Currently, the problem of automated data analysis and statistics collection from stock quotation charts has not been fully resolved. Most of the analysis of visual data falls on the physical work of the analyst, or on obsolete software solutions. The process of summarizing the information...

Today's dependence on technology increases human need for iron ore products, and forecasts suggest that by the year 2035 Demand for steel 60 Percent increase ( Mohammadi , Soltani Mohammadi , and the donor of security 2035) For this reason, predicting the price of metals such as iron ore using...

It is well known that the development of drug resistance in cancer cells can lead to a change in cell morphology. We reasoned that machine-learning techniques could thus be used to elucidate far greater insight into the relationship between cell shape and signaling. To test this hypothesis we...

In the present generation, the web domain is the most rising and open knowledge medium. The Web Domain consists of contents for numerous areas, including multimedia, organized, semi-structured and unstructured data, which are accessible on the web to users with knowledge that is relevant. But...

In this paper, we disentangle the changes that the rise of Artificial Intelligence Technologies (AITs) is inducing in the semiconductor industry. The prevailing von Neumann architecture at the core of the established “intensive” technological trajectory of chip production is currently...

In real-time decision-making problems for complicated stochastic systems, a covariate that reflects the state of the system is observed in real time and a state-dependent decision needs to be made immediately to optimize some system performance. Such system performances, for complicated...

Contemporary supply chain management systems need not only be adaptive to changing needs of the consumers but also to the varying terms amongst the members of the supply chain. Moreover, the on- time availability of services and goods is imperative for retaining customers. Hence, irrespective of...

Traditional statistical methods pose challenges in data analysis due to irregularity in the financial data. To improve accuracy, financial researchers use machine learning architectures for the past two decades. Neural Networks (NN) are a widely used architecture in financial research. Despite...

The application of artificial neural networks to finance has received a great deal of attention from both investors and researchers, especially as a forecasting method. When the number of predictors is high, these methods suffer from the so-called "curse of dimensionality" and produce biased...