<scp>Privacy‐preserving</scp> data mining and machine learning in healthcare: Applications, challenges, and solutions

Naresh, Vankamamidi S.; Thamarai, Muthusamy

doi:10.1002/widm.1490

Cited by 14 publications

(5 citation statements)

References 143 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Tesla employs AI to optimize the energy management of its electric vehicles (EVs) and battery storage systems. AI algorithms analyze driving patterns, weather conditions, and energy prices to optimize charging schedules and improve battery performance (Naresh et al, 2024). This approach enhances the sustainability of EVs by reducing energy consumption and extending battery life.…”

Section: Developing Ai Models For Environmental Impact Assessmentmentioning

confidence: 99%

Leveraging AI for sustainable accounting: Developing models for environmental impact assessment and reporting

Beatrice Oyinkansola Adelakun,

Bernard Owusu Antwi,

Afari Ntiakoh

et al. 2024

Financ. account. res. j.

View full text Add to dashboard Cite

The integration of Artificial Intelligence (AI) in sustainable accounting represents a transformative approach to enhancing the accuracy, efficiency, and comprehensiveness of environmental impact assessment and reporting. This paper explores the development of AI-driven models aimed at advancing sustainable accounting practices, focusing on environmental impact assessment and transparent reporting. AI technologies, particularly machine learning (ML) and natural language processing (NLP), play a pivotal role in automating and refining data collection, analysis, and reporting processes. These technologies enable the processing of vast amounts of heterogeneous data from multiple sources, including IoT sensors, satellite imagery, and corporate disclosures. By leveraging ML algorithms, organizations can identify patterns, predict trends, and assess the environmental impact of their operations with unprecedented precision. One of the key advantages of AI in sustainable accounting is its ability to enhance data accuracy and reliability. Traditional methods often suffer from manual errors and inconsistencies. AI models, however, can continuously learn and adapt, improving their accuracy over time. For instance, predictive analytics can forecast future environmental impacts based on historical data, allowing companies to implement proactive measures to mitigate adverse effects. Furthermore, AI facilitates real-time monitoring and reporting. IoT devices equipped with environmental sensors can stream data to AI systems, which process and analyze the information instantaneously. This capability is crucial for timely reporting and compliance with environmental regulations. Real-time data analytics also empower organizations to make informed decisions swiftly, optimizing their sustainability strategies and reducing their ecological footprint. Another significant contribution of AI is in enhancing transparency and accountability in environmental reporting. NLP algorithms can analyze and interpret regulatory texts, corporate reports, and public records, ensuring that organizations adhere to sustainability standards and guidelines. Additionally, AI can automate the generation of comprehensive and comprehensible sustainability reports, making them accessible to a broader audience, including stakeholders and regulators. Developing robust AI models for sustainable accounting involves several critical steps. Initially, data preprocessing is essential to clean and harmonize diverse datasets, ensuring quality input for AI algorithms. Next, model training and validation are conducted using historical and real-time data to refine predictive capabilities. Continuous model evaluation and adjustment are necessary to maintain accuracy and relevance in dynamic environmental contexts. Collaboration between AI experts, environmental scientists, and accounting professionals is paramount in this development process. Interdisciplinary teams can ensure that AI models are not only technically sound but also aligned with environmental science principles and accounting standards. This collaboration also fosters innovation, leading to the development of more sophisticated tools for environmental impact assessment and reporting. The adoption of AI-driven sustainable accounting models offers numerous benefits, including enhanced efficiency, accuracy, and compliance. However, challenges such as data privacy, algorithmic transparency, and the need for substantial initial investments must be addressed. Future research should focus on overcoming these obstacles and exploring the potential of emerging AI technologies, such as deep learning and blockchain, to further revolutionize sustainable accounting practices. AI holds significant promise for transforming sustainable accounting by improving environmental impact assessment and reporting. Through advanced data analytics, real-time monitoring, and enhanced transparency, AI can help organizations achieve their sustainability goals, ensuring a more sustainable future. The continuous development and refinement of AI models, supported by interdisciplinary collaboration, are essential for realizing these benefits and addressing the complex challenges of environmental sustainability. Keywords: Sustainable Accounting, Environmental Impact Assessment, AI, Developing Models, Reporting.

show abstract

Section: Developing Ai Models For Environmental Impact Assessmentmentioning

confidence: 99%

Leveraging AI for sustainable accounting: Developing models for environmental impact assessment and reporting

Beatrice Oyinkansola Adelakun,

Bernard Owusu Antwi,

Afari Ntiakoh

et al. 2024

Financ. account. res. j.

