Unintended bias against protected groups has become a key obstacle to the widespread adoption of machine learning methods. This work presents modeling procedure that carefully builds models around class information in order make sure final model is independent status, even nonlinear sense. works for any method. The was tested on subprime credit card data combined with demographic by zip code fr...

The vast advances in Machine Learning over the last ten years have been powered by availability of suitably prepared data for training purposes. future ML-enabled enterprise hinges on data. As such, there is already a vibrant market offering annotation services to tailor sophisticated ML models. In this paper, we present research practical problem obtaining order improve accuracy We consider an...

In the Design of Experiments, we seek to relate response variables explanatory factors. Response Surface methodology (RSM) approximates relation between output and a polynomial transform using linear model. Some researchers have tried adjust other types models, mainly nonlinear nonparametric. We present large panel Machine Learning approaches that may be good alternatives classical RSM approxim...

We describe a method for training accurate Transformer machine-translation models to run inference using 8-bit integer (INT8) hardware matrix multipliers, as opposed the more costly single-precision floating-point (FP32) hardware. Unlike previous work, which converted only 85 multiplications INT8, leaving 48 out of 133 them in FP32 because unacceptable accuracy loss, we convert all INT8 without...

The field of optimization and machine learning are increasingly interplayed and optimization in different problems leads to the use of machine learning approaches‎. ‎Machine learning algorithms work in reasonable computational time for specific classes of problems and have important role in extracting knowledge from large amount of data‎. ‎In this paper‎, ‎a methodology has been employed to opt...

Hyperspectral sensors enable the study of the chemical properties of scene materials remotely for the purpose of identification, detection, and chemical composition analysis of objects in the environment. Hence, hyperspectral images captured from earth observing satellites and aircraft have been increasingly important in agriculture, environmental monitoring, urban planning, mining, and defense...

Bitcoin as the current leader in cryptocurrencies is a new asset class receiving significant attention in the financial and investment community and presents an interesting time series prediction problem. In this paper, some forecasting models based on classical like ARIMA and machine learning approaches including Kriging, Artificial Neural Network (ANN), Bayesian method, Support Vector Machine...

Machine learning (ML) refers to the processes that enable computers think based on various methods. It can be also called domain which is a subset of Artificial Intelligence (AI). Deep (DL) has been promising, new and modern technique for data analysis in recent years. shown as improved version Neural Networks (ANN) one popular AI methods today. The population world increasing day by importance...