Engineering Applications of Artificial Intelligence最新文献

Fault diagnosis method of mining vibrating screen mesh based on an improved algorithm

IF 7.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2025-02-26 DOI: 10.1016/j.engappai.2025.110343

Fusheng Niu , Jiahui Wu , Jinxia Zhang , ZhiHeng Nie , Guang Song , Xiongsheng Zhu , Shuo Wang

Artificial intelligence fault diagnosis technology based on machine vision, due to its low cost and high efficiency, has become an indispensable part of production processes across various industries. Compared to traditional fault diagnosis methods, artificial intelligence diagnosis of common mechanical failures, such as ‘clogging’, ‘wear’, and ‘breakage’ in vibrating screen meshes within the mining screening sector, improves detection efficiency, accuracy, and sustainability. Since small target faults in large screening areas are challenging to detect through manual diagnosis, it reduces screening efficiency and shorter equipment lifespan, negatively impacting mining enterprises' safe and efficient production. A fault diagnosis model with a better speed-precision trade-off is proposed to improve detection precision based on the You Only Look Once version 5 single-stage object detection algorithm. This model is optimized in feature extraction and fusion by integrating autocode masking, re-parameterization, and omni-dimensional attention. The model's performance is primarily evaluated using precision, recall, balanced score, and mean average precision. The improved algorithm achieves a precision of 97.2%, a recall of 93.3%, a balanced score of 95.21%, and a mean average precision of 97.0%. Experimental results demonstrate that the improved algorithm increases the mean average precision by 3.1% compared to the original model. The results show that the improved algorithm is more effective than the original in fault diagnosis, with enhanced screen mesh detection precision. Thus, it ensures production safety and stable screening efficiency. Moreover, the proposed algorithm provides a reference for advancing intelligent and efficient fault diagnosis technology in the mining screening field.

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引用次数: 0

An enhanced failure mode and effect analysis method based on preference disaggregation in risk analysis of intelligent wearable medical devices

IF 7.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2025-02-26 DOI: 10.1016/j.engappai.2025.110384

Huchang Liao , Xiaoyan Yin , Xingli Wu , Romualdas Bausys

Conducting a risk analysis on potential failure modes that may damage the performance of intelligent wearable medical devices is imperative since the failure of the devices could directly impact human health. Failure mode and effect analysis (FMEA) is an evaluative instrument for potential failure modes in risk management. This paper presents an enhanced FMEA technique grounded in preference disaggregation analysis considering the interrelationships between failure modes to improve the precision of risk analysis. First, the initial evaluation of failure mode occurrence is updated by the overall influence-strength matrix among failure modes. The matrix formation considers the indirect interrelationships between failure modes, the positive/negative effects of failure modes, and the initial strength of failure modes. Then, a preference disaggregation method is applied to derive the weights of risk factors and the overall utilities of failure modes from historical decision examples. Failure modes are categorized from the most severe to the least severe according to their utilities. Smart bracelets, as a type of intelligent wearable medical devices, apply artificial intelligence technology in health monitoring. Through an illustrative case study of smart bracelets, the efficacy of the proposed approach is validated.

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引用次数: 0

An integrated method of hotel site selection based on probabilistic linguistic multi-attribute group decision making

IF 7.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2025-02-26 DOI: 10.1016/j.engappai.2025.110328

Jiu-Ying Dong , Ying-Ying Yao , Shyi-Ming Chen , Shu-Ping Wan

The site selection acts a pivotal role in determining the success of a construction project. Since the site selection needs to gather the wisdom of a group of decision makers (DMs) and involves many factors, it can be regarded as a multi-attribute group decision making (MAGDM) problem in artificial intelligence. The assessments of alternatives on attributes are expressed by probabilistic linguistic term sets (PLTSs). A new two-stage normalization method is proposed for PLTSs considering the psychological states of decision makers. A new score function for PLTS is defined. The best and worst method is extended for fuzzy preference relation. The individual objective attribute weights are determined via information entropy. The individual subjective attribute weights are derived through the extended best and worst method. The individual comprehensive attribute weights are derived by minimum relative entropy principle. The weights of DMs are acquired through an optimization model. It minimizes the deviation between the opinions of all DMs and the deviation between the individual and collective comprehensive attribute weight vectors, simultaneously, which effectively overcomes the drawback of only minimizing single deviation. A new method is presented for MAGDM with PLTSs. A hotel site selection example is demonstrated and comparative analyses are executed to verify the validity and advantages of the proposed method. The test statistic

Z

values of Spearman's rank-correlation test are all smaller than 1.645, which shows that the ranking order obtained by the proposed method is statistically sharply distinct from that produced by other methods and thus further validates the proposed method.

