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Journal of Artificial Intelligence and Big Data Disciplines (JAIBDD)

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📢 Latest Update: New special issue call for papers on "Emerging Technologies in Research" - Submit by March 31, 2025

📢 Latest Update: New special issue call for papers on "Emerging Technologies in Research" - Submit by March 31, 2025

January-March 2025

Volume 2, Issue 1 - $2025

Volume 2 Issue 1 Cover

Issue Details:

Volume 2 Issue 1
Published:Invalid Date

Editorial: January-March 2025

Welcome to the 2025 issue of Journal of Artificial Intelligence and Big Data Disciplines (JAIBDD). This issue showcases the remarkable breadth and depth of contemporary research across multiple disciplines. From cutting-edge applications of machine learning in climate science to the revolutionary potential of quantum computing in drug discovery, our featured articles demonstrate the power of interdisciplinary collaboration in addressing global challenges.

We are particularly excited to present research that bridges traditional academic boundaries, reflecting our journal's commitment to fostering innovation through cross-disciplinary dialogue. The integration of artificial intelligence with environmental science, the application of blockchain technology to supply chain management, and the convergence of urban planning with smart city technologies exemplify the transformative potential of collaborative research.

As we continue to navigate an era of rapid technological advancement and global challenges, the research presented in this issue offers both insights and solutions that will shape our future. We thank our authors, reviewers, and editorial board members for their continued dedication to advancing knowledge and promoting scientific excellence.

Dr. Aaluri Seenu
Editor-in-Chief
Journal of Artificial Intelligence and Big Data Disciplines (JAIBDD)

Articles in This Issue

Showing 6 of 6 articles
Research PaperID: jaibdd120006

A Comprehensive Study of Big Data Utilization in AI-Driven ERP Cybersecurity Applications

Gowthamm Mandala

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.

Big Datacybersecurityenterprise resource planningERP systemsAI-driven ERP securitydata protection+15 more
1,750 views
642 downloads
DOI:
10.70179/8f4s0892

Contributors:

 Gowthamm Mandala
Research PaperID: jaibdd120007

AI and Big Data Integration Strategies for Secure and Efficient ERP System Deployments

Venkata Siva Rama Prasad C

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.

enterprise resource planningERP systemsAI-driven ERPbig data analyticsenterprise automationregulatory compliance+14 more
2,112 views
625 downloads
DOI:
10.70179/fnpdtw95

Contributors:

 Venkata Siva Rama Prasad C
Research PaperID: jaibdd120013

Designing Neural Network Frameworks for Big Data Analysis in ERP Systems to Counter Cyber Threats

Zakera Yasmeen

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.

enterprise resource planningERP systemscybersecuritycyberattack predictionneural network frameworksdeep learning+15 more
2,437 views
756 downloads
DOI:
10.70179/2rt9k031

Contributors:

 Zakera Yasmeen
Research PaperID: jaibdd120014

Enhancing ERP Systems with Big Data Analytics and AI-Driven Cybersecurity Mechanisms

Pallav kumar Kaulwar

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.

enterprise resource planningERP systemsbig data analyticsAI-driven cybersecuritybusiness intelligenceintrusion detection+16 more
2,509 views
712 downloads
DOI:
10.70179/32w32914

Contributors:

 Pallav kumar Kaulwar
Research PaperID: JAIBDD120015

Machine Learning Algorithms for Optimizing Big Data-Enhanced Cybersecurity in ERP Ecosystems

Shakir Syed

The advent of big data has had a significant impact on enterprise resource planning (ERP) ecosystems, particularly when it comes to supporting scalability and addressing the limitations of existing cybersecurity frameworks in ERP ecosystems. Big data technologies enhance cybersecurity in ERP ecosystems by improving cyber forensics readiness. However, the use of big data-enhanced cybersecurity solutions in ERP ecosystems can result in several cybersecurity concerns regarding the privacy, protection, and preservation of ERP cybersecurity data. This creates a need for machine learning-based algorithms to optimize data analytics-based cybersecurity initiatives in ERP ecosystems. Five machine learning algorithms are developed to optimize ERP big data-enhanced cybersecurity. In developing these machine learning models, a framework has been created for securing ERP big data by identifying and selecting the most appropriate machine learning algorithm that can be utilized to develop an effective ERP cybersecurity solution. Each of the algorithms has been analyzed in terms of its evaluation metrics and other performance and learning attributes. While all the algorithms can be effectively used for ERP big data-enhanced cybersecurity, the following are the outstanding strengths of each algorithm: logistic regression for ensuring scalability and making classification predictions based on real-time assessments; decision tree for easily integrating with existing ERP systems; random forest for its ensemble learning-based power to enhance overall ERP ecosystem security; k-nearest neighbors for its simple and easy-to-understand methodology; and support vector machine for its potential in ERP system security data clustering and addressing the system security multi-dimensionality challenge. This paper also presents the limitations of each evaluated algorithm, as well as areas for additional research to maximize the overall effectiveness of the machine learning techniques.

2,464 views
813 downloads
DOI:
10.70179/mnqh8179

Contributors:

 Shakir Syed
Research PaperID: jaibdd120016

The Intersection of Big Data, Cybersecurity, and ERP Systems: A Deep Learning Perspective

Venkata Narasareddy Annapareddy

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!

big datacybersecurityenterprise resource planningERP systemsdigital technologiesIT security+16 more
2,503 views
933 downloads
DOI:
10.70179/z385t072

Contributors:

 Venkata Narasareddy Annapareddy

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