A decision support system for order selection in electronic commerce based on fuzzy neural network supported by real-coded genetic algorithm

Healthcare industry is undergoing changes at a tremendous rate due to healthcare innovations. Predictive analytics is increasingly being used to diagnose the patient’s ailments and provide actionable insights into already existing healthcare data. The paper looks at a decision support system for determining the health status of the foetus from cardiotographic data using deep learning neural networks. The foetal health records are classified as normal, suspect and pathological. As the multiclass cardiotographic datset of the foetus shows a high degree of imbalance a weighted deep neural network is applied. To overcome the accuracy paradox due to the multiclass imbalance, relevant metrics such as the sensitivity, specificity, F1 Score and Gmean are used to measure the performance of the classifier rather than accuracy. The metrics are applied to the individual classes to ensure that the positive cases are identified correctly. The weighted DNN based classifier is able to classify the positive instances with Gmean score of 91% which is better than than the SVM classifier.

Download Full-text

Optimization of parameters of neuro-fuzzy model

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v17.i3.pp1206-1209 ◽

2020 ◽

Vol 17 (3) ◽

pp. 1206

Author(s):

Lyalya Bakievna Khuzyatova ◽

Lenar Ajratovich Galiullin

Keyword(s):

Neural Network ◽

Decision Support ◽

Decision Support Systems ◽

Fuzzy Neural Network ◽

Support Systems ◽

Fuzzy Model ◽

Knowledge Bases ◽

Fuzzy Neural Networks ◽

Neural Network Models ◽

Fuzzy Neural

<p>The questions and problems of the formation of knowledge bases of intelligent man-machine decision support systems are considered. The neuron-fuzzy model used in the work is described. The need for increasing the efficiency of the neuron-fuzzy model in the formation of knowledge bases is being updated. The task is to develop methods and algorithms for presetting and optimizing the parameters of a fuzzy neural network. To solve difficult formalized tasks, it is necessary to develop decision support systems - expert systems based on a knowledge base. ES developers are constantly faced with the problems of “extraction” and formalization of knowledge, as well as the search for new ways to obtain it. To do this, use the extraction, acquisition and formation of knowledge. Currently, the formation of knowledge bases is relevant for the creation of hybrid technologies - fuzzy neural networks that combine the advantages of neural network models and fuzzy systems. The analysis of the efficiency of the fuzzy neural network carried out in the work showed that the quality of training of the NN largely depends on the choice of the number of fuzzy granules for input drugs. In addition, to use fuzzy information formalized by the mathematical apparatus of fuzzy logic, procedures are required for selecting optimal forms and presetting the parameters of the corresponding membership functions (MF).</p>

Download Full-text

Logistics Distribution Center Location Evaluation Based on Genetic Algorithm and Fuzzy Neural Network

Communications in Computer and Information Science - Computational Intelligence and Intelligent Systems ◽

10.1007/978-3-642-04962-0_35 ◽

2009 ◽

pp. 305-312

Author(s):

Yuxiang Shao ◽

Qing Chen ◽

Zhenhua Wei

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Fuzzy Neural Network ◽

Distribution Center ◽

Logistics Distribution ◽

Center Location ◽

Fuzzy Neural ◽

Distribution Center Location

Download Full-text

Clinical Decision Support System for Early Disease Detection and Management

Pre-Screening Systems for Early Disease Prediction, Detection, and Prevention - Advances in Medical Diagnosis, Treatment, and Care ◽

10.4018/978-1-5225-7131-5.ch005 ◽

2019 ◽

pp. 108-155

Author(s):

Likewin Thomas ◽

Manoj Kumar M. V. ◽

Annappa B.

Keyword(s):

Neural Network ◽

Decision Support ◽

Decision Support System ◽

Clinical Decision Support ◽

Support System ◽

Medical Error ◽

Clinical Decision Support System ◽

Clinical Decision ◽

Healthcare Management ◽

Statistical Techniques

Medical error is an adverse event of a failure in healthcare management, causing unintended injuries. Proper clinical care can be provided by employing a suitable clinical decision support system (CDSS) for healthcare management. CDSS assists the clinicians in identifying the severity of disease at the time of admission and predicting its progression. In this chapter, CDSS was developed with the help of statistical techniques. Modified cascade neural network (ModCNN) was built upon the architecture of cascade-correlation neural network (CCNN). ModCNN first identifies the independent factors associated with disease and using that factor; it predicts its progression. A case progressing towards severity can be given better care, avoiding later stage complications. Performance of ModCNN was evaluated and compared with artificial neural network (ANN) and CCNN. ModCNN showed better accuracy than other statistical techniques. Thus, CDSS developed in this chapter is aimed at providing better treatment planning by reducing medical error.

Download Full-text