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引用次数: 0

Enhancing cross-lingual hate speech detection through contrastive and adversarial learning

IF 7.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2025-02-26 DOI: 10.1016/j.engappai.2025.110296

Asseel Jabbar Almahdi, Ali Mohades, Mohammad Akbari, Soroush Heidary

The rise of hate speech on social media platforms, particularly in low-resource languages, necessitates innovative solutions. In response, we introduce a zero and few-shot model combining supervised contrastive learning and adversarial training. To address the scarcity of labeled data in diverse languages, our approach adapts features from well-resourced languages to efficiently detect hate speech in low-resource contexts. The proposed framework first leverages supervised contrastive learning, maximizing the utility of limited labeled data by transferring knowledge from source languages. This adaptation empowers the accurate detection of hate speech in underrepresented languages, optimizing available resources. We then introduce contrastive adversarial training, refining hate speech representations in low-resource languages. This approach ensures a nuanced understanding of hate speech across linguistic boundaries, significantly enhancing the model’s adaptability and accuracy. To validate our approach, we conducted zero-shot and few-shot cross-lingual evaluations in three languages. Our results demonstrate the superiority of the proposed contrastive learning-based models. To ensure reproducibility, the code associated with this paper is available on GitHub (Almahdi, 2024). .

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引用次数: 0

Not all samples are equal: Boosting action segmentation via selective incremental learning

IF 7.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2025-02-26 DOI: 10.1016/j.engappai.2025.110334

Feng Huang , Xiao-Diao Chen , Wen Wu , Weiyin Ma

Temporal action segmentation (TAS) seeks to perform classification for each frame in a video. Existing methods tend to design diverse network architectures, while overlooking the intrinsic characteristics of training samples. Notably, two key issues arise: (1) Frames around action boundaries are more ambiguous and thus pose greater difficulties for training compared to other frames; and (2) beyond the commonly used categorical labels, the total number of action instances within a video may serve as an additional, potentially vital, supervision cue. To address these issues, this paper introduces a novel method that combines a model-agnostic training strategy with an instance number alignment loss, designed to enhance the performance of existing models. Specifically, a selective incremental learning (SIL) strategy is proposed to alleviate the impact of noisy samples by progressively training the model in an easy-to-difficult manner through a dynamic sample selection mechanism. Furthermore, an instance number alignment loss (INAL) is developed to capture both global and local features simultaneously by incorporating a multi-task learning module. Extensive evaluations are conducted on three benchmark datasets, namely 50Salads, Georgia Tech egocentric activities (GTEA), and Breakfast. The experimental results demonstrate that the proposed method achieves substantial performance improvements over state-of-the-art approaches.

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引用次数: 0

Novel q-Rung Orthopair Fuzzy distance based similarity measure and score function in real life decision making

IF 7.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2025-02-26 DOI: 10.1016/j.engappai.2025.110348

