سلام دوستان حداقل ماين مي كنينن NFT ماين كنين كه يه چيزي گيرتون بياد. به نظرم اساس كوين هارو بخونين بعد ماين كنين. پروژه پايين از تمامي مواردي كه فرستادين برام بهتر بوده.

https://hottg.com/SpinnerCoin_bot/app?startapp=r_280673

🎙 Real-time in-browser speech recognition



▪Сode: https://github.com/xenova/transformers.js/tree/v3/examples/webgpu-whisper
▪Hf: https://huggingface.co/spaces/Xenova/realtime-whisper-webgpu

@Machine_learn

https://hottg.com/SpinnerCoin_bot/app?startapp=r_280673


تنها پروژه اي كه اين روزا از نظرم اهميت داره Spinner هستش كه به ماين NFT مي پردازه.
💎 +750 $SPN as a first-time gift

سلام
این مقالمون در مرحله ی ریوایزد از دوستان اگر کسی خواست می تونیم به مقالاتشون سایت برنیم.

Title
Comparative Study of Long Short-Term Memory (LSTM), Bidirectional LSTM, and Traditional Machine Learning Approaches for Energy Consumption Prediction
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Short title
Machine Learning, XGBoost, Tree-based Algorithm, Solar Energy Production, LSTM, Artificial Intelligence, Machine Learning, time-series,Bi-LSTM
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Abstract
Responsible, efficient, and environmentally conscious energy consumption practices are increasingly essential for ensuring the reliability of the modern electricity grid. This study focuses on leveraging time series analysis to enhance the accuracy of forecasting. Time series forecasting is a critical task in various application domains, as real-world time series data often exhibit non-linear patterns with complexities that conventional forecasting techniques struggle to capture. To address this, our approach proposes the utilization of long short-term memory (LSTM) and Bi-LSTM models for precise time series forecasting. To ensure a fair evaluation, the performance of our proposed approach is compared with traditional neural networks, time-series forecasting methods, and conventional decline curves. Additionally, individual models based on LSTM and Bi-LSTM, along with other machine learning methods, are implemented for a comprehensive assessment. The experimental results in this study consistently demonstrate that our proposed model outperforms all benchmarking methods in terms of mean absolute error (MAE) across most datasets. To address the imbalance between activations by both groups of consumers and prosumers, our prediction results show that the proposed method exhibits higher prediction performance compared to several traditional forecasting methods, such as the autoregressive integrated moving average model (ARIMA) and Seasonal autoregressive integrated moving average model (SARIMA). Specifically, the root mean square error (RMSE) of Bi-LSTM is 5.35%, 46.08%, and 50.6% lower than LSTM, ARIMA, and SARIMA, respectively, on the May test data.
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journal
Energy Exploration & Exploitation (SAGE)

@Raminmousa
@Machine_learn
@paper4money

🔈 Separating the "Chirp" from the "Chat": Self-supervised Visual Grounding of Sound and Language



▪Paper: https://arxiv.org/abs/2406.05629
▪Website: https://mhamilton.net/denseav
▪Code: https://github.com/mhamilton723/DenseAV
▪Video: https://youtu.be/wrsxsKG-4eE

@Machine_learn

🔥 Astrologers have announced a week of video generation models!

Following the hype around the Kling, Luma and Runway models, a new open source version of Open-Sora has been released.

Open-Sora 1.2 from Hpcoretech has been published on huggingface.

Basic moments:

The new 1.1B model is trained on 20M videos and generates videos up to 14 seconds long at 720p resolution.

▪Diffusion Model: https://huggingface.co/hpcai-tech/OpenSora-STDiT-v3
▪VAE model: https://huggingface.co/hpcai-tech/OpenSora-VAE-v1.2
▪Technical report: https://github.com/hpcaitech/Open-Sora/blob/main/docs/report_03.md
▪Demo: https://huggingface.co/spaces/hpcai-tech/open-sora

@Machine_learn

با عرض سلام پك يادگيري ماشين و يادگيري عميق به همراه ٣٦ پروژه با داكيومنت فارسي رو براي دوستان تهيه كرديم از دوستان كسي خواست مي تونه به ايدي بنده پيام بده.

