Data Security Research Articles

Transforming Healthcare: The Role of Artificial Intelligence in Revolutionising Patient Care

Artificial Intelligence (AI) has emerged as a powerful tool in the healthcare industry. It has enhanced patient care, diagnostics, treatment planning, and operational efficiency. This article explores the diverse applications of AI. Machine learning algorithms, natural language processing, and predictive analytics reshape clinical practices. These technologies allow healthcare providers to focus more on patient care by enhancing diagnostic accuracy, personalising treatments, and automating tasks. However, challenges such as data security, algorithmic bias, and ethical concerns must be addressed for AI to be fully integrated into healthcare. Overcoming these obstacles will enable AI to revolutionise patient outcomes and healthcare innovation.

Transforming Healthcare: The Role of Artificial Intelligence in Revolutionising Patient Care

Artificial Intelligence (AI) has emerged as a powerful tool in the healthcare industry. It has enhanced patient care, diagnostics, treatment planning, and operational efficiency. This article explores the diverse applications of AI. Machine learning algorithms, natural language processing, and predictive analytics reshape clinical practices. These technologies allow healthcare providers to focus more on patient care by enhancing diagnostic accuracy, personalising treatments, and automating tasks. However, challenges such as data security, algorithmic bias, and ethical concerns must be addressed for AI to be fully integrated into healthcare. Overcoming these obstacles will enable AI to revolutionise patient outcomes and healthcare innovation.

Unlocking the potential of ChatGPT in academic libraries

This paper provides an overview of Generative Pretrained Transformer (GPT) and its application in academic libraries. Utilizing a qualitative approach to content analysis, this study synthesizes existing scholarship and addresses gaps in the literature. The scope and utility of ChatGPT in academic libraries are explored, revealing its potential to function as an intelligent virtual librarian. ChatGPT's implementation can alleviate the workload of librarians and effectively address complex queries from patrons. Additionally, the study highlights ChatGPT's ability to offer personalized assistance and research guidance through its natural language processing capabilities, enabling seamless communication with users. ChatGPT aids patrons in discovering resources that would typically demand significant effort from library staff. Despite its advantages, the study identifies areas for improvement, such as data security, dependence on technology, and the risk of providing incomplete or inaccurate information on specialized and complex topics. Nonetheless, ChatGPT emerges as a noteworthy tool for academic libraries in the current century. This paper contributes valuable insights into ChatGPT and offers practical guidelines for library practitioners globally. The content is derived from peer-reviewed research and serves as a comprehensive resource for librarians worldwide.

A Blockchain and Hybrid Deep Learning for Secure and Efficient Healthcare Data Transmission and Management

A Blockchain and Hybrid Deep Learning for Secure and Efficient Healthcare Data Transmission and Management

Applying artificial intelligence in predicting educational excellence in higher education institutions: A case study in Jordanian universities

Monitoring the responsible application of Artificial Intelligence (AI) in higher education requires the establishment of robust regulatory frameworks and thorough policy guidelines. The study concentrates on important elements that could impede the excellence of education, including issues with data security, privacy, and policies as well as legal frameworks. By examining these variables, the study aims to address the particular opportunities and difficulties encountered in this setting and to gain a deeper knowledge of how AI deployment affects educational excellence in Jordanian higher education institutions. A survey research methodology has been chosen. Institutions in Jordan that have started implementing AI or metaverse technology were given a pre-made questionnaire. The population of students in Jordanian higher education institutions, including both local and foreign students at different educational levels, is the main subject of this study. During the three-month data collection phase, the sample size was cautiously raised to more than 457 individuals in order to boost the research's robustness. The results show that the AI adoption, trust in technology (by data privacy and security), and policy & regulations in Jordanian higher institutions have significant impacts on educational excellence. Our results highlight the urgent need for policymakers to reevaluate and explain current regulatory frameworks in order to safeguard educational excellence, while also confirming the transformative possible of AI implementation in improving instructive resources and services. This study demonstrates the possible benefits of integrating AI technologies into educational backgrounds by confirming the strong correlation between AI adoption and educational excellence. In order to increase the effectiveness, usability, and caliber of educational resources and services, schools ought to think about implementing AI-driven tools and platforms.

