nowadays diabetes disease is one of the main problems of health domain and it’s known as the fourth factor of death in the world. the main problem with this dangerous disease is the late or weak diagnosis. the reason of weak diagnosis is because sometimes doctors aren’t able to select the right patterns or they can’t use the standard patterns very well, so the outcome is that the disease will b...

background: reported cases of uncontrolled use of pesticides and its produced effects by direct or indirect exposition, represent a high risk for human health. therefore, in this paper, it is shown the results of the development and execution of an algorithm that predicts the possible effects in endocrine system in fisher 344 (f344) rats, occasioned by ingestion of malathion. methods: it was re...

Data management and machine learning are two important tasks in data science. However, they have been independently studied so far. We argue that they should be complementary to each other. On the one hand, machine learning requires data management techniques to extract, integrate, clean the data, to support scalable and usable machine learning, making it user-friendly and easily deployable. On...

This Paper represents a literature review of Swarm intelligence algorithm in the area of semi-supervised classification. There are many research papers for applying swarm intelligence algorithms in the area of machine learning. Some algorithms of SI are applied in the area of ML either solely or hybrid with other ML algorithms. SI algorithms are also used for tuning parameters of ML algorithm, ...

Support Vector Machines (SVMs) are well-established Machine Learning (ML) algorithms. They rely on the fact that i) linear learning can be formalized as a well-posed optimization problem; ii) non-linear learning can be brought into linear learning thanks to the kernel trick and the mapping of the initial search space onto an high dimensional feature space. The kernel is designed by the ML exper...

Software Testing (ST) processes attempt to verify and validate the capability of a software system to meet its required attributes and functionality. As software systems become more complex, the need for automated software testing methods emerges. Machine Learning (ML) techniques have shown to be quite useful for this automation process. Various works have been presented in the junction of ML a...

Cyber-physical systems (CPS), such as automotive systems, are starting to include sophisticated machine learning (ML) components. Their correctness, therefore, depends on properties of the inner ML modules. While learning algorithms aim to generalize from examples, they are only as good as the examples provided, and recent efforts have shown that they can produce inconsistent output under small...