deep learning

Comparison of some CNN architectures for detecting cardiomegaly from chest X-ray images

In medical image analysis, deep learning and convolutional neural networks (CNN) are widely employed, particularly in tasks such as classification and segmentation.  This study specifically addresses their application in healthcare for detecting cardiomegaly, a condition characterized by an enlarged heart, often related to factors such as hypertension or coronary artery diseases.  The primary objective is to develop an algorithm to identify cardiomegaly in chest X-ray images, constituting a binary classification problem (whether the image exhibits cardiomegaly or not). 

Ensemble Methods Based on Centering for Image Segmentation

Ensemble methods can be used for many tasks, some of the most popular being: classification, regression, and image segmentation. Image segmentation is a challenging task, where the use of ensemble machine learning methods provides an opportunity to improve the accuracy of neural network predictions.

ML MODELS AND OPTIMIZATION STRATEGIES FOR ENHANCING THE PERFORMANCE OF CLASSIFICATION ON MOBILE DEVICES

The paper highlights the increasing importance of machine learning (ML) in mobile applications, with mobile devices becoming ubiquitous due to their accessibility and functionality. Various ML models, including Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), are explored for their applications in real-time classification on mobile devices. The paper identifies key challenges in deploying these models, such as limited computational resources, battery consumption, and the need for real-time performance.

Advanced YOLO models for real-time detection of tomato leaf diseases

The increasing focus on smart agriculture in the last decade can be attributed to various factors, including the adverse effects of climate change, frequent extreme weather events, increasing population, the necessity for food security, and the scarcity of natural resources.  The government of Morocco adopts preventative measures to combat plant illnesses, specifically focusing on tomatoes.  Tomatoes are widely acknowledged as one of the most important vegetable crops, but they are highly vulnerable to several diseases that significantly decrease their productivity.  De

Revolutionizing tomato pest management: Synergy of Deep Learning, IoT, and Precision Agriculture

The increasing worldwide demand for agricultural goods, particularly tomatoes, underscores the need for effective pest control.  Key pests such as Whiteflies, Fruit Fly, and Helicoverpa Armigera pose significant threats to tomato crops.  This research proposes a novel approach by integrating modern technologies such as deep learning and the Internet of Things (IoT) to revolutionize traditional pest management methods.  Using a portable Pest Counting Device equipped with the YOLOv8 deep learning model on a Raspberry Pi 4B, coupled with the Firebase IoT platform, facilita

Intelligent Fake News Prediction System Based on NLP and Machine Learning Technologies

The article describes a study of identification of fake news based on natural language processing, big data analysis and deep learning technology. The developed system automatically checks the news for signs of fake news, such as the use of manipulative language, unverified sources and unreliable information. Data visualization is implemented on the basis of a friendly user interface that displays the results of news analysis in a convenient and understandable format.

Intelligent driver assistance systems based on computer vision and deep learning

This article presents an integrated Advanced Driver Assistance System (ADAS) that combines several key functional modules, such as collision warning, lane detection, traffic sign recognition, and pothole detection, which are implemented using modern deep learning models, particularly YOLOv8n. The system is optimized for devices with limited computational resources, such as Raspberry Pi or NVIDIA Jetson Nano, by employing a modular architecture and parallel data processing to ensure realtime performance.

UNDERSTANDING LARGE LANGUAGE MODELS: THE FUTURE OF ARTIFICIAL INTELLIGENCE

The article examines the newest direction in artificial intelligence - Large Language Models, which open a new era in natural language processing, providing the opportunity to create more flexible and adaptive systems. With their help, a high level of understanding of the context is achieved, which enriches the user experience and expands the fields of application of artificial intelligence. Large language models have enormous potential to redefine human interaction with technology and change the way we think about machine learning.

A data-driven fusion of deep learning and transfer learning for orange disease classification

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

Enhancing the vision graph model by elevating the precision diagnostics with attention and convolutions in medical imaging

The COVID-19 showed us that rapid and accurate diagnostics is a necessity.  Therefore, researchers began to implement deep learning models that can help the doctors to reach faster and reliable results, but there are more development to be done.  In our research paper, we introduced an innovative approach to enhance the Vision Graph model's accuracy for better results.  Our method exploits the strength of the ConvMixer architecture and Attention mechanism.  We start by utilizing Depthwise convolution and Pointwise convolution to capture spatial information in detail whi