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مقاله ی زیر تماما نگارش شده و اماده سابمیت از دوستان کسی خواست نفرات ۳ و ۴ اش خالی هست.
IEEE Geoscience and Remote Sensing Letter
Impact factor 4
CiteScore 7.6
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Title: Enhanced-HisSegNet: An Enhanced Histagram Layered Segmentation Network for SAR Image-based Flood Segmentation
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Abstract:
Floods are among the most frequent natural disasters, causing loss of life and significant economic and environmental damage, with direct impacts on agriculture, urban infrastructure, and transportation networks. Therefore, it is crucial to accurately and efficiently identify flooded areas in the aftermath of such events. Synthetic Aperture Radar (SAR) imagery plays a vital role in this process, as water surfaces reflect less microwave energy compared to land due to their smooth texture and low surface roughness. In this study, we present a multimodal fusion strategy that enhances the existing model by Turkmenli et al. [1] through the integration of fine-tuned histograms and Deep Neural Networks (DNNs) for improved flood mapping. Specifically, we introduce fine-tuned histogram extraction layers designed for SAR data, which are integrated into Deep Segmentation Neural Networks (DSNNs). The model was tested on two real SAR datasets, with cross-dataset validation using an external cohort, representing a second innovation in our approach. Experimental results demonstrate that our model, with fine-tuned histogram layers, outperforms previous approaches by up to 4% in intersection over union (IoU) and provides a comprehensive evaluation through metrics such as Precision, Recall, Average Precision (AP), Mean Average Precision (mAP), False Positive Rate (FPR), and Mean Average Recall (mAR). Importantly, these improvements come with minimal additional learnable parameters. The code for this work will be made available at https://github.com/Mohsena1990/Enhanced-HistSegNet
@Raminmousa
@Machine_learn
@Paper4monry
مقاله ی زیر تماما نگارش شده و اماده سابمیت از دوستان کسی خواست نفرات ۳ و ۴ اش خالی هست.
IEEE Geoscience and Remote Sensing Letter
Impact factor 4
CiteScore 7.6
------------------------------
Title: Enhanced-HisSegNet: An Enhanced Histagram Layered Segmentation Network for SAR Image-based Flood Segmentation
------------------------------
Abstract:
Floods are among the most frequent natural disasters, causing loss of life and significant economic and environmental damage, with direct impacts on agriculture, urban infrastructure, and transportation networks. Therefore, it is crucial to accurately and efficiently identify flooded areas in the aftermath of such events. Synthetic Aperture Radar (SAR) imagery plays a vital role in this process, as water surfaces reflect less microwave energy compared to land due to their smooth texture and low surface roughness. In this study, we present a multimodal fusion strategy that enhances the existing model by Turkmenli et al. [1] through the integration of fine-tuned histograms and Deep Neural Networks (DNNs) for improved flood mapping. Specifically, we introduce fine-tuned histogram extraction layers designed for SAR data, which are integrated into Deep Segmentation Neural Networks (DSNNs). The model was tested on two real SAR datasets, with cross-dataset validation using an external cohort, representing a second innovation in our approach. Experimental results demonstrate that our model, with fine-tuned histogram layers, outperforms previous approaches by up to 4% in intersection over union (IoU) and provides a comprehensive evaluation through metrics such as Precision, Recall, Average Precision (AP), Mean Average Precision (mAP), False Positive Rate (FPR), and Mean Average Recall (mAR). Importantly, these improvements come with minimal additional learnable parameters. The code for this work will be made available at https://github.com/Mohsena1990/Enhanced-HistSegNet
@Raminmousa
@Machine_learn
@Paper4monry
GitHub
GitHub - Mohsena1990/Enhanced-HistSegNet: The code related to HistSegNet approach was submitted to IEEE Geoscience and Remote Sensing…
The code related to HistSegNet approach was submitted to IEEE Geoscience and Remote Sensing Letters. - Mohsena1990/Enhanced-HistSegNet
What is this attraction of unprecedented generosity? Your queries will probably be used to train new models (although this is not accurate).
