizing different hand gestures. The current myoelectric devices usually implement very few hand gestures. In order to approximate a real hand functionality, a myoelectric prosthesis should implement a large number of hand and finger gestures. However, increasing number of gestures can lead to a decrease in recognition accuracy. In this work a Myo arm band device is used to recognize fourteen gestures (five build in gestures of Myo armband in addition to nine new gestures). The data in this research is collected from three body-able subjects for a period of 7 seconds per gesture. The proposed method uses a pattern recognition technique based on Multi-Layer Perceptron Neural Network (MLPNN). The results show an average accuracy of 90.5% in recognizing the proposed fourteen gestures.

Myoelectric prostheses have been researched widely, and some cases have been implemented to be used by amputees in real life. However, natural control of an active prothesis remains a challenge. This work presents an exploration of an intelligent controller for upper prostheses based on myoelectric signals. A simple intelligent classifier for a small control system is designed and incorporated into a hand prosthesis to be used by the amputees in Iraq and similar developing countries. To achieve this, a Multi-Layer Perceptron Neural Networks (MLPNN) classification system is developed. The proposed system uses pattern recognition based on features extracted from eight raw EMG signals collected using a Myo armband. Five different classes of hand gestures are recognised. The system also applies remove silence process and overlapped segmentation to the collected EMG data. Continuous real values that represent class types are sent to the controller to move the prosthesis. This work shows that, by adding appropriate pre-processing, a considerable increase in the accuracy of the proposed MLP classifier can be obtained. The required hardware circuits were assembled and software scripts written to implement the intelligent myoelectric hand prosthesis.

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A prediction of students' achievements is important for educational organizations. It helps to revise plans and improve students' achievements throughout their education period. A neurofuzzy system for predicting student achievement is presented in this study. The motivation behind it is to propose a promising achievement predictor for real-time systems associated with e-learning courses. The proposed neuro-fuzzy predictor uses the time that a student needs to answer a question and the difficulty level of that question as input variables. The predictor output was the level of the student's achievement. Real data were used from e-learning courses at the University of Kerbala, Iraq. The proposed system achieved an excellent accuracy of up to 99% and an root mean square error (RMSE) value of 0.0965 for recognizing unknown test samples. The proposed prediction system based on adaptive neuro-fuzzy inference system (ANFIS) achieved better results than previous techniques. It is hoped that the results of this work will improve college admission processes and support future planning in educational organizations.