Andreas Dengel

SolarFCD: A Large-Scale Dataset and Benchmark for Solar Fault Classification in Photovoltaic Systems

Misbah Ijaz, Saif ur Rehman Khan, Abd Ur Rehman, Arooj Zaib et autres

We provide SolarFCD, a comprehensive multi-modal collection of 3742 annotated images from three public sources that span thermal and RGB modalities. We establish detailed-class defect taxonomy that includes expert validated label mappings across all source datasets and exact semantic descriptions. High-quality image-level …

SolarFCD: A Large-Scale Dataset and Benchmark for Solar Fault Classification in Photovoltaic Systems

Misbah Ijaz, Saif ur Rehman Khan, Abd Ur Rehman, Arooj Zaib et autres

We provide SolarFCD, a comprehensive multi-modal collection of 3742 annotated images from three public sources that span thermal and RGB modalities. We establish detailed-class defect taxonomy that includes expert validated label mappings across all source datasets and exact semantic descriptions. High-quality image-level …

Additional file 2 of VariantMedium: sensitive and generalizable somatic point mutation calling with 3D DenseNets trained and evaluated on experimental data

Özlem Muslu, Thomas Bukur, Pablo Riesgo-Ferreiro, Sameesh Kher et autres

Additional file 2. Supplementary Figures (Fig. S1-S16).

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VariantMedium: sensitive and generalizable somatic point mutation calling with 3D DenseNets trained and evaluated on experimental data

Özlem Muslu, Thomas Bukur, Pablo Riesgo-Ferreiro, Sameesh Kher et autres

Abstract Background Accurately identifying somatic variants from genomic sequencing is crucial for understanding and treating cancer. Previously, methods based on statistics and heuristics, as well as methods based on machine learning were proposed for somatic single nucleotide variant (SNV) calling from matched …

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Additional file 1 of VariantMedium: sensitive and generalizable somatic point mutation calling with 3D DenseNets trained and evaluated on experimental data

Özlem Muslu, Thomas Bukur, Pablo Riesgo-Ferreiro, Sameesh Kher et autres

Additional file 1. Supplementary Tables (Table S1-S13).

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Additional file 1 of VariantMedium: sensitive and generalizable somatic point mutation calling with 3D DenseNets trained and evaluated on experimental data

Özlem Muslu, Thomas Bukur, Pablo Riesgo-Ferreiro, Sameesh Kher et autres

Additional file 1. Supplementary Tables (Table S1-S13).

de (code pays fourni par la source)

VariantMedium: sensitive and generalizable somatic point mutation calling with 3D DenseNets trained and evaluated on experimental data

Özlem Muslu, Thomas Bukur, Pablo Riesgo-Ferreiro, Sameesh Kher et autres

Abstract Background Accurately identifying somatic variants from genomic sequencing is crucial for understanding and treating cancer. Previously, methods based on statistics and heuristics, as well as methods based on machine learning were proposed for somatic single nucleotide variant (SNV) calling from matched …

de (code pays fourni par la source)

Additional file 2 of VariantMedium: sensitive and generalizable somatic point mutation calling with 3D DenseNets trained and evaluated on experimental data

Özlem Muslu, Thomas Bukur, Pablo Riesgo-Ferreiro, Sameesh Kher et autres

Additional file 2. Supplementary Figures (Fig. S1-S16).

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Additional file 1 of FloraSyntropy-net: scalable deep learning with novel FloraSyntropy archive for large-scale plant disease diagnosis

Saif Ur Rehman Khan, Muhammad Nabeel Asim, Sebastian Vollmer, Andreas Dengel

Supplementary Material 1.

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FloraSyntropy-net: scalable deep learning with novel FloraSyntropy archive for large-scale plant disease diagnosis

Saif Ur Rehman Khan, Muhammad Nabeel Asim, Sebastian Vollmer, Andreas Dengel

Abstract Early diagnosis of plant diseases is critical for global food safety, yet most AI solutions lack the generalization required for real-world agricultural diversity. These models are typically constrained to specific species, failing to perform accurately across the broad spectrum of cultivated …

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Additional file 1 of FloraSyntropy-net: scalable deep learning with novel FloraSyntropy archive for large-scale plant disease diagnosis

Saif Ur Rehman Khan, Muhammad Nabeel Asim, Sebastian Vollmer, Andreas Dengel

Supplementary Material 1.

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FloraSyntropy-net: scalable deep learning with novel FloraSyntropy archive for large-scale plant disease diagnosis

Saif Ur Rehman Khan, Muhammad Nabeel Asim, Sebastian Vollmer, Andreas Dengel

Abstract Early diagnosis of plant diseases is critical for global food safety, yet most AI solutions lack the generalization required for real-world agricultural diversity. These models are typically constrained to specific species, failing to perform accurately across the broad spectrum of cultivated …

de (code pays fourni par la source)