Binary number system based digital logic design has been in use for long with phenomenal increase in circuit sizes, working with binary logic system is becoming increasingly difficult. Multi-valued logic system reduces the significant amount of design effort. Multi-valued ternary logic under GF (3) and quaternary logic under GF (4) are available in the literature, but circuit design based on these logic systems is very few. As traditional computing devices based on irreversible logic are approaching their limit in terms of heat dissipation, power and speed requirement. Reversible computing is emerging as an alternative technology. Usage of multi-valued logic for irreversible computing is also growing. Ternary and quaternary logic based reversible gate have been proposed recently. Ternary logic based design has further been enhanced using balanced logic levels. But, the same is not available for quaternary logic. In this paper, we propose balanced quaternary logic and synthesis approach, which offers significant advantages in logic design. Small circuits like adder subtractor have also been designed based on that approach. We feel that balanced logic based approach will open a new era in multivalued logic design.
In these days the stock market or stock exchange is the most significant event in the world of financial market. The present the economy of the world is significantly subject to the cost of financial exchange. People of background of business or educational are very attractive to the stock market. They choose to put resources into securities exchange based on some expectation or earlier fundamental information. This prediction is basically performed by two ways one is using analyzing the information by gathering from social media, online news, comments and second is by historical stock data. Many factors are involved in the prediction of stock like – rational and irrational behavior, physical factors vs. physiological, market rumors, investor sentiment etc. This article is an extensive study as it incorporates pre-handling of the stock market dataset, use of different component designing procedures, joined with a tweaked profound learning-based framework for securities exchange cost pattern expectation.
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