With the rapid growth of AI technology, deep learning algorithms have made remarkable achievements in the field of natural language processing, especially showing great potential in processing Chinese, the world's...
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This paper introduces μARCHiFI, an open-source tool dedicated to the formal modeling and verification of microarchitecture-level fault injections and their effects on complex hardware/software systems. First, we addr...
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The "Automated Brain Disease Diagnosis and Patient Support system"is a web application that aids hospital administration in diagnosis of five classes of brain disorders: Alzheimer's Disease, Parkinson...
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With the rise of the big data era, universities are encountering significant challenges in safeguarding data security and ensuring privacy protection. Blockchain technology, renowned for its decentralized, secure, and...
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Predicting faults in a software system can be performed using software reliability models. Reliability is a real- world aspect that is related to several real-life issues which depends on several factors. Different al...
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This research investigates the use of reinforcement learning (RL) algorithms for optimal control systems in electrical engineering. The article discusses four popular RL algorithms - Q-learning, Deep Q Network (DQN), ...
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In today's complex world, the intricate connections between human health, socio-economic factors, and environmental conditions necessitate a holistic approach to health challenges. This paper highlights the critic...
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ISBN:
(纸本)9783031751097;9783031751103
In today's complex world, the intricate connections between human health, socio-economic factors, and environmental conditions necessitate a holistic approach to health challenges. This paper highlights the critical need for the One Health paradigm, emphasizing a comprehensive framework that integrates human, economic, and environmental health considerations. By incorporating Explainable Artificial Intelligence (XAI) principles, this paper showcases a pioneering method to make the sophisticated insights derived from AI more accessible and actionable for a broad range of stakeholders, including healthcare professionals, environmental experts, and policy makers. Within the framework of the MISTRAL H2020 European project, we illustrate a healthcare model that employs XAI to demystify AI algorithms, thereby fostering trust and enhancing understanding among its users. This innovative approach not only improves clinical decision-making but also addresses environmental health issues by rendering the opaque decision-making processes ofMachine Learning (ML) models transparent and understandable. The framework's significance is underlined by its capacity to inform Health Impact Assessments (HIA) and predict disease risks, facilitating precise interventions and informed policy development. Furthermore, we propose a method for engineering and accessing this framework online, allowing users to engage directly with the system.
In the rapidly evolving landscape of commodity markets, accurate forecasting and classification of price trends are crucial for informed decision-making by investors, traders, and policymakers. This study presents a n...
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In recent years, the rapid development of the Internet and information technology has exacerbated the complexity and threat of the network security environment, and enterprises are facing unprecedented challenges. To ...
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This paper describes the application status and existing problems of the nocturnal patrol technology of the overhead transmission lines in our country. A system for carrying night vision of unmanned aerial vehicle (UA...
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