Advances in Computing and Engineering Journal

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Advances in Computing and Engineering Journal

Journal of Advances in Computing and Engineering (ACE) is a peer-reviewed, open access, interdisciplinary journal, with two issues yearly.

The focus of the journal is on theories, methods, and applications in computing and engineering.

20/08/2025

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]
📣 Follow us:
• Facebook: facebook.com/ace.apc.aastmt
• Twitter/X: twitter.com/ace_journal_apc

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

09/07/2025

We’re excited to announce that the June issue of the Advances in Computing and Engineering Journal (ACE), Volume 5, Issue 1, has been officially published
Read the full issue at: https://apc.aast.edu/ojs/index.php/ACE/issue/view/91/showToc

25/06/2025

Call for Papers – Advances in Computing and Engineering (ACE) Journal
Published by: Academy Publishing Center, Arab Academy for Science, Technology, and Maritime Transport (AASTMT), Alexandria, Egypt
Submission Deadline: May 30, 2025
Next Issue: June 2025
Website: http://apc.aast.edu/ojs/index.php/ACE/index
Submission Link: http://apc.aast.edu/ojs/index.php/ace/user/register
________________________________________
About the Journal
Advances in Computing and Engineering (ACE) is a biannual, open-access, international, peer-reviewed journal. It provides a global platform for the publication of original research articles, case studies, and reviews focusing on the latest developments in computing, engineering, and related interdisciplinary fields.
Key Features:
• Indexed in: DOAJ, Google Scholar, EuroPub, and EBSCO
• Future Indexing Goal: Scopus
• License: Creative Commons Attribution-NonCommercial 4.0 International (authors retain copyright)
• No Submission or Article Processing Charges (APCs)
________________________________________
Topics of Interest Include (but are not limited to):
• Theoretical and applied computing
• AI, machine learning, and data science
• Emerging technologies and interdisciplinary research
• Software and systems engineering
• Smart systems and IoT
• Cybersecurity and networks
• Bioinformatics
________________________________________
Follow Us Online:
• Email: [email protected]
• Facebook: https://www.facebook.com/ace.apc.aastmt
• Twitter/X: https://twitter.com/ace_journal_apc

14/04/2025

Call for Papers: Advances in Computing and Engineering (ACE)
Journal of Advances in Computing and Engineering (ACE) invites submissions for our upcoming issue , ACE is a peer-reviewed, open access, interdisciplinary journal, with two issues yearly. The focus of the journal is on theories, methods, and applications in computing and engineering. The journal covers all areas of computing, engineering, and technology, including interdisciplinary topics. ACE also publishes survey and review articles.
We welcome submissions covering topics such as: Computer science theory; Algorithms; Intelligent computing; Bioinformatics; Health informatics; Deep learning; Networks; Wireless communication systems; Signal processing; Robotics; Optical design engineering; Sensors.
Submission Link: https://apc.aast.edu/ojs/index.php/ACE/user/register

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Website

http://apc.aast.edu/ojs/index.php/ACE

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