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learning method » learning methods (Expand Search)
quantification » identification (Expand Search)
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Probabilistic AutoRegressive Neural Networks for Accurate Long-Range Forecasting
Published 2023“…While numerous statistical and machine learning methods have been proposed, real-life prediction problems often require hybrid solutions that bridge classical forecasting approaches and modern neural network models. …”
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COVID-19 infection localization and severity grading from chest X-ray images
Published 2021“…An extensive set of experiments was performed using the state-of-the-art segmentation networks, U-Net, U-Net++, and Feature Pyramid Networks (FPN). …”
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Hydrogen Sulfide (H<sub>2</sub>S) Sensor: A Concept of Physical Versus Virtual Sensing
Published 2021“…The merits of the proposed system are as follows: 1) a virtual sensing concept is combined with a physical sensing platform to enhance the proposed model’s estimation power in quantifying H<sub>2</sub>S in air samples; 2) a new feature extraction method based on fractional derivatives is proposed to further enhance the model’s learning capabilities; 3) an array of four gas sensors is fabricated in the in-house foundry to record and analyze the signature of H<sub>2</sub>S at various concentration levels; 4) a shallow neural network (NN) model is trained and tested on the recorded data, and based on the NN’s input–output relation, a mathematical model is presented for the quantification of H<sub>2</sub>S; and 5) the proposed model is a highly sensitive and reliable H<sub>2</sub>S gas sensing scheme with the ability to detect the gas instantaneously. …”
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An Overview on XML Semantic Disambiguation from Unstructured Text to Semi-Structured Data: Background, Applications, and Ongoing Challenges
Published 2016“…Third, we describe current and potential application scenarios that can benefit from XML semantic analysis, including: data clustering and semantic-aware indexing, data integration and selective dissemination, semantic-aware and temporal querying, web and mobile services matching and composition, blog and social semantic network analysis, and ontology learning. Fourth, we describe and discuss ongoing challenges and future directions, including: the quantification of semantic ambiguity, expanding XML disambiguation context, combining structure and content, using collaborative/social information sources, integrating explicit and implicit semantic analysis, emphasizing user involvement, and reducing computational complexity.…”
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