In order to promote the evaluation performance of deep learning infrared automatic target recognition (ATR) algorithms in the complex environment of air-to-air missile research, we proposed an analytic hierarchy proce...
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In sequential decision-making problems, the information structure describes the causal dependencies between system variables, encompassing the dynamics of the environment and the agents' actions. Classical models ...
Causal explanations of the predictions of NLP systems are essential to ensure safety and establish trust. Yet, existing methods often fall short of explaining model predictions effectively or efficiently and are often...
We explore the earliest arrival time problem in public transport journey planning. A journey typically consists of multiple scheduled public transport legs. The actual time required to transfer between these legs can ...
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Cyber attacks are a growing concern for network services, especially with the rapid advancement of online platforms. Innovative technologies and methods are continuously being developed to counteract these evolving cy...
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Electricity market forecasting is very useful for the different actors involved in the energy sector to plan both the supply chain and market operation. Nowadays, energy demand data are data coming from smart meters a...
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Knowledge-based Visual Qustion-answering (K-VQA) often requires the use of background knowledge beyond the image. However, we discover that a single knowledge generation strategy is often insufficient for all K-VQA qu...
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Floods remain one of the most devastating weather-induced disastersworldwide, resulting in numerous fatalities each year and severelyimpacting socio-economic development and the ***, the ability to predict flood-prone...
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Floods remain one of the most devastating weather-induced disastersworldwide, resulting in numerous fatalities each year and severelyimpacting socio-economic development and the ***, the ability to predict flood-prone areas in advance is crucialfor effective risk management. The objective of this research is to assessand compare three convolutional neural networks, U-Net, WU-Net, andU-Net++, for spatial prediction of pluvial flood with a case study at atropical area in the north of Vietnam. They are relative new convolutionalgorithms developed based on U-shaped architectures. For this task, ageospatial database with 796 historical flood locations and 12 floodindicators was prepared. For training the models, the binary crossentropywas employed as the loss function, while the Adaptive momentestimation (ADAM) algorithm was used for the optimization of themodel parameters, whereas, F1-score and classification accuracy (Acc)were used to assess the performance of the models. The results unequivocally highlight the high performance of the three models,achieving an impressive accuracy rate of 96.01%. The flood susceptibility maps derived from this research possess considerable utility for local authorities, providing valuable insights and informationto enhance decision-making processes and facilitate the implementation of effective risk management strategies.
Alzheimer's disease (AD) presents a significant challenge for older adults in marginalized communities. The application of machine learning (ML) techniques through predictive models holds promise for enhancing the...
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Conformal prediction is a powerful tool for uncertainty quantification, but its application to time-series data is constrained by the violation of the exchangeability assumption. Current solutions for time-series pred...
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