Search

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]

Abstract: The analysis of blood cells in microscope images can provide useful information concerning the health of patients. There are three major blood cell types, namely, erythrocytes (red), leukocytes (white), and platelets. Manual classification is time consuming and susceptible to error due to the different morphological features of the cells. This paper presents an intelligent system that simulates a human visual inspection and classification of the three blood cell types. The proposed system comprises two phases: The image preprocessing phase where blood cell features are extracted via global pattern averaging, and the neural network arbitration phase where training is the first and then classification is carried out. Experimental results suggest that the proposed method performs well in identifying blood cell types regardless of their irregular shapes, sizes and orientation, thus providing a fast, simple and efficient rotational and scale invariant blood cell identification system which can be used in automating laboratory reporting. [Copyright &y& Elsevier]