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.

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
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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)
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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
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• 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

27/03/2025

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

03/03/2025

Advances in Computing and Engineering Journal
Volume4, Issue 2, 2024
https://apc.aast.edu/ojs/index.php/ACE/issue/view/77/showToc

Dynamic demand response strategies for load management using machine learning across consumer segments
Ravi Kumar Goli, Nazeer Shaik, Manju Sree Yalamanchili
DOI: https://dx.doi.org/10.21622/ACE.2024.04.2.1082

Abstract

Grid optimization and stability are essential for sustainable power management while energy demand keeps increasing. Demand Response (DR) programs, which provide financial incentives to promote participation, aim to modify customer energy usage patterns, especially during periods of peak demand. The effectiveness of six demand response models in the residential, commercial, and industrial sectors is investigated in this research. These systems efficiently support load adjustment tactics, such as load shifting and curtailment, to achieve notable peak load reductions by utilizing sophisticated prediction approaches, such as machine learning, statistical methods, and reinforcement learning. The study assesses each model’s performance in terms of load reduction and other metrics, emphasizing how customized incentive programs and sophisticated predictive analytics affect grid stability.

23/02/2025

Advances in Computing and Engineering Journal
Volume4, Issue 2, 2024
https://apc.aast.edu/ojs/index.php/ACE/issue/view/77/showToc

Development of policy research-evidence organizer and public health-policy evaluation tool (prophet): a computing paradigm for promoting evidence-informed policymaking in Nigeria
Kingsley Otubo Igboji, Chigozie Jesse Uneke, Fergus U. Onu, Onyedikachi Chukwu
DOI: https://dx.doi.org/10.21622/ACE.2024.04.2.1076

Abstract

Background: in vast majority of low-and middle-income countries, performance of health systems continues to be abysmally poor with unacceptably low health outcomes. This is not unconnected with implementation of evidence-deficient health policies. Critical research evidence contributes in strengthening health policies to ensure clear cut targets and context specifics that adequately addresses identified health challenges and inequities. This study modeled a computing paradigm for brokering knowledge translation process and assisting health policymakers in promoting evidenced-informed policymaking. It strategically evaluates and assess level of evidence content, and predict implementation prospects of health policy documents.

Methods: its development process adopted object-oriented methodology for structural analysis and design specifications. Visual Basic.net and standard query language server were deployed at the front-end and back-end implementation processes respectively. The study designed an algorithm based on discrete choice experiment technique in an iterative four-scaled user-defined parametric options for rating policy features and assessment of overall policy prospect. Salient policy features/attributes were assembled as assessable variable entities. It adapted machine learning linear model to classify attributes into 6-domains to reflect the WHO promoted 6-policy cycle of a health system. Aggregated scores of policy features across all domains are utilized to compute policy overall grade-point in percentage weight.

Results: PROPHET, was used to assess thirty-three (33) national health policies extracted from online repository warehousing health policy documents in Nigeria known as policy information platform. The result shows that only 11 out of the 33 (33.3%) policies passed with at least 50% grade-point fixed in this study as minimum benchmark for implementation considerations.

Conclusion: This system rates policy features, assesses overall implementation prospect of policies with seamless real-time data validation and referencing across modules. PROPHET is expected to aid health policymakers in amplifying evidence-informed policymaking for improved health outcomes.

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