Advances in Engineering Innovation

Open access

Print ISSN: 2977-3903

Online ISSN: 2977-3911

About AEI

Advances in Engineering Innovation (AEI) is a peer-reviewed, fast-indexing open access journal hosted by Tianjin University Research Centre on Data Intelligence and Cloud-Edge-Client Service Engineering and published by EWA Publishing. AEI is published irregularly, and it is a comprehensive journal focusing on multidisciplinary areas of engineering and at the interface of related subjects, including, but not limited to, Artificial Intelligence, Biomedical Engineering, Electrical and Electronic Engineering, Materials Engineering, Traffic and Transportation Engineering, etc.

For the details about the AEI scope, please refer to the Aims and Scope page. For more information about the journal, please refer to the FAQ page or contact info@ewapublishing.org.

Aims & scope of AEI are:
· Artificial Intelligence
· Computer Sciences
· Aerospace Engineering
· Architecture & Civil Engineering
· Biomedical Engineering
· Electrical and Electronic Engineering
· Energy and Power Engineering
· Materials Engineering
· Mechanical Engineering
· Traffic and Transportation Engineering

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Editors View full editorial board

Sharidya Rahman
Monash University
Australia
Editorial Board
Alvin Gatto
Brunel University London
United Kingdom
Editorial Board
Cemil Keskinoğlu
Cukurova University
Editorial Board
Lee Ching Hao
Taylor's University
Malaysia
Editorial Board

Latest articles View all articles

Research Article
Published on 17 January 2025 DOI: 10.54254/2977-3903/2025.20556
Yichen Xu, Yaoyu Chen

Increasing amounts of financial data demand sophisticated analytics to develop sound recommendation models. This article discusses combining time series analysis and association rule mining for big data in Hadoop and Spark to enrich financial product recommendation engines. The paper is an integrated analysis of two types of prediction algorithms: AutoRegressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) networks to forecast user behavior and demand for financial services in the future from transactional history. The ARIMA model is used as the default while the LSTM model is used to represent non-linear dependencies and give a more dynamic forecast. association rule mining – in particular the Apriori algorithm – is used to find latent patterns and relationships between user transactions and financial products. This article illustrates how time series forecasting and association rule mining can be merged to bring a more useful financial recommendation. The hybrid approach, which combines both approaches, proves to increase user interaction and recommendation accuracy by 20% compared to the previous systems, according to experiments. The paper emphasises the possibilities of using big data in the construction of scalable, individualized financial recommendation systems.

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Xu,Y.;Chen,Y. (2025).Time series data analysis and association rule mining in financial recommendation systems using Hadoop and Spark.Advances in Engineering Innovation,15,35-39.
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Research Article
Published on 17 January 2025 DOI: 10.54254/2977-3903/2025.20553
Guigen Jin, Dian Jin, Xiaoxia Ding

In intelligent warehousing and transportation processes, the centralization of material units significantly enhances storage and handling efficiency. Among these, the centralized unitization of material pallets is in high demand and widely applied in practical operations. In multi-SKU scenarios, achieving efficient palletizing—particularly online mixed palletizing—poses a major challenge in logistics operations. This process aims to save manpower while ensuring operational efficiency. To address this issue, this paper presents a combined heuristic algorithm that integrates an anthropomorphic heuristic algorithm with a greedy algorithm incorporating local perturbations. The proposed approach accounts for constraints such as mass, volume, center of gravity, non-overlapping placement, and stability. Experimental results demonstrate that this algorithm effectively resolves the palletizing challenges for multi-SKU goods, significantly reducing space waste.

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Jin,G.;Jin,D.;Ding,X. (2025).Research on online mixed palletizing strategy of robotic arms for multi-SKU scenarios.Advances in Engineering Innovation,15,26-34.
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Research Article
Published on 15 January 2025 DOI: 10.54254/2977-3903/2025.20517
Yankun Li

Urbanization is a significant driver of land use change, particularly in rapidly growing metropolitan areas. This research investigates Greenfield City land use change in the 20-year period (2000-2020) using GIS and satellite data. The mapping shows where the greatest land-use changes occurred, ranging from increased residential and commercial developments to the loss of agricultural fields and the omission of green space. The work applies multi-temporal analyses of Landsat satellite images taken in 2000, 2010 and 2020 to estimate land cover change and its effects on urban planning and sustainability. They indicate that there’s a clear rise in housing and business developments, but also a steep decline in farming and greenspace. These transformations affect the environment, with habitat loss, biodiversity destruction and encroachment on natural resources. The paper wraps up by focusing on the issues of sustainability in urban planning and how better land use planning is required to reduce the negative environmental effects of urban sprawl.

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Li,Y. (2025).Multi-temporal analysis of land use change using GIS and satellite imagery: Implications for sustainable urban planning.Advances in Engineering Innovation,15,21-25.
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Research Article
Published on 15 January 2025 DOI: 10.54254/2977-3903/2025.20514
Yifan Zhou, Yuhui Wang, Chao Yao, Wandi Sun, An Yang, Xiuxiu Sun

In intelligent scenarios, large language models (LLMs) are used to create characters that interact with users, providing guidance and relevant information. The higher the degree of anthropomorphism of these roles, the better the emotional experience they provide, which is beneficial for user interaction and enhances user experience. Therefore, evaluating the character-creation capabilities of LLMs is essential. This study used a questionnaire and used another LLM (ChatGPT-4o) to assess the impact of emoji usage and language style on the anthropomorphism and emotional expression of content generated by LLMs. The results indicate that when using emojis, the characters exhibit higher levels of anthropomorphism and emotional expression. Additionally, informal language styles contribute to enhancing both anthropomorphism and emotional expression.

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Zhou,Y.;Wang,Y.;Yao,C.;Sun,W.;Yang,A.;Sun,X. (2025).Evaluation of character creation of large language models.Advances in Engineering Innovation,15,14-20.
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Volumes View all volumes

2025

Volume 15January 2025

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2024

Volume 14December 2024

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Volume 13November 2024

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Volume 12October 2024

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Indexing

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