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Admin: @Raminmousa ID: @Machine_learn link: https://t.me/Machine_learn
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Complex Sig: Complex deep model for signal classification
Abstract: The ability to classify signals is an important task that provides the opportunity for many different applications. In the early research for signal classification, they had to
decompose the signal using FT (Fourier transform), SIFT, MFCC or other manual methods using statistical modulation features, then classify these signals by a traditional machine learning approach. In the last few years, the process of learning deep models that lead to the automatic extraction of features has positively affected classification. Different deep-learning models with di erent depths have been proposed in the literature.
This article proposes different approaches to classify signals in different SNR conditions. ResNet-based approaches perform well for high SNRs but poorly when dealing with low SNRs. Therefore, TRansforme-based approaches were proposed for classification, reaching an average accuracy of 0.7056 in low SNR and an average of 0.9089 in high SNR.
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@Raminmousa
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@Machine_learn
🔹 Title: Predicting the Order of Upcoming Tokens Improves Language Modeling
🔹 Publication Date: Published on Aug 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.19228
• PDF: https://arxiv.org/pdf/2508.19228
• Github: https://github.com/zaydzuhri/token-order-prediction
@Machine_learn
رمضان الکریم ❤️
@Machine_learn
How we made Python's packaging library 3x faster
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@Machine_learn
🔹 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
🚀 AI Agents for Android Apps
📌 GitHub: https://github.com/actionstatelabs/android-action-kernel
@Machine_learn
Sharing State Between Prompts and Programs
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@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
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
💻 ++101 Linux commands Open-source eBook
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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
Video-LMM Post-Training: A Deep Dive into Video Reasoning with Large Multimodal Models
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@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
Fri, 27 Feb 2026 (showing first 50 of 206 entries )
[1] arXiv:2602.23352 [pdf, html, other]
Stark localization of interacting particles
Wojciech De Roeck, Amirali Hannani, Alessio Lerose, Nathan Vandenbosch
Subjects: Mathematical Physics (math-ph); Disordered Systems and Neural Networks (cond-mat.dis-nn)
[2] arXiv:2602.23350 [pdf, html, other]
A strengthening of the dimensional Brunn-Minkowski conjecture implies the (B)-Conjecture
Sotiris Armeniakos, Jacopo Ulivelli
Comments: Comments are welcome!
Subjects: Functional Analysis (math.FA); Metric Geometry (math.MG)
[3] arXiv:2602.23343 [pdf, html, other]
Cyclic sieving for a class of rectangular domino tableaux
Laura Colmenarejo, Bridget Eileen Tenner, Camryn E. Thompson
Comments: 17 pages
Subjects: Combinatorics (math.CO)
[4] arXiv:2602.23340 [pdf, html, other]
Combinatorial Properties of the Raisonnier Filter
Spyridon Dialiatsis, Yurii Khomskii
Subjects: Logic (math.LO)
[5] arXiv:2602.23326 [pdf, html, other]
Spin Glass Concepts in Computer Science, Statistics, and Learning
Andrea Montanari
Comments: 33 pages; 2 pdf figures
Subjects: Probability (math.PR); Disordered Systems and Neural Networks (cond-mat.dis-nn)
[6] arXiv:2602.23325 [pdf, html, other]
Spanning tight components in 4-uniform hypergraphs
Francesco Di Braccio, Brian Hearn, Joanna Lada, Mihir Neve, Lu-Ming Zhang
Comments: 24 pages, 4 figures
Subjects: Combinatorics (math.CO)
[7] arXiv:2602.23323 [pdf, html, other]
Modeling Large-Scale Adversarial Swarm Engagements using Optimal Control
Claire Walton, Isaac Kaminer, Qi Gong, Abram H. Clark, Theodoros Tsatsanifos
Comments: arXiv admin note: substantial text overlap with arXiv:2108.02311. substantial text overlap with arXiv:2108.02311
Subjects: Optimization and Control (math.OC)
@Machine_learn
✨RoboCurate: Harnessing Diversity with Action-Verified Neural Trajectory for Robot Learning
📝 Summary:
RoboCurate enhances synthetic robot learning data by evaluating action quality through simulator replay consistency. It also augments observation diversity via image editing and video transfer techniques. This leads to substantial improvements in robot task success rates compared to using real da...
🔹 Publication Date: Published on Feb 21
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.18742
• PDF: https://arxiv.org/pdf/2602.18742
• Project Page: https://seungkukim.github.io/robocurate/
@Machine_learn
هر ثانیه توقف، یعنی از دست رفتن زمان، هزینه و فرصت…
پایداری دیگر یک انتخاب نیست؛ یک ضرورت است.
🚀 ایرانGPU؛جایی که پروژهها متوقف نمیشوند.
🏛 تنها و اولین شرکت بورسی هوش مصنوعی ایران
🕒 بیش از ۵ سال سابقه فعالیت حرفهای
🌐 شبکهای از ۲۰+ دیتاسنتر غیرمتمرکز در سراسر کشور
🧠 مناسب تیمها، پژوهشگران و سازمانهای حرفهای AI
🛟 پشتیبانی ۲۴ ساعته، ۷ روز هفته
📈 تضمین SLA با دسترسپذیری 99.9٪ و ارائه سرور داخل ایران
📩 ثبت درخواست مشاوره | شروع مسیر هوشمندانه
https://b2n.ir/qk8423
🔹 Title: CODA: Coordinating the Cerebrum and Cerebellum for a Dual-Brain Computer Use Agent with Decoupled Reinforcement Learning
🔹 Publication Date: Published on Aug 27
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.20096
• PDF: https://arxiv.org/pdf/2508.20096
• Project Page: https://github.com/OpenIXCLab/CODA
• Github: https://github.com/OpenIXCLab/CODA
@Machine_learn
Dataset Name: Gallstone Dataset (UCI)
Basic Description: Gallstone Dataset (UCI Machine Learning Repository)
📥 DATASET DOWNLOAD INFORMATION
==================================
🔴 Dataset Size: Download dataset as zip (81 kB)
🔰 Direct dataset download link:
URL not found
📊 Additional information:
==================================
File count not found
Views: 1,128
Downloads: 246
📚 RELATED NOTEBOOKS:
==================================
1. Heart Attack Risk Prediction Dataset | Upvotes: 274
URL: https://www.kaggle.com/datasets/iamsouravbanerjee/heart-attack-prediction-dataset
@Machine_learn
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: 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
🔹 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
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
🔹 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
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
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@Machine_learn
@paper4money
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
Python Programming Hans-Petter Halvorsen
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@Machine_learn
با عرض سلام برای مقاله زیر نیاز به نفر 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
3 :15 milion
@Raminmousa
@Machine_learn
@paper4money
🛠️OpenAI just released new guide on how coding agents like GPT-5.1-Codex-Max plug into everyday engineering workflow
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@Machine_learn