Otitis media is a medical concept that represents a range of inflammatory middle ear disorders. The high costs of medical devices used by field experts for diagnosis of the disease relevant to otitis media are the mos...
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An online Special Interest Group is a group of people with the same interest gather to form an online community through the Internet. In certain cases where the knowledge is being manipulated, the portal of a Special ...
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An online Special Interest Group is a group of people with the same interest gather to form an online community through the Internet. In certain cases where the knowledge is being manipulated, the portal of a Special Interest Group is in a form of knowledge portal. This portal allows interaction among its community members. Interaction through forum in the portal makes activities such as discussion of problems and knowledge sharing among each other possible. The need to classify users’ expertise in a Special Interest Group is crucial task. This paper describes a Point-based Semiautomatic Expertise classification method to classify users’ expertise in a Special Interest Group knowledge portal.
We introduce in this paper a generic approach to combine implicit crowdsourcing and language learning in order to mass-produce language resources (LRs) for any language for which a crowd of language learners can be in...
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In this paper we study an ODE model on the microRNA-mRNA dynamics. We prove the existence of two equilibrium points (one with strictly positive compo-nents) and obtain a biologically consistent, sufficient asymptotic ...
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An approach to reducing the computation time taken by neural nets for the searching process is introduced. We combine both fast and cooperative modular neural nets to enhance the performance of the detection process. ...
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An approach to reducing the computation time taken by neural nets for the searching process is introduced. We combine both fast and cooperative modular neural nets to enhance the performance of the detection process. Such an approach is applied to identify human faces automatically in cluttered scenes. In the detection phase, neural nets are used to test whether a window of 20/spl times/20 pixels contains a face or not. The major difficulty in the learning process comes from the large database required for face/nonface images. A simple design for cooperative modular neural nets is presented to solve this problem by dividing these data into three groups. Such division results in reduction of computational complexity and thus decreasing the time and memory needed during the test of an image. Simulation results for the proposed algorithm show a good performance.
Objective Diabetes mellitus is a serious disease where the body of affected patients are failed to produce enough insulin that causes an abnormality of blood *** disease happens for a number of reasons including moder...
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Objective Diabetes mellitus is a serious disease where the body of affected patients are failed to produce enough insulin that causes an abnormality of blood *** disease happens for a number of reasons including modern lifestyle,lethargic attitude,unhealthy food consumption,family history,age,overweight,*** aim of this study was to propose a machine learning based prediction model that detected diabetes at the *** In this work,we collected 520 patients records from the University of California,Irvine(UCI)machine learning repository of Sylhet Diabetes Hospital,***,a similar questionnaire of that hospital was followed and assembled 558 patients records from all over Bangladesh through this ***,we accumulated patient records of these two *** the next step,these datasets were cleaned and applied thirty five state-of-arts classifiers such as logistic regression(LR),K nearest neighbors(KNN),support vector classifier(SVC),Nave Byes(NB),decision tree(DT),random forest(RF),stochastic gradient descent(SGD),Perceptron,AdaBoost,XGBoost,passive aggressive classifier(PAC),ridge classifier(RC),Nu-support vector classifier(NuSVC),linear support vector classifier(LSVC),calibrated classifier CV(CCCV),nearest centroid(NC),Gaussian process classifier(GPC),multinomial NB(MNB),complement NB,Bernoulli NB(BNB),categorical NB,Bagging,extra tree(ET),gradiant boosting classifier(GBC),Hist gradiant boosting classifier(HGBC),one vs rest classifier(OVsRC),multi-layer perceptron(MLP),label propagation(LP),label spreading(LS),stacking,ridge classifier CV(RCCV),logistic regression CV(LRCV),linear discriminant analysis(LDA),quadratic discriminant analysis(QDA),and light gradient boosting machine(LGBM)to explore best stable predictive *** performance of the classifiers has been measured using five metrics such as accuracy,precision,recall,F1-score,and area under the receiver operating ***,these outcomes were interpret
The main goal of the paper is to study the equilibria of a nonlinear system, proving the existence and uniqueness of an equilibrium point in the positive ortant. We also provide numerically tractable conditions (by us...
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Evolutionary programming (EP) is one of the main classes of evolutionary algorithms (EAs). Improving existing EAs is necessary in order to achieve better results and overcome their costly computational complexity. In ...
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Evolutionary programming (EP) is one of the main classes of evolutionary algorithms (EAs). Improving existing EAs is necessary in order to achieve better results and overcome their costly computational complexity. In this paper, we present a new version of EP called Directed Evolutionary Programming (DEP) in which more directing strategies with learned termination criteria are invoked to overcome some drawbacks of EP. In DEP, the mutated children are given the chance to improve themselves with the guidance of their parents. The search process in DEP is supported by diversification and intensification schemes in order to keep the diversity, achieve faster convergence and equip the search with an automatic termination criteria. The computational experiments show that DEP is efficient and cheaper than some well-known versions of EP.
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