big data analytics

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.

Enterprise resource planning (ERP) systems are often viewed as a necessary burden that usually delivers less value than originally anticipated. Enterprise resource planning systems are supposed to provide organizations with the information they need for central control, regulatory compliance, and business processes that are supported by information flowing through the system. Big Data and AI models present an opportunity to redefine how ERP is implemented and maintained. It has the potential to move the ERP space from being mundane to being a significant driver of future business performance. AI, by automating human performance, can also automate system ownership and improve customer satisfaction. In this way, it is a force multiplier that changes the economics of the ERP ecosystem. The data being fed into the model and the results of that model present new challenges for ensuring that all of this can be done at the proper level of security. This is a central challenge for ensuring the long-term security of both the ERP implementation and the enterprise itself. This paper will examine the protocols that must be observed to retain security and maximize this potential transformation associated with the AI-driven future of ERP solutions. Both the opportunities and the potential pitfalls will be discussed in the hopes that this will enable a secure and efficient path to that future, minimizing the number of disruptions that a company will have to encounter on that path.

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.

This paper introduces a framework to enhance the present enterprise resource planning (ERP) systems by integrating big data analytics and state-of-the-art artificial intelligence (AI)-driven cybersecurity mechanisms. Nowadays, due to the diversity and high volume of data, the use of business intelligence and analytics solutions is paramount for ERP systems. This use results in the optimization of enterprise resource planning functions. AI can be used with cybersecurity mechanisms to predict and stop the behavior of a potential threat, thus leading to an optimal cybersecurity model. However, the biggest challenge is the integration of run-time cybersecurity solutions with the enterprise resources in the ERP systems. The proposed solutions incorporate advanced AI-driven cybersecurity techniques for intrusion detection, anomaly detection innovation, and prediction-based mechanisms to mitigate potential threats at the onset. In particular, the paper proposes a set of measures and guidelines for IT stakeholders and business executives on how to integrate technology innovation while maintaining the ERP systems to be modern, relevant, and adaptive in a competitive business environment. We believe that our work is beneficial for both researchers and practitioners to systematically understand the significance of integrating big data analytics with the existing ERP functions and the application of AI in the cybersecurity model for enterprises. Our results emphasize that the implementation of big data and AI-based solutions within the organization will support innovation, safeguard security mechanisms, and lead to a sustainable position in the digital market.

Real-time traffic congestion is a challenging problem in smartcities. An Intelligent Transportation System (ITS) is a big data application integrating sensor hardware and network technologies, which can intelligently capture traffic information, efficiently transform data into knowledge, and organize and manage transportation resources. Urban Traffic Control (UTC) is a critical component of ITS, analyzing real-time traffic information and coordinating traffic signal timing plans to optimize traffic network performance and improve vehicle travel speeds. However, traditional UTC based on centralized architecture would be challenged in data transmission, architecture malfunctions, system bottlenecks, etc. As a solution, the multi-Agent based RTC (ATC) with smart intersections is proposed. Additionally, more comprehensive traffic data can be captured with advanced detection techniques, and Regional Traffic Control (RTC) systems can be designed with advanced optimal control algorithms. Digital Banking (DB) Infrastructures powered with AI can be trained on historical data to simulate human understanding of patterns and trends when leveraging custom models tuned to understand banking transactions. Integrating artificial intelligence (AI) and automation into the digital banking infrastructure can result in a stable pipeline implementation of sorting transactions data for anomalies per the bank thresholds. Moreover, with the integration of AI systems, captured data can be analysed to study the traffic characteristics of the bank and determine how efficiently it is working. AI can also be used to examine this data, identify problematic data, perform risk prediction, timely tracking, and further determine whether it fits the standards of bank transactions. AI can warn of difficulties in bank transactions. When an unauthorised transaction is detected, that transaction can be prohibited in real-time. Furthermore, it can help to significantly increase banks' risk management levels, improved efficiency and a near-zero error output requirement of regular activity.