Abstract: This study examines the effectiveness of using machine learning-based image recognition model for classifying common diseases in crops. This study addresses the critical need for swift and ...

CNN senior political data reporter Harry Enten marveled on Friday at how the Democratic Party has not only failed to find a new leader, but continues to disappoint voters. CNN News Central host Kate ...

Features Loads pre-trained neural network parameters from CSV files Processes the MNIST dataset (60,000 images) Interactive visualization of MNIST images with SDL2 Forward pass implementation for ...

Introduction: Accurate environmental image classification is essential for ecological monitoring, climate analysis, disaster detection, and sustainable resource management. However, traditional ...

Abstract: The recognition of handwritten digits has been among the most enduring fundamental problems explored in the field of machine learning and computer vision. The objective of this work is to ...

The MNIST dataset is a well-known benchmark in computer vision and consists of handwritten digits (0-9). The goal of this project is to build a Neural Network (NN) model that can accurately classify ...

Weed management presents a major challenge to vegetable growth. Accurate identification of weeds is essential for automated weeding. However, the wide variety of weed types and their complex ...

Methods: This study prospectively evaluated 357 participants (101 with sarcopenia and 256 without sarcopenia) for training, encompassing three types of data: muscle ultrasound images, clinical ...