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One of the chronic diseases is Diabetes, Diabetes is a metabolic disorder category caused by continued high levels of blood sugar. It is regarded as one of the most deadly diseases in the world. If accurate early prediction is possible, Diabetes severity and risk factor can be significantly lowered. Machine learning has become more popular in the medical community as a result of its ascent, and in the field of diseases in particular. In this paper, we propose a model that can predict where the patient has or hasn't have diabetes. Our model is based on the prediction precision of certain powerful machine learning (ML) algorithms based on different measures such as precision, recall, and F1-measure. The Pima Indian Diabetes (PIDD) dataset has been used, that can predict diabetic onset based on diagnostics manner. The results we obtained using Logistic Regression (LR), Naive Bayes (NB), and K-nearest Neighbor (KNN) algorithms were 94%, 79%, and 69% respectively. The results show that LR is more efficient at predicting diabetes Compared to other algorithms.

The World Wide Web (WWW) has become infinite repository of information over the world because of the vast amount of information that they can gain. Also the access to the web enables any user easily to get information anytime and anywhere. But with these advantages, there is also some disadvantages such as the some websites contains harmful material like terrorism, hate, violence, and pornographic. Therefore developing a system that can filter these contents is become necessary. In this paper we introduce a web content filtering using multi agents, in this system we used FCM algorithm and some webpage feature in classification process. Experimental results based on (2000) different websites are given us an efficient results. On the testing set, the system was able to achieve correct acceptance and reject of (97.5). English pornographic websites uses as a case study.

The Internet has fundamentally changed the way we collect, access, and deliver information. However, this now means that finding the exact information we need is a significant problem. While search engines can find information based on the keywords we provide, using this technique alone is insufficient for rich information retrieval. Consequently, solutions, which lack the understanding of the syntax and semantics of content, find it difficult to accurately access the information we need. New approaches have been proposed that try to overcome this limitation by utilising Semantic Web and Linked Data techniques. Content is serialised using RDF, and queries executed using SPARQL. This approach requires an exact match between the query structure and the RDF content. While this is an improvement to keyword-based search, there is no support for probabilistic reasoning to show how close a query is to the content being searched. In this paper, we address this limitation by converting RDF content into a matrix of features and treat queries as a classification problem. We have successfully developed a working prototype system to demonstrate the applicability of our approach.