Cardiac magnetic resonance perfusion (CMRP) images are used to assess the local function and permeability of the heart muscle. The perfusion analysis requires the segmentation of cardiac inner and outer walls of the left ventricle (LV). However, the available perfusion datasets are limited or have no annotations. A fair dataset was annotated to employ the latest and most effective Deep Learning (DL) methodologies. In this paper, we employ similar cardiac imaging protocols in terms of cardiac geometry by initially training using CINE images and performing domain adaptation to CMRP images using

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

Type 2 Diabetes (T2D) is a complex chronic illness that affects around 90% of diabetic patients worldwide. Prediabetes is an elementary phase for T2D that is recommended to be early diagnosed to prevent its progression. In this study, we used 16S rRNA data from the gut and nasal cavity of prediabetic and control patients to identify common and exclusive diabetic pathways for each body site. Furthermore, using the Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) as well as MicobiomeExplorer in the pathway enrichment analysis, we also identified the

The communication barriers faced by people with disabilities, particularly the deaf or hard of hearing, nonverbal, deaf-mute, and blind have a significant impact on their quality of life and social inclusion. Our research aims to provide real-time translation from sign language to speech and vice versa. The ability to provide real-time speech-to-text and text-to-sign language translation will help alleviate these barriers, improve communication, and increase social inclusivity for this community ensuring they are not left out in conversations and social interactions. A significant amount of

This paper presents a novel approach to Arabic Word Sense Disambiguation (WSD) leveraging transformer-based models to tackle the complexities of the Arabic language. Utilizing the SALMA dataset, we applied several techniques, including Sentence Transformers with Siamese networks and the SetFit framework optimized for few-shot learning. Our experiments, structured around a robust evaluation framework, achieved a promising F1-score of up to 71%, securing second place in the ArabicNLU 2024: The First Arabic Natural Language Understanding Shared Task competition. These results demonstrate the

Coronavirus (CoV) disease 2019 (COVID-19) is a severe pandemic affecting millions worldwide. Due to its rapid evolution, researchers have been working on developing diagnostic approaches to suppress its spread. This study presents an effective automated approach based on genomic image processing (GIP) techniques to rapidly detect COVID-19, among other human CoV diseases, with high acceptable accuracy. The GIP technique was applied as follows: first, genomic graphical mapping techniques were used to convert the genome sequences into genomic grayscale images. The frequency chaos game

Recently, sequencing technologies have become readily available, and scientists are more motivated to conduct metagenomic research to unveil the potential of a myriad of ecosystems and biomes. Metagenomics studies the composition and functions of microbial communities and paves the way to multiple applications in medicine, industry, and ecology. Nonetheless, the immense amount of sequencing data of metagenomics research and the few user-friendly analysis tools and pipelines carry a new challenge to the data analysis. Web-based bioinformatics tools are now being developed to facilitate the

Ad hoc networks have been through extensive research in the last decade. Even with their desirable characteristics, major issues related to their security need to be considered. Various security solutions have been proposed to reduce the risks of malicious actions. They mainly focus on key management, authentication, secure localization, and aggregation techniques. These techniques have been proposed to secure wireless communications but they can only deal with external threats. Therefore, they are considered the first line of defense. Intrusion detection systems are always required to

Fall prediction is a critical process in ensuring the safety and well-being of individuals, particularly the elderly population. This paper focuses on the development of a fall detection and prediction system using wearable sensors and machine learning algorithms. The system issues an alarm upon predicting the occurrence of falling and sends alerts to a monitoring centre for timely assistance. Wearable sensor devices, including Inertial Measurement Units (IMUs) equipped with accelerometers, gyroscopes, and magnetometers are utilized for data collection. UPFALL, a comprehensive online freely

The automatic generation of textual descriptions from images, known as image captioning, holds significant importance in various applications. Image captioning applications include accessibility for the visually impaired, social media enhancement, automatic image description for search engines, assistive technology for education, and many more. While extensive research has been conducted in English, exploring this challenge in Arabic remains limited due to its complexity. Arabic is one of the world's most widely spoken languages. Around 420 million native people speak this language. It is also