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

Catheters are life support devices. Human expertise is often required for the analysis of X-rays in order to achieve the best positioning without misplacement complications. Many hospitals in underprivileged regions around the world lack the sufficient radiology expertise to frequently process X-rays for patients with catheters and tubes. This deficiency may lead to infections, thrombosis, and bleeding due to misplacement of catheters. In the last 2 decades, deep learning has provided solutions to various problems including medical imaging challenges. So instead of depending solely on

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

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

The increasing adoption of programming education necessitates efficient and accurate methods for evaluating students’ coding assignments. Traditional manual grading is time-consuming, often inconsistent, and prone to subjective biases. This paper explores the application of large language models (LLMs) for the automated evaluation of programming assignments. LLMs can use advanced natural language processing capabilities to assess code quality, functionality, and adherence to best practices, providing detailed feedback and grades. We demonstrate the effectiveness of LLMs through experiments

Nuclei segmentation is a critical task in biological image analysis, with numerous applications in cancer diagnosis, grading, staging, and treatment planning. However, this task is challenging, particularly when dealing with low-resolution and low signal-to-noise ratio microscopy images. Segmentation problems arise, such as touching and missing cells, which make the process even more challenging. Deep learning models, including Attention U-Net and TransUNet, have demonstrated exceptional performance in medical image segmentation. Nonetheless, the choice of optimizer can significantly impact

correct understanding of the Holy Quran is an essential duty for all Muslims. Tajweed rules guide the reciter to perform Holy Quran reading exactly as it was uttered by Prophet Muhammad peace be upon him. This work focused on the recognition of one Quranic recitation rule. Qalqalah rule is applied to five letters of the Arabic Alphabet (Baa/Daal/Jeem/Qaaf/Taa) having sukun vowelization. The proposed system used the Mel Frequency Cepstral Coefficients (MFCC) as the feature extraction technique, and the Convolutional Neural Networks (CNN) model was used for recognition. The available dataset

Precise Recitation of Holy Quran is a religious duty that must be performed with great care. Tajweed rules are constructed to guide the reader to utter the Holy Quran text as it was originally uttered by prophet Muhammad. An automatic pattern detection algorithm is implemented to allocate basic Tajweed rules. The rules addressed in this paper, are Madd, Noon Sakinah, Tanween and Meem Sakinah rules. These rules are characterized by well-defined uttered style. The rules studied in this paper, were allocated at 487 positions in forty verses of Surat El-Anfal of Sheikh El-Hosary's recitation. Data

Current Autism Spectrum Disorder (ASD) diagnosis methods exhibit some limitations as they are based on clinical interviews and observations of behaviors, characteristics, and abilities. Moreover, considering the current challenges in identifying the causes and mechanisms associated with ASD, there is an essential need for automated techniques capable of providing an accurate classification between ASD and typically developed (TD). In this paper, we present a convolutional neural network model that can differentiate ASD from TD. This proposed system is trained and validated on the well-known