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In this research work, we have implemented an intelligent system for diagnosis of diabetic retinopathy. Diabetic retinopathy is a damage caused by diabetes to the retinal blood vessels. This causes the leak of blood and other fluid that resulted into swelling of retina tissue and cloudy vision. This can be classified into two; non-proliferative diabetic retinopathy and proliferative diabetic retinopathy. The dataset used for the implementation of this intelligent system is obtained from the freely available UCI machine learning repository. This system will aid physicians to accurately diagnose the disease. Such a system will make the diagnosis faster, accurate and easier as compared to manual diagnosis. Our novel system uses a feedforward neural network trained with backpropagation neural network. The results obtained in this work are compared with previously proposed systems using the same dataset. Experimental results indicate that our novel system outperforms the other systems in diagnosing diabetic retinopathy. [ABSTRACT FROM AUTHOR]

Emotional processes and responses are biological and psychological processes in humans. The modelling of these biological processes in machines is considered a challenging and often controversial task in the field of bio-inspired computing. This is because of the complexity of emotional processes and rarity of available emotional computational models. However, research into possible mechanisms that allow emotional phenomena to influence cognitive and behavioural processes and how they can be implemented in robots has recently been gaining momentum with successful results in modelling emotions within systems. The motive for this trend is that future machines and robots will be capable of making better decisions if their cognition is enhanced by emotions. We present in this paper an emotional neural network based on an improved emotional back propagation (EmBP) learning algorithm. The modified algorithm, namely the iEmBP learning algorithm, models within its configuration three emotional responses: anxiety, confidence and impression. The proposed iEmBP-based emotional neural network will be implemented to a blood cell type identification problem, and its performance will be compared to two other neural networks. Experimental results show that the iEmBP-based emotional neural network has improved performance when compared to the EmBP-based network, and significantly outperforms the conventional back propagation (BP)-based neural networks in recognition results and time cost. [ABSTRACT FROM AUTHOR]

Abstract: Credit scoring and evaluation is one of the key analytical techniques in credit risk evaluation which has been an active research area in financial risk management. Artificial neural networks (NNs) have been considered to be accurate tools for credit analysis among others in the credit industry. Lately, emotional neural networks (EmNNs) have been suggested and applied successfully for pattern recognition. In this paper we investigate the efficiency of EmNNs and compare their performance to conventional NNs when applied to credit risk evaluation. In total 12 neural networks; based equally on emotional and conventional neural models; are arbitrated under three learning schemes to classify whether a credit application is approved or declined. The learning schemes differ in the ratio of training-to-validation data used during training and testing the neural networks. The emotional and conventional neural models are trained using real world credit application cases from the Australian credit approval datasets which has 690 cases; each case with 14 numerical attributes; based on which an application is accepted or rejected. The performance of the 12 neural networks will be evaluated using certain criteria. Experimental results suggest that both emotional and conventional neural models can be used effectively for credit risk evaluations, however the emotional models outperform their conventional counterparts in decision making speed and accuracy, thus, making them ideal for implementation in fast automatic processing of credit applications. [Copyright &y& Elsevier]

Abstract: 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. [ABSTRACT FROM AUTHOR]

Abstract: This paper describes a credit risk evaluation system that uses supervised neural network models based on the back propagation learning algorithm. We train and implement three neural networks to decide whether to approve or reject a credit application. Credit scoring and evaluation is one of the key analytical techniques in credit risk evaluation which has been an active research area in financial risk management. The neural networks are trained using real world credit application cases from the German credit approval datasets which has 1000 cases; each case with 24 numerical attributes; based on which an application is accepted or rejected. Nine learning schemes with different training-to-validation data ratios have been investigated, and a comparison between their implementation results has been provided. Experimental results will suggest which neural network model, and under which learning scheme, can the proposed credit risk evaluation system deliver optimum performance; where it may be used efficiently, and quickly in automatic processing of credit applications. [Copyright &y& Elsevier]

The idea of machines having emotions sounds like science fiction, however, few decades ago the idea of machines with intelligence seemed also like fiction, but today we are developing intelligent machines with successful applications. We have always overlooked the emotional factors during machine learning and decision making; however, it is quite conceivable to artificially model certain emotions in machine learning. This paper presents an emotional neural network (EmNN) that is based on the emotional back propagation (EmBP) learning algorithm. The EmNN has emotional weights and two emotional parameters; anxiety and confidence, which are updated during learning. The performances of the EmNN and a conventional BP-based neural network, using two topologies for each network, will be compared when applied to a blood cell type identification problem. Experimental results show that the additional emotional parameters and weights improved the identification rate as well as the classification time. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]