Genomic microarray databases encompass complex high dimensional gene expression samples. Imbalanced microarray datasets refer to uneven distribution of genomic samples among different contributed classes which can negatively affect the classification performance. Therefore, gene selection from imbalanced microarray dataset can give rise to misleading, and inconsistent nominated genes that would alter the classification performance. Such unsatisfactory classification performance is due to the skewed distribution of the samples across the microarrays toward the majority class. In this paper, we

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

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

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

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

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

People with visual impairments often have difficulty locating misplaced objects. This can be a major barrier to their independence and quality of life. Retinal prostheses can restore some vision to people with severe vision loss. We introduce a novel real-time system for locating any misplaced objects for people with visual impairments using retinal prostheses. The system combines One For All (OFA) for Visual Grounding and Google Speech Recognition to identify the object to be located. It then uses an image processing technique called grabCut to extract the object from the background to

For many years, lung cancer has been ranked among the deadliest illnesses in the world. Therefore, it must be anticipated and detected at an early stage. We need to build a computer-aided diagnosis (CAD) system to help physicians to provide better treatment. In this study, the whole pipeline and the process of the CAD system for lung cancer detection in Chest X-ray are provided. It demonstrates the limitations and the problems facing lung cancer detection. New work is highlighted to be explored by the researchers in this area. Existing studies in the field are reviewed, including their

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

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