AI algorithms

The term "Big Data" refers to data that has massive volume, is varied in nature, and is generated at a high velocity. This data is difficult to process using conventional database management tools, and it grows over time. Cybersecurity is not limited to securing software applications or infrastructures; instead, it focuses on safeguarding data systems from any kind of breach or unauthorized access. Enterprise Resource Planning, or ERP system, is an information system that integrates all business transactions, and the data present in the system flow between various units or departments of the company. Such systems are more often targets of cyberattacks because of the data they hold. AI-driven ERP cybersecurity applications are imperative for securing such systems against modern cyber threats. AI technologies provide automation, and when integrated with Big Data, it becomes a stronger tool for enhancing the security of any data system. This study is focused on AI and Big Data techniques that can be used in ERP cybersecurity systems. The proposed methodology can be used by any cybersecurity developer for building a data processing system or AI mechanism that can effectively process data from an enterprise system or any data system. Associative classification techniques can be impressively effective compared to conventional classification solutions in the development of an AI algorithm for detecting cyber threats against ERP systems. It is more effective in comparison to traditional algorithms, as very few works are available on associating associative rules on a complete log file of an organization containing organizational data for work purposes. Additionally, the proposed framework can be imperatively helpful for real-time surveillance and data management systems and may lead to future growth in the cybersecurity and digitization domain.

Quality is the driving factor of the manufacturing industry. With the advent of Industry 4.0, the integration of advanced sensors, edge computing, artificial intelligence, digital twins, and 3D printing has great potential to achieve resilient manufacturing. The first part of this study addresses real-time quality control with edge computing. A digital imaging system is introduced, equipped with an edge device for real-time defect detection in additive manufacturing. AI promises great potential for achieving zero-defect manufacturing, which was barely possible in the past. Then, as the main structure provider for Industry 4.0, edge computing is described. The computer is used in quality control features of edge computing by real-time cloud monitoring and real-time elasticity for cloud services. In the second part, a predictive maintenance framework is proposed by integrating the digital twin, advanced data processing, and AI algorithms. AI finally determines the degradation status of products with high confidence to maintain the resilience of the product life cycle. Expensive fault events on products can be avoided with the help of AI-based advanced planning of maintenance at an appropriate time. The approach could mitigate potential risks of shortening lifespan, increasing maintenance costs, triggering catastrophic events, or even causing social impact if combined with real-time edge computing and advanced sensors. Such an approach advances the deployment of the manufacturing industry in the era of Industry 4.0.