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Admin: @Raminmousa ID: @Machine_learn link: https://t.me/Machine_learn
Mostrar más📈 Análisis del canal de Telegram Machine learning books and papers
El canal Machine learning books and papers (@machine_learn) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 24 517 suscriptores, ocupando la posición 8 056 en la categoría Educación y el puesto 13 757 en la región Irán.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 24 517 suscriptores.
Según los últimos datos del 24 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de -165, y en las últimas 24 horas de -3, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 6.78%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.90% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 1 663 visualizaciones. En el primer día suele acumular 465 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 1.
- Intereses temáticos: El contenido se centra en temas clave como disorder, psy, مقاله, framework, graph.
📝 Descripción y política de contenido
El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“Admin: @Raminmousa
ID: @Machine_learn
link: https://t.me/Machine_learn”
Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 25 junio, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Educación.
24 517
Suscriptores
-324 horas
-477 días
-16530 días
Archivo de publicaciones
Forecasting of Bitcoin Prices Using Hashrate Features: Wavelet and Deep Stacking Approach
✅NEW PAPER
Link: https://arxiv.org/abs/2501.13136
Abstract: Digital currencies have become popular in the last decade due to their non-dependency and decentralized nature. The price of these currencies has seen a lot of fluctuations at times, which has increased the need for prediction. As their most popular, Bitcoin(BTC) has become a research hotspot. The main challenge and trend of digital currencies, especially BTC, is price fluctuations, which require studying the basic price prediction model. This research presents a classification and regression model based on stack deep learning that uses a wavelet to remove noise to predict movements and prices of BTC at different time intervals. The proposed model based on the stacking technique uses models based on deep learning, especially neural networks and transformers, for one, seven, thirty and ninety-day forecasting. Three feature selection models, Chi2, RFE and Embedded, were also applied to the data in the pre-processing stage. The classification model achieved 63\% accuracy for predicting the next day and 64\%, 67\% and 82\% for predicting the seventh, thirty and ninety days, respectively. For daily price forecasting, the percentage error was reduced to 0.58, while the error ranged from 2.72\% to 2.85\% for seven- to ninety-day horizons. These results show that the proposed model performed better than other models in the literature.
@Machine_learn
Repost from Papers
با عرض سلام ما نياز به يك co-author براي مقاله زير داريم
Title : A Comprehensive Analysis on Machine Learning for Lung Cancer Level
Abstract
Lung cancer remains a significant global health concern, necessitating accurate and efficient diagnostic methodologies. This study undertakes a comprehensive exploration into the utilization of machine learning (ML) techniques for the precise classification of lung cancer levels. A meticulous examination is conducted to mitigate overfitting challenges while optimizing model efficacy, incorporating strategies such as monitoring minimum child weight and fine-tuning learning rates. Diverse ML models, encompassing XGBoost, LGBM, Adaboost, Logistic Regression, Decision Tree, Random Forest, CatBoost, and k-NN, are meticulously employed and rigorously evaluated. Furthermore, the study scrutinizes the intricate relationships between features and targets through the lens of Deep Neural Networks (DNN), elucidating their potential for complex pattern recognition. The findings underscore the efficacy of various ML models in accurately stratifying lung cancer levels. Despite the intricacies of DNN architectures, conventional ML models, notably XGBoost, LGBM, and Logistic Regression, demonstrate exceptional performance. Comprehensive comparative metrics including accuracy, precision, recall, and F-1 score underscore the superiority of select models in the realm of lung cancer prediction.
Keywords: Lung cancer prediction, Machine learning, Overfitting, Model performance, Deep Neural Networks.
جهت هماهنگي با ايدي بنده در ارتباط باشين
@Raminmous
@Machine_learn
https://t.me/+SP9l58Ta_zZmYmY0
List of 500+AI Agent projects/UseCases
https://github.com/DataSpoof/500-AI-Agents-Projects
@Machine_learn
Repost from Papers
با عرض سلام نفر چهارم از مقاله ي زير قابل اضافه شدن مي باشد.
