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Background: Eye melanoma is deforming in the eye, growing and developing in tissues inside the middle layer of an eyeball, resulting in dark spots in the iris section of the eye, changes in size, the shape of the pupil, and vision., Objective: The current study aims to diagnose eye melanoma using a gray level co-occurrence matrix (GLCM) for texture extraction and soft computing techniques, leading to the disease diagnosis faster, time-saving, and prevention of misdiagnosis resulting from the physician's manual approach., Material and Methods: In this experimental study, two models are proposed for the diagnosis of eye melanoma, including backpropagation neural networks (BPNN) and radial basis functions network (RBFN). The images used for training and validating were obtained from the eye-cancer database., Results: Based on our experiments, our proposed models achieve 92.31% and 94.70% recognition rates for GLCM+BPNN and GLCM+RBFN, respectively., Conclusion: Based on the comparison of our models with the others, the models used in the current study outperform other proposed models., Competing Interests: None, (Copyright: © Journal of Biomedical Physics and Engineering.)

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.

Humans are apt at recognizing patterns and discovering even abstract features which are sometimes embedded therein. Our ability to use the banknotes in circulation for business transactions lies in the effortlessness with which we can recognize the different banknote denominations after seeing them over a period of time. More significant is that we can usually recognize these banknote denominations irrespective of what parts of the banknotes are exposed to us visually. Furthermore, our recognition ability is largely unaffected even when these banknotes are partially occluded. In a similar analogy, the robustness of intelligent systems to perform the task of banknote recognition should not collapse under some minimum level of partial occlusion. Artificial neural networks are intelligent systems which from inception have taken many important cues related to structure and learning rules from the human nervous/cognition processing system. Likewise, it has been shown that advances in artificial neural network simulations can help us understand the human nervous/cognition system even furthermore. In this paper, we investigate three cognition hypothetical frameworks to vision-based recognition of banknote denominations using competitive neural networks. In order to make the task more challenging and stress-test the investigated hypotheses, we also consider the recognition of occluded banknotes. The implemented hypothetical systems are tasked to perform fast recognition of banknotes with up to 75 % occlusion. The investigated hypothetical systems are trained on Nigeria's Naira banknotes and several experiments are performed to demonstrate the findings presented within this work.

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.

In our continuous attempts to model natural intelligence and emotions in machine learning, many research works emerge with different methods that are often driven by engineering concerns and have the common goal of modeling human perception in machines. This paper aims to go further in that direction by investigating the integration of emotion at the structural level of cognitive systems using the novel emotional DuoNeural Network (DuoNN). This network has hidden layer DuoNeurons, where each has two embedded neurons: a dorsal neuron and a ventral neuron for cognitive and emotional data processing, respectively. When input visual stimuli are presented to the DuoNN, the dorsal cognitive neurons process local features while the ventral emotional neurons process the entire pattern. We present the computational model and the learning algorithm of the DuoNN, the input information-cognitive and emotional-parallel streaming method, and a comparison between the DuoNN and a recently developed emotional neural network. Experimental results show that the DuoNN architecture, configuration, and the additional emotional information processing, yield higher recognition rates and faster learning and decision making., (Copyright © 2010 Elsevier Ltd. All rights reserved.)

Credit scoring is one of the key analytical techniques in credit risk evaluation which has been an active research area in financial risk management. This paper presents a credit risk evaluation system that uses a neural network model based on the back propagation learning algorithm. We train and implement the neural network to decide whether to approve or reject a credit application, using seven learning schemes and real world credit applications from the Australian credit approval datasets. A comparison of the system performance under the different learning schemes is provided, furthermore, we compare the performance of two neural networks; with one and two hidden layers following the ideal learning scheme. Experimental results suggest that neural networks can be effectively used in automatic processing of credit applications.

Much of the research work into artificial intelligence (AI) has been focusing on exploring various potential applications of intelligent systems with successful results in most cases. In our attempts to model human intelligence by mimicking the brain structure and function, we overlook an important aspect in human learning and decision making: the emotional factor. While it currently sounds impossible to have "machines with emotions," it is quite conceivable to artificially simulate some emotions in machine learning. This paper presents a modified backpropagation (BP) learning algorithm, namely, the emotional backpropagation (EmBP) learning algorithm. The new algorithm has additional emotional weights that are updated using two additional emotional parameters: anxiety and confidence. The proposed "emotional" neural network will be implemented to a facial recognition problem, and the results will be compared to a similar application using a conventional neural network. Experimental results show that the addition of the two novel emotional parameters improves the performance of the neural network yielding higher recognition rates and faster recognition time.

Advances in digital technologies have allowed us to generate more images than ever. Images of scanned documents are examples of these images that form a vital part in digital libraries and archives. Scanned degraded documents contain background noise and varying contrast and illumination, therefore, document image binarisation must be performed in order to separate foreground from background layers. Image binarisation is performed using either local adaptive thresholding or global thresholding; with local thresholding being generally considered as more successful. This paper presents a novel method to global thresholding, where a neural network is trained using local threshold values of an image in order to determine an optimum global threshold value which is used to binarise the whole image. The proposed method is compared with five local thresholding methods, and the experimental results indicate that our method is computationally cost-effective and capable of binarising scanned degraded documents with superior results.

Classification of blood cell types can be time consuming and susceptible to error due to the different morphological features of the cells. This paper presents a blood cell identification system that simulates a human visual inspection and identification of the three blood cell types. The proposed system uses global pattern averaging to extract cell features, and a neural network to classify the cell type. Two neural networks are investigated and a comparison between these networks is drawn. Experimental results suggest that the proposed system provides fast, simple and efficient identification which can be used in automating laboratory reporting.