Raili Basu, Sayanta Chakraborty, Apu Kumar Saha

The distance and similarity measure are the most promising information measures to estimate the distance and similarity between two sets. In the present study, novel distance and similarity measure are introduced and affirmed their proficiencies by theoretical explications and numerical examples. To demonstrate the applicability of the proposed similarity measure (pSMr), medical diagnosis and pattern recognition problems have been solved. This study aims to develop a novel ranking method under q-Rung Orthopair Fuzzy Environment (q-ROFE) based on the proposed measure to solve multi-criteria group decision making (MCGDM) problem. This method aims to identify the most promising alternative based on similarity to positive ideal solution (PIS). The focus is to comprehend the closeness of performance of an alternative from desired performance relative to its distance from ideal solutions. Furthermore, an improved q-Rung orthopair fuzzy score function (SF) has also been proposed elaborating all the significant properties. Moreover, Full consistency method (FUCOM) has been reclaimed under q-ROFE with the aid of q-Rung orthopair fuzzy Weighted Averaging Operator (q-ROFWA). To validate the acceptability of the proposed ranking method, a problem of healthcare waste management (HCWM) has been structured and solved. The feasibility and effectiveness of integrated model have been encountered with comparative and sensitivity examinations. The proposed measures outperform over existing measures to discriminate two q-ROF numbers, which has been validated by comparison. Moreover, the novel

S F

is competent enough to rank any type of q-ROF numbers regardless of their membership and non-membership grades and the dominance has been illustrated through comparisons.

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引用次数: 0

Interpreting injection molding quality defect using explainable artificial intelligence and analysis of variance

IF 7.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2025-02-26 DOI: 10.1016/j.engappai.2025.110362

Faouzi Tayalati, Ikhlass Boukrouh, Abdellah Azmani, Monir Azmani

Injection molding is a widely employed manufacturing process known for its efficiency, precision, and scalability in producing complex plastic components. However, persistent quality defects, particularly shrinkage, remain a significant challenge, directly impacting product reliability and performance. Traditional studies have relied heavily on Analysis of Variance (ANOVA) to identify and analyze the parameters influencing such defects. While ANOVA is effective in determining the significance of individual factors, it often falls short in capturing the nonlinear interactions and complex dependencies characteristic of injection molding processes. Our study addresses this limitation by adopting a hybrid methodology that integrates ANOVA with explainable artificial intelligence (XAI) techniques, specifically SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-Agnostic Explanations). This innovative approach offers a deeper and more interpretable analysis of the factors affecting shrinkage. We investigate five key parameters: mold temperature, melt temperature, injection time, packing time, and packing pressure. The results reveal that the hybrid approach not only identifies significant parameters but also uncovers complex interactions overlooked by ANOVA alone. This nuanced understanding of process dynamics facilitates improved defect prediction and control, advancing the quality of injection-molded products. By bridging the gap in conventional methodologies, our research blends statistical precision with XAI's interpretability, providing a novel framework for optimizing injection molding processes and enhancing product reliability.

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引用次数: 0

Automated acquisition and analysis of illegal fund accounts in gambling websites

IF 7.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2025-02-25 DOI: 10.1016/j.engappai.2025.110326

Shenao Zheng , Yanan Cheng, Guoying Sun , Zhaoxin Zhang

While the rapid growth of the internet has made life more convenient, it has also made it easier for people to access online gambling sites. This has led to an increase in fraud, resulting in significant financial losses and serious societal issues. However, the abundance of gambling sites, diverse development frameworks, and complex identity verification methods pose significant challenges to gaining comprehensive access to their illegal funds. To address online gambling fraud and assist law enforcement in cutting off their funding chains, we investigate gambling websites to uncover information about the physical funding accounts used by criminal groups. Given the extensive scale of these websites, we propose an auto-registration framework based on You Only Look Once version 4 (YOLOv4) to automate account registration and retrieve illegal fund account details. Additionally, we construct a dataset of user interface (UI) elements from 17 types of gambling websites for model training. The YOLOv4 model achieves an F1-score of 0.84 and a mean Average Precision (mAP) of 83.96%. The proposed framework achieves a registration success rate of 60.58%. After extracting data from numerous gambling websites in seven batches, we identify 7496 entity account details and 47 payment methods, analyze the wealth of entity information, and highlight six new payment methods. Finally, by identifying recurring illegal fund accounts across multiple domains, we confirm 23 criminal gangs, providing substantial support to law enforcement agencies in combating online gambling-related crimes.

