This study presents a detailed numerical investigation of axisymmetric, incompressible, pressure-driven flow in circular pipes representative of two major pipeline systems in Sudan: The Greater Nile (GN) and Petrodar (PD) pipelines. Both simplified numerical simulation approaches and a full Navier-Stokes solver are employed to analyze axial velocity profiles, pressure distributions, and vorticity fields. Velocity and pressure are computed using finite-difference, finite-volume, and finite-elemen...

Computer Engineering 

Morphological segmentation is foundational for Natural Language Processing in morphologically rich languages, such as Amharic, yet progress is constrained by limited gold annotations and fragmented toolchains. We present an end-to-end framework that jointly addresses data creation and modeling for Amharic segmentation, part-of-speech tagging, and dependency parsing. Our approach begins with a silver-data generation pipeline that bootstraps segmentation labels from a rule-based analyzer and refin...

Computer Engineering 

Deep learning has come to be one of the most transformative generations in current clinical imaging, imparting advanced answers for diagnosis, segmentation, and sickness class. This complete evaluation explores the most essential deep learning architectures—inclusive of convolutional neural networks (CNNs), recurrent neural networks (RNNs), and deep perception networks (DBNs)—and their packages in the course of diverse imaging modalities, such as CT, MRI, PET, and fundus images. The examine high...

Computer Engineering 

Bipolar disorder is a severe psychiatric condition characterized by mood instability, and a specific subtype known as rapid cycling presents with frequent mood episodes, complicating diagnosis and treatment. Accurate identification of rapid cycling is critical for early intervention and personalized care strategies. This study explores the feasibility of using Transformer-based deep learning models to predict rapid cycling in bipolar disorder through an integrative analysis of synthetic multimod...

Computer Engineering  Artificial Intelligence  Psychiatry & Psychology 

This article introduces a study on optimizing LEACH parameters to improve the efficiency of wireless sensor networks. The focus of the research is to find the optimal algorithm configuration parameter to maximize the performance and prolong the lifetime of wireless sensor batteries. This study aims to improve the overall energy efficiency of wireless sensor networks by adjusting the LEACH parameters. The results will contribute to the development of more sustainable and energy-e...

Computer Engineering  Technology 

Node renumbering is an important step in the solution of sparse systems of equations. It aims to reduce the bandwidth and profile of the matrix. This allows for the speeding up of the solution of the system or allows for the addressing of even larger systems than otherwise would be possible. Research on this topic dates to the late sixties. In most of these algorithms nodal degree is an important consideration. In the new algorithm, we consider the maximum difference in adjacent node numbers as ...

Software Engineering  Computer Engineering 

Bipolar depression with comorbid obsessive-compulsive disorder (OCD) presents a significant clinical challenge due to its complex symptomatology, unpredictable treatment responses, and high relapse rates. Traditional ap-proaches to treatment planning lack reliable tools for predicting pa-tient-specific outcomes, leaving clinicians with limited options for personal-izing care. This study leverages advanced machine learning (ML), specifical-ly XGBoost, to develop a predictive framework capable of ...

Computer Engineering  Artificial Intelligence  Psychiatry & Psychology 

Accurate prediction of treatment response in bipolar disorder patients with comorbid obsessive-compulsive disorder (OCD) is essential to improving clinical outcomes and minimizing ineffective interventions. The complex interplay between bipolar disorder and OCD often complicates pharmacological treatment, leading to inconsistent results. This study aims to leverage machine learning (ML) techniques to develop predictive models that enhance the precision of quetiapine monotherapy outcomes. The pri...

Computer Engineering  Artificial Intelligence  Psychiatry & Psychology 

The treatment of major depressive disorder (MDD) often involves antidepressants, yet non-response to initial therapies remains a significant clinical and economic burden. This research aims to evaluate the comparative efficacy and cost-efficiency of 13 commonly prescribed antidepressants, spanning four major drug classes: SSRIs, SNRIs, NaSSAs, and TCAs. By employing machine learning and simulated patient data, we model non-response rates over two years, highlighting each drug’s cumulative risk t...

Computer Engineering  Artificial Intelligence  Psychiatry & Psychology 

Depressive disorders are complex, multifactorial conditions that exhibit significant variability in treatment response, often influenced by gender differences. This study leverages advanced machine learning (ML) techniques to predict antidepressant response to sertraline and imipramine, addressing the pressing need for personalized treatment strategies. By employing the Synthetic Minority Oversampling Technique (SMOTE), the research overcomes class imbalance—a common limitation in clinical datas...

Computer Engineering  Artificial Intelligence  Psychiatry & Psychology