In recent years, the Kurdo-Zagrosian mountains in western Iran and northern Iraq have faced numerous wildfire fires. Mapping forest fire susceptibility is crucial for several reasons, including its role in prevention ...
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ISBN:
(纸本)9781510681026
In recent years, the Kurdo-Zagrosian mountains in western Iran and northern Iraq have faced numerous wildfire fires. Mapping forest fire susceptibility is crucial for several reasons, including its role in prevention and mitigation, resource allocation, ecological conservation, early warning systems, policy development, insurance and risk management, and wildfire risk mapping. Machine Learning (ML) has found numerous applications in remote sensing, including fire detection, severity assessment, fuel moisture content estimation, fire spread prediction, fire susceptibility mapping, smoke plume detection, air quality monitoring, post-fire recovery monitoring, and decision support systems for fire management. This study employs a new approach to leveraging non-negative matrix factorization (NMF) for detecting fire-susceptible areas in the Kurdo-Zagrosian forests of Marivan and Sarvabad in Kurdistan Province, western Iran. The NMF is a ML method used for dimensionality reduction and feature extraction. NMF differs from traditional matrixfactorization methods by enforcing non-negativity constraints on the factor matrices, making the resulting factors interpretable and often more suitable for real-world data analysis. Sentinel-2 satellite imagery, elevation, distance to the road network, and Zagros Grass Index (ZGI) have been used as the primary inputs of the model, combined with in situ data for verifying and interpreting the resulting maps. The results showed that, besides providing useful information in extracting fire susceptible areas, NMF handles wide study areas efficiently, especially for tasks like feature extraction from large-scale datasets such as satellite images or multispectral data. The results especially revealed that ZGI has specifically demonstrated improved accuracy and reliability. The resulting map also showed a very close overlap between the fired area provided by Sentinel imagery from 2021 to 2023 and the areas labeled as highly susceptible regions
In this abstract, a new formulation of the non-negative matrix factorization problem for topic modeling will be presented. It allows the user to iteratively improve the topic model with a higher level of detail than c...
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Most of traditional singing voice separation methods usually assume that the vocal model is the source-filter model thatextracts the transfering functions of the vibration source and the filters separately. In recent ...
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non-negative matrix factorization (NMF) has previously been shown to be a useful decomposition tool for multivariate data. non-negative bases allow strictly additive combinations which have been shown to be part-based...
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non-negative matrix factorization (NMF) is a data dimensionality reduction method, which can process large scale and high dimensional data more efficiently. Among them, kernel-based non-negative matrix factorization m...
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There are two nasty classical problems of synonymy and polysemy in the filtering systems of Chinese documents. To deal with these two problems, we would ideally like to represent documents not by words, but by the sem...
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ISBN:
(纸本)3540221158
There are two nasty classical problems of synonymy and polysemy in the filtering systems of Chinese documents. To deal with these two problems, we would ideally like to represent documents not by words, but by the semantic relations between words. non-negative matrix factorization (NMF) is applied to dimensionality reduction of the words space. NMF is distinguished from the latent semantic indexing (LSI) by its non-negativity constraints. These constraints lead to a parts-based representation because they allow only additive, not subtractive, combinations. Also, NMF computation is based on the simple iterative algorithm;it is therefore advantageous for applications involving large sparse matrices. The experimental results show that, comparing with LSI, NMF method not only improves filtering precision markedly, but also has the merits of fast computing speed and less memory occupancy.
In this paper, we propose a noise reduction method based on non-negative matrix factorization (NMF) for noise-robust automatic speech recognition (ASR). Most noise reduction methods applied to ASR front-ends have been...
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non-negative matrix factorization (NMF) is a problem with many applications, ranging from facial recognition to document clustering. However, due to the variety of algorithms that solve NMF, the randomness involved in...
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This paper proposes to use non-negative matrix factorization based speech enhancement in robust automatic recognition of mixtures of speech and music. We represent magnitude spectra of noisy speech signals as the non-...
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With the rapid advancement of large language models, academic topic identification and topic evolution analysis are crucial for enhancing AI’s understanding capabilities. Dynamic topic analysis provides a powerful ap...
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