View full text Add to dashboard Cite

show abstract

“…It is important to note that battery technology and methodologies for determining battery parameters are continually evolving, and new AI and ML-based techniques are likely to emerge in the future. As of my last update, AI and ML applications were not yet widely integrated into commercial EVs and ESS, but ongoing research and development in this domain was occurring [112,113].…”

Section: Determination Of Battery Parameters In Evsmentioning

confidence: 99%

Recent Progress of Deep Learning Methods for Health Monitoring of Lithium-Ion Batteries

Madani,

Ziebert,

Vahdatkhah

et al. 2024

Batteries

View full text Add to dashboard Cite

In recent years, the rapid evolution of transportation electrification has been propelled by the widespread adoption of lithium-ion batteries (LIBs) as the primary energy storage solution. The critical need to ensure the safe and efficient operation of these LIBs has positioned battery management systems (BMS) as pivotal components in this landscape. Among the various BMS functions, state and temperature monitoring emerge as paramount for intelligent LIB management. This review focuses on two key aspects of LIB health management: the accurate prediction of the state of health (SOH) and the estimation of remaining useful life (RUL). Achieving precise SOH predictions not only extends the lifespan of LIBs but also offers invaluable insights for optimizing battery usage. Additionally, accurate RUL estimation is essential for efficient battery management and state estimation, especially as the demand for electric vehicles continues to surge. The review highlights the significance of machine learning (ML) techniques in enhancing LIB state predictions while simultaneously reducing computational complexity. By delving into the current state of research in this field, the review aims to elucidate promising future avenues for leveraging ML in the context of LIBs. Notably, it underscores the increasing necessity for advanced RUL prediction techniques and their role in addressing the challenges associated with the burgeoning demand for electric vehicles. This comprehensive review identifies existing challenges and proposes a structured framework to overcome these obstacles, emphasizing the development of machine-learning applications tailored specifically for rechargeable LIBs. The integration of artificial intelligence (AI) technologies in this endeavor is pivotal, as researchers aspire to expedite advancements in battery performance and overcome present limitations associated with LIBs. In adopting a symmetrical approach, ML harmonizes with battery management, contributing significantly to the sustainable progress of transportation electrification. This study provides a concise overview of the literature, offering insights into the current state, future prospects, and challenges in utilizing ML techniques for lithium-ion battery health monitoring.

show abstract

“…Cybersecurity may also be referred to as information technology security. Securing a system's information flow is a gigantic issue because of the numerous types of possible attacks/threats, that is why cybersecurity experts are emphasizing a customized security system for modern‐era domains such as healthcare (Naresh & Thamarai, 2023), Telemetry (Abreu & Pereira, 2022), IoT (Almagrabi, 2023), and other systems with big data analytics (Pramanik et al, 2021). The general communication framework for emerging connected and autonomous vehicles poses a severe security challenge as its safety can be compromised by several diverse types of cyberattacks.…”

Section: Introductionmentioning

confidence: 99%

Machine learning and blockchain technologies for cybersecurity in connected vehicles

Ahmad,

Zia,

Naqvi

et al. 2023

WIREs Data Min & Knowl

View full text Add to dashboard Cite

Future connected and autonomous vehicles (CAVs) must be secured against cyberattacks for their everyday functions on the road so that safety of passengers and vehicles can be ensured. This article presents a holistic review of cybersecurity attacks on sensors and threats regarding multi‐modal sensor fusion. A comprehensive review of cyberattacks on intra‐vehicle and inter‐vehicle communications is presented afterward. Besides the analysis of conventional cybersecurity threats and countermeasures for CAV systems, a detailed review of modern machine learning, federated learning, and blockchain approach is also conducted to safeguard CAVs. Machine learning and data mining‐aided intrusion detection systems and other countermeasures dealing with these challenges are elaborated at the end of the related section. In the last section, research challenges and future directions are identified.This article is categorized under: Commercial, Legal, and Ethical Issues > Security and Privacy Technologies > Machine Learning Technologies > Internet of Things

show abstract

Privacy‐preserving data mining and machine learning in healthcare: Applications, challenges, and solutions

Cited by 14 publications

References 143 publications

Leveraging AI for sustainable accounting: Developing models for environmental impact assessment and reporting

Leveraging AI for sustainable accounting: Developing models for environmental impact assessment and reporting

Recent Progress of Deep Learning Methods for Health Monitoring of Lithium-Ion Batteries

Machine learning and blockchain technologies for cybersecurity in connected vehicles

Contact Info

Product

Resources

About