互联网的迅猛发展在为人们的生活带来便利的同时，也使人们更容易访问在线赌博网站。这导致了欺诈行为的增加，造成了巨大的经济损失和严重的社会问题。然而，大量的赌博网站、多样化的开发框架和复杂的身份验证方法给全面获取非法资金带来了巨大挑战。为了应对网络赌博欺诈，协助执法部门切断其资金链，我们对赌博网站进行调查，以发现犯罪集团使用的实际资金账户信息。鉴于这些网站规模庞大，我们提出了一个基于 You Only Look Once version 4（YOLOv4）的自动注册框架，以自动注册账户并获取非法资金账户的详细信息。此外，我们还构建了一个包含 17 种赌博网站用户界面（UI）元素的数据集，用于模型训练。YOLOv4 模型的 F1 分数为 0.84，平均精度 (mAP) 为 83.96%。拟议框架的注册成功率为 60.58%。在分七批次从众多赌博网站提取数据后，我们识别出 7496 个实体账户详情和 47 种支付方式，分析了大量实体信息，并突出显示了六种新的支付方式。最后，通过识别多个域名中重复出现的非法资金账户，我们确认了 23 个犯罪团伙，为执法部门打击网络赌博相关犯罪提供了有力支持。

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引用次数: 0

Dual-clustering-based Two-population Co-evolutionary Algorithm for segmentation coding in flash memory

IF 7.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2025-02-25 DOI: 10.1016/j.engappai.2025.110329

Jianjun Luo , Menghao Chen , Boming Huang , Hailuan Liu , Lingyan Fan , Lijuan Gao , Guorui Feng

To ensure the reliability of stored data, error checking and correction (ECC) module is widely used in flash memory-based storage devices. With the increase of raw bit error rate (RBER), the traditional error correction coding mode is not only hard to satisfy the requirements of storage devices, but also reduces the efficiency of data recovery when error data cannot be located. In this paper, a novel segmentation coding mode is proposed to improve the drawbacks of traditional coding mode. To our knowledge, this is an innovative study of the error correction coding mode in ECC. It can make the coding mode used in ECC to become more flexible and efficient. Since the performance of this coding mode is related to segmentation, we propose a dual-population co-evolutionary algorithm based on clustering algorithm to optimize the performance. The proposed algorithm adopts clustering algorithm to measure the diversity of the population and dynamic weight allocation strategy to regulate evolution indicators of the population. Some experiments are conducted on benchmark problems and the segmentation coding problem, respectively. Experimental results show that the proposed algorithm is superior to other state-of-the-art evolutionary algorithms.

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引用次数: 0

Joint prediction of multi-aircraft trajectories in terminal airspace: A Flight Pattern-Guided Social Long-Short Term Memory network

IF 7.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2025-02-25 DOI: 10.1016/j.engappai.2025.110325

Xiao Chu , Weili Zeng , Lingxiao Wu

Aircraft trajectory prediction is a critical foundation for tasks such as conflict detection and resolution, and monitoring of abnormal aircraft behavior, thus making it a key technology for the next generation of air traffic systems. Airport terminal areas, serving as the convergence points of the entire air transportation network, accommodate the highest density of aircraft within the aviation system. Among these densely packed aircraft, there are inherent and potential interactions that are invisible yet influential, posing a significant challenge for trajectory prediction in the terminal area. To address this issue, we propose a Flight Pattern-Guided Social Long-Short Term Memory (FPG-SLSTM) network to jointly predict multiple aircraft trajectories. Firstly, it introduces a flight pattern matching method based on classification concepts to assign a flight pattern to each trajectory. Following this, the flight intent trajectory is generated based on that flight pattern. Then, the loss function is improved by incorporating the flight intent trajectory as prior physical information, and the Social Long-Short Term Memory (Social LSTM) neural network is employed to model the trajectory prediction problem for multiple aircraft, with social pooling operations to integrate the mutual influences among aircraft. A real-world dataset was constructed to validate the proposed approach. Experimental results show that the method achieved a 14.5% improvement in horizontal prediction accuracy and a 29.5% improvement in height prediction accuracy on average for a 6-minute horizon, compared to Binary Encoding Representation for Flight Trajectory Prediction (FlightBERT). These findings highlight that the proposed framework outperforms other baseline models in terms of prediction accuracy, particularly in complex traffic environments during busy periods, demonstrating the model’s ability to further enhance both the precision and robustness of trajectory prediction.

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引用次数: 0