this study presents a comprehensive framework for credit scoring in loan application processes. the research presents a framework that uses data analysis and machine learning techniques to evaluate creditworthiness, i...
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Capacity management of Enterprise applications (EAs) encompasses critical IT processes that maximize the IT system's performance while minimizing operational costs. Effective management of EAs capacity can be enha...
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the evolution of human civilization has been intrinsically linked to advancements in technology, leading to the development of multiple languages as mediums of communication. However, this linguistic diversity poses s...
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Parkinson's disease, a degenerative disorder of the nervous system, has a significant impact on a substantial population across the globe, leading to gradual deterioration in both, non-motor and motor, capabilitie...
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In the contemporary research context, path planning for autonomous vehicles in dynamic and uncertain environments represents a significant challenge. In this paper, we propose a dynamic path planning and optimisation ...
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Over 50% of India's population is employed in agriculture, which is vital to the country's economy but confronts enormous hurdles from environmental variability. this study reviews machine learning approaches ...
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Efficient numerical optimization methods can improve performance and reduce the environmental impact of computing in many applications. this work presents a proof-of-concept study combining primitive state representat...
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ISBN:
(纸本)9798350373981;9798350373974
Efficient numerical optimization methods can improve performance and reduce the environmental impact of computing in many applications. this work presents a proof-of-concept study combining primitive state representations and agent-environment interactions as first-order optimizers in the setting of budget-limited optimization. through reinforcement learning (RL) over a set of training instances of an optimization problem class, optimal policies for sequential update selection of algorithmic iteration steps are approximated in generally formulated low-dimensional partial state representations that consider aspects of progress and resource use. For the investigated case studies, deployment of the trained agents to unseen instances of the quadratic optimization problem classes outperformed conventional optimal algorithms with optimized hyperparameters. the results show that elementary RL methods combined with succinct partial state representations can be used as heuristics to manage complexity in RL-based optimization, paving the way for agentic optimization approaches.
Residential architectural plan design must comply with a number of building requirements, including fire prevention and sunlight exposure. At the same time, there are several design options, such as irregular plot out...
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this study addresses the challenges of real-time data synchronization and big data processing in the construction of digital twin workshops under the background of intelligent manufacturing. A solution that integrates...
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Reinforcement learning (RL) has emerged as a vital component in the development of autonomous systems. However, several challenges, such as high computational demands, limited generalization in dynamic environments, a...
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