deep learning

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 article outlines the development of neural network frameworks to counter future cyberattacks in Enterprise Resource Planning (ERP) systems. A lack of security can lead to major risks. Hence, an ERP system is more prone to cyberattacks. Many researchers have suggested the integration of big data analytics to counter cyberattacks. We have designed a neural network framework for binary classification to predict the attack classes in real-time. We have compared various types of neural networks to check which neural network is more effective and has less error in predicting cyberattacks. The findings revealed that out of the proposed methodologies, the ensemble of the Recurrent Neural Network as an autoencoder is the most effective design. We proposed three designs, and we have found that the ensemble of deep learning provides a 97.5% error rate. This design is most effective for big data architecture in the real-time pipeline of ERP. The trends of deep learning work on big data but face some practical issues in implementing the models. The study is beneficial for organizations using ERP, which is the largest ERP vendor and the most costly product widely used worldwide. Hence, it provides research in the field of cybersecurity and contributes to the latest technology approach redesign for practitioners. Deep learning methodologies face practical challenges in the real-time representation of any event. The relevance of the computing approach of the novel and deep learning model in practice is identified, and it is performed by the researchers.

In the modern world of digital technologies, several technological and managerial aspects share a strong intersection. Among others, three important aspects are big data, cybersecurity, and enterprise resource planning systems. In the IT era, organizations have been relying on applications for their growth and day-to-day activities. While it is essential to manage and operate ERPs to improve and attain new business heights, there are also big questions about continuous cyberattacks, security, and hacking issues. The ignorance and negligence of the management and staff in any of these have led them to a significant level of loss. Some instances have practically shown companies' reputations and prospects went down drastically due to such uncontrollable white-collar crimes. The growth of the digital era forces us to devise adept mechanisms and take stringent measures. Consequently, there is a need to transcend these major challenges in unison. In this exploratory study, we have proposed the potential and the possibility for deep learning in the area of big data and cybersecurity when closely knit with enterprise resource planning systems. Contemporary ERPs are like the central nervous system of a living organism: providing vital data and ensuring efficient operations. The key elements of today's organizations are reporting systems and transaction processing systems. The database systems that store business data are often architected with a mixture of different data management technologies. These conglomerate systems are often hybrids with a complex array of proprietary, open-source, and emerging cloud-based convergences. Considering the fast and rapidly changing IT environments, ERPs have to be adaptable to these data challenges. The security of an organization's data is a core issue and must be protected from IT-based security threats. Organizations must have reserved data hacked or be demoralized by hacking attempts. The harmonic convergence of data management, cybersecurity analytics, and deep learning provides profound new approaches for hardening the systems that manage and protect the data!