Advances in Computing and Engineering Journal, 9 Abou Qear, Abu Qir Al Gharbeyah, Alexandria Governorate, Montaza 2 (2026)

04/03/2026

We are pleased to invite you to submit your research manuscript to the upcoming issue of the Advances in Computing and Engineering (ACE) journal, published by the Academy Publishing Center of the Arab Academy for Science, Technology, and Maritime Transport (AASTMT), Alexandria, Egypt.
ACE is a biannual, international, open-access, and peer-reviewed journal providing a platform for high-quality research in computing, engineering, and interdisciplinary innovations at the intersection of science and technology.
🔹 Next Issue: DECEMBER 2025
🔹 Journal Website: http://apc.aast.edu/ojs/index.php/ACE/index
🔹 Submit Here: http://apc.aast.edu/ojs/index.php/ace/user/register
🔍 Areas of Interest (including but not limited to):
• Theoretical and Applied Computing
• Artificial Intelligence, Machine Learning & Data Science
• Emerging Technologies & Interdisciplinary Research
• Software and Systems Engineering
• Smart Systems & Internet of Things (IoT)
• Cybersecurity & Networks
• Bioinformatics
🏆 Key Features:
• Indexed in: DOAJ, Google Scholar, EuroPub, and EBSCO
• Targeting Future Indexing: Scopus
• License: Creative Commons Attribution-NonCommercial 4.0 (authors retain copyright)
• No Article Processing or Submission Charges
We encourage submissions of original research articles, case studies, and review papers that reflect novel contributions to the computing and engineering fields.
📬 For inquiries: [email protected]
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26/02/2026

Advances in Computing and Engineering
Vol 5, No 2 (2025)
https://apc.aast.edu/ojs/index.php/ACE/issue/view/100/showToc

CNN-based hybrid fusion for robust multimodal Face-Iris biometric authentication system
Afolabi Ifedayo Awodeyi, Philip Asuquo, Bliss Utibe-Abasi Stephen
DOI: https://dx.doi.org/10.21622/ACE.2025.05.2.1406

Abstract

This research presents a robust CNN driven biometric authentication system that combines face and iris recognition through both feature-level and score-level fusion. The framework addresses key limitations of unimodal systems which includes pose variation, lighting inconsistencies and spoofing by leveraging the strengths of each biometric trait. Two parallel CNN branches extract deep features from face and iris images, which are then fused and classified. Simultaneously, similarity scores from individual classifiers are combined using a weighted average. A hybrid decision rule integrates both outputs to enhance reliability and reduce false acceptances. The model was tested on the ORL and CASIA-IrisV4 datasets under realistic conditions. It achieved a recognition accuracy of 99.65% and a 0.00% FAR, outperforming unimodal and single-fusion approaches. This confirms the system’s potential for high-security applications. Future research will explore scalability with larger datasets, inclusion of additional modalities like fingerprint and deployment on mobile or edge devices.This research presents a robust CNN driven biometric authentication system that combines face and iris recognition through both feature-level and score-level fusion. The framework addresses key limitations of unimodal systems which includes pose variation, lighting inconsistencies and spoofing by leveraging the strengths of each biometric trait. Two parallel CNN branches extract deep features from face and iris images, which are then fused and classified. Simultaneously, similarity scores from individual classifiers are combined using a weighted average. A hybrid decision rule integrates both outputs to enhance reliability and reduce false acceptances. The model was tested on the ORL and CASIA-IrisV4 datasets under realistic conditions. It achieved a recognition accuracy of 99.65% and a 0.00% FAR, outperforming unimodal and single-fusion approaches. This confirms the system’s potential for high-security applications. Future research will explore scalability with larger datasets, inclusion of additional modalities like fingerprint and deployment on mobile or edge devices.

Read the full issue here:
https://apc.aast.edu/.../index.../ACE/issue/view/100/showToc

18/02/2026

Advances in Computing and Engineering
Vol 5, No 2 (2025)
https://apc.aast.edu/ojs/index.php/ACE/issue/view/100/showToc
Vision beyond the human eye: advancing early breast cancer detection through computer vision and ai-enhanced imaging
Godfrey Perfectson Oise, Babalola Eyitemi Akilo, Joy Akpowehbve Odimayomi, Unuigbokhai Nkem Belinda, Chioma Julia Onwuzo, Onoriode Michael Atake, Sofiat Kehinde Bakare
DOI: https://dx.doi.org/10.21622/ACE.2025.05.2.1343
Abstract

