با عرض سلام در حال تنظیم مقاله ای تحت عنوان
Title: MediSeg: Medical Segmentation and classification Recommender system ....
Journal: IEEE Transactions on Medical Imaging
If: 9.8
این کار ۶ ماه طول خواهد کشید و به مسائل بهینه سازی انرژی، جلوگیری از اموزش مجدد شبکه ها، و مسائل تولید کربن در شبکه ها ی عصبی پرداخته خواهد شد.
هزینه مشارکت :
2: 600$
3:500 $
4: 400$
5:300$
6: 200$
7:200$
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Title: MediSeg: Medical Segmentation and classification Recommender system ....
Journal: IEEE Transactions on Medical Imaging
If: 9.8
این کار ۶ ماه طول خواهد کشید و به مسائل بهینه سازی انرژی، جلوگیری از اموزش مجدد شبکه ها، و مسائل تولید کربن در شبکه ها ی عصبی پرداخته خواهد شد.
هزینه مشارکت :
2: 600$
3:500 $
4: 400$
5:300$
6: 200$
7:200$
@Raminmousa
@Machine_learn
@Paper4money
Machine learning books and papers pinned «با عرض سلام در حال تنظیم مقاله ای تحت عنوان Title: MediSeg: Medical Segmentation and classification Recommender system .... Journal: IEEE Transactions on Medical Imaging If: 9.8 این کار ۶ ماه طول خواهد کشید و به مسائل بهینه سازی انرژی، جلوگیری از اموزش مجدد…»
🔹 Title: OmniHuman-1.5: Instilling an Active Mind in Avatars via Cognitive Simulation
🔹 Publication Date: Published on Aug 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.19209
• PDF: https://arxiv.org/pdf/2508.19209
• Project Page: https://omnihuman-lab.github.io/v1_5/
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🔹 Publication Date: Published on Aug 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.19209
• PDF: https://arxiv.org/pdf/2508.19209
• Project Page: https://omnihuman-lab.github.io/v1_5/
@Machine_learn
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🔹 Title: UltraMemV2: Memory Networks Scaling to 120B Parameters with Superior Long-Context Learning
🔹 Publication Date: Published on Aug 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.18756
• PDF: https://arxiv.org/pdf/2508.18756
• Github: https://github.com/ZihaoHuang-notabot/Ultra-Sparse-Memory-Network
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🔹 Publication Date: Published on Aug 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.18756
• PDF: https://arxiv.org/pdf/2508.18756
• Github: https://github.com/ZihaoHuang-notabot/Ultra-Sparse-Memory-Network
@Machine_learn
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📹 AI in Bioinformatics Overcoming Pitfalls in Statistical, ML and Generative AI Approaches
🎞 Watch
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🎞 Watch
@Machine_learn
YouTube
AI in Bioinformatics Overcoming Pitfalls in Statistical, ML and Generative AI Approaches
Unlock the complexities of AI in Bioinformatics in this engaging webinar, “AI in Bioinformatics: Overcoming Pitfalls in Statistical, ML and Generative AI Approaches.”
Dr. Juan Felipe Beltrán, scientist and software engineer, takes you inside the real-world…
Dr. Juan Felipe Beltrán, scientist and software engineer, takes you inside the real-world…
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Title: Independently Recurrent Neural Network XGBoost (IXGBOOST) proposed method for Short term load forecasting
Abstract: Short-term load forecasting (STLF) is one of the most important and critical issue for power system operators. Therefore, it plays a fundamental role in improving the reliability of the power system, facilitating the integration of renewable energy sources and making demand response processes more efficient. Today, electricity forecasting based on sensor data with the increasing popularity of smart meter applications. On the other hand, STLF is one of the most critical inputs for the power plant planning undertaking. STLF reduces the overall scheduling uncertainty added by the intermittent generation of renewable resources. Therefore, it helps to minimize the cost of hydrothermal power generation in a power grid. Machine learning (ML) models have obtained acceptable results in this field. These approaches require manual feature extraction, which is challenging. Because of feature selection, deep learning approaches have automatically achieved results in prediction problems. This research proposes a network approach based on IndRNN+XGBoost to forecast electricity consumption in three modes: hourly, daily and weekly. ....
Journal: Optik
2: 20 milion
3:15 milion
4:10 milion
@Raminmousa
@Machine_learn
@paper4money
Title: Independently Recurrent Neural Network XGBoost (IXGBOOST) proposed method for Short term load forecasting
Abstract: Short-term load forecasting (STLF) is one of the most important and critical issue for power system operators. Therefore, it plays a fundamental role in improving the reliability of the power system, facilitating the integration of renewable energy sources and making demand response processes more efficient. Today, electricity forecasting based on sensor data with the increasing popularity of smart meter applications. On the other hand, STLF is one of the most critical inputs for the power plant planning undertaking. STLF reduces the overall scheduling uncertainty added by the intermittent generation of renewable resources. Therefore, it helps to minimize the cost of hydrothermal power generation in a power grid. Machine learning (ML) models have obtained acceptable results in this field. These approaches require manual feature extraction, which is challenging. Because of feature selection, deep learning approaches have automatically achieved results in prediction problems. This research proposes a network approach based on IndRNN+XGBoost to forecast electricity consumption in three modes: hourly, daily and weekly. ....
Journal: Optik
2: 20 milion
3:15 milion
4:10 milion
@Raminmousa
@Machine_learn
@paper4money