Integrating AI in Healthcare: Innovations in Petroleum-Based Fraud Detection and Its Implications for Medical Diagnostics
DOI:
10.47709/ijmdsa.v3i4.4655Keywords:
Artificial Intelligence, Machine Learning, Fraud Detection, Anomaly Detection, Fraud Prevention, Data Integrity, Regulatory Compliance, Pattern Recognition, Predictive AnalyticsDimension Badge Record
Abstract
Artificial intelligence (AI) is transforming a number of industries through increasing operational effectiveness, detecting fraudulent activity, and boosting diagnostic accuracy. In order to demonstrate the transformational potential of AI techniques across different areas, this review paper examines the convergence of petroleum-based fraud detection and AI applications in healthcare. The study looks at how artificial intelligence is currently being used in healthcare, particularly in tailored and medical diagnostics. After that, it explores how artificial intelligence (AI) is utilized in petroleum-based fraud detection, going over methods like data mining, anomaly detection, and machine learning algorithms that are used to find and stop fraud. The review emphasizes the possible advantages and synergies as it looks further into how fraud detection findings from the petroleum business might be applied to healthcare. Notwithstanding these advantages, the paper discusses the main obstacles and restrictions related to integrating AI, such as system integration, data security and privacy, accuracy and dependability, and ethical and legal issues. The study intends to provide significant insights into the efficient deployment of AI technologies and the potential for cross-industry applications to stimulate innovation and enhance results by giving a thorough review of these subjects.
Abstract viewed = 101 times
References
N. Cunningham. The 10 worst energy-related disasters of modern times. https://oilprice.com/Energy/Coal/ Coal-The-Worlds-Deadliest-Source-Of-Energy.html, last accessed on 08/10/20
N. E. Institution. Chernobyl accident and its consequences. https://www.nei.org/resources/factsheets/chernobyl-accident-and-its-consequences, last accessed on 04/03/21
S. Institute. Confirmation of a coordinated attack on the Ukrainian power grid. https://www.sans.org/blog/ confirmation-of-a-coordinated-attack-on-the-ukrainian-power-grid/, last accessed on 01/01/21.
N. E. Services. Energy theft and fraud reduction. Https: //www.smart-energy.com/industry-sectors/energy-grid-management/ energy-theft-and-fraud-reduction/, last accessed on 02/11/21
Husnain, A., Alomari, G., & Saeed, A. AI-Driven Integrated Hardware and Software Solution for EEG-Based Detection of Depression and Anxiety.
M. V. Barros, R. Salvador, C. M. Piekarski, A. C. de Francisco, and F. M. C. S. Freire, “Life cycle assessment of electricity generation: a review of the characteristics of existing literature,” The International Journal of Life Cycle Assessment, vol. 25, no. 1, pp. 36–54, 2020.
B. L. Lee, C. Wilson, P. Simshauser, and E. Majiwa, “Deregulation, efficiency and policy determination: An analysis of australia’s electricity distribution sector,” Energy Economics, p. 105210, 2021.
Zeb, S., Nizamullah, F. N. U., Abbasi, N., & Fahad, M. (2024). AI in Healthcare: Revolutionizing Diagnosis and Therapy. International Journal of Multidisciplinary Sciences and Arts, 3(3).
J. D. Hunt, E. Byers, Y. Wada, S. Parkinson, D. E. Gernaat, S. Langan, D. P. van Vuuren, and K. Riahi, “Global resource potential of seasonal pumped hydropower storage for energy and water storage,” Nature communications, vol. 11, no. 1, pp. 1–8, 2020
Zeb, S., Nizamullah, F. N. U., Abbasi, N., & Qayyum, M. U. (2024). Transforming Healthcare: Artificial Intelligence's Place in Contemporary Medicine. BULLET: Jurnal Multidisiplin Ilmu, 3(4).
T. Simla and W. Stanek, “Reducing the impact of wind farms on the electric power system by the use of energy storage,” Renewable Energy, vol. 145, pp. 772–782, 2020
HUSNAIN, A., & SAEED, A. (2024). AI-Enhanced Depression Detection and Therapy: Analyzing the VPSYC System.
