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There is an increase in death rate yearly as a result of heart diseases. One of the major factors that cause this increase is misdiagnoses on the part of medical doctors or ignorance on the part of the patient. Heart diseases can be described as any kind of disorder that affects the heart. In this research work, causes of heart diseases, the complications and the remedies for the diseases have been considered. An intelligent system which can diagnose heart diseases has been implemented. This system will prevent misdiagnosis which is the major error that may occur by medical doctors. The dataset of statlog heart disease has been used to carry out this experiment. The dataset comprises attributes of patients diagnosed for heart diseases. The diagnosis was used to confirm whether heart disease is present or absent in the patient. The datasets were obtained from the UCI Machine Learning. This dataset was divided into training, validation set and testing set, to be fed into the network. The intelligent system was modeled on feed forward multilayer perceptron, and support vector machine. The recognition rate obtained from these models were later compared to ascertain the best model for the intelligent system due to its significance in medical field. The results obtained are 85%, 87.5% for feedforward multilayer perceptron, and support vector machine respectively. From this experiment we discovered that support vector machine is the best network for the diagnosis of heart disease.

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, 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 “PI-EmNN” (Prototype-Incorporated Emotional Neural Network). 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 back propagation neural network (EmNN), deep networks and an exemplar classification model, k-nearest neighbor (k-NN).

Mortality rate increases all over the world on a daily basis. The reason for this could be largely adduced to the increase in the number of patients with cardiovascular diseases. To worsen the case, many physicians have been known for misdiagnosis of patients reporting heart related ailment. In this paper, an intelligent system has been design which will help in effective diagnosis of the patient to avoid misdiagnosis. The dataset of UCI statlog heart disease has been used in this experiment. The dataset is comprises thirteen features which are vital in diagnosis of heart diseases. The system is model on a multilayer neural network trained with backpropagation and simulated on feedforward neural network. The recognition of 85% was obtained from testing of the network.

There is a need for quality production at a very fast rate in food processing industry. Therefore, developing a system that can perform the visual perception of the human operator in making decisions at a very fast rate will be of great advantage. Such machine vision system will reduce human errors such as individual perception differences in determining whether a product is healthy or defective for production. In this research, an intelligent identification system for grading banana fruit has been developed to replace or aid the human operator who may suffer from inconsistent slow decision-making. This work is divided into three phases. The first phase is the acquisition of the images and preparation of the database required for our experiments. In the second phase, several image processing techniques are employed to extract banana features for use in the last phase; which is the classification phase. Here, a neural network classifier is arbitrated using extracted banana image features in order to classify and grade the fruit. The sufficient classification rates obtained in this work, and the minimal time costs required when compared with previous works indicate that our novel banana grading system can be efficiently used in real life applications in the food processing industry. Practical Applications Our novel grading system has been developed to be used in a fruit (e.g. banana) production factory where quality control and sorting is required. Over the years, human operators had always been employed to grade raw material and product in order to determine if the raw material is suitable for production or marketing. This operation by human workers was considered as very slow when it comes to decision making and there may also be inconsistent in their decision on the product. Thus, we believe that our proposed intelligent grading system can be successfully implemented in practice in a banana production factory, in order to sort out defective or good banana prior to marketing, thus improving the quantity and quality of banana production.2015 Wiley Periodicals, Inc