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Manuel Zamudio López, Hamidreza Zareipour and Mike Quashie
This research proposes an investigative experiment employing binary classification for short-term electricity price spike forecasting. Numerical definitions for price spikes are derived from economic and statistical thresholds. The predictive task employ...
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Jiaming Li, Ning Xie and Tingting Zhao
In recent years, with the rapid advancements in Natural Language Processing (NLP) technologies, large models have become widespread. Traditional reinforcement learning algorithms have also started experimenting with language models to optimize training. ...
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Darian M. Onchis, Flavia Costi, Codruta Istin, Ciprian Cosmin Secasan and Gabriel V. Cozma
(1) Background: Lung cancers are the most common cancers worldwide, and prostate cancers are among the second in terms of the frequency of cancers diagnosed in men. Automatic ranking of the risk groups of such diseases is highly in demand, but the clinic...
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Xin Tian and Yuan Meng
The judicious configuration of predicates is a crucial but often overlooked aspect in the field of knowledge graphs. While previous research has primarily focused on the precision of triples in assessing knowledge graph quality, the rationality of predic...
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Tamás Kegyes, Alex Kummer, Zoltán Süle and János Abonyi
We analyzed a special class of graph traversal problems, where the distances are stochastic, and the agent is restricted to take a limited range in one go. We showed that both constrained shortest Hamiltonian pathfinding problems and disassembly line bal...
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Alexey Liogky and Victoria Salamatova
Data-driven simulations are gaining popularity in mechanics of biomaterials since they do not require explicit form of constitutive relations. Data-driven modeling based on neural networks lacks interpretability. In this study, we propose an interpretabl...
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Parisa Mahya and Johannes Fürnkranz
Recently, some effort went into explaining intransparent and black-box models, such as deep neural networks or random forests. So-called model-agnostic methods typically approximate the prediction of the intransparent black-box model with an interpretabl...
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Jing Chen, Gang Zhou, Jicang Lu, Shiyu Wang and Shunhang Li
Fake news detection has become a significant topic based on the fast-spreading and detrimental effects of such news. Many methods based on deep neural networks learn clues from claim content and message propagation structure or temporal information, whic...
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Varada Vivek Khanna, Krishnaraj Chadaga, Niranajana Sampathila, Srikanth Prabhu, Venkatesh Bhandage and Govardhan K. Hegde
Polycystic Ovary Syndrome (PCOS) is a complex disorder predominantly defined by biochemical hyperandrogenism, oligomenorrhea, anovulation, and in some cases, the presence of ovarian microcysts. This endocrinopathy inhibits ovarian follicle development ca...
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Vidhya Kamakshi and Narayanan C. Krishnan
Explainable Artificial Intelligence (XAI) has emerged as a crucial research area to address the interpretability challenges posed by complex machine learning models. In this survey paper, we provide a comprehensive analysis of existing approaches in the ...
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