Abbasi, N., Nizamullah, F. N. U., & Zeb, S. (2023). AI in Healthcare: Integrating Advanced Technologies with Traditional Practices for Enhanced Patient Care. BULLET: Jurnal Multidisiplin Ilmu, 2(3), 546-556.
F. Leach, G. Kalghatgi, R. Stone, and P. Miles, “The scope for improving the efficiency and environmental impact of internal combustion engines,” Transportation engineering, p. 100005, 2020.
Abbasi, N., Nizamullah, F. N. U., & Zeb, S. (2023). AI IN HEALTHCARE: USING CUTTING-EDGE TECHNOLOGIES TO REVOLUTIONIZE VACCINE DEVELOPMENT AND DISTRIBUTION. JURIHUM: Jurnal Inovasi dan Humaniora, 1(1), 17-29.
Valli, L. N. (2024). A succinct synopsis of predictive analytics for fraud detection and credit scoring in BFSI. JURIHUM: Jurnal Inovasi dan Humaniora, 2(2), 200-213.
S. Wang, D. Wang, Z. Yu, X. Dong, S. Liu, H. Cui, and B. Sun, “Advances in research on petroleum biodegradability in soil,” Environmental Science: Processes & Impacts, vol. 23, no. 1, pp. 9–27, 2021
Lashari, Z. A., Lalji, S. M., Ali, S. I., Kumar, D., Khan, B., & Tunio, U. (2024). Physiochemical analysis of titanium dioxide and polyacrylamide nanofluid for enhanced oil recovery at low salinity. Chemical Papers, 78(6), 3629-3637.
Z. U. ZANGO, “Review of petroleum sludge thermal treatment and utilization of ash as a construction material, a way to environmental sustainability,” International Journal of Advanced and Applied Sciences, vol. 7, no. 12, 2020.
World energy outlook 2017. https://www.iea.org/reports/ world-energy-outlook-2017, last accessed on 12/12/20.
Hussain, S. M. Arif, and M. Aslam, “Emerging renewable and sustainable energy technologies: State of the art,” Renewable and Sustainable Energy Reviews, vol. 71, pp. 12–28, 2017
Mehta, A., Niaz, M., Uzowuru, I. M., & Nwagwu, U. Implementation of the Latest Artificial Intelligence Technology Chatbot on Sustainable Supply Chain Performance on Project-Based Manufacturing Organization: A Parallel Mediation Model in the American Context.
S. Cao, Y. Chen, G. Cheng, F. Du, W. GAO, Z. He, S. Li, S. Lun, H. Ma, Q. Su et al., “Preliminary study on evaluation of smart-cities technologies and proposed uv lifestyles,” in 2018 4th International Conference on Universal Village (UV). IEEE, 2018, pp. 1–49
Valli, L. N. (2024). Predictive Analytics Applications for Risk Mitigation across Industries; A review. BULLET: Jurnal Multidisiplin Ilmu, 3(4), 542-553.
Lodhi, S. K., Hussain, H. K., & Gill, A. Y. (2024). Renewable Energy Technologies: Present Patterns and Upcoming Paths in Ecological Power Production. Global Journal of Universal Studies, 1(1), 108-131.
D. W. Kweku, O. Bismark, A. Maxwell, K. A. Desmond, K. B. Danso, E. A. Oti-Mensah, A. T. Quachie, and B. B. Adormaa, “Greenhouse effect: greenhouse gases and their impact on global warming,” Journal of Scientific research and reports, pp. 1–9, 2017
Choudhary, V., Mehta, A., Patel, K., Niaz, M., Panwala, M., & Nwagwu, U. (2024). Integrating Data Analytics and Decision Support Systems in Public Health Management. South Eastern European Journal of Public Health, 158-172.
Lodhi, S. K., Gill, A. Y., & Hussain, I. (2024). 3D Printing Techniques: Transforming Manufacturing with Precision and Sustainability. International Journal of Multidisciplinary Sciences and Arts, 3(3), 129-138.
Underdal and K. Hanf, International environmental agreements and domestic politics: The case of acid rain. Routledge, 2019.
