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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 24 577 suscriptores, ocupando la posición 8 094 en la categoría Educación y el puesto 13 766 en la región Irán.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 24 577 suscriptores.
Según los últimos datos del 14 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de -157, y en las últimas 24 horas de -9, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 6.65%. Durante las primeras 24 horas tras publicar, el contenido suele obtener N/A% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 1 634 visualizaciones. En el primer día suele acumular 0 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 5.
- 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 15 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.
24 577
Suscriptores
-924 horas
-207 días
-15730 días
Archivo de publicaciones
با عرض سلام برای مقاله زیر نیاز به نفرات ۲ و ۳ داریم.
KG-Psy: A Knowledge-Graph and GPT-5 Based Framework for Personalized Clinical Decision Support in Bipolar Disorder and Borderline Personality Disorder
Abstract: Accurate diagnosis and personalized treatment planning for complex psychiatric disorders such as Bipolar Disorder (BD) and Borderline Personality Disorder (BPD) remain major challenges due to overlapping symptoms, fluctuating mood patterns, and heterogeneous clinical presentations. To address these challenges, we introduce KG-Psy, a hybrid neuro-symbolic framework that combines a domain-specific psychiatric Knowledge Graph (KG) with the advanced reasoning capabilities of GPT-5.
KG-Psy constructs multi-layer psychiatric knowledge graphs encoding symptom trajectories, neural correlates, pharmacological mechanisms, therapeutic guidelines, comorbidities, and behavioral patterns extracted from large-scale clinical literature. GPT-5 is employed to extract clinical entities, infer latent symptom-neural relationships, assess diagnostic likelihoods, and generate patient-specific treatment recommendations. The integration of structured KG reasoning with LLM-based inference allows KG-Psy to produce interpretable, evidence-supported, and clinically actionable outputs.
We evaluated KG-Psy on 310 de-identified psychiatric case reports and 12 expert-validated benchmark scenarios. The framework achieved 91.5% F1-score in distinguishing BD from BPD and an average pathway confidence of 86.9%, indicating robust multi-step inference. In personalized treatment recommendation tasks, KG-Psy achieved 88.7% accuracy, outperforming LLM-only and KG-only baselines by 23% and 31%, respectively.
....
Keywords: Bipolar Disorder, Borderline Personality Disorder, Knowledge Graph, GPT-5, Personalized Treatment
2 :20 milion
3 :15 milion
@Raminmousa
@Machine_learn
@paper4money
🔹 Title: Mind the Third Eye! Benchmarking Privacy Awareness in MLLM-powered Smartphone Agents
🔹 Publication Date: Published on Aug 27
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.19493
• PDF: https://arxiv.org/pdf/2508.19493
• Project Page: https://zhixin-l.github.io/SAPA-Bench
• Github: https://github.com/Zhixin-L/SAPA-Bench
@Machine_learn
🔹 Title: Self-Rewarding Vision-Language Model via Reasoning Decomposition
🔹 Publication Date: Published on Aug 27
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.19652
• PDF: https://arxiv.org/pdf/2508.19652
@Machine_learn
🚀 AI Agents for Android Apps
📌 GitHub: https://github.com/actionstatelabs/android-action-kernel
@Machine_learn
سلام اين مقاله امشب سابميت ميشه اگر از دوستان كسي نياز داشت با من هماهنگ بشه
@Raminmousa
با عرض سلام ما برای این مقاله نیاز به نفر دوم داریم و تنها مقاله دو نفر جایگاه داره. دوستانی که نیاز دارن می تونن به پی وی بنده پیام بدن
@Raminmousa
⚠️ فردا اخرین مهلت ...!
