The construction of classification models is widely used in data mining. There are concerns about the privacy of data owners because of the need to collect data to build models. In this paper, a NAÏVE BAYES classification model construction plan is presented, which performs the model construction operation with the participation of data owners and without the need to collect the original data. Instead of collecting data, the scheme uses encryption of bit strings from counting without disclosing the original data to perform the process of creating the NAÏVE BAYES model. This design allows the model to be built with appropriate performance without the need for trust in a third party with a minimum number of encryptions, so that in terms of time complexity, up to 87% improvement in time cost can be observed. In addition, memory consumption has not increased significantly when compared to designs that use encryption operations.

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 and Aim: Despite the implementation of effective preventive and therapeutic programs, no significant success has been achieved in the reduction of tuberculosis. One of the reasons is the delay in diagnosis. Therefore, the creation of a diagnostic aid system can help to diagnose early Tuberculosis. The purpose of this research was to evaluate the role of the Naive BAYES ALGORITHM as a tool for the diagnosis of pulmonary Tuberculosis. Materials and Methods: In this practical study, the study population included Patients with TB symptoms, the study sample is recorded data of 582 individuals with primary Tuberculosis symptoms in Tehran's Masih Daneshvari Hospital. The data of samples were investigated in two classes of pulmonary Tuberculosis and non-Tuberculosis. A Naive BAYES ALGORITHM for screening pulmonary Tuberculosis using primary symptoms of patients has been used in Python software version 3. 7. Results: Accuracy, sensitivity and specificity after the implementation of the Naive BAYES ALGORITHM for diagnosis of pulmonary Tuberculosis were %95. 89, %93. 59 and %98. 53, respectively, and the Area under curve was calculated %98. 91. Conclusion: The performance of a Naive BAYES model for diagnosis of pulmonary Tuberculosis is accurate. This system can be used to help the patient and manage illness in remote areas with limited access to laboratory resources and healthcare professional and cause to diagnose early Tuberculosis. It can also lead to timely and appropriate proceedings to control the transmission of TB to other people and to accelerate the recovery of the disease.

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.