Adnan Khashman

Prototype-Incorporated Emotional Neural Network

Artificial neural networks (ANNs) aim to simulate the biological neural activities. Interestingly, many “engineering” prospects in ANN have relied on motivations from cognition and psychology studies. So far, two important learning theories that have been subject of active research are the prototype and adaptive learning theories. The learning rules employed for ANNs can be related to adaptive learning theory, where several examples of the different classes in a task are supplied to the network for adjusting internal parameters. Conversely, the prototype-learning theory uses prototypes (representative examples); usually, one prototype per class of the different classes contained in the task. These prototypes are supplied for systematic matching with new examples so that class association can be achieved. In this paper, we propose and implement a novel neural network algorithm based on modifying the emotional neural network (EmNN) model to unify the prototype- and adaptive-learning theories. We refer to our new model as “prototype-incorporated EmNN”. Furthermore, we apply the proposed model to two real-life challenging tasks, namely, static hand-gesture recognition and face recognition, and compare the result to those obtained using the popular back-propagation neural network (BPNN), emotional BPNN (EmNN), deep networks, an exemplar classification model, and k-nearest neighbor.

Non-Destructive Prediction of Concrete Compressive Strength Using Neural Networks

Abstract Our thirst for progress as humans is reflected by our continuous research activities in different areas leading to many useful emerging applications and technologies. Artificial intelligence and its applications are good examples of such explored fields with varying expectations and realistic results. Generally, artificially intelligent systems have shown their capability in solving real-life problems; particularly in non-linear tasks. Such tasks are often assigned to an artificial neural network (ANN) model to arbitrate as they mimic the structure and function of a biological brain; albeit at a basic level. In this paper, we investigate a newly emerging application area for ANNs; namely civil engineering. We design, implement and test an ANN model to predict and classify the compressive strength of different concrete mixes into low, moderate or high strength. Traditionally, the performance of concrete is affected by many non-linear factors and testing its strength comprises a destructive procedure of concrete samples. Numerical results in this work show high efficiency in correctly classifying the compressive strength, thus making it possible to use in real-life applications.

Disk hernia and spondylolisthesis diagnosis using biomechanical features and neural network.

Artificial neural networks have found applications in various areas of medical diagnosis. The capability of neural networks to learn medical data, mining useful and complex relationships that exist between attributes has earned it a major domain in decision support systems. This paper proposes a fast automatic system for the diagnosis of disk hernia and spondylolisthesis using biomechanical features and neural network. Such systems as described within this work allow the diagnosis of new cases using trained neural networks; patients are classified as either having disk hernia, spondylolisthesis, or normal. Generally, both disk hernia and spondylolisthesis present similar symptoms; hence, diagnosis is prone to inter-misclassification error. This work is significant in that the proposed systems are capable of making fast decisions on such somewhat difficult diagnoses with reasonable accuracies. Feedforward neural network and radial basis function networks are trained on data obtained from a public database. The results obtained within this research are promising and show that neural networks can find applications as efficient and effective expert systems for the diagnosis of disk hernia and spondylolisthesis.

Performance Evaluation of Binarization Methods for Document Images

In scanned documents, where noise, contrast, and illumination vary, classifying pixels as foreground or background pixels is still a difficult and challenging problem. Several evaluation studies on binarization methods for document images were previously performed, however, performing an objective evaluation to determine an optimal binarization method is not trivial because of the application-dependency of the different methods and the varieties in document databases. In this paper, the aim is to determine an optimal binarization method that can be effectively used with a variety of scanned documents. Firstly, a comparative study of thirteen binarization methods applied to gray level images of degraded historical documents, artificially created words, and handwritten documents is presented. Secondly, three new image quality parameters, used for performance evaluation in addition to visual inspection of binarized images are proposed. Experimental results suggest that local method Water Flow Model and global methods Kapur and Otsu methods outperform the other ten binarization methods on all images.