3 research outputs found
The Efficacy of Choosing Strategy with General Regression Neural Network on Evolutionary Markov Games
Nowadays, Evolutionary Game Theory which studies the learning model of players,has attracted more attention than before. These Games can simulate the real situationand dynamic during processing time. This paper creates the Evolutionary MarkovGames, which maps playersā strategy-choosing to a Markov Decision Processes(MDPs) with payoffs. Boltzmann distribution is used for transition probability andthe General Regression Neural Network (GRNN) simulating the strategy-choosing inEvolutionary Markov Games. Prisonerās dilemma is a problem that uses the methodand output results showing the overlapping the human strategy-choosing line andGRNN strategy-choosing line after 48 iterations, and they choose the same strate-gies. Also, the error rate of the GRNN training by Tit for Tat (TFT) strategy is lowerthan similar work and shows a better re
An efficient deep multiātask learning structure for covidā19 disease
Abstract COVIDā19 has had a profound global impact, necessitating the development of infection detection systems based on machine learning. This paper presents a Multiātask architecture that addresses the classification and segmentation tasks for COVIDā19 detection. The model comprises an encoder for feature representation, a decoder for segmentation, and a multiālayer perceptron for classification. Evaluations conducted on two datasets demonstrate the model's performance in both classification and segmentation. To enhance efficiency and diagnosis accuracy, CTāscan images undergo preāprocessing using image processing algorithms like histogram equalization, median filtering, and mathematical morphology operations. The combination of the median filter preāprocessing and the proposed model yields impressive results in the classification task, achieving high accuracy, sensitivity, and specificity, with values of 0.97, 0.97, and 0.96, respectively, for dataset 1, and 0.96 in mentioned metrics for dataset 2. For segmentation, the proposed model, particularly with the average morphology preāprocessing, exhibits excellent performance with high accuracy, low mean squared error, high peak signalātoānoise ratio, high structural similarity index, and a mean dice coefficient of 88.86Ā Ā±Ā 0.05 for dataset 1, and 87.97Ā Ā±Ā 0.02 for dataset 2. Furthermore, the preātrained models consistently demonstrate the superiority of the median filter and proposed model in the classification task on the same datasets. In conclusion, the proposed multiātask model, incorporating image processing techniques, achieves remarkable results in both classification and segmentation. The utilization of preāprocessing algorithms and the multiātask framework significantly contribute to superior performance metrics. This study encourages further exploration of combining diverse image processing algorithms to advance infection diagnosis and treatment
Diagnosis of Heart Disease Using Feature Selection Methods Based On Recurrent Fuzzy Neural Networks}, \cjournal{IPTEK The Journal of Technology and Science
The World Health Organization (WHO) estimated one-third of all global deaths reason by cardiovascular diseases. Nowadays, artificial intelligence attracts many considerations in diagnosing heart disease. This study used trained recurrent fuzzy neural networks (RFNN) for diagnosing heart disease. This study also used five kinds of feature selection and extraction models for comparing the action of a model, such as data envelopment analysis (DEA), Linear Discriminative Analysis (LDA), Principle Component Analysis (PCA), Correlation Feature Selection (CFS), and Relief. By using these methods, this paper diagnosed whether the patient has a heart disease problem or not. The results showed that Correlation feature selection has the best operation in feature selection in RFNN by accuracy of 98.4%