Title : A Comprehensive Analysis on Machine Learning for Lung Cancer Level
Abstract
Lung cancer remains a significant global health concern, necessitating accurate and efficient diagnostic methodologies. This study undertakes a comprehensive exploration into the utilization of machine learning (ML) techniques for the precise classification of lung cancer levels. A meticulous examination is conducted to mitigate overfitting challenges while optimizing model efficacy, incorporating strategies such as monitoring minimum child weight and fine-tuning learning rates. Diverse ML models, encompassing XGBoost, LGBM, Adaboost, Logistic Regression, Decision Tree, Random Forest, CatBoost, and k-NN, are meticulously employed and rigorously evaluated. Furthermore, the study scrutinizes the intricate relationships between features and targets through the lens of Deep Neural Networks (DNN), elucidating their potential for complex pattern recognition. The findings underscore the efficacy of various ML models in accurately stratifying lung cancer levels. Despite the intricacies of DNN architectures, conventional ML models, notably XGBoost, LGBM, and Logistic Regression, demonstrate exceptional performance. Comprehensive comparative metrics including accuracy, precision, recall, and F-1 score underscore the superiority of select models in the realm of lung cancer prediction.
Keywords: Lung cancer prediction, Machine learning, Overfitting, Model performance, Deep Neural Networks.
جهت هماهنگي با ايدي بنده در ارتباط باشين
@Raminmous
@Machine_learn
https://t.me/+SP9l58Ta_zZmYmY0
با عرض سلام پروژه جدیدمون شروع شد.
هدف اصلی این پروژه اموزش یک مدل پیشنهاد دهنده ی مدل برای مسائله طبقه بندی تصاویر پزشکی میباشد که از اموزش مجدد مدل ها جلوگیری میکند. این مسائله با جنبه جلوگیری از مصرف انرژی اموزشی و زمان اموزش مدل ها ارائه می شود. برای این منظور ۵۰۰۰ مقاله در این زمینه جمع اوری شده است. جزئیات بیشتر در لینک گیت قرار دارد.
Project Title: MedRec: Medical recommender system for image classification without retraining
Github: https://github.com/Ramin1Mousa/MedicalRec
Journal: IEEE Transactions on Pattern Analysis and Machine Intelligence
Impact factor: 20.8
۷ نفر دیگر امکان اضافه شدن به این پروژه رو دارند. هر شخص نیاز هست که حدودا داده های ۴۰۰ مقاله رو بررسی کند. زمان تقریبی هر مقاله ۵-۱۰ دقیقه می باشد. هزینه مشارکت در مقاله:
🔹 2- 600$
🔺 3- 500$
💠 4- 400$
🔺 5- 300$
🔹 6- 200$
🔸 7- 200$
جهت مشارکت می تونید به ایدی بنده پیام بدین.
🔹شنبه شروع این پروژه هست🔹
@Raminmousa
🔎 Depth Anything
git clone https://github.com/DepthAnything/Video-Depth-Anything
cd Video-Depth-Anything
pip install -r requirements.txt
▪GitHub
▪Paper
▪Model Small
▪Model Large
▪Demo
@Machine_learn
🧑🍳 New Cookbook guide: How to use the Usage API and Cost API to monitor your OpenAI usage
📚 Book
@Machine_learn
Transformers 2: Self-adaptive LLMs
Paper: https://arxiv.org/pdf/2501.06252v2.pdf
Code:
https://github.com/SakanaAI/self-adaptive-llms
https://github.com/codelion/adaptive-classifier
Datasets: GSM8K - HumanEval - MATH
MBPP - TextVQA - OK-VQA - ARC (AI2 Reasoning Challenge)
@Machine_learn
دوستاني كه نياز به همكاري در يه مقاله خوب دارند اين مقاله جايگاه ٤ و ٥ باقي مونده...!
