Many real-world analytics problems involve two significant challenges: prediction and optimization. Because of the typically complex nature each challenge, standard paradigm is predict-then-optimize. By large, machine learning tools are intended to minimize error do not account for how predictions will be used in downstream optimization problem. In contrast, we propose a new very general framew...

A majority of efforts in terms of technology commercialization have failed; however, the issue of commercialization and its high importance are agreed upon by policymakers, entrepreneurs, and researchers. This shows the high complexity of the commercialization process. One of the main solutions to overcome the commercialization problems is to predict the success of technology commercialization ...

Awareness of the level of river flow and its fluctuations at different times is one of the significant factor to achieve sustainable development for water resource issues. Therefore, the present study two hybrid models, Wavelet- Adaptive Neural Fuzzy Interference System (WANFIS) and Wavelet- Artificial Neural Network (WANN) are used for flow prediction of Gamasyab River (Nahavand, Hamedan, Iran...

The increasing volatility in pricing and growing potential for profit in digital currency have made predicting the price of cryptocurrency a very attractive research topic. Several studies have already been conducted using various machine-learning models to predict crypto currency prices. This study presented in this paper applied a classic Autoregressive Integrated Moving Average(ARIMA) model ...