✅Title:
Automatic Image Annotation (AIA) of AlmondNet-20 Method for Almond Detection by Improved CNN-based Model
✅Short title
Machine Learning, Convolutional Neural Networks (CNNs), Image Annotation, Food Industry, Almond, Nuts Detection

Abstract:
In response to the global demand for high-quality agricultural products, especially in the competitive nut market, we present an innovative approach to enhance the grading of almonds and their shells. Leveraging Deep Convolutional Neural Networks (AlmondNet-20), we achieved over 99% accuracy through 20 layers of CNN, employing data augmentation for robust almond-shell differentiation. Our model, trained over 1000 epochs, demonstrated a remarkable accuracy of 99%, with a low loss function of 0.0567. Test evaluations revealed perfect precision, recall, and F1-score for almond detection. This advanced classification system not only boosts grading accuracy but also ensures reliability in distinguishing almonds from shells globally, benefiting both experts and non-experts. The application of deep learning algorithms opens avenues for product patents, contributing to the economic value of our country.
Field
Food Industry, Agricultural Engineering, Industrial Engineering, Computer Engineering.
1. Agronomy (3.7 CiteScore, 5.2 Impact Factor)
2. Biosystems Engineering (10.1 CiteScore, 5.1 Impact Factor)
3. Precision Agriculture (9.9 CiteScore, 6.2 Impact Factor)

@Raminmousa@Machine_learn@Paper4money

نفرات ۱ تا ۴ مقاله ی زیر خالی می باشد از دوستان اگر کسی خواست در خدمتیم

Title

Solar Energy Production Forecasting: A Comparative Study of LSTM, Bi-LSTM, and XGBoost Models with Activation Function Analysis

Abstract
This research focuses on the integration of Machine Learning (ML) methodologies and climatic parameters to predict solar panel energy generation, with a specific emphasis on addressing consumption-production imbalances. Leveraging a dataset sourced from the Kaggle platform, the study is conducted in the context of Estonia, aiming to optimize solar energy utilization in this geographic region. The dataset, obtained from Kaggle, encompasses comprehensive information on climatic variables, including sunlight intensity, temperature, and humidity, alongside corresponding solar panel energy output. Through the utilization of machine learning algorithms, such as XGBoost regression and neural networks, our predictive model endeavors to discern intricate patterns and correlations within these datasets. By tailoring the model to Estonia's climatic nuances, we seek to enhance the accuracy of energy production forecasts and, consequently, better manage the challenges associated with consumption-production imbalances. Furthermore, the research investigates the adaptability of the proposed model to diverse climatic conditions, ensuring its applicability for similar endeavors in other geographical locations. By utilizing Kaggle's rich dataset and employing advanced machine learning techniques, this study aims to contribute valuable insights that can inform sustainable energy policies and practices, ultimately promoting a more efficient and reliable renewable energy infrastructure.

Related Fields
Business, Marketing, Industrial Engineering, Computer Engineering.

Candidate Journals
1. Sustainability (5.8 CiteScore, 3.9 Impact Factor)
2. Archives of Computational Methods in Engineering (14.1 CiteScore, 9.7 Impact Factor)
3. Journal of Building Engineering (8.3 CiteScore, 6.4 Impact Factor)

@Raminmousa
@paper4money
@Machine_learn

A quick introduction to Docker.

📓 book

@Machine_learn

The first channel on Telegram that offers exciting questions, answers, and tests in data science, artificial intelligence, machine learning, and programming languages.

#interviews #datascience #python

https://t.me/DataScienceQ

Telegram

Data Science Questions, Answers, Quizzes, Interviews

The first channel on Telegram that offers exciting questions, answers, and tests in data science, artificial intelligence, machine learning, and programming languages.

با عرض سلام نياز به نفر دوم اين مقاله داريم.
ابتدا اركايو مقاله تا دو هفته ديگه فرستاده ميشه سپس براي Knowledge-based Systems فرستاده ميشه. كسايي كه نياز دارن به بنده مراجعه كنن
@Raminmousa

با عرض سلام دو پکیچ یادگیری ماشین و یادگیری عمیق را برای دوستانی که می خواهند تا فرداشب با تخفیف ۵۰٪ مجدد قرار دادیم این تخفیف اخرین سری از تخفیف های این دو پکیچ می باشد
1: introduction to machine learning
2: Regression (linear and non-linear)
3: Tensorflow introduction
4: Tensorflow computaion graph
5: Tensorflow optimizer and loss function
6: Tensorflow linear and non linear regression
7: logistic regression
8: Tensorflow regression
___________
9: introduction to traditional machine learning
10: knn and desicion tree
11: desicion tree and Naive bayes
12: desicion tree, knn, Naive bayes implementation
13: k-means
14: Guassion Mixture Model(GMM)
15: implementation K-means and GMM _16: introduction to Artificial Neural Network
17: Multi-level Neural Network
18: Introduction to Convolution Neural Network
19: Tensorflow Multi-level Neural Network
20:Tensorflow CNN
21:CNN image clasaification
22: Cnn text clasaification
23: Recurrent Neural Network(RNN)

جهت تهیه می تونین به ایدی بنده مراجعه کنین

@Raminmousa

ViT-CoMer: Vision Transformer with Convolutional Multi-scale Feature Interaction for Dense Predictions🖥 Github: https://github.com/Traffic-X/ViT-CoMer

📕 Paper: https://arxiv.org/pdf/2403.07392.pdf

✨ Tasks: https://paperswithcode.com/task/object-detection

🔥Dataset: https://paperswithcode.com/dataset/coco
@Machine_learn

Book: 📚grokking Deep Reinforcement Learning
Authors: Miguel Morales Foreword by Charles Isbell, Jr.
year: 2020
pages: 472
Tags: #RL #DRL
@Machine_learn

Book: 📚Exercises in Machine Learning
Authors: Michael U. Gutmann
year: 2024
pages: 211
Tags: #ML
@Machine_learn

⚡️ ResAdapter: Domain Consistent Resolution Adapter for Diffusion Models

▪page: https://res-adapter.github.io
▪paper: https://arxiv.org/abs/2403.02084
▪code: https://github.com/bytedance/res-adapter

@Machine_learn