Applied Intelligence最新文献_第9页

An original model for multi-target learning of logical rules for knowledge graph reasoning

IF 3.4 2区计算机科学

Applied Intelligence Pub Date : 2024-12-12 DOI: 10.1007/s10489-024-05966-1

Haotian Li, Bailing Wang, Kai Wang, Rui Zhang, Yuliang Wei

引用次数: 0

Cross-lingual prompting method with semantic-based answer space clustering

IF 3.4 2区计算机科学

Applied Intelligence Pub Date : 2024-12-12 DOI: 10.1007/s10489-024-06101-w

Ahtamjan Ahmat, Yating Yang, Bo Ma, Rui Dong, Rong Ma, Lei Wang

{"title":"Cross-lingual prompting method with semantic-based answer space clustering","authors":"Ahtamjan Ahmat, Yating Yang, Bo Ma, Rui Dong, Rong Ma, Lei Wang","doi":"10.1007/s10489-024-06101-w","DOIUrl":"10.1007/s10489-024-06101-w","url":null,"abstract":"<div><p>Prompt learning has achieved remarkable performance in various natural language understanding scenarios as it intuitively bridges the gap between pre-training and fine-tuning. However, directly applying monolingual prompting methods to cross-lingual tasks leads to discrepancies between source-language training and target-language inference, namely language bias in cross-lingual transfer. To address this gap, we propose a novel model called Cross-lingual Semantic Clustering Prompt (X-SCP). Specifically, in the prompt engineering stage, we design a language-agnostic prompt template and introduce a progressive code-switching approach to enhance the alignment between source and target languages. In the answer engineering stage, we construct a unified multilingual answer space through semantic consistency-guided clustering. The model trains a cluster-based verbalizer by learning a pre-clustered multilingual answer space. In this way, X-SCP alleviates language bias in both prompt engineering and answer engineering. Experimental results show that our model outperforms the strong baselines under zero-shot cross-lingual settings on both the XGLUE-NC and MLDoc document classification datasets.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 2","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Can neural networks estimate parameters in epidemiology models using real observed data?

IF 3.4 2区计算机科学

Applied Intelligence Pub Date : 2024-12-12 DOI: 10.1007/s10489-024-06012-w

Muhammad Jalil Ahmad, Korhan Günel

{"title":"Can neural networks estimate parameters in epidemiology models using real observed data?","authors":"Muhammad Jalil Ahmad, Korhan Günel","doi":"10.1007/s10489-024-06012-w","DOIUrl":"10.1007/s10489-024-06012-w","url":null,"abstract":"<div><p>The primary objective of this study is to address the challenges associated with estimating parameters in mathematical epidemiology models, which are crucial for understanding the dynamics of infectious diseases within a population. The scope of this research includes the development and application of a two-phase neural network for parameter estimation, specifically within the context of epidemic compartmental models. This study presents a novel approach by integrating an extreme learning machine with a heuristic population-based optimization method within a two-phase neural network framework. The networks are driven by a heuristic population-based optimization method, enhancing the accuracy and efficiency of parameter estimation in mathematical epidemiology models. The effectiveness of the method is validated using actual COVID-19 data provided by the Turkish Ministry of Health. The data includes cases categorized as Susceptible, Exposed, Infected, Removed, and Deceased, which are crucial components of epidemic compartmental models. The obtained results highlight the capability of the proposed method to provide insights into the spread of infectious diseases by offering reliable estimates of model parameters. This, in turn, supports better understanding and forecasting of disease dynamics. The methodology provides a significant contribution to the field by offering a new, efficient technique for parameter estimation in epidemiological models.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 2","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A reinforced final belief divergence for mass functions and its application in target recognition

IF 3.4 2区计算机科学

Applied Intelligence Pub Date : 2024-12-12 DOI: 10.1007/s10489-024-05955-4

Fuxiao Zhang, Zichong Chen, Rui Cai

{"title":"A reinforced final belief divergence for mass functions and its application in target recognition","authors":"Fuxiao Zhang, Zichong Chen, Rui Cai","doi":"10.1007/s10489-024-05955-4","DOIUrl":"10.1007/s10489-024-05955-4","url":null,"abstract":"<div><p>As an extension of Bayesian probability theory, the Dempster-Shafer (D-S) evidence theory uses mass function instead of traditional probability distribution. This theory is famous for multi-sensor data fusion and can well represent uncertainty. However, if there are conflicting mass functions, the D-S evidence theory will fail. The existing methods for handling conflicting mass functions do not fully consider the interaction between focal elements. Therefore, to solve the conflict problem, this paper defines the similarity factor and quantity factor of the focal element and then considers the impact of their interaction. After that, we propose a novel reinforced final belief divergence (RFBD) measure to solve the conflicting problem in mass functions from the perspective of divergence measurement. We use several numerical examples to verify the superiority of RFBD in handling conflicting evidence under uncertain conditions. Finally, we combine belief entropy and ambiguity measure to propose the RFBD-based multi-sensor data fusion approach, then achieve target recognition in UCI datasets. The experimental results show that our RFBD is better than the advanced divergence methods currently available.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 2","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

