transfer learning

Detecting plant diseases on time is imperative to improve agricultural productivity and mitigate economic losses.  This study presents a novel artificial intelligence framework for classifying plant diseases, combining advanced deep learning technologies with a hyperparameter optimization strategy.  Specifically, we employed a hybrid architecture that concatenates two pre-trained models, MobileNetV2 and DenseNet201, enriched with custom layers developed by the researchers.  Bayesian Optimization was employed to enhance model performance, focusing on four critical hyperp

Emerging as a crucial tool for companies in many industries, emotion recognition offers a greater understanding of consumer views of goods and services, hence strengthening client connections, improving service delivery, and guiding emotionally intelligent marketing strategies.  Particularly with the use of transfer learning techniques, end-to-end image-based emotion categorization has gained popularity in the last several years.  In tackling such challenging tasks, deep learning models have shown great efficacy.  Though reading emotions from visual data presents diffic

Violence detection in video surveillance systems is a critical challenge for ensuring public safety in smart cities.  This study presents a comprehensive analysis of deep learning architectures and transfer learning techniques for violence detection, evaluating their performance across benchmark datasets, including Hockey Fight, Movies, and UCF-Crime.  ResNet50, MobileNetV2, ConvLSTM, DenseNet121, and Xception models are compared in terms of accuracy, computational efficiency, and real-world applicability.  Results highlight ResNet50 as the most effective model, achievi

In agriculture, early detection of crop diseases is imperative for sustainability and maximizing yields.  Rooted in Agriculture 4.0, our innovative approach  combines pre-trained Convolutional Neural Networks (CNNs) models with data-driven solutions to address global challenges related to water scarcity.  By integrating the combined $L_{1}/L_{2}$ regularization technique to our model layers, we enhance their flexibility, reducing the risk of the overfitting effect of the model.  In the orange dataset used in our experiments, we have 1790 orange images, including a class

A Question Generation System (QGS) is a sophisticated piece of AI technology designed to automatically generate questions from a given text, document, or context.  Recently, this technology has gained significant attention in various fields, including education, and content creation.  As AI continues to evolve, these systems are likely to become even more advanced and viewed as an inherent part of any modern e-learning or knowledge assessment system.  In this research paper, we showcase the effectiveness of leveraging pre-trained checkpoints for Arabic questions generat