This study presents "X-LeafNet,"a novel approach for identifying tea leaf diseases using a modified Xception model integrated with Explainable Artificial Intelligence (XAI) techniques. The system aims to cla...
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This study investigates the relationship between task difficulty and performance outcomes in virtual reality (VR) piloting tasks. Utilizing data from simulated flight sessions, we analyzed whether significant statisti...
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In India, the role of agriculture is pivotal contributing to the growth of the Indian economy and employment. It is essential to focus on the security of food which ensures individuals' health. Pest plays a major ...
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Recently, the combination of Deep Learning (DL) methods within the Internet of Things (IoTs) has developed in the agricultural field, especially in the domain of pest management. This study considers the implementatio...
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Alzheimer's disease is becoming more common over time. It is a long-term brain disease that gradually impairs people's memory, thinking skills, and finally their ability to perform even the most basic tasks. E...
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One of the most rapidly developing technologies that helps reduce the amount of work done by humans is known as 'home automation.' People who are looking for luxury and sophisticated home automation platforms ...
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The Model-View-Controller (MVC) design pattern is widely used in software engineering for developing user interfaces. While MVC offers many benefits, handling data in a way that is efficient and effective can be a cha...
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This study presents an integrated system devised to combat plastic pollution in lakes autonomously, eliminating the need for human intervention. Utilizing sensor data and camera imagery processed through the YOLO algo...
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Inverse Reinforcement Learning (IRL) and Reinforcement Learning from Human Feedback (RLHF) are pivotal methodologies in reward learning, which involve inferring and shaping the underlying reward function of sequential...
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Inverse Reinforcement Learning (IRL) and Reinforcement Learning from Human Feedback (RLHF) are pivotal methodologies in reward learning, which involve inferring and shaping the underlying reward function of sequential decision-making problems based on observed human demonstrations and feedback. Most prior work in reward learning has relied on prior knowledge or assumptions about decision or preference models, potentially leading to robustness issues. In response, this paper introduces a novel linear programming (LP) framework tailored for offline reward learning. Utilizing pre-collected trajectories without online exploration, this framework estimates a feasible reward set from the primal-dual optimality conditions of a suitably designed LP, and offers an optimality guarantee with provable sample efficiency. Our LP framework also enables aligning the reward functions with human feedback, such as pairwise trajectory comparison data, while maintaining computational tractability and sample efficiency. We demonstrate that our framework potentially achieves better performance compared to the conventional maximum likelihood estimation (MLE) approach through analytical examples and numerical experiments. Copyright 2024 by the author(s)
Sarcasm-also termed as verbal irony, when the speaker presents something in a mocking or humorous way such that he or she actually conveys the opposite of what they seem to say. This article has a detailed analysis of...
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