@Raminmousa
MiniCPM-V: A GPT-4V Level MLLM on Your Phone
The recent surge of Multimodal Large Language Models (MLLMs) has fundamentally reshaped the landscape of #AI research and industry, shedding light on a promising path toward the next AI milestone. However, significant challenges remain preventing MLLMs from being practical in real-world applications. The most notable challenge comes from the huge cost of running an MLLM with a massive number of parameters and extensive computation. As a result, most MLLMs need to be deployed on high-performing cloud servers, which greatly limits their application scopes such as mobile, offline, energy-sensitive, and privacy-protective scenarios. In this work, we present MiniCPM-V, a series of efficient #MLLMs deployable on end-side devices. By integrating the latest MLLM techniques in architecture, pretraining and alignment, the latest MiniCPM-Llama3-V 2.5 has several notable features: (1) Strong performance, outperforming GPT-4V-1106, Gemini Pro and Claude 3 on OpenCompass, a comprehensive evaluation over 11 popular benchmarks, (2) strong #OCR capability and 1.8M pixel high-resolution #image perception at any aspect ratio, (3) trustworthy behavior with low hallucination rates, (4) multilingual support for 30+ languages, and (5) efficient deployment on mobile phones. More importantly, MiniCPM-V can be viewed as a representative example of a promising trend: The model sizes for achieving usable (e.g., GPT-4V) level performance are rapidly decreasing, along with the fast growth of end-side computation capacity. This jointly shows that GPT-4V level MLLMs deployed on end devices are becoming increasingly possible, unlocking a wider spectrum of real-world AI applications in the near future.
Paper: https://arxiv.org/pdf/2408.01800v1.pdf
Codes:
https://github.com/OpenBMB/MiniCPM-o
https://github.com/openbmb/minicpm-v
Datasets: Video-MME
@Machine_learn
Title: Breast Cancer Ultrasound Image Segmentation Using Improved 3DUnet++
🔹🔹🔹🔹🔹🔹🔹🔹
Author: @Raminmousa
🔹🔹🔹🔹🔹🔹🔹🔹
Cite: https://doi.org/10.1016/j.wfumbo.2024.100068
ABSTRACT: Breast cancer is the most common cancer and the main cause of cancer-related deaths in women around the world. Early detection reduces the number of deaths. Automated breast ultrasound (ABUS) is a new and promising screening method for examining the entire breast. Volumetric ABUS examination is time-consuming, and lesions may be missed during the examination. Therefore, computer-aided cancer diagnosis in ABUS volume is highly expected to help the physician for breast cancer screening. In this research, we presented 3D structures based on UNet, ResUNet, and UNet++ for the automatic detection of cancer in ABUS volume to speed up examination while providing high detection sensitivity with low false positives (FPs). The three investigated approaches were evaluated on equal datasets in terms of training and testing as well as with proportional hyperparameters. Among the proposed approaches in classification and segmentation problems, the UNet++ approach was able to achieve more acceptable results. The UNet++ approach on the dataset of the Tumor Segmentation, Classification, and Detection Challenge on Automated 3D Breast Ultrasound 2023 (Named TSCD-ABUS2023) was able to achieve Accuracy=0.9911 and AUROC=0.9761 in classification and Dice=0.4930 in segmentation.
@Machine_learn
📄 A comprehensive bibliometric analysis on social network anonymization: current approaches and future directions
📎 Study the paper
@Machine_learn
نفرات ٤ و ٥ از اين پروژه باقي موندن دوستاني كه حاضر به همكاري هستن به ايدي بنده مراجعه كنند.
@Raminmousa
WIS Python programming course started in 2024.04
📖 Github
@Machine_learn
DeepSeek-V3 Technical Report
We present DeepSeek-V3, a strong Mixture-of-Experts (MoE) language model with 671B total parameters with 37B activated for each token. To achieve efficient inference and cost-effective training, DeepSeek-V3 adopts Multi-head Latent Attention (MLA) and DeepSeekMoE architectures, which were thoroughly validated in #DeepSeek V2. Furthermore, DeepSeek-V3 pioneers an auxiliary-loss-free strategy for load balancing and sets a multi-token prediction training objective for stronger performance. We pre-train DeepSeek-V3 on 14.8 trillion diverse and high-quality tokens, followed by Supervised Fine-Tuning and Reinforcement Learning stages to fully harness its capabilities. Comprehensive evaluations reveal that DeepSeek-V3 outperforms other open-source models and achieves performance comparable to leading closed-source models. Despite its excellent performance, DeepSeek-V3 requires only 2.788M H800 GPU hours for its full training. In addition, its training process is remarkably stable. Throughout the entire training process, we did not experience any irrecoverable loss spikes or perform any rollbacks. The model checkpoints are available at https://github.com/deepseek-ai/DeepSeek-V3.
Paper: https://arxiv.org/pdf/2412.19437v1.pdf
Code: https://github.com/deepseek-ai/deepseek-v3
@Machine_learn