با عرض سلام ما برای این مقاله نیاز به نفر دوم داریم و تنها مقاله دو نفر جایگاه داره. دوستانی که نیاز دارن می تونن به پی وی بنده پیام بدن
@Raminmousa
Repost from Papers
Title: Fundamental Challenges of Neural Network in Handling Sequential Feature of Time Series: Np-hard Challenge
Journal: IEEE transaction on soft computing
Author : 2
Price: 1200 USDT
@Raminmousa
@Machine_learn
@Paper4money
🔹 Title: Select to Know: An Internal-External Knowledge Self-Selection Framework for Domain-Specific Question Answering
🔹 Publication Date: Published on Aug 21
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.15213
• PDF: https://arxiv.org/pdf/2508.15213
@Machine_learn
Sharing State Between Prompts and Programs
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@Machine_learn
Dataset Name: Online Payments Fraud Detection Dataset
Basic Description: Online payment fraud big dataset for testing and practice purpose
📖 FULL DATASET DESCRIPTION:
The below column reference:
📥 DATASET DOWNLOAD INFORMATION
🔴 Dataset Size: Download dataset as zip (186 MB)
🔰 Direct dataset download link:
https://www.kaggle.com/api/v1/datasets/download/rupakroy/online-payments-fraud-detection-dataset
@Machine_learn
Dataset Name: Linked In Job Postings (2023 - 2024)
Basic Description: LinkedIn Job Postings (2023 - 2024)
📖 FULL DATASET DESCRIPTION:
Scraper Code - https://github.com/ArshKA/LinkedIn-Job-Scraper
Every day, thousands of companies and individuals turn to LinkedIn in search of talent. This dataset contains a nearly comprehensive record of 124,000+ job postings listed in 2023 and 2024. .
🔰 Direct dataset download link:
https://www.kaggle.com/api/v1/datasets/download/arshkon/linkedin-job-postings
📊 Additional information:
File count not found
Views: 126,000
Downloads: 53,100
📚 RELATED NOTEBOOKS:
1. "Decoding the Job Market: An In-depth Exploration | Upvotes: 84
URL: https://www.kaggle.com/code/pratul007/decoding-the-job-market-an-in-depth-exploration
2. LinkedIn Job Postings 2023 Data Analysis | Upvotes: 58
URL: https://www.kaggle.com/code/enricofindley/linkedin-job-postings-2023-data-analysis
@Machine_learn
🔹 Title: Forecasting Probability Distributions of Financial Returns with Deep Neural Networks
🔹 Publication Date: Published on Aug 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.18921
• PDF: https://arxiv.org/pdf/2508.18921
• Github: https://github.com/jmichankow/deep_learning_probability
@Machine_learn
Dataset Name: Real Life Violence Situations Dataset
Basic Description: 1000 videos containing real street fight and 1000 video from other classes
🔴 Dataset Size: Download dataset as zip (4 GB)
🔰 Direct dataset download link:
https://www.kaggle.com/api/v1/datasets/download/mohamedmustafa/real-life-violence-situations-dataset
1. Real Time Violence Detection | MobileNet Bi-LSTM | Upvotes: 424
URL: https://www.kaggle.com/code/abduulrahmankhalid/real-time-violence-detection-mobilenet-bi-lstm
2. Real life violence detection using InceptionV3 | Upvotes: 395
URL: https://www.kaggle.com/code/nandinibagga/real-life-violence-detection-using-inceptionv3
3. Real Life Violence Detection / KERAS-TENSORFLOW | Upvotes: 115
URL: https://www.kaggle.com/code/brsdincer/real-life-violence-detection-keras-tensorflow
4. Video Fights Dataset | Upvotes: 24
URL: https://www.kaggle.com/datasets/shreyj1729/cctv-fights-dataset
@Machine_learn
Repost from Papers
با عرض سلام برای مقاله زیر نیاز به نفرات ۲ و ۳ داریم.
