The Speech Emotion Recognition (SER) system is one of the essential human-computer interface applications. Despite the rapid advancement of technology, there is still a gap in SER research in the Arabic language corpus. The goal of this research is to build an Arabic-based SER based on a feature set that has both high performance and low computational cost. Two novel feature sets were implemented using a mix of spectral and prosodic features. An Arabic semi-natural corpus 'EYASE' was adopted for testing the proposed system. Five machine learning classifiers using the different feature sets

The Speech Emotion Recognition (SER) system is an approach to identify individuals' emotions. This is important for human-machine interface applications and for the emerging Metaverse. This work presents a bilingual Arabic-English speech emotion recognition system based on EYASE and RAVDESS datasets. A novel feature set was composed by using spectral and prosodic parameters to obtain high performance at a low computational cost. Different classification models were applied. These machine learning classifiers are Random Forest, Support Vector Machine, Logistic Regression, Multi-Layer Perceptron

Speech Emotion Recognition (SER) systems are widely regarded as essential human-computer interface applications. Extracting emotional content from voice signals enhances the communication between humans and machines. Despite the rapid advancement of Speech Emotion Recognition systems for several languages, there is still a gap in SER research for the Arabic language. The goal of this research is to build an Arabic-based SER system using a feature set that has both high performance and low computational cost. Two novel feature sets were created using a mix of spectral and prosodic features

Since late 2019, Covid-19 has broken out causing immense pressure on healthcare systems worldwide. Fast detection of Covid-19 has become crucial in controlling and slow-pacing the virus outbreak. Innovative methods that are cheap, fast, and accurate for Covid-19 detection are of high importance to aid in the efforts of containment of the disease. In this study a novel method is proposed for Covid-19 detection through analysis of ECG image records. Three models are introduced for three classification schemas, Normal vs Covid-19, Covid-19 vs non Covid-19, Normal vs Covid-19 vs Abnormal HeartBeat

Human drivers employ their attentional systems during driving to focus on critical items and make judgments. Because gaze data can indicate human attention, collecting and analyzing gaze data has emerged in recent years to improve autonomous driving technologies. In safety-critical situations, it is important to predict not only where the driver focuses his attention but also on which objects. In this work, we propose DAP, a novel framework for driver attention prediction that bridges the attention prediction gap between pixels and objects. The DAP Framework is evaluated on the Berkeley

The coronavirus disease 2019 (COVID-19) pandemic has been spreading quickly, threatening the public health system. Consequently, positive COVID-19 cases must be rapidly detected and treated. Automatic detection systems are essential for controlling the COVID-19 pandemic. Molecular techniques and medical imaging scans are among the most effective approaches for detecting COVID-19. Although these approaches are crucial for controlling the COVID-19 pandemic, they have certain limitations. This study proposes an effective hybrid approach based on genomic image processing (GIP) techniques to

Severe acute respiratory syndrome coronavirus 2 (SARS–CoV-2) is a worldwide health concern, and new treatment strategies are needed. Targeting inflammatory innate immunity pathways holds therapeutic promise, but effective molecular targets remain elusive. Here, we show that human caspase-4 (CASP4) and its mouse homolog, caspase-11 (CASP11), are up-regulated in SARS–CoV-2 infections and that CASP4 expression correlates with severity of SARS–CoV-2 infection in humans. SARS–CoV-2–infected Casp112/2 mice were protected from severe weight loss and lung pathology, including blood vessel damage

Sepsis is a potentially life-threatening medical condition that increases mortality in pediatric populations admitted in the intensive care unit (ICU). Due to the unpredictable nature of the disease course, it was challenging to find the informative genetic biomarkers at the earliest stages. Consequently, a considerable attention has been paid for the early prediction of pediatric sepsis based on genetic biomarkers analysis that would promote the early medical intervention. Therefore, the proposed study attempted to demonstrate the feasibility of Henry Gas Solubility Optimization (HGSO) in

Precision medicine has gained considerable popularity since the “one-size-fits-all” approach did not seem very effective or reflective of the complexity of the human body. Subsequently, since single-omics does not reflect the complexity of the human body's inner workings, it did not result in the expected advancement in the medical field. Therefore, the multi-omics approach has emerged. The multi-omics approach involves integrating data from different omics technologies, such as DNA sequencing, RNA sequencing, mass spectrometry, and others, using computational methods and then analyzing the

In a search for new drug candidates for one of the neglected tropical diseases, leishmaniasis, twenty quinoline-isatin hybrids were synthesized and tested for their in vitro antileishmanial activity against Leishmania major strain. All the synthesized compounds showed promising in vitro activity against the promastigote form in a low micromolar range (IC50 = 0.5084–5.9486 μM) superior to the reference miltefosine (IC50 = 7.8976 μM). All the target compounds were then tested against the intracellular amastigote form and showed promising inhibition effects (IC50 = 0.60442–8.2948 μM versus 8.08