Articles


The explosive growth of social networks has led to the generation of vast, complex datasets, necessitating advanced methods for real-time analysis. Graph-based algorithms provide a natural framework for representing and analyzing social networks, with nodes representing users and edges encapsulating interactions. However, the scale, velocity, and diversity of this data pose significant challenges for traditional analytical techniques. This paper explores scalable graph-based algorithms designed for real-time analysis of big data in social networks, focusing on innovations in parallel and distributed processing, streaming algorithms, and machine learning on graphs. Applications such as community detection, sentiment analysis, and anomaly detection are examined, demonstrating the potential of these algorithms in solving critical problems in social network analysis. Case studies highlight real-world implementations and performance metrics, shedding light on practical challenges and solutions. Finally, this work addresses open issues, including algorithmic scalability, system-level constraints, and ethical considerations, while proposing future directions to enhance the capabilities of graph-based systems for real-time insights in the evolving landscape of social networks

Accurate financial reporting is a cornerstone of organizational transparency, accountability, and operational efficiency. It forms the basis for acquiring confidence among stakeholders, meeting requirements by regulatory bodies, and helping the management make sound decisions based on the same. For financial reporting inaccuracies that come out, there are ripple effects of financial mismanagement, legal penalties, reputational damage, and loss of stakeholder confidence. Some reasons for these inaccuracies are human error, fraud, or systemic inefficiency within the financial processes themselves. This research investigates the multifaceted risks of poor financial reporting and emphasizes the key role of ERP systems in responding to these challenges. ERP systems, with the ability to integrate current data, automate financial workflows, and monitor compliance, offer a comprehensive solution for enhancing the accuracy and reliability of financial reports. It therefore places a special emphasis on studying case stories, challenges, and best practices of ERP implementation to present a comprehensive road map of how ERP systems can serve as a financial reporting risk mitigation tool to improve the quality of corporate financial governance.

Among the theoretical frameworks used in empirical research of technology acceptance and use, the Unified Theory of Acceptance and Use of Technology (UTAUT) model has been identified as the most comprehensive and valid one in the context of a variety of organizations. This research adopts the UTAUT model to analyse the adoption of NetSuite ERP which is a leading cloud-based ERP system commonly utilized for business process optimisation. Aim of the study masters on the performance expectancy, effort expectancy, social influence and facilitating condition that affects level of user acceptance and usage of the system. Furthermore, it looks at the mediated moderator roles of the demographic variables including the gender, age, experience and voluntariness of use.


This research employs both quantitative and qualitative methods to analyse data gathered from a sampled population of NetSuite ERP users within sectors. These preliminary results present key enablers and barriers when implementing and using NetSuite ERP, and identify implications for organisations aimed at enhancing usability and effectiveness of the system, and supporting people through NetSuite training. The study hence innovates on the adoption strategies and calls for usability-based approaches to enhance NetSuite ERP value. Implications for organizations implementing cloud-based ERP systems as well as ERP vendors are presented based on the findings of this study, coupled with directions for future research on the use of UTAUT in cloud based ERP systems.


 

The integration of data engineering and artificial intelligence (AI) has emerged as a transformative force in healthcare, enabling predictive analysis that significantly improves patient outcomes, operational efficiency, and cost management. This study proposes a robust predictive analysis framework that combines advanced data engineering techniques with AI models to address the inherent complexities of healthcare data. Healthcare systems generate vast and heterogeneous data from electronic health records (EHRs), imaging modalities, wearable devices, and laboratory results, presenting challenges such as data fragmentation, interoperability, and scalability. Leveraging data engineering, the framework ensures seamless data ingestion, preprocessing, and storage, creating a unified pipeline that supports real-time analytics. AI algorithms, including machine learning (ML) and deep learning models, are then employed to derive actionable insights for disease prediction, resource optimization, and personalized treatment strategies.The proposed framework is validated using diverse healthcare datasets, demonstrating high predictive accuracy, scalability, and practical applicability. It outperforms existing models by addressing critical limitations, such as handling data silos, ensuring data privacy, and adapting to varying clinical workflows. Furthermore, the study discusses the ethical implications and potential challenges, including data security and algorithmic biases, while suggesting future directions to refine the framework. This integration of data engineering and AI has the potential to revolutionize healthcare by enabling predictive, preventive, and precision medicine

Thus, Machine learning ( ML) has become innovative tool in the area to Data engineering that provide an innovative ways for processing, analyzing and using the data in today’s business environment. This article discusses the conceptual integration of ML into data engineering, with an emphasis on the capability of data-oriented companies to achieve new levels of productivity. This way, using preliminarily trained ML algorithms corporations can omit time-consuming tasks like data cleansing, data wrangling, and outliers identification and increase the efficiency of analytic results.


The work emphasizes on major areas that explain how and when various applications of ML enhance data engineering including scalable data pipelines, predictive data analytics and real time decision making. It also describes difficulties such as implementation issues and finding the correct approach to work with big data assets; structures data governance. Employing qualitative and quantitative data, this article demonstrates best practices that some firms can adopt to improve the utilization of ML as part of their big data management plans.


Finally, the article establishes the pivotal importance of ML in realizing AI potential and preparing data-focused businesses for dominance in the world where data is becoming the chief source of value. This work is therefore a conceptual map and a roadmap of sorts for incorporating machine learning into data engineering at scale.

The Impact of Artificial Intelligence on Project Management Practices

Vinay Chowdary Manduva

Mathematics and Computer Science Journal ,Volume 2024 , Page 138-176
https://doi.org/10.18535/mcsj/v2024.06

The integration of Artificial Intelligence (AI) into project management practices is revolutionizing traditional methodologies by enhancing efficiency, accuracy, and decision-making capabilities. This paper examines the transformative impact of AI on core project management functions, including task automation, predictive analytics, risk management, resource optimization, and team collaboration. By leveraging AI-powered tools, project managers can address longstanding challenges such as scheduling inefficiencies, communication gaps, and risk mitigation with unprecedented precision. However, the adoption of AI also introduces challenges, including high implementation costs, ethical concerns, and potential overreliance on technology. This study explores the balance between human expertise and AI capabilities, highlighting real-world applications and emerging trends that are shaping the future of project management. The findings underscore the potential for AI to not only streamline project execution but also redefine the strategic role of project managers in a rapidly evolving digital landscape.