This paper reviews the application of computer vision and artificial intelligence (AI) in enhancing breast cancer detection, exploring how deep learning models, particularly Convolutional Neural Networks (CNNs), augment traditional screening techniques. The review examines the current state of computer vision applications in breast cancer detection, emphasizing deep learning-based approaches, and discusses how CNNs are integrated into clinical workflows, the empirical evidence supporting their effectiveness, and the practical challenges involved in their clinical adoption. The methodology also includes a deep learning-based approach to classify and segment breast ultrasound images using a publicly available dataset. CNN-based systems demonstrate performance on par with or even surpassing human radiologists in specific diagnostic tasks. Studies show that MobileNetV3, a lightweight CNN, holds strong potential for integration into edge AI systems for point-of-care diagnostics, as well as in privacy-preserving frameworks such as federated learning. The MobileNetV3-based classification model demonstrated robust performance across the three diagnostic categories: normal, benign, and malignant, with an overall test set accuracy of 91.2%. Key performance metrics, including precision (benign: 0.85, malignant: 0.74, normal: 0.83), recall (benign: 0.84, malignant: 0.74, normal: 0.88), F1-score (benign: 0.85, malignant: 0.74, normal: 0.86), and accuracy (0.82), are examined to evaluate the efficacy of these AI-driven approaches. The review identifies emerging trends, such as multi-modal learning and federated learning, which aim to enhance model robustness and privacy. The integration of AI into clinical workflows holds promise for improving diagnostic accuracy and reducing healthcare disparities by expanding access to high-quality screening services. This paper contributes to a deeper understanding of how AI-driven innovations are reshaping breast cancer detection and inspires further research toward their responsible and widespread implementation.
Read the full issue here:
https://apc.aast.edu/ojs/index.php/ACE/issue/view/100/showToc

03/02/2026

🌟ACE Volume 5, Issue 2 – December 2025 is Out! 🌟
Check out the latest Table of Contents for ACE Volume 5, Issue 2 (December 2025).

Table of Contents
Articles
Vision beyond the human eye: advancing early breast cancer detection through computer vision and ai-enhanced imaging
Godfrey Perfectson Oise, Babalola Eyitemi Akilo, Joy Akpowehbve Odimayomi, Unuigbokhai Nkem Belinda, Chioma Julia Onwuzo, Onoriode Michael Atake, Sofiat Kehinde Bakare

CNN-based hybrid fusion for robust multimodal Face-Iris biometric authentication system
Afolabi Ifedayo Awodeyi, Philip Asuquo, Bliss Utibe-Abasi Stephen

Interpretable air quality classification for public health using machine learning
Godfrey Perfectson Oise, Cyprian C. Konyeha, Chioma Julia Onwuzo, Ejenarhome Prosper Otega, Babalola Eyitemi Akilo, Olayinka Tosin Comfort, Joy Akpowehbve Odimayomi, Unuigbokhai Nkem Belinda

Smart trolley with IoT-based automatic billing and secure locking system
S. S. Vidya Balantrapu, Praveen Kumar Bathula, Arjun Sanjay B., HSV Ganesh P., Sanjeevi M., Muralli Krishna P.

A federated framework for speech-based early detection of Alzheimer’s disease
Mohamed Mourad Abdellattif, Abdelrahman Mohamed Farouk, Nada Hamada Ahmed, Nadine Ahmed Elquersh, Ahmed Hamdy Elshennawy, Noha S. Tawfik

A simulation-based smart city architecture using arduino and cisco packet tracer
Md Rakeen Islam Nahin

Machine learning approaches for detecting hate speech in African languages on social media: a systematic literature review
Banchale Adhi Gufu, Audrey Mbogho, Edward Ombui

Using multilevel policy to mitigate database threats from former employees: a case of public sector organisations in Zanzibar
Rogers P. Bhalalusesa, Ibrahim Salum Salehe

Swarm intelligence–driven mobilenet optimization for breast cancer classification in ultrasound images
Marwa A. ElShenawy, Rania Kadry

A healthcare 5.0 compliant workforce digital twin framework for healthcare personnel in resource-constrained medical facilities in sub-Saharan Africa
Ebun P. Fasina, Babatunde A. Sawyerr, Ayomide I. Hassan, Abdulazeez Murainah, Chika P. Ojiako

📄 View full version : https://apc.aast.edu/ojs/index.php/ACE/issue/view/100/showToc
For inquiries, contact: [email protected]

28/01/2026

We’re excited to announce that the December issue of Advances in Computing and Engineering (ACE), Volume 5, Issue 2 is now officially published!
📖 Read the full issue here: https://apc.aast.edu/ojs/index.php/ACE/issue/view/100/showToc

05/08/2025

Advances in Computing and Engineering
Vol 5, No 1 (2025)
https://apc.aast.edu/ojs/index.php/ACE/issue/view/91/showToc

Fog computing-enabled smart seating systems: optimizing latency and network bandwidth efficiency in classrooms
Evans Obu, Michael Asante, Eric Opoku Osei
DOI: https://dx.doi.org/10.21622/ACE.2025.05.1.1335

Abstract

In modern educational settings, overcrowded classrooms challenge student engagement and learning efficiency. To address these issues, we propose a novel smart seating system powered by Fog Computing that leverages Wireless Sensor Networks (WSN), Internet of Things (IoT), Fog Computing (FC) and Cloud Computing (CC) technologies. Our work introduces the first fog computing-driven smart seating system for classroom settings. It demonstrates significant improvements in latency (3.29 ms in Fog-based vs. 108.69 ms in cloud-based systems), while maintaining comparable network efficiency. Our findings highlight fog computing’s potential to transform real-time classroom management. Using iFogSim, we conducted a comparative study between traditional cloud-centric architectures and our fog-based system across various classroom scenarios. Results demonstrate that the fog-based architecture delivers superior real-time responsiveness, making it particularly suitable for dynamic educational environments. This research provides both technical insights into performance improvements and practical implementation guidelines for educational institutions seeking to optimize classroom management systems.

