Using sensors and actuators as engine control mechanisms brings technical complexity to rule-based approach to diagnosis as it is difficult to establish a complete association between sensor data and the symptoms. Diagnostic evaluation of critical components in vehicle engines has only gained little attention, whereas the interdependent nature of sensors and propeller requires continuous monitoring for stability and temperature control. In this study, the Bayesian probability approach was used to provide intelligence logic with mathematical formulation for detecting overheating in vehicle engines; by providing the architectural design of the proposed system.The proposed framework was implemented using Microsoft Visual Basic.NET with integrated ActiveX Data Object (VB ADO) to experiment with the model for performance evaluation. Its usability testing and computational pattern were carried out with comparative analysis. Therefore, this study recommended that the problem domain for automobile diagnosis should be explicit about inculcating all engine-related problems other than overheating.

With increasing speed of information and documents on the Web, our need to classify them in different categories and clusters is more necessary. Clustering tries to find related structures in data sets which they are not categorized, yet. Concerning the needs, a new approach for text documents categorization is presented in this paper which includes three phases: pre-processing documents and selection feature, K-Means clustering and Naïve Bayes (NB) optimization. The proposed model uses K-Means and NB algorithms that utilize K-Means algorithm to find minimum distances between features from the center of clusters and NB algorithm for computing the probability of each feature into documents and using them to cluster features, separately. The proposed model optimizes performance of K-Means algorithm by using NB properties in clustering. Therefore, the model overcomes to the challenges of labeling different documents and origin of K-Means algorithm which it refers to categorizing text documents as un-supervised model. Finally, the experiment results of proposed model and K-Means algorithms are evaluated based on evaluation methods and are compared in validated datasets.

Background: Reported cases of uncontrolled use of pesticides and its produced effects by director indirect exposition, represent a high risk for human health. Therefore, in this paper, it is shownthe results of the development and execution of an algorithm that predicts the possible effects inendocrine system in Fisher 344 (F344) rats, occasioned by ingestion of malathion.Methods: It was referred to ToxRefDB database in which different case studies in F344 ratsexposed to malathion were collected. The experimental data were processed using NaïveBayes (NB) machine learning classifier, which was subsequently optimized using geneticalgorithms (GAs). The model was executed in an application with a graphical user interfaceprogrammed in C#.Results: There was a tendency to suffer bigger alterations, increasing levels in the parathyroidgland in dosages between 4 and 5 mg/kg/day, in contrast to the thyroid gland for doses between739 and 868 mg/kg/day. It was showed a greater resistance for females to contract effects onthe endocrine system by the ingestion of malathion. Females were more susceptible to sufferalterations in the pituitary gland with exposure times between 3 and 6 months.Conclusions: The prediction model based on NB classifiers allowed to analyze all the possiblecombinations of the studied variables and improving its accuracy using GAs. Excepting thepituitary gland, females demonstrated better resistance to contract effects by increasing levelson the rest of endocrine system glands.

In risk analysis based on Bayesian framework, premium calculation requires specification of a prior distribution for the risk parameter in the heterogeneous portfo-lio. When the prior knowledge is vague, the E-Bayesian and robust Bayesian analysis can be used to handle the uncertainty in specifying the prior distribution by consid-ering a class of priors instead of a single prior. In this paper, we study the E-Bayes and robust Bayes premium estimation and prediction in exponential model under the squared log error loss function. A prequential analysis in a simulation study is carried out to compare the proposed predictors. Finally, a real data example is included for illustrating the results.