Examining the Challenges of Implementing Artificial Intelligence in the Water Supply Sector: A Case Study

Challenges in the water supply sector have hindered the advanced implementation of artificial intelligence (AI) compared to other sectors. These challenges have not been sufficiently examined in the existing literature. An empirical study was conducted within a public utilities organization in the United Arab Emirates (UAE) to address this gap. An integrated approach combining interpretive structural modeling (ISM) and fuzzy cross-impact matrix multiplication applied to classification (MICMAC) analysis was utilized to identify the critical challenges and to model and analyze the relationships among them. The ISM model provides significant advantages by enabling decision-makers to visualize complex interactions, supporting the development of an effective AI implementation strategy. The strategy should prioritize four critical challenges: the lack of technical skills and knowledge, the limited availability of ready-to-use AI solutions, inadequate water infrastructure, and concerns regarding privacy and data security. These challenges were identified based on their positioning at the lowest level of the ISM model and their classification as independent in the fuzzy MICMAC analysis. Addressing these four challenges will help to mitigate the remaining six. The study’s findings and implications are expected to offer valuable guidance for decision-makers in implementing AI technologies within water supply organizations, both in the UAE and in countries with similar environments.

Asking for Traces: A Vignette Study on Acceptability Norms and Personal Willingness to Donate Digital Trace Data

Digital trace data are increasingly used in the social sciences. Given the risks associated with data access via application programming interfaces (APIs) as well as ethical discussions around the use of such data, data donations have been proposed as a methodologically reliable and ethically sound way of collecting digital trace data. While data donations have many advantages, study participants may be reluctant to share their data, for example, due to privacy concerns. To assess which factors in a data donation request are relevant for participants’ acceptance and decisions, we conducted a vignette experiment investigating the general acceptability and personal willingness to donate various data types (i.e., data from GPS, web browsing, LinkedIn/Xing, Facebook, and TikTok) for research purposes. The preregistered study was implemented in the probability-based German Internet Panel (GIP) and gathered responses from n = 3821 participants. Results show that people rate the general acceptability of data donation requests higher than their own willingness to donate data. Regarding the different data types, respondents indicated that they would be more willing to donate their LinkedIn/Xing, TikTok, and GPS data compared to web browsing and Facebook data. In contrast, information about whether the donated data would be shared with other researchers and data security did not affect the responses to the respective donation scenarios. Based on these results, we discuss implications for studies employing data donations.

Large Language Models in Traditional Chinese Medicine: A Scoping Review.

The application of large language models (LLMs) in medicine has received increasing attention, showing significant potential in teaching, research, and clinical practice, especially in knowledge extraction, management, and understanding. However, the use of LLMs in Traditional Chinese Medicine (TCM) has not been thoroughly studied. This study aims to provide a comprehensive overview of the status and challenges of LLM applications in TCM. A systematic search of five electronic databases and Google Scholar was conducted between November 2022 and April 2024, using the Arksey and O'Malley five-stage framework to identify relevant studies. Data from eligible studies were comprehensively extracted and organized to describe LLM applications in TCM and assess their performance accuracy. A total of 29 studies were identified: 24 peer-reviewed articles, 1 review, and 4 preprints. Two core application areas were found: the extraction, management, and understanding of TCM knowledge, and assisted diagnosis and treatment. LLMs developed specifically for TCM achieved 70% accuracy in the TCM Practitioner Exam, while general-purpose Chinese LLMs achieved 60% accuracy. Common international LLMs did not pass the exam. Models like EpidemicCHAT and MedChatZH, trained on customized TCM corpora, outperformed general LLMs in TCM consultation. Despite their potential, LLMs in TCM face challenges such as data quality and security issues, the specificity and complexity of TCM data, and the nonquantitative nature of TCM diagnosis and treatment. Future efforts should focus on interdisciplinary talent cultivation, enhanced data standardization and protection, and exploring LLM potential in multimodal interaction and intelligent diagnosis and treatment.