KG-Psy: A Knowledge-Graph and GPT-5 Based Framework for Personalized Clinical Decision Support in Bipolar Disorder and Borderline Personality Disorder
Abstract: Accurate diagnosis and personalized treatment planning for complex psychiatric disorders such as Bipolar Disorder (BD) and Borderline Personality Disorder (BPD) remain major challenges due to overlapping symptoms, fluctuating mood patterns, and heterogeneous clinical presentations. To address these challenges, we introduce KG-Psy, a hybrid neuro-symbolic framework that combines a domain-specific psychiatric Knowledge Graph (KG) with the advanced reasoning capabilities of GPT-5.
KG-Psy constructs multi-layer psychiatric knowledge graphs encoding symptom trajectories, neural correlates, pharmacological mechanisms, therapeutic guidelines, comorbidities, and behavioral patterns extracted from large-scale clinical literature. GPT-5 is employed to extract clinical entities, infer latent symptom-neural relationships, assess diagnostic likelihoods, and generate patient-specific treatment recommendations. The integration of structured KG reasoning with LLM-based inference allows KG-Psy to produce interpretable, evidence-supported, and clinically actionable outputs.
We evaluated KG-Psy on 310 de-identified psychiatric case reports and 12 expert-validated benchmark scenarios. The framework achieved 91.5% F1-score in distinguishing BD from BPD and an average pathway confidence of 86.9%, indicating robust multi-step inference. In personalized treatment recommendation tasks, KG-Psy achieved 88.7% accuracy, outperforming LLM-only and KG-only baselines by 23% and 31%, respectively.
....
Keywords: Bipolar Disorder, Borderline Personality Disorder, Knowledge Graph, GPT-5, Personalized Treatment
2 :20 milion
3 :15 milion
@Raminmousa
@Machine_learn
@paper4money
💻 ++101 Linux commands Open-source eBook
📚 Read
@Machine_learn
ایده داری… اما اجراش زمانبره؟
هزینه نیرو بالا رفته؟
تولید محتوا کند پیش میره؟
تو وبینار رایگان ایرانGPU یاد میگیری چطور AI
⚡ سرعت کار رو چند برابر میکنه
⚡ هزینهها کم میشه
⚡ تیم هوشمندتر کار میکنه
📅 ۱۴ دی | ساعت ۱۹
🎤 پوریا حداد
🎁 فرصت ارزشمند فقط برای شرکتکنندگان
💥 تخفیف تا ۴۰٪ روی یک محصول AI
⭐فرصت بردن دو جایزه ۲۰۰ میلیون تومانی
لینک ثبتنام
https://B2n.ir/hw4212
Dataset Name: FIFA23 OFFICIAL DATASET
Basic Description: From FIFA17 to FIFA23 statistics for each football player
📖 FULL DATASET DESCRIPTION:
The dataset contains +17k unique players and more than 60 columns, general information and all KPIs the famous videogame offers. As the esport scene keeps rising espacially on FIFA, I thought it can be useful for the community (kagglers and/or gamers)
📥 DATASET DOWNLOAD INFORMATION
🔴 Dataset Size: Download dataset as zip (14 MB)
🔰 Direct dataset download link:
https://www.kaggle.com/api/v1/datasets/download/bryanb/fifa-player-stats-database
📊 Additional information:
File count not found
Views: 107,000
Downloads: 66,500
@Machine_learn
Dataset Name: Malaria Bounding Boxes
Basic Description: P. vivax (malaria) infected human blood smears
📖 FULL DATASET DESCRIPTION:
Malaria is a disease caused by Plasmodium parasites that remains a major threat in global health, affecting 200 million people and causing 400,000 deaths a year. The main species of malaria that affect humans are Plasmodium falciparum and Plasmodium vivax.
📥 DATASET DOWNLOAD INFORMATION
🔴 Dataset Size: Download dataset as zip (5 GB)
🔰 Direct dataset download link:
https://www.kaggle.com/api/v1/datasets/download/kmader/malaria-bounding-boxes
📊 Additional information:
File count not found
Views: 54,400
Downloads: 4,657
@Machine_learn
Python Programming Hans-Petter Halvorsen
📚 Read
@Machine_learn
