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Admin: @Raminmousa ID: @Machine_learn link: https://t.me/Machine_learn
Show more📈 Analytical overview of Telegram channel Machine learning books and papers
Channel Machine learning books and papers (@machine_learn) in the English language segment is an active participant. Currently, the community unites 24 517 subscribers, ranking 8 031 in the Education category and 13 728 in the Iran region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 24 517 subscribers.
According to the latest data from 26 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -162 over the last 30 days and by -2 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 5.76%. Within the first 24 hours after publication, content typically collects 1.79% reactions from the total number of subscribers.
- Post reach: On average, each post receives 1 412 views. Within the first day, a publication typically gains 440 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 1.
- Thematic interests: Content is focused on key topics such as disorder, psy, مقاله, framework, graph.
📝 Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
“Admin: @Raminmousa
ID: @Machine_learn
link: https://t.me/Machine_learn”
Thanks to the high frequency of updates (latest data received on 27 June, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Education category.
24 517
Subscribers
-224 hours
-337 days
-16230 days
Posts Archive
🔸برترین کانالهای آموزشی در زمینه های هوشمصنوعی, پایتون و یادگیری ماشین
❯ هوش مصنوعی:
1️⃣ @Ai_Tv
2⃣ @HomeAI
3⃣ @eventai
4⃣ @Ai_NewsTv
❯ علم داده :
1️⃣ @DataPlusScience
❯ یادگیری ماشین :
1️⃣@Machine_learn
❯ یادگیری عمیق :
1️⃣ @cvision
❯ آموزش پایتون:
1⃣ @raspberry_python
2⃣ @Python4all_pro
❯ منابع و کتابهای پایتون ، علم داده و یادگیری ماشین :
1⃣ @programmingPDF
Repost from Papers
Title:
CNN-based Labelled Crack Detection for Image Annotation
Short title:
Machine Learning, Convolutional Neural Networks (CNNs),Image Annotation, Crack Detection
Abstract
Numerous image processing techniques (IPTs) have been employed to detect crack defects, offering an alternative to human-conducted onsite inspections. These IPTs manipulate images to extract defect features, particularly cracks in surfaces produced through Additive Manufacturing (AM). This article presents a vision-based approach that utilizes deep convolutional neural networks (CNNs) for crack detection in AM surfaces. Traditional image processing techniques face challenges with diverse real-world scenarios and varying crack types. To overcome these challenges, our proposed method leverages CNNs, eliminating the need for extensive feature extraction. Annotation for CNN training is facilitated by LabelImg without the requirement for additional IPTs. The trained CNN, enhanced by OpenCV preprocessing techniques, achieves an outstanding 99.54% accuracy on a dataset of 14,982 annotated images with resolutions of 1536 × 1103 pixels. Evaluation metrics exceeding 96% precision, 98% recall, and a 97% F1-score highlight the precision and effectiveness of the entire process.
Field
Mechanical Engineering, Material Engineering, Industrial Engineering, Computer Engineering, Civil Engineering, Aerospace Engineering
Journal
1. Optics and Laser Technology (8.3 CiteScore, 5.0 Impact Factor)
2. Optics and Lasers in Engineering (9.3 CiteScore, 4.6 Impact Factor)
3. The International Journal of Advanced Manufacturing Technology (3.4 CiteScore, 3.226 Impact Factor)
با عرض سلام نفرات ١ تا ٤ اين مقاله جهت ارسال به ژورنال خالي مي باشد. دوستاني كه نياز دارند به ايدي بنده پيام بدن.
@Raminmousa
@paper4money
Title:
CNN-based Labelled Crack Detection for Image Annotation
Short title:
Machine Learning, Convolutional Neural Networks (CNNs),Image Annotation, Crack Detection
Abstract
Numerous image processing techniques (IPTs) have been employed to detect crack defects, offering an alternative to human-conducted onsite inspections. These IPTs manipulate images to extract defect features, particularly cracks in surfaces produced through Additive Manufacturing (AM). This article presents a vision-based approach that utilizes deep convolutional neural networks (CNNs) for crack detection in AM surfaces. Traditional image processing techniques face challenges with diverse real-world scenarios and varying crack types. To overcome these challenges, our proposed method leverages CNNs, eliminating the need for extensive feature extraction. Annotation for CNN training is facilitated by LabelImg without the requirement for additional IPTs. The trained CNN, enhanced by OpenCV preprocessing techniques, achieves an outstanding 99.54% accuracy on a dataset of 14,982 annotated images with resolutions of 1536 × 1103 pixels. Evaluation metrics exceeding 96% precision, 98% recall, and a 97% F1-score highlight the precision and effectiveness of the entire process.
Field
Mechanical Engineering, Material Engineering, Industrial Engineering, Computer Engineering, Civil Engineering, Aerospace Engineering
Journal
1. Optics and Laser Technology (8.3 CiteScore, 5.0 Impact Factor)
2. Optics and Lasers in Engineering (9.3 CiteScore, 4.6 Impact Factor)
3. The International Journal of Advanced Manufacturing Technology (3.4 CiteScore, 3.226 Impact Factor)
با عرض سلام نفرات ١ تا ٤ اين مقاله جهت ارسال به ژورنال خالي مي باشد. دوستاني كه نياز دارند به
ايدي بنده پيام بدن.
@Raminmousa
با عرض سلام دو پكيچ يادگيري ماشين و يادگيري عميق با تخفيف ٧٥٪ براي دوستان در نظر گرفتيم.
دوستاني كه نياز دارند به ايدي بنده پيام بدن.
@Raminmousa
🧬 Evolving New Foundation Models: Unleashing the Power of Automating Model Development
▪Blog: https://sakana.ai/evolutionary-model-merge/
▪Paper: https://arxiv.org/abs/2403.13187
@Machine_learn
با عرض سلام به خاطر ماه مبارك رمضا دو پكيچ يادگيري ماشين و يادگيري عميق با تخفيف ٧٥٪ براي دوستان در نظر گرفتيم.
دوستاني كه نياز دارند به ايدي بنده پيام بدن.
@Raminmousa
🗂 بزرگترین کامیونیتی فعالان هوشمصنوعی و یادگیری ماشین ایران
در ابن کانالها مطالب آموزشی در خصوص ماشین و دیپ لرنینگ، دیتا ساینس، هوشمصنوعی، مدل های زیانی ، چت بات ها ، پرامپت نویسی و .... هر آنچه نیاز دارید ارائه می شود
📥 با زدن دکمه Add این کامیونیتی تخصصی را به تلگرامتان اضافه کنید
👇👇👇👇
https://t.me/addlist/uiFaC-MW4yllMTRk
Repost from Machine Learning with Python
This channels is for Programmers, Coders, Software Engineers.
0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages
✅ https://t.me/addlist/8_rRW2scgfRhOTc0
✅ https://t.me/DataScienceM
