Journal of Artificial Intelligence and Big Data Disciplines

The retail and consumer sector is a major contributor to global economic output but faces challenges in terms of uncertain consumer behavior, fast-changing technology, and retail convenience. Accurate demand forecasting is of paramount importance in the overall retail decision-making process. It is a prerequisite of broader application areas, such as stock control, merchandising, and pricing. Inadequate demand forecasting can lead to lost margin opportunities in terms of out-of-stock scenarios, as well as heavy discounting of goods in response to overstocking. In online retail markets, convenience disappears if goods are not available when the consumer wants or a store closes, as goods sell out at a full price. In a world of volatile trading conditions, the forecast of future demand is more difficult and less reliable than it used to be, due to both internal business complexity and increased external market disruption. Demand signal data need to be incorporated into the forecasting process, and it is necessary to use the techniques available to improve the efficiency and speed of the demand forecasting process to enhance the level of operational control. To cater to all these challenges and opportunities, recent academic research has covered a wide range of themes in retail demand forecasting. Retailers themselves are investing heavily in information systems, data management, analytical, and forecasting capabilities. Software providers offer a broad array of demand forecasting tools, some of which are cloud-hosted. Options are attractive as they allow access to broader data and more sophisticated models. Some business problems require solutions that are not covered by off-the-shelf software and therefore may require more customization to be suitable for enterprise implementation. Such solutions could aim at automating the demand forecasting process, leveraging a broad range of external data sources, creating outside views of future demand for bricks-and-mortar retail space, as well as seamless integration with operations and other decision-making cycles. Such AI-driven demand forecasting solutions can achieve positive outcomes, such as enhanced customer experience and supply chain optimization

Developing a quantum computing algorithm that outperforms its classical counterpart is widely viewed as a major milestone for the field. We achieve this milestone by offering an explicit, efficiently implementable algorithm that solves a fundamental problem in investing. One would want faster algorithms for better models at the same scale before worrying about learning the entire wealth distribution. We propose efficient quantum algorithms for both of these key subproblems.For example, quantum computers can efficiently reverse-engineer private shares to attain an accurate estimate of price-sensitive inside information. In recent years, the role of artificial intelligence has grown considerably in the operational decision-making process and, in particular, in the financial services industry. This paper takes advantage of the progress in quantum computing, addressing the problem of wealth distribution prediction in a big data set, a key problem in the deployment of trading strategies. Through a high-performance cloud computing architecture, we assess the impact of quantum computing technologies in comparison with the classical approach through several prediction models and analytical methodologies in both machine and deep learning.

The manipulation of atomic and molecular structures has been a topic of interest in recent years owing to the broad range of applications that its control entails. Researchers in areas such as macromolecular science are highly interested in protein folding problems, while direct drug discovery methods focus on strategies for designing ligands or modulators that target proteins of interest. In addition to targeting a specific protein, one of the principal objectives of work related to drug production is the modification of drugs in such a way that their performance profile is improved concerning that of other drugs. In this regard, the large amount of chemical data and their respective biological activities encoded in big data analytics will be a cornerstone in dealing with problems related to drug design. As a result of the many different data sources employed, strategies for the analysis of big data emerging from the world of research, especially the computer and health sciences are currently quite varied.