About ACEThe 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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Our blind and multi-reviewer process ensures that all articles are rigorously evaluated based on their intellectual merit and contribution to the field.
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United Kingdom
Malaysia
United Kingdom
United Kingdom
yilun.shang@northumbria.ac.uk
Latest articles View all articles

Among the numerous deep learning-based object detection algorithms, the YOLO (You Only Look Once) series has become a preferred choice for various detection scenarios due to its fast detection speed, high accuracy, and strong adaptability. However, its performance remains suboptimal when applied to unconventional images, such as aerial images, which often feature complex content and small detection targets. To overcome this limitation, a novel model called YOLOv8-TDE (Tiny Detection Enhanced) is introduced in this work. First, to more effectively differentiate between key features and noise and to comprehensively capture multi-scale target features, the feature extraction network is improved by employing pooling convolution kernels of various sizes and incorporating a lightweight attention mechanism. Second, the GeoIoU (Geometric Intersection over Union) loss function is introduced to reduce sensitivity to aspect ratio and center point distance, addressing the limitations of the original CIoU loss function, which is overly sensitive to small changes in these parameters. Finally, a novel detection head, the FAD Head, is proposed to dynamically generate detection head parameters based on the input image's features, enabling better feature extraction for targets of different sizes in complex scenes. This enhancement improves the model's adaptability across various scenarios, as well as detection accuracy and stability. Experiments on the VisDrone2019 dataset demonstrate that the proposed model outperforms the original YOLOv8n, achieving a 15.5% improvement in mAP@0.5 and a 17.6% improvement in mAP@0.5:0.95.

Accurate stock price prediction is crucial for investors and financial institutions, yet the complexity of the stock market makes it highly challenging. This study aims to construct an effective model to enhance the prediction ability of General Electric's stock price trend. The CNN - LSTM model is adopted, combining the feature extraction ability of CNN with the long - term dependency handling ability of LSTM, and the Adam optimizer is used to adjust the parameters. In the data preparation stage, historical trading data of General Electric's stock is collected. After cleaning, handling missing values, and feature engineering, features with strong correlations to the closing price are selected and dimensionality reduction is performed. During model training, the data is divided into training, validation, and testing sets in a ratio of 7:2:1. The Stochastic Gradient Descent algorithm is used with a dynamic learning rate adjustment and L2 regularization, and the Mean Squared Error is used as the loss function, evaluated by variance, R - squared score, and maximum error. Experimental results show that the model loss decreases steadily, and the predicted values align well with the actual values, providing a powerful tool for investment decisions. However, the model's performance in real - time and extreme market conditions remains to be tested, and future improvements could consider incorporating more data sources.

The gradual development and expansion of online social media provides a stage for people to share their personal views and opinions. Many experts and scholars have collected and analyzed these emotions, created numerous emotional models and data sets, which are widely used in various fields including architectural interior design. Through sentiment analysis techniques, it has become an important branch in interior design to investigate how to optimise user’s emotional experience, such as comfort, sense of belonging and well-being. This paper selects GoEmotions as the dataset for sentiment classification and analysis of text, which is currently the largest manually annotated dataset. By analyzing the comments on Instagram posts related to interior design, the study investigates the impact of residential interior space design on people's emotions, and then trains a Low-Rank Adaptation (LoRA) model that can generate interior spaces with certain emotional feelings.

Detecting objects in urban traffic images presents considerable difficulties because of the following reasons: 1) These images are typically immense in size, encompassing millions or even hundreds of millions of pixels, yet computational resources are constrained. 2) The small size of vehicles in certain scenarios leads to insufficient information for accurate detection. 3) The uneven distribution of vehicles causes inefficient use of computational resources. To address these issues, we propose YOLOSCM (You Only Look Once with Segmentation Clustering Module), an efficient and effective framework. To address the challenges of large-scale images and the non-uniform distribution of vehicles, we propose a Segmentation Clustering Module (SCM). This module adaptively identifies clustered regions, enabling the model to focus on these areas for more precise detection. Additionally, we propose a new training strategy to optimize the detection of small vehicles and densely packed targets in complex urban traffic scenes. We perform extensive experiments on urban traffic datasets to demonstrate the effectiveness and superiority of our proposed approach.
Volumes View all volumes
Volume 134February 2025
Find articlesProceedings of the 5th International Conference on Signal Processing and Machine Learning
Conference website: https://2025.confspml.org/
Conference date: 12 January 2025
ISBN: 978-1-83558-955-7(Print)/978-1-83558-956-4(Online)
Editor: Stavros Shiaeles
Volume 133February 2025
Find articlesProceedings of the 5th International Conference on Signal Processing and Machine Learning
Conference website: https://2025.confspml.org/
Conference date: 12 January 2025
ISBN: 978-1-83558-943-4(Print)/978-1-83558-944-1(Online)
Editor: Stavros Shiaeles
Volume 132February 2025
Find articlesProceedings of the 2nd International Conference on Machine Learning and Automation
Conference website: https://2024.confmla.org/
Conference date: 21 November 2024
ISBN: 978-1-83558-941-0(Print)/978-1-83558-942-7(Online)
Editor: Mustafa ISTANBULLU
Volume 131February 2025
Find articlesProceedings of the 2nd International Conference on Machine Learning and Automation
Conference website: https://2024.confmla.org/
Conference date: 21 November 2024
ISBN: 978-1-83558-939-7(Print)/978-1-83558-940-3(Online)
Editor: Mustafa ISTANBULLU
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