Insights into the Advancements of Artificial Intelligence and Machine Learning, the Present State of Art, and Future Prospects: Seven Decades of Digital Revolution

Bindra, Prabhleen; Kshirsagar, Meghana; Ryan, Conor; Vaidya, Gauri; Gupt, Krishn Kumar; Kshirsagar, Vivek

doi:10.1007/978-981-16-0878-0_59

Cited by 10 publications

(4 citation statements)

References 21 publications

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“…Smart homes, industrial automation, and wearable devices are just the beginning. The ability to gather and share real-time data is enhancing efficiency, enabling predictive maintenance, and creating a more connected and responsive world (Bindra et. al., 2021, Emmert-Streib, 2021, Jordan, 2019.…”

Section: Emerging Technologiesmentioning

confidence: 99%

Data Confidentiality and Integrity: A Review of Accounting and Cybersecurity Controls in Superannuation Organizations

Anthony Anyanwu,

Temidayo Olorunsogo,

Temitayo Oluwaseun Abrahams

et al. 2024

Comput. sci. IT res. j.

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In an era dominated by digital transformation, superannuation organizations face unprecedented challenges in safeguarding the confidentiality and integrity of sensitive financial data. This review explores the intricate relationship between accounting practices and cybersecurity controls within the context of superannuation entities. By examining the existing literature, regulatory frameworks, and industry best practices, this paper synthesizes the key considerations essential for ensuring robust data protection. The study delves into the critical role of accounting systems in managing financial information and the subsequent implications for data confidentiality. It investigates how evolving accounting standards and practices intersect with cybersecurity protocols to fortify the integrity of financial records within superannuation organizations. The dynamic nature of cyber threats necessitates a comprehensive analysis of technological safeguards, risk management frameworks, and compliance measures to uphold data confidentiality. Furthermore, the review underscores the imperative for a multidimensional approach to cybersecurity in the superannuation sector. It discusses the integration of advanced technologies such as encryption, blockchain, and anomaly detection alongside traditional accounting controls to create a resilient defense against emerging threats. The exploration extends to the examination of employee training programs, incident response strategies, and third-party risk assessments as integral components of a comprehensive cybersecurity posture. As superannuation organizations navigate the complex landscape of data management, a holistic understanding of the interplay between accounting and cybersecurity controls becomes paramount. This review contributes to the existing body of knowledge by providing insights into the challenges and opportunities presented by the evolving technological landscape, offering practitioners and policymakers a foundation for enhancing data confidentiality and integrity in the superannuation sector. Keywords: Data Confidentiality; Accounting; Cybersecurity; Superannuation Organization; Data Integrity.

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Section: Emerging Technologiesmentioning

confidence: 99%

Data Confidentiality and Integrity: A Review of Accounting and Cybersecurity Controls in Superannuation Organizations

Anthony Anyanwu,

Temidayo Olorunsogo,

Temitayo Oluwaseun Abrahams

et al. 2024

Comput. sci. IT res. j.

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“…The decision tree (DT) is a tree structure (can be binary or non-binary). DT includes the ID3 algorithm [39] , C4.5 algorithm [40] , and CART algorithm [41] , which are the core technologies for classification and prediction [42] . The ID3 algorithm mainly aims to construct a DT recursively and select features at each node of the DT by applying the information gain criterion.…”

Section: Decision Treementioning

confidence: 99%

Predicted Mean Vote of Subway Car Environment Based on Machine Learning

Huang

et al. 2023

Big Data Min. Anal.

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The thermal comfort of passengers in the carriage cannot be ignored. Thus, this research aims to establish a prediction model for the thermal comfort of the internal environment of a subway car and find the optimal input combination in establishing the prediction model of the predicted mean vote (PMV) index. Data-driven modeling utilizes data from experiments and questionnaires conducted in Nanjing Metro. Support vector machine (SVM), decision tree (DT), random forest (RF), and logistic regression (LR) were used to build four models. This research aims to select the most appropriate input variables for the predictive model. All possible combinations of 11 input variables were used to determine the most accurate model, with variable selection for each model comprising 102 350 iterations. In the PMV prediction, the RF model was the best when using the correlation coefficients square (R 2 / as the evaluation indicator (R 2 : 0.7680, mean squared error (MSE): 0.2868). The variables include clothing temperature (CT), convective heat transfer coefficient between the surface of the human body and the environment (CHTC), black bulb temperature (BBT), and thermal resistance of clothes (TROC). The RF model with MSE as the evaluation index also had the highest accuracy (R 2 : 0.7676, MSE: 0.2836). The variables include clothing surface area coefficient (CSAC), CT, BBT, and air velocity (AV). The results show that the RF model can efficiently predict the PMV of the subway car environment.

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“…The recent evidence also shows the low sides of incomplete EHRs, and integrated data as a major barrier in providing personalized healthcare 11 . With COVID‐9, this need has been significantly increased to have a complete idea of the patient engaging in personalized and intelligent health care services 11 which can be attained through artificial intelligence 12 . These ideas of integration and sharing of personal data also bring in two crucial concerns: privacy and security 13 …”

Section: Introductionmentioning

confidence: 99%

SENSIBLE: SEquestered aNd SynergIstic BLockchain Ecosystem

Kshirsagar

Vaidya

Yao

et al. 2022

Engineering Reports

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Health care interoperability unfolds the way for personalized health care services at a reduced cost. Furthermore, a decentralized system holds the promise to prevent compromises such as cyber-attacks due to data breaches. Hence, there is a need for a framework that seamlessly integrates and shares data across the system stakeholders. We propose SEquestered aNd SynergIstic BLockchain Ecosystem (SENSIBLE), a blockchain-powered, knowledge-driven data-sharing framework that gives patients complete control of their medical history and can extract rich information hidden in it using knowledge graphs (KGs). By incorporating both blockchain and KGs, we can provide a platform for secure data sharing among stakeholders by maintaining data privacy and integrity through data authentication and robust data integration. We present a Proof-of-Concept of the SENSIBLE network with Ethereum to share dynamic knowledge across stakeholders. Dynamic knowledge generation on the blockchain provides a two-fold advantage of cooperation and communication amongst the stakeholders in the health care ecosystem. This leads to operational ease through sharing relevant portions of complex information while also ensuring the isolation of sensitive medical data.

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Insights into the Advancements of Artificial Intelligence and Machine Learning, the Present State of Art, and Future Prospects: Seven Decades of Digital Revolution

Cited by 10 publications

References 21 publications

Data Confidentiality and Integrity: A Review of Accounting and Cybersecurity Controls in Superannuation Organizations

Data Confidentiality and Integrity: A Review of Accounting and Cybersecurity Controls in Superannuation Organizations

Predicted Mean Vote of Subway Car Environment Based on Machine Learning

SENSIBLE: SEquestered aNd SynergIstic BLockchain Ecosystem

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