ECCDN-Net: A deep learning-based technique for efficient organic and recyclable waste classification.

Efficient waste management is essential to minimizing environmental harm as well as encouraging sustainable progress. The escalating volume and sophistication of waste present significant challenges, prompting innovative methods for effective waste categorization and management. Deep learning models have become highly intriguing tools for automating trash categorization activities, providing effective ways to optimize processes for handling waste.

Examining Drinking Water Preferences among University Students: A Comparative Assessment.

In recent years, there has been a clear increase in bottled water consumption globally, which has led to significant health and environmental concerns. This cross-sectional study aims to understand the attitude and preferences of university students in Qatar towards the use of bottled and tap drinking water using an online survey questionnaire (September and October 2022). The number of students who responded to the online survey was 14% ( = 688) of the student population, with a mean age of 22.

Assessing the Variation in COVID-19 Severity Among the Different Nationalities Living in Qatar.

Background Coronavirus disease (COVID-19) is a highly infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and it has resulted in a global pandemic. The COVID-19 pandemic has resulted in numerous reports on clinical outcomes and risk factors associated with morbidity and mortality. However, the extent to which nationality influences the severity of COVID-19 is not fully understood.

Differences in mental health status between individuals living with diabetes, and pre-diabetes in Qatar: A cross-sectional study.

Aims: The aims of this study was to determine the prevalence and to compare depression and anxiety screening scores by type of diabetes: type 2 diabetes mellitus (T2DM), type 1 diabetes mellitus (T1DM), and pre-diabetes. The secondary aim was to examine sex differences in screening scores by type of diabetes.

Methods: This cross-sectional study was conducted in Doha, Qatar using primary data collection (N = 150), and stratified random sampling at a diabetes primary healthcare center.

BIO-CXRNET: a robust multimodal stacking machine learning technique for mortality risk prediction of COVID-19 patients using chest X-ray images and clinical data.

Unlabelled: Nowadays, quick, and accurate diagnosis of COVID-19 is a pressing need. This study presents a multimodal system to meet this need. The presented system employs a machine learning module that learns the required knowledge from the datasets collected from 930 COVID-19 patients hospitalized in Italy during the first wave of COVID-19 (March-June 2020).

Deep Learning Assisted Automated Assessment of Thalassaemia from Haemoglobin Electrophoresis Images.

Haemoglobin (Hb) electrophoresis is a method of blood testing used to detect thalassaemia. However, the interpretation of the result of the electrophoresis test itself is a complex task. Expert haematologists, specifically in developing countries, are relatively few in number and are usually overburdened.

Microwave brain imaging system to detect brain tumor using metamaterial loaded stacked antenna array.

In this paper, proposes a microwave brain imaging system to detect brain tumors using a metamaterial (MTM) loaded three-dimensional (3D) stacked wideband antenna array. The antenna is comprised of metamaterial-loaded with three substrate layers, including two air gaps. One 1 × 4 MTM array element is used in the top layer and middle layer, and one 3 × 2 MTM array element is used in the bottom layer.

QCovSML: A reliable COVID-19 detection system using CBC biomarkers by a stacking machine learning model.

The reverse transcription-polymerase chain reaction (RT-PCR) test is considered the current gold standard for the detection of coronavirus disease (COVID-19), although it suffers from some shortcomings, namely comparatively longer turnaround time, higher false-negative rates around 20-25%, and higher cost equipment. Therefore, finding an efficient, robust, accurate, and widely available, and accessible alternative to RT-PCR for COVID-19 diagnosis is a matter of utmost importance. This study proposes a complete blood count (CBC) biomarkers-based COVID-19 detection system using a stacking machine learning (SML) model, which could be a fast and less expensive alternative.