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.
Humans use facial expressions successfully for conveying their emotional states. However, replicating such success in the human-computer interaction domain is an active research problem. In this paper, we propose deep convolutional neural network (DCNN) for joint learning of robust facial expression features from fused RGB and depth map latent representations. We posit that learning jointly from both modalities result in a more robust classifier for facial expression recognition (FER) as opposed to learning from either of the modalities independently. Particularly, we construct a learning pipeline that allows us to learn several hierarchical levels of feature representations and then perform the fusion of RGB and depth map latent representations for joint learning of facial expressions. Our experimental results on the BU-3DFE dataset validate the proposed fusion approach, as a model learned from the joint modalities outperforms models learned from either of the modalities.
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 APPLICATIONSOur 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
This work presents the diagnosis of iris nevus using a convolutional neural network (CNN) and deep belief network (DBN). Iris nevus is a pigmented growth (tumor) found in the front of the eye or around the pupil. It is seen that racial and environmental factors affect the iris color (e.g., blue, hazel, brown) of patients; hence, pigmented growths may be masked in the eye background or iris. In this work, some image processing techniques are applied to images to reinforce areas of interests in them, after which the considered classifiers are trained. We describe the automated diagnosis of iris nevus using neural network-based systems for the classification of eye images as "nevus affected" and "unaffected". Recognition rates of 93.35% and 93.67% were achieved for the CNN and DBN, respectively. Hence, the systems described in this work can be used satisfactorily for diagnosis or to reinforce the confidence in manual-visual diagnosis by medical experts.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.