Uni-Buddy is an advanced AI system developed to simplify university life at Nile University. It efficiently handles questions in everyday language, accesses real-time university databases, and simultaneously provides accurate responses for multiple users. Its goals include assisting with course registration, academic advising, financial inquiries, campus navigation, and research support. The evaluation demonstrates Uni-Buddy's user-friendly design, effective navigation, language comprehension, and database connectivity proficiency. Compared to similar studies, it stands out for its ease of use

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

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

Brain Connectivity refers to networks of functional and anatomical connections found throughout the brain. Multiple neural populations are connected by intricate connectivity circuits and interact with one another to exchange information, synchronize their activity, and participate in the accomplishment of complex cognitive tasks. Issues about how various brain regions contribute to cognition and their reciprocal roles have drawn the attention of researchers since the beginning of neuroscience. The interest in brain connection estimation has grown significantly due to the advancement of

Alzheimer’s Disease (AD) is a dangerous disease that is known for its characteristics of eroding memory and destroying the brain. The classification of Alzheimer's disease is an important topic that has recently been addressed by many studies using Machine Learning (ML) and Deep Learning (DL) methods. Most research papers tackling early diagnosis of AD use these methods as a feature extractor for neuroimaging data. In our research paper, the proposed algorithm is to optimize the performance of the prediction of early diagnosis from the multimodal dataset by a multi-step framework that uses a