A hybrid deep learning model-based LSTM and modified genetic algorithm for air quality applications.

Over time, computing power and storage resource advancements have enabled the widespread accumulation and utilization of data across various domains. In the field of air quality, analyzing data and developing air quality models have become pivotal in safeguarding public health. Despite significant progress in modeling, the critical need for accurate pollutant predictions persists.

Compliance and Determinants of Spectacle Wear Among Moroccan Adults Residing Beni-Mellal Khénifra Region.

Background And Objective: Ensuring a high level of adherence to wearing spectacles is essential to preserve eye health and achieve optimal vision correction. Comprehending the factors influencing adherence to wearing spectacles can inform strategies to improve eye care outcomes. This study aimed to assess the prevalence and factors influencing adherence to wearing spectacles among Moroccan adults residing the Beni-Mellal Khenifra region.

A Review on Climate, Air Pollution, and Health in North Africa.

Purpose Of Review: The aim of this review is to summarize and provide clear insights into studies that evaluate the interaction between air pollution, climate, and health in North Africa.

Recent Findings: Few studies have estimated the effects of climate and air pollution on health in North Africa. Most of the studies highlighted the evidence of the link between climate and air pollution as driving factors and increased mortality and morbidity as health outcomes.

Learning From Mouse CT-Scan Brain Images To Detect MRA-TOF Human Vasculatures.

The earlier studies on brain vasculature semantic segmentation used classical image analysis methods to extract the vascular tree from images. Nowadays, deep learning methods are widely exploited for various image analysis tasks. One of the strong restrictions when dealing with neural networks in the framework of semantic segmentation is the need to dispose of a ground truth segmentation dataset, on which the task will be learned.

Petersen Graph Multi-Orientation Based Multi-Scale Ternary Pattern (PGMO-MSTP): An Efficient Descriptor for Texture and Material Recognition.

Classifying and modeling texture images, especially those with significant rotation, illumination, scale, and view-point variations, is a hot topic in the computer vision field. Inspired by local graph structure (LGS), local ternary patterns (LTP), and their variants, this paper proposes a novel image feature descriptor for texture and material classification, which we call Petersen Graph Multi-Orientation based Multi-Scale Ternary Pattern (PGMO-MSTP). PGMO-MSTP is a histogram representation that efficiently encodes the joint information within an image across feature and scale spaces, exploiting the concepts of both LTP-like and LGS-like descriptors, in order to overcome the shortcomings of these approaches.

Building roof segmentation from aerial images using a lineand region-based watershed segmentation technique.

In this paper, we present a novel strategy for roof segmentation from aerial images (orthophotoplans) based on the cooperation of edge- and region-based segmentation methods. The proposed strategy is composed of three major steps. The first one, called the pre-processing step, consists of simplifying the acquired image with an appropriate couple of invariant and gradient, optimized for the application, in order to limit illumination changes (shadows, brightness, etc.