Creating a machine learning solution for a real world problem often becomes an iterative process of training, evaluation and improvement where the best practices and generic solutions are few and far between. Our work presents a novel solution for an essential step of this cycle: the process of understanding the root causes of ’bugs’ (particularly consequential or confusing test errors) discove...

Abstract This paper studies the performance of Machine Learning models in inflation forecasting. The most popular algorithms have been used, respectively Support Vector Regression, Neural Networks, LSTM, Random Forest, XGBoost and LightGBM both univariate multivariate form, order to predict Romania, expressed as CPI, Core-1, Core-2 Core-3, on multiple time horizons. results suggest that heurist...

The reflections recorded on satellite images have been affected by various environmental factors. In these images, some of these factors are combined with other environmental factors that cannot be distinguished. Therefore, it seems wise to model these environmental phenomena in the form of hybrid indicators. In this regard, satellite imagery and machine learning methods can play a unique role ...

Fault diagnosis has always been an essential aspect of control system design. This is necessary due to the growing demand for increased performance and safety of industrial systems is discussed. Support vector machine classifier is a new technique based on statistical learning theory and is designed to reduce structural bias. Support vector machine classification in many applications in v...

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...

Introduction: To develop different radiomic models based on radiomic features and machine learning methods to predict early intensity modulated radiation therapy (IMRT) response.   Materials and Methods: Thirty prostate patients were included. All patients underwent pre ad post-IMRT T2 weighted and apparent diffusing coefficient (ADC) magnetic resonance imagi...