U. of Haifa. Exposure to ’white’ light leds appears to suppress body’s production of melatonin more than certain other lights, research suggests. https://www.sciencedaily.com/releases/2011/ 09/110912092554.htm, last accessed on 04/04/21.
Lodhi, S. K., Gill, A. Y., & Hussain, H. K. (2024). Green Innovations: Artificial Intelligence and Sustainable Materials in Production. BULLET: Jurnal Multidisiplin Ilmu, 3(4), 492-507.
Lashari, Z. A., Lalji, S. M., Ali, S. I., Kumar, D., Khan, B., & Tunio, U. (2024). Physiochemical analysis of titanium dioxide and polyacrylamide nanofluid for enhanced oil recovery at low salinity. Chemical Papers, 78(6), 3629-3637.
Okulicz-Kozaryn and M. Altman, “The happiness-energy paradox: Energy use is unrelated to subjective well-being,” Applied Research in Quality of Life, vol. 15, no. 4, pp. 1055–1067, 2020.
Mining and quarrying. https://www.ilo.org/ipec/areas/ Miningandquarrying/lang--en/index.htm, last accessed on 12/12/20.
L. Cheng and T. Yu, “A new generation of ai: A review and perspective on machine learning technologies applied to smart energy and electric power systems,” International Journal of Energy Research, vol. 43, no. 6, pp. 1928–1973, 2019.
E. Mollasalehi, D. Wood, and Q. Sun, “Indicative fault diagnosis of wind turbine generator bearings using tower sound and vibration,” Energies, vol. 10, no. 11, p. 1853, 2017.
M. Akhloufi and N. Benmesbah, “Outdoor ice accretion estimation of wind turbine blades using computer vision,” in 2014 Canadian Conference on Computer and Robot Vision. IEEE, 2014, pp. 246–253
F. Miralles, N. Pouliot, and S. Montambault, “State-of-the-art review ` of computer vision for the management of power transmission lines,” in Proceedings of the 2014 3rd International Conference on Applied Robotics for the Power Industry. IEEE, 2014, pp. 1–6.
T. Azar, A. Khamis, N. A. Kamal, and B. Galli, “Short term electricity load forecasting through machine learning,” in Joint European-US Workshop on Applications of Invariance in Computer Vision. Springer, 2020, pp. 427–437.
Lodhi, S. K., Hussain, I., & Gill, A. Y. (2024). Artificial Intelligence: Pioneering the Future of Sustainable Cutting Tools in Smart Manufacturing. BIN: Bulletin of Informatics, 2(1), 147-162.
L. Du, J. Guo, and C. Wei, “Impact of information feedback on residential electricity demand in china,” Resources, Conservation and Recycling, vol. 125, pp. 324–334, 2017
P. Conde-Clemente, J. M. Alonso, and G. Trivino, “Toward automatic generation of linguistic advice for saving energy at home,” Soft Computing, vol. 22, no. 2, pp. 345–359, 2018.
Lodhi, S. K., Hussain, H. K., & Hussain, I. (2024). Using AI to Increase Heat Exchanger Efficiency: An Extensive Analysis of Innovations and Uses. International Journal of Multidisciplinary Sciences and Arts, 3(4), 1-14.
R. Jurowetzki, “Unpacking big systems–natural language processing meets network analysis. A study of smart grid development in denmark.” A Study of Smart Grid Development in Denmark. (May 21, 2015). SWPS, vol. 15, 2015.
Lodhi, S. K., Gill, A. Y., & Hussain, I. (2024). AI-Powered Innovations in Contemporary Manufacturing Procedures: An Extensive Analysis. International Journal of Multidisciplinary Sciences and Arts, 3(4), 15-25.
R. Jing, Y. Lin, N. Khanna, X. Chen, M. Wang, J. Liu, and J. Lin, “Balancing the energy trilemma in energy system planning of coastal cities,” Applied Energy, p. 116222, 2020
Downloads
ARTICLE Published HISTORY
Issue
Section
License
Copyright (c) 2024 Muhammad Fahad, Muhammad Ibrar, Muhammad Umer Qayyum, Ali Husnain
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.