30/07/2025

Advances in Computing and Engineering
Vol 5, No 1 (2025)
https://apc.aast.edu/ojs/index.php/ACE/issue/view/91/showToc

An AI-based framework for improving efficiency and fairness in the interview process PDF
Mohannad Taman, Yahia Khaled, Dalia Sobhy
DOI: https://dx.doi.org/10.21622/ACE.2025.05.1.1317

Abstract

Artificial intelligence (AI) technologies have advanced to the point where they can help human resource specialists, such as recruiters, by automating major parts of the hiring process and filtering the list of candidates. However, little research has evaluated the use of AI in virtual interviews. This paper presents InstaJob, an AI-powered framework designed to improve efficiency and fairness in the hiring process. It uses deep learning models for face emotion detection, text emotion analysis, and filler word detection in interviews to evaluate candidates’ soft skills, ensuring unbiased assessments. The proposed face emotion detection model achieved a validation accuracy of 77%, which outperforms the other state-of-the-art approaches.

22/07/2025

Advances in Computing and Engineering
Vol 5, No 1 (2025)
https://apc.aast.edu/ojs/index.php/ACE/issue/view/91/showToc

Real-time mobile broadband quality of service prediction using AI-driven customer-centric approach PDF
Ayokunle A. Akinlabi, Folasade M. Dahunsi, Jide J. Popoola, Lawrence B. Okegbemi
DOI: https://dx.doi.org/10.21622/ACE.2025.05.1.1332

Abstract

Statistical methods employed in evaluating the quality of service (performance) of mobile broadband (MBB) networks face drawbacks relating to the accurate and reliable processing of the huge amounts of heterogenous real time traffic data generated from MBB networks. Since the traffic patterns experienced in MBB networks are largely complex, highly dynamic and heterogenous in nature; hence, statistical methods may not adjust adequately to the changing network conditions. The highlighted gap can be addressed by machine learning (ML), as it has been effectively used in the past to support the analysis and knowledge discovery of communication systems’ traffic data through identification of intricate and hidden patterns. This paper presents the application of ML techniques to predict MBB QoS in real-time, using a custom-built mobile application (MBPerf) that collects five (5) network metrics (DNS lookup, speeds, latency, signal strength), location information and device characteristics across diverse network conditions in South West of Nigeria. The QoS modeling task was carried out using MBPerf pre-processed dataset. Three (3) classification algorithms including Random Forest (RF), Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost) were trained using the QoS dataset and then evaluated in order to determine the most effective model based on certain evaluation metrics – accuracy, precision, F1-Score and recall. Following hyperparameter tuning to improve the model's performance, the selected model was deployed in a real-world network environment to classify QoS into "Above Average," "Average," and "Below Average," categories. Mobile customers receive real-time notifications with actionable insights based on the predicted QoS class, empowering them to optimize their usage and troubleshoot issues. From the performance results obtained for the 3 ML models trained with MBPerf dataset, SVM (95%) and XGBoost (97%) significantly outperformed RF (59%) in terms of accuracy. However, the performance difference between SVM and XGBoost models are not significant. Interestingly, the 3 models showed great capability to accurately make predictions on the three QoS categories (classes) as depicted by the ROC-AUC and mlogloss curves. Lastly, the feature importance plot shows that QoS is the collective effect of service performance and not a function QoS metrics only that determines the degree of satisfaction of a user of the service. This Artificial Intelligence (AI) powered system promotes a more transparent and efficient MBB experience for all stakeholders in Nigeria's fast evolving digital landscape.

16/07/2025

Advances in Computing and Engineering Journal
Volume 5, Issue 1, 2025
https://apc.aast.edu/ojs/index.php/ACE/issue/view/91/showToc

Table of Contents
Articles
1. Real-time mobile broadband quality of service prediction using AI-driven customer-centric approach PDF
Ayokunle A. Akinlabi, Folasade M. Dahunsi, Jide J. Popoola, Lawrence B. Okegbemi
DOI: https://dx.doi.org/10.21622/ACE.2025.05.1.1332

2. An AI-based framework for improving efficiency and fairness in the interview process PDF
Mohannad Taman, Yahia Khaled, Dalia Sobhy
DOI: https://dx.doi.org/10.21622/ACE.2025.05.1.1317

3. Fog computing-enabled smart seating systems: optimizing latency and network bandwidth efficiency in classrooms PDF
Evans Obu, Michael Asante, Eric Opoku Osei
DOI: https://dx.doi.org/10.21622/ACE.2025.05.1.1335

Read the full issue at:
https://apc.aast.edu/ojs/index.php/ACE/issue/view/91/showToc

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