Applied and Computational Engineering

Open access

Print ISSN: 2755-2721

Online ISSN: 2755-273X

About ACE

The proceedings series Applied and Computational Engineering (ACE) is an international peer-reviewed open access series that publishes conference proceedings from various methodological and disciplinary perspectives concerning engineering and technology. ACE is published irregularly. The series contributes to the development of computing sectors by providing an open platform for sharing and discussion. The series publishes articles that are research-oriented and welcomes theoretical and applicational studies. Proceedings that are suitable for publication in the ACE cover domains on various perspectives of computing and engineering.

Aims & scope of ACE are:
·Computing
·Machine Learning
·Electrical Engineering & Signal Processing
·Applied Physics & Mechanical Engineering
·Chemical & Environmental Engineering
·Materials Science and Engineering

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Editors View full editorial board

Hisham AbouGrad
University of East London
United Kingdom
Editorial Board
Mian Umer Shafiq
UCSI University
Malaysia
Editorial Board
Bilyaminu Auwal Romo
University of East London
United Kingdom
Editorial Board
Yilun Shang
Northumbria University
United Kingdom
Associate Editor
yilun.shang@northumbria.ac.uk

Latest articles View all articles

Research Article
Published on 4 July 2025 DOI: 10.54254/2755-2721/2025.BJ24684
Zhaolin Yu

Semantic segmentation has undergone a remarkable transformation from traditional computer vision approaches to sophisticated deep learning architectures, culminating in the revolutionary capabilities introduced by foundation models. This comprehensive survey examines the technical progression of semantic segmentation methodologies, with particular emphasis on vision foundation models, such as the Segment Anything Model (SAM) and Contrastive Language-Image Pre-training (CLIP). This paper systematically analyzes how these large-scale pretrained models enable previously unattainable capabilities, including zero-shot learning and cross-domain generalization while identifying persistent challenges regarding computational efficiency and boundary precision. The investigation encompasses critical applications across medical imaging, remote sensing, and video understanding domains, revealing both transformative benefits and technical limitations. It concludes that foundation models represent a fundamental paradigm shift requiring hybrid approaches that effectively combine general capabilities with domain-specific optimizations.

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Yu,Z. (2025). Semantic Segmentation in the Era of Foundation Models: Technical Evolution and Applications. Applied and Computational Engineering,177,10-15.
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Research Article
Published on 4 July 2025 DOI: 10.54254/2755-2721/2025.BJ24681
Nuo Chen

As urban traffic congestion continues to intensify, predicting short-term traffic flow has become essential to enabling real-time control in intelligent transportation systems (ITS). However, traditional models face significant limitations in capturing the spatiotemporal and nonlinear characteristics of traffic data. Long Short-Term Memory (LSTM) networks, with their gated mechanisms, can effectively model long-term dependencies and periodic patterns in traffic flow. The accuracy of these predictions directly influences decision-making in scenarios such as traffic guidance and emergency management, offering substantial practical value for improving road network efficiency. This study constructs an optimized LSTM model to evaluate its effectiveness in short-term traffic prediction and to compare predictive performance across different time granularities. A dual-layer LSTM architecture is employed, incorporating the Adam optimizer, Dropout, and early stopping as regularization strategies. Using urban traffic monitoring data from the United States, both hourly and daily prediction models are developed for experimental validation. Results indicate that the hourly prediction model (MSE = 0.0709) markedly surpasses the daily model (MSE = 0.2987), effectively identifying recurring patterns like rush periods in the morning and evening. These outcomes offer a practical solution for adaptive traffic regulation.