Digital Twins for Smarter Iranian Cities: A Future Studies Perspective

This study explores the future of Urban Digital Twin (UDT) in urban planning systems of developing countries, with a focus on Iran. Despite UDT's growing popularity, its implementation in developing countries is limited. The research identifies critical factors influencing UDT development, including organisational acceptance, urban infrastructure, policy and legislation, and technology and innovation. Using a futures studies approach, the study employs the Delphi method, MICMAC (Matrix Impact Cross-Reference Multiplication Applied to a Classification) technique, and SISMW (Strategic Uncertainties and Strengths Weaknesses Opportunities and Threats Matrix) methodologies to analyse these factors. The study reveals that international sanctions, organisational factors, technological factors, and infrastructure limitations hinder UDT development in Iran. However, UDT technology has the potential to transform urban planning in developing countries. The study provides a roadmap for collaboration between public and private sectors and research institutes to facilitate UDT implementation, highlighting the importance of legislative frameworks, digital infrastructure, innovation, and stakeholder engagement. Policy implications suggest that governments should prioritise supportive policies, investments in digital infrastructure, and collaborative efforts to address data privacy, security, and ownership issues. By addressing these challenges, developing countries can leverage UDT technology to improve urban planning, resource management, and quality of life.

The Impact of Demand Forecasting Accuracy on Customer Satisfaction

Purpose: The aim of the study was to analyze the impact of demand forecasting accuracy on customer satisfaction Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low-cost advantage as compared to field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Accurate demand forecasting significantly enhances customer satisfaction by reducing stockouts, improving delivery reliability, and aligning inventory with consumer needs. Advanced predictive models, such as AI and machine learning, enable businesses to adapt to market dynamics, fostering trust and loyalty. Studies show improved repeat purchase rates and customer retention, highlighting forecasting precision as crucial for competitive advantage. Unique Contribution to Theory, Practice and Policy: Resource-Based View (RBV) Theory, Expectancy-Disconfirmation Theory (EDT) and Systems Theory in supply chain and management maybe used to anchor future studies on the impact of demand forecasting accuracy on customer satisfaction. Research should focus on developing integrative frameworks that combine demand forecasting accuracy with customer satisfaction metrics, emphasizing the role of advanced technologies like AI and machine learning. Companies should also invest in training programs to equip supply chain professionals with the skills to utilize advanced forecasting tools effectively. Governments and regulatory bodies should establish guidelines to encourage secure data sharing between supply chain stakeholders, enhancing forecasting accuracy

AI-Driven Data Processing and Decision Optimization in IoT through Edge Computing and Cloud Architecture

This study discussion point of this paper is to make an in-depth analysis of the development impact of the Internet of Things combined with edge computing and artificial intelligence. In the analysis process, the importance and criticality of data processing and decision making of edge computing as well as the challenges faced should be elaborated respectively. With the rapid popularization and development of Internet of Things devices, edge computing has brought more innovative solutions for different application scenarios such as intelligent furniture industrialization, automatic driving and intelligent transportation by reducing the delay of processing data and improving the characteristics of security data, film and television. Resource and energy efficiency have certain limitations, so it is necessary to combine artificial intelligence to enhance edge computing devices, hardware accelerators, and its utility and federated learning technologies, which can effectively improve the performance and scalability of edge computing and promote the development of more self-service network systems for smart devices. The core of this study is how to promote the Internet through AI-driven edge computing to further develop and provide insights for research priorities and suggest related future research directions.

Data Migration and security in cloud

Data migration and security in the cloud" refers to the process of transferring data from an on-premises system to a cloud environment while prioritizing security measures to protect sensitive information during transit and once stored in the cloud, including implementing strong access controls, encryption, and regular monitoring to prevent data breaches and ensure compliance with relevant regulations. In this time of digital evolution, cloud migration and its security have become essential topics that cannot be avoided. With more companies turning to cloud services for support, the demand for large-scale data storage and data management has also increased. Along with this rising need, the risk of cyber attacks on cloud data has also increased. To better protect yourself from these risks, it’s essential to ensure that you and your organization are up-to-date on the latest security technologies and safety precautions. Keywords : Data Migration, Data Security, cloud, ETL, Data Integration

