Applied and Computational Engineering

- The Open Access Proceedings Series for Conferences

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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. 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.

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December 21, 2022

Applied and Computational Engineering - Gender and Diversity pledge


We pledge to our series community:

  • We're committed: we put diversity and inclusion at the heart of our activities
  • We champion change: we're working to increase the percentage of women, early career ...

December 6, 2021

Applied and Computational Engineering - Disclaimer


  • The statements, opinions and data contained in the series Applied and Computational Engineering (ACE) are solely those of the individual authors and contributors and not of the publisher and the editor(s). Applied and Computational Engineering stays neutral with regard to jurisdictional claims in published maps and ...
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    News

  • February 22, 2023, Good News! Welcome Dr. Marwan Omar from Illinois Institute of Technology to give a speech at CONF-CDS 2023!
  • February 21, 2023, Good News! Welcome Dr. Roman Bauer from University of Surrey to give a speech at CONF-CDS 2023!
  • January 18, 2023: Prof. Festus Adedoyin from Bournemouth University was invited to deliver a keynote speech at CONF-MSS 2023.
  • February 15, 2023: Good News! Welcome Dr. Ioannis Spanopoulos from University of South Florida to give a speech at CONF-MCEE 2023!
  • February 14, 2023, Good News! Welcome Dr. Roman Bauer from University of Surrey to give a speech at CONF-SPML 2024!
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    Latest articles

    Open Access | Article

    In this paper, we propose an optimised image segmentation method by structurally innovating and improving the traditional Unet model and integrating the latest GSConv module. In our experiments, we integrate the GSConv module into the encoder and decoder parts of U-Net to take advantage of its excellent feature extraction and information transfer capabilities. In comparing the training process of the two models of Unet and GSConv Unet, it is found that GSConv Unet has faster convergence speed and better generalisation ability, and finally shows higher segmentation accuracy and iou values in the test part. From the segmentation results, GSConv Unet delineates the lung region more accurately and meticulously compared to Unet, providing an effective idea for lung X-ray image segmentation tasks. This research is of great significance, which not only improves the effectiveness of the image segmentation task but also brings new technological breakthroughs in the field of medical imaging. By introducing the GSConv module and optimising the Unet structure, we have successfully improved the precision and efficiency of lung X-ray image segmentation, providing doctors with a more reliable and accurate diagnostic tool.

    Open Access | Article

    The integration of Artificial Intelligence (AI) with Environmental, Social, and Governance (ESG) models represents a significant shift in corporate strategy and sustainability efforts. This paper explores the transformative role of deep learning and machine learning technologies in enhancing the precision, efficiency, and effectiveness of ESG frameworks. By utilizing convolutional neural networks (CNNs) and natural language processing (NLP), businesses can now process vast amounts of data, gaining insights that were previously unattainable. The study delves into quantitative analyses involving regression models and scenario analyses, backed by Monte Carlo simulations, to demonstrate the predictive power of AI-enhanced ESG models. Furthermore, the paper discusses the challenges and solutions related to data quality, computational demands, and ethical considerations in implementing AI in ESG assessments. The empirical evidence and theoretical analysis presented underline the superiority of AI-integrated models over traditional methods, showcasing improvements in time-to-insight, predictive accuracy, and cost efficiency. This study not only highlights the practical applications of AI in corporate sustainability efforts but also addresses the ethical and operational challenges faced during implementation.

    Open Access | Article

    The large integer multiplication is the basis of many computer science algorithms, ranging from cryptography to complex calculations in various scientific fields. Contemporary society excessively depends on complex computing tasks. Hence, the need for good algorithms is becoming increasingly apparent as well. This text gives the reader an in-depth knowledge of the multiplication algorithms of large integers by contrasting traditional algorithms with the new Algorithm developed by Karatsuba. This research methodology involves a comparative analysis of the components using an advanced analysis framework that primarily focuses on execution times, efficiency metrics, and resource utilization. Incontrovertibly, the experimental results confirm the Karatsuba algorithm's undoubted hastiness compared to the conventional approaches. This study extends our grasp of the evolution of algorithms in computational optimization, enabling people to get unique and relevant findings that will benefit numerous areas where large integer multiplications are involved. In addition to these findings, the study also highlights the importance of algorithm selection in ensuring computational efficiency and accuracy in large integer multiplications across various applications.

    Open Access | Article

    This paper mainly studies the current mainstream vehicle object detection methods and improvement methods in specific scenarios. The research provides ideas and methods for further enhancing the accuracy and stability of vehicle object detection, which is beneficial in reducing missed and incorrect detections. This can improve the accuracy of target detection in urban areas, construction sites, and other scenarios, thereby ensuring safety. The purpose of this research is to improve vehicle object detection methods and identify directions for enhancement. In this essay, the main way of research is that reading a lot of papers and contrasting the advantage and disadvantage among them. The main finding of this paper is that in different application scenarios, the directions for improvement vary, and the improved algorithms cannot simultaneously accommodate both accuracy and robustness, and the improved methods cannot be widely used. Based on this information, pointing out the future development direction and clear problems.

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