16 research outputs found
Applying a multi-agent classifier system with a novel trust measurement method to classifying medical data
Full text linkIn this paper, we present the application of a Multi-Agent Classifier System (MACS) to medical data classification tasks. The MACS model comprises a number of Fuzzy Min-Max (FMM) neural network classifiers as its agents. A trust measurement method is used to integrate the predictions from multiple agents, in order to improve the overall performance of the MACS model. An auction procedure based on the sealed bid is adopted for the MACS model in determining the winning agent. The effectiveness of the MACS model is evaluated using the Wisconsin Breast Cancer (WBC) benchmark problem and a real-world heart disease diagnosis problem. The results demonstrate that stable results are produced by the MACS model in undertaking medical data classification tasks. © 2014 Springer Science+Business Media Singapore
Removal of humic acid from peat water using untreated powdered eggshell as a low cost adsorbent
No full textContemplating the feasibility of vermiculate blended chitosan for heavy metal removal from simulated industrial wastewater
No full textIn situ formed magnetic chitosan nanoparticles functionalized with polyethylenimine for effective U(VI) sorption
No full textOne-pot synthesis of Cd1−x In x Te semiconductor as a sensitizer on TiO2 mesoporous for potential solar cell devices
No full textKinetics and isotherm study on adsorption of chromium on nano crystalline iron oxide/hydroxide: linear and nonlinear analysis of isotherm and kinetic parameters
No full textNobel Materials (ZnO Nanoparticles and ZnO Nanoparticles Supported on a Zeolite) for the Removal of Tartrazine from Aqueous Solutions
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Experimental demonstration on the deterministic quantum key distribution based on entangled photons
No full textStochastic Perturbation-Based Finite Element for Free Vibration of Functionally Graded Beams with an Uncertain Elastic Modulus
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