Has generative AI become of age

Small and Medium Enterprises (SMEs) in South Africa previously faced challenges due to limited resources, restricted access to technology, and the need to constantly adapt to a dynamic business environment. The introduction of Generative Artificial Intelligence (AI) emerged as a potential solution to these issues, promising to enhance operational efficiency and strategic decision-making. As a representative of developing economies, South Africa experienced a growing interest in AI technologies. This study was conducted to explore the impact of generative AI on SME productivity in South Africa, an area which had been underexplored. Employing a qualitative methodology, the study evaluated the current state and implications of generative AI in South African SMEs. It involved in-depth interviews to gather perceptions, experiences, challenges, and benefits from SME owners and managers regarding the adoption of generative AI technologies. The findings analysed via R Statistical Software revealed significant insights into the specific areas where generative AI substantially impacted SME productivity. It also identified the challenges and opportunities associated with the adoption of generative AI by SMEs, as well as the potential long-term implications. Key findings included notable improvements in data-driven decision-making, operational efficiencies, and market expansion strategies. However, the study also highlighted barriers such as the lack of technical expertise, initial setup costs, and concerns over data security. Overall, the impact of generative AI on SMEs in South Africa was found to be predominantly positive, paving the way for further technological advancements and adoption in the sector.

Leveraging Artificial Intelligence and Blockchain for Medicaid Optimization: Enhancing Access, Efficiency, and Data Security

Leveraging Artificial Intelligence and Blockchain for Medicaid Optimization: Enhancing Access, Efficiency, and Data Security

An efficient signed SSL/TLS-based data security in the cloud using LTT-DDBM and TECC

An efficient signed SSL/TLS-based data security in the cloud using LTT-DDBM and TECC

Security and Compliance in Cloud ERP Systems: A Deep Dive into Workday's Framework

This paper provides a comprehensive analysis of the security and compliance framework within cloud- based ERP systems, with a particular focus on Workday, a leader in the market for enterprise resource planning (ERP) solutions. As businesses increasingly transition to cloud ERP systems, ensuring robust security and regulatory compliance has become critical to safeguarding sensitive data, maintaining operational integrity, and meeting industry-specific regulations. The paper begins by introducing the essential concepts of cloud ERP security, highlighting the challenges of data breaches, data integrity, and access control. It further explores Workday's advanced security infrastructure, including its encryption protocols, secure data storage, and real-time monitoring systems, as well as its adherence to global compliance standards such as GDPR, SOC 2, and ISO 27001. The analysis also emphasizes Workday’s tailored security features for specific industries like finance, healthcare, and government, demonstrating how its platform fosters customer trust and drives adoption across diverse sectors. Looking to the future, the paper discusses emerging trends, including the integration of Artificial Intelligence (AI), Machine Learning (ML), and blockchain technology, which hold potential for enhancing data security and compliance in cloud ERP environments. This deep dive into Workday’s security and compliance framework underscores its role in mitigating risks, ensuring regulatory adherence, and positioning itself as a trusted solution in an evolving cloud landscape. Keywords: AI/ML, Cloud ERP Security, Workday Security Framework, Cloud ERP Compliance, Workday Compliance Standards, Data Security, Workday Data Encryption, Regulatory Compliance, SOC 2 Compliance, Identity and Access Management, GDPR Compliance, Risk Mitigation.

Modern Data Governance Technologies and Their Role in Regulatory Compliance: A Study on GDPR and HIPAA