https://docs.mistral.ai/getting-started/models/
@Machine_learn
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@Machine_learn
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LLaMA-Omni: Seamless Speech Interaction with Large Language Models
Paper: https://arxiv.org/pdf/2409.06666v1.pdf
Code: https://github.com/ictnlp/llama-omni
✅ @Machine_learn
Paper: https://arxiv.org/pdf/2409.06666v1.pdf
Code: https://github.com/ictnlp/llama-omni
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MiniCPM-V
MiniCPM-V 2.6: A GPT-4V Level MLLM for Single Image, Multi Image and Video on Your Phone
Creator: OpenBMB
Stars ⭐️: 11.4k
Forked By: 798
GitHub Repo:
https://github.com/OpenBMB/MiniCPM-V
➖➖➖➖➖➖➖➖➖➖➖➖➖➖
Join✅ https://www.tg-me.com/deep_learning_proj
✅ @Machine_learn
MiniCPM-V 2.6: A GPT-4V Level MLLM for Single Image, Multi Image and Video on Your Phone
Creator: OpenBMB
Stars ⭐️: 11.4k
Forked By: 798
GitHub Repo:
https://github.com/OpenBMB/MiniCPM-V
➖➖➖➖➖➖➖➖➖➖➖➖➖➖
Join
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GitHub
GitHub - OpenBMB/MiniCPM-o: MiniCPM-o 2.6: A GPT-4o Level MLLM for Vision, Speech and Multimodal Live Streaming on Your Phone
MiniCPM-o 2.6: A GPT-4o Level MLLM for Vision, Speech and Multimodal Live Streaming on Your Phone - OpenBMB/MiniCPM-o
ban.pdf
1.4 MB
INDCAPS: The IndRNN Capsule Approach for Persian Multi-
Domain Sentiment Analysis
یکی از بحث های که این روزها خیلی ترند هستش بحث مربوط به طبقه بندی احساسات چندجمله ای می باشد. در این مقاله ما یک مجموعه داده که روی داده های دیجی کالا می باشند رو جمع اوری کردیم. جمع اوری این داده ها ۳ ماه طول کشیده و این ریپورت گزارش مربوط به این داده هاست.
@Raminmousa
@Machine_learn
Domain Sentiment Analysis
یکی از بحث های که این روزها خیلی ترند هستش بحث مربوط به طبقه بندی احساسات چندجمله ای می باشد. در این مقاله ما یک مجموعه داده که روی داده های دیجی کالا می باشند رو جمع اوری کردیم. جمع اوری این داده ها ۳ ماه طول کشیده و این ریپورت گزارش مربوط به این داده هاست.
@Raminmousa
@Machine_learn
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OmniGen: Unified Image Generation
Paper: https://arxiv.org/pdf/2409.11340v1.pdf
Code: https://github.com/vectorspacelab/omnigen
Dataset: DreamBooth | MagicBrush
✅ @Machine_learn
Paper: https://arxiv.org/pdf/2409.11340v1.pdf
Code: https://github.com/vectorspacelab/omnigen
Dataset: DreamBooth | MagicBrush
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📑 Advancing biomedical discovery and innovation in the era of big data and artificial intelligence
💥 Perspective Article
📎 Study the paper
✅ @Machine_learn
💥 Perspective Article
📎 Study the paper
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📃 Natural Language Processing Methods for the Study of Protein-Ligand Interactions
🗓Publish year: 2024
📎 Study the paper
✅@Machine_learn
🗓Publish year: 2024
📎 Study the paper
✅@Machine_learn
Here are some Hyperparameter (HP) tuning & optimization packages you can use in your projects:
- Scikit-Optimize: https://lnkd.in/gbJqdFq9
- Optuna: https://optuna.org/
- Hyperopt: https://lnkd.in/gPSRhW_6
- Ray.tune: https://lnkd.in/gzrDAbHg
- Keras tuner: https://lnkd.in/g_HDHiug
- BayesianOptimization: https://lnkd.in/g8UKEvjc
- Metric Optimization Engine (MOE): https://lnkd.in/g89JGFB2
- Spearmint: https://lnkd.in/gJwG3AwE
- GPyOpt: https://lnkd.in/g4cWEBPz
- SigOpt: https://sigopt.com/
✅ @Machine_learn
- Scikit-Optimize: https://lnkd.in/gbJqdFq9
- Optuna: https://optuna.org/
- Hyperopt: https://lnkd.in/gPSRhW_6
- Ray.tune: https://lnkd.in/gzrDAbHg
- Keras tuner: https://lnkd.in/g_HDHiug
- BayesianOptimization: https://lnkd.in/g8UKEvjc
- Metric Optimization Engine (MOE): https://lnkd.in/g89JGFB2
- Spearmint: https://lnkd.in/gJwG3AwE
- GPyOpt: https://lnkd.in/g4cWEBPz
- SigOpt: https://sigopt.com/
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lnkd.in
LinkedIn
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