An intelligent sleep-awake energy management system for wireless sensor network

Vellela, Sai Srinivas; Balamanigandan, R.

doi:10.1007/s12083-023-01558-x

Cited by 15 publications

(1 citation statement)

References 34 publications

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“…Joze M. Rozanec et al [22] proposed an architecture for the XAI using semantic and AI technologies, and it is used to detect demand forecasting and deployed in the real world. The Knowledge graph is used to provide the explanation about the process of demand forecasting at a higher level of explanation than the specific features.…”

Section: Explainable Ai In Forecastingmentioning

confidence: 99%

Towards Trustworthy Telemedicine: Applying Explainable AI for Remote Healthcare Recommendations

2023

IJPREMS

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In the rapidly evolving landscape of healthcare, telemedicine has emerged as a pivotal tool, offering patients remote access to medical consultations and treatments. However, the trustworthiness of telemedicine platforms remains a concern, especially when artificial intelligence (AI) is employed to provide healthcare recommendations. This research paper delves into the application of Explainable AI (XAI) in telemedicine to enhance its trustworthiness and transparency. We investigate the current challenges faced by telemedicine platforms, particularly in the context of AIdriven recommendations, and explore how XAI can address these issues by offering clear, understandable explanations for AI-generated outputs. Our findings indicate that integrating XAI into telemedicine not only bolsters patient trust but also empowers healthcare professionals to make more informed decisions based on AI recommendations. We conclude by proposing a framework for the seamless integration of XAI in telemedicine platforms and discuss its potential implications for the future of remote healthcare.

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Section: Explainable Ai In Forecastingmentioning

confidence: 99%