Data governance has evolved into cornerstone of regulatory compliance in the data-driven organizations of today. Strict data privacy regulations as the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA) as well as the explosive increase in volume and complexity of data make effective data governance today vitally important. These guidelines, which follow notions of openness, responsibility, and data minimization, mandate that businesses provide data security, integrity, and access. Modern tools including Apache Atlas, AWS Lake Formation, Databricks Unity Catalog, and Alation are emerging as critical solutions allowing businesses to automate metadata management, enforce access limits, trace data history, and create auditable logs. In this paper, these tools are evaluated in relation to scalability, usability, compliance, and interoperability. Although these technologies substantially simplify regulatory compliance, they are not without limits including complexity, integration problems, and cost constraints. This article tackles future developments that may change data governance, the benefits and disadvantages of this, and emphasizes in a nutshell that these tools are to be used to assure compliance. Keywords Data Governance, GDPR Compliance, HIPAA Compliance, Metadata Management, Data Lineage, Access Control, Data Privacy, Regulatory Compliance, Blockchain in Data Governance, AI in Data Governance, Privacy-Preserving Technologies, Multi-Cloud Governance, Data Security, Audit Logging, Role-Based Access Control.

Data Cataloging as a Catalyst for Effective Data Governance and Improved Decision-Making

Data governance is a foundational component for organizations managing data as a strategic asset in today’s data- driven environments. Effective data governance ensures data quality, security, accessibility, and regulatory compliance, all of which are crucial for maintaining organizational integrity and competitive advantage. This paper explores how data cataloging, as a core tool within data governance frameworks, facilitates metadata management, data lineage tracking, and role-based access control (RBAC). We analyze the technical mechanisms through which data cataloging contributes to governance objectives, such as maintaining data quality and enabling regulatory compliance. Additionally, this study examines the impact of data cataloging on decision-making within organizations, highlighting improvements in decision accuracy, speed, and accountability. By drawing from examples in sectors such as finance and healthcare, this paper outlines the challenges associated with data cataloging implementation, including integration complexity and scalability issues. The paper concludes with future directions, focusing on artificial intelligence (AI)- enabled metadata tagging and cloud-native cataloging solutions, which promise to enhance data cataloging capabilities and make governance frameworks more agile and scalable. Keywords Data Governance, Data Cataloging, Metadata Management, Data Quality, Data Lineage, Role-Based Access Control (RBAC), Compliance, AI-Driven Cataloging, Cloud-Native Solutions, Data-Driven Decision-Making.

The Role of Blockchain in Secure and Scalable Distributed Learning Systems

Blockchain technology has emerged as a transformative tool for enhancing security, transparency, and scalability in distributed learning systems. This paper explores how blockchain can address critical challenges in these systems, such as data integrity, trust among participants, and scalability of learning models. By leveraging blockchain's decentralized ledger and smart contract capabilities, it becomes possible to establish secure data sharing, mitigate privacy concerns, and promote collaborative learning without reliance on central authorities. Furthermore, we discuss novel consensus mechanisms optimized for distributed learning, ensuring efficiency and scalability. Real-world applications in sectors like healthcare, finance, and education are also reviewed to highlight the practical benefits of integrating blockchain into distributed learning systems. Finally, the paper identifies open research challenges and future directions to advance this interdisciplinary field.

Using UMAP for Partially Synthetic Healthcare Tabular Data Generation and Validation

In healthcare, vast amounts of data are increasingly collected through sensors for smart health applications and patient monitoring or diagnosis. However, such medical data often comprise sensitive patient information, posing challenges regarding data privacy, and are resource-intensive to acquire for significant research purposes. In addition, the common case of lack of information due to technical issues, transcript errors, or differences between descriptors considered in different health centers leads to the need for data imputation and partial data generation techniques. This study introduces a novel methodology for partially synthetic tabular data generation, designed to reduce the reliance on sensor measurements and ensure secure data exchange. Using the UMAP (Uniform Manifold Approximation and Projection) visualization algorithm to transform the original, high-dimensional reference data set into a reduced-dimensional space, we generate and validate synthetic values for incomplete data sets. This approach mitigates the need for extensive sensor readings while addressing data privacy concerns by generating realistic synthetic samples. The proposed method is validated on prostate and breast cancer data sets, showing its effectiveness in completing and augmenting incomplete data sets using fully available references. Furthermore, our results demonstrate superior performance in comparison to state-of-the-art imputation techniques. This work makes a dual contribution by not only proposing an innovative method for synthetic data generation, but also studying and establishing a formal framework to understand and solve synthetic data generation and imputation problems in sensor-driven environments.