1-Deep Learning Basic
-01_Introduction
--01_How_TensorFlow_Works
--02_Creating_and_Using_Tensors
--03_Implementing_Activation_Functions
-02_TensorFlow_Way
--01_Operations_as_a_Computational_Graph
--02_Implementing_Loss_Functions
--03_Implementing_Back_Propagation
--04_Working_with_Batch_and_Stochastic_Training
--05_Evaluating_Models
-03_Linear_Regression
--linear regression
--Logistic Regression
-04_Neural_Networks
--01_Introduction
--02_Single_Hidden_Layer_Network
--03_Using_Multiple_Layers
-05_Convolutional_Neural_Networks
--Convolution Neural Networks
--Convolutional Neural Networks Tensorflow
--TFRecord For Deep learning Models
-06_Recurrent_Neural_Networks
--Recurrent Neural Networks (RNN)
2-Classification apparel
-Classification apparel double capsule
-Classification apparel double cnn
3-ALZHEIMERS USING CNN(ResNet)
4-Fake News (Covid-19 dataset)
-Multi-channel
-3DCNN model
-Base line+ Char CNN
-Fake News Covid CapsuleNet
5-3DCNN Fake News
6-recommender systems
-GRU+LSTM MovieLens
7-Multi-Domain Sentiment Analysis
-Dranziera CapsuleNet
-Dranziera CNN Multi-channel
-Dranziera LSTM
8-Persian Multi-Domain SA
-Bi-GRU Capsule Net
-Multi-CNN
9-Recommendation system
-Factorization Recommender, Ranking Factorization Recommender, Item Similarity Recommender (turicreate)
-SVD, SVD++, NMF, Slope One, k-NN, Centered k-NN, k-NN Baseline, Co-Clustering(surprise)
10-NihX-Ray
-optimized CNN on FullDataset Nih-Xray
-MobileNet
-Transfer learning
-Capsule Network on FullDataset Nih-Xray

@Raminmousa

سلام اين مقالمون براي نيچر نوشته شده از دوستان كسي نياز داشت نفرات ١ تا ٤ اش خالي هستش .

Brain Tumor Detection Through Diverse CNN Architectures in IoT healthcare industries: Fast R-CNN, UNet, Transfer Learning-Based CNN, and Fully Connected CNN

Abstract
Artificial intelligence-powered deep learning methods have significantly advanced the diagnosis of brain tumors in Internet of Thing (IoT)-healthcare systems, achieving high accuracy by processing extensive datasets. Brain health is crucial for human life, and accurate diagnosis is vital for effective treatment. Magnetic Resonance Imaging (MRI) provides critical data for diagnosing brain health issues, offering a substantial source of big data for artificial intelligence applications in image classification. In this study, we aimed to classify brain tumors, specifically glioma, meningioma, and pituitary tumors, from MRI images using Region-based Convolutional Neural Network (R-CNN) and UNet architectures. Additionally, we employed Convolutional Neural Networks (CNN) and CNN-based models such as Inception-V3, EfficientNetB4, and VGG19, leveraging transfer learning methods for classification tasks. The models were evaluated using F-score, recall, precision, and accuracy metrics. Our findings revealed that the Fast R-CNN model achieved the highest accuracy at 99%, with an F-score of 98.5%, an Area Under the Curve (AUC) value of 99.5%, a recall of 99.4%, and a precision of 98.5%. The integration of R-CNN, UNet, and transfer learning models plays a pivotal role in the early diagnosis and prompt treatment of brain tumors in IoT-healthcare systems, significantly improving patient outcomes.

Keywords: Region-based Convolutional Neural Network, UNet, Brain tumor, Transfer learning, Medical imaging

Scientific Reports, Nature Springer

@Raminmousa
@paper4money
@Machine_learn