📃 Plant-based anti-cancer drug discovery using computational approaches*📎*** Study the paper
@Machine_learn

Constrained Diffusion Implicit Models!

We use diffusion models to solve noisy inverse problems like inpainting, sparse-recovery, and colorization. 10-50x faster than previous methods!

Paper: arxiv.org/pdf/2411.00359

Demo: https://t.co/m6o9GLnnZF

@Machine_learn

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✅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