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Chen,N. (2025). Construction of a Short-Term Traffic Flow Prediction Model Based on Improved LSTM and Performance Evaluation Across Multiple Time Granularities. Applied and Computational Engineering,177,1-9.
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Research Article
Published on 4 July 2025 DOI: 10.54254/2755-2721/2025.24689
Jingyu Tang

This study proposes a novel malware detection framework integrating dynamic and static analysis, and realizes the collaborative processing of bi-modal data through a unified graph neural network architecture. Specifically: extracting the control flow and data dependency features from binary disassembly, and capturing the system call sequence with time attributes in the sandbox environment; After encoding the two types of features into heterogeneous relationship graphs, a two-branch network is adopted to process the static topology (graph convolutional layer) and dynamic sequence (graph attention layer) respectively; Finally, the classification decision-making is achieved by the feature fusion module. In the benchmark test set of EMBER, VirusShare, and CIC-MalMem, the accuracy rate of the framework exceeded 95%, which is 4 to 7 percentage points higher than the single-modal baseline. The recall rate of unknown malware families remained above 92%, and the single-sample detection time was less than 50 milliseconds. The ablation experiment confirmed that static features effectively resist shell confusion and dynamic temporal attributes improve the recognition of distorted viruses. The current system has limitations on anti-sandbox detection technology. Further research suggests combining reinforcement learning to dynamically adjust the sandbox depth and introducing contractive learning to optimize the discriminative ability of graph embedding.

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Tang,J. (2025). Fusion of Static and Dynamic Features for Malware Detection: A Graph Neural Network Approach to Behavioral Representation and Classification. Applied and Computational Engineering,176,16-22.
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Research Article
Published on 4 July 2025 DOI: 10.54254/2755-2721/2025.24683
Yimeng Wang

In video content analysis, accurate tracking and recognition of objects is a complex task. Current research has primarily focused on the development of complex scenes and fast-moving targets. Yet, there are challenges of small objects, long time-series dependencies, and object occlusion. In this paper, we propose the Intelli-context transformer to detect objects in a dynamic environment. Addressing this challenge, attention mechanisms, contextual information, and semantic information are integrated into Intelli-Context Transformer to enhance the accuracy of video object tracking. Intelli-Context Transformer employs an end-to-end training approach and incorporates a Contextual Spatiotemporal Attention Module, which dynamically adjusts the focus on different information to improve recognition accuracy. The proposed method is capable of capturing and analyzing the spatiotemporal features of a single target in videos in real time, effectively handling tracking tasks in complex scenes. Compared with state-of-the-art methods, Intelli-Context Transformer demonstrates its strong generalization capability in video object recognition. This research provides an efficient and reliable approach for dynamic target tracking in complex scenes and offers technical support for functions such as behavior analysis and anomaly detection, contributing to the development of intelligent video surveillance and navigation.

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Wang,Y. (2025). Moving Object Tracking Using Context-Aware Attention Transformer. Applied and Computational Engineering,176,8-15.
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Volumes View all volumes

Volume 177July 2025

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Proceedings of CONF-MLA 2025 Symposium: Applied Artificial Intelligence Research

Conference website: https://2025.confmla.org/

Conference date: 3 September 2025

ISBN: 978-1-80590-241-6(Print)/978-1-80590-242-3(Online)

Editor: Hisham AbouGrad

Volume 176July 2025

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Proceedings of the 3rd International Conference on Machine Learning and Automation

Conference website: 978-1-80590-240-9

Conference date: 17 November 2025

ISBN: 978-1-80590-239-3(Print)/978-1-80590-240-9(Online)

Editor: Hisham AbouGrad

Volume 175July 2025

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Proceedings of CONF-CDS 2025 Symposium: Application of Machine Learning in Engineering

Conference website: https://www.confcds.org

Conference date: 19 August 2025

ISBN: 978-1-80590-237-9(Print)/978-1-80590-238-6(Online)

Editor: Marwan Omar, Mian Umer Shafiq

Volume 174July 2025

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Proceedings of CONF-CDS 2025 Symposium: Data Visualization Methods for Evaluatio

Conference website: https://2025.confcds.org/portsmouth.html

Conference date: 30 July 2025

ISBN: 978-1-80590-235-5(Print)/978-1-80590-236-2(Online)

Editor: Marwan Omar, Elisavet Andrikopoulou

Indexing

The published articles will be submitted to following databases below: