Introduction: Visual evoked potentials contain certain diagnostic information which have proved to be of importance in the visual systems functional integrity. Due to substantial decrease of amplitude in extra macular stimulation in commonly used pattern VEPs, differentiating normal and abnormal signals can prove to be quite an obstacle. Due to developments of use of machine l...

In this study, we apply several advanced machine learning techniques including extreme gradient boosting (XGBoost), support vector (SVM), and a deep neural network to predict bankruptcy using easily obtainable financial data of 3728 Belgian Small Medium Enterprises (SME’s) during the period 2002–2012. Using above-mentioned techniques, bankruptcies with global accuracy 82–83% only three ratios: ...

Machine learning techniques play an increasingly prominent role in medical diagnosis. With the use of these techniques, patients’ data can be analyzed to find patterns or facts that are difficult explain, making diagnoses more reliable and convenient. The purpose this research was compare efficiency diabetic classification models using four machine techniques: decision trees, random forests, su...

The rate of diabetes is rapidly increasing worldwide. Early detection can help prevent or delay the onset by initiating lifestyle changes and taking appropriate preventive measures. Until now, prediabetes type 2 have proved to be early problems. There a need for easy, rapid, accurate diagnostic tools diagnosis in this context. Machine learning algorithms diagnose diseases early. Numerous studie...

In the past, traditional chemical heuristics have been very important for discovery of new materials. Machine learning approaches started to replace those in recent years, and they offer opportunities materials science. Both are strongly interconnected.Classical typically rely on less data than machine approaches. There two different types science: one relies features inspired by classical heur...