GRUDMU-DSCNN: An edge computing method for fault diagnosis with missing data

IF 3.4 2区计算机科学

Applied Intelligence Pub Date : 2024-12-12 DOI: 10.1007/s10489-024-06104-7

Ziyang Yu, Yanzhi Wang, Xiaofeng Zong, Jinhong Wu, Qi Zhou

{"title":"GRUDMU-DSCNN: An edge computing method for fault diagnosis with missing data","authors":"Ziyang Yu, Yanzhi Wang, Xiaofeng Zong, Jinhong Wu, Qi Zhou","doi":"10.1007/s10489-024-06104-7","DOIUrl":"10.1007/s10489-024-06104-7","url":null,"abstract":"<div><p>Traditional deep learning methods for rolling bearing fault diagnosis require a lot of computational time and resources. At the same time, the accuracy of fault diagnosis is affected by missing data collected due to the instability of sensors or data acquisition systems. In this paper, we propose a fault diagnosis method based on Gated Recurrent Unit with Decays and Maskless Update—Depthwise Separable Convolution Neural Network (GRUDMU-DSCNN). First, we use the trainable attenuation mechanism in GRUDMU for effective imputation of missingness and change the position of mask vectors to deal with missing data and solve the problem of missing data affecting the accuracy of fault diagnosis. In addition, we combine GRUDMU with DSCNN and deploy the model to edge devices. This improves the effectiveness of real-time fault diagnosis in edge computing scenarios. Furthermore, to verify whether the proposed method is effective in improving the accuracy of fault diagnosis in two missing patterns, namely Interval Missing and Missing Completely At Random (MCAR), we used a customized experimental equipment dataset and open experiments. The NVIDIA Jetson Xavier NX suite served as the edge computing platform to verify the effectiveness and superiority of the proposed model. The results indicate an average improvement in classification accuracy of 8.07% and 9.65% on both datasets when compared to existing methods.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 2","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142811090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing explainability in medical image classification and analyzing osteonecrosis X-ray images using shadow learner system

IF 3.4 2区计算机科学

Applied Intelligence Pub Date : 2024-12-12 DOI: 10.1007/s10489-024-05916-x

Yaoyang Wu, Simon Fong, Liansheng Liu

{"title":"Enhancing explainability in medical image classification and analyzing osteonecrosis X-ray images using shadow learner system","authors":"Yaoyang Wu, Simon Fong, Liansheng Liu","doi":"10.1007/s10489-024-05916-x","DOIUrl":"10.1007/s10489-024-05916-x","url":null,"abstract":"<p>Numerous applications have explored medical image classification using deep learning models. With the emergence of Explainable AI (XAI), researchers have begun to recognize its potential in validating the authenticity and correctness of results produced by black-box deep learning models. On the other hand, current diagnostic approaches for osteonecrosis face significant challenges, including difficulty in early detection, subjectivity in image interpretation, and reliance on surgical interventions without a comprehensive diagnostic foundation. This paper presents a novel Medical Computer-Aid-Diagnosis System—the Shadow Learning System framework—which integrates a convolutional neural network (CNN) with an Explainable AI method. This system not only performs conventional computer-aiding-diagnosis functions but also uniquely exploits misclassified data samples to provide additional medically relevant information from the machine learning model’s perspective, assisting doctors in their diagnostic process. The implementation of XAI techniques in our proposed system goes beyond merely validating CNN model results; it also enables the extraction of valuable information from medical images through an unconventional machine learning perspective. Our paper aims to enhance and extend the general structure and detailed design of the Shadow Learner System, making it more advantageous not only for human users but also for the deep learning model itself. A case study on femoral head osteonecrosis was conducted using our proposed system, which demonstrated improved accuracy and reliability in its prediction results. Experimental results interpreted using XAI methods are visualized to prove the confidence of our proposed model that generates reasonable results, confirming the effectiveness of the proposed model.</p>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 2","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multifactorial modality fusion network for multimodal recommendation

IF 3.4 2区计算机科学

Applied Intelligence Pub Date : 2024-12-12 DOI: 10.1007/s10489-024-06038-0

Yanke Chen, Tianhao sun, Yunhao Ma, Huhai Zou

{"title":"Multifactorial modality fusion network for multimodal recommendation","authors":"Yanke Chen, Tianhao sun, Yunhao Ma, Huhai Zou","doi":"10.1007/s10489-024-06038-0","DOIUrl":"10.1007/s10489-024-06038-0","url":null,"abstract":"<div><p>Multimodal recommendation systems aim to deliver precise and personalized recommendations by integrating diverse modalities such as text, images, and audio. Despite their potential, these systems often struggle with effective modality fusion strategies and comprehensive modeling of user preferences. To address these issues, we propose the Multifactorial Modality Fusion Network (MMFN). MMFN overcomes the limitations of previous models by following pivotal architectures. First, this novel approach employs three Graph Neural Networks (GNN) to extract foundational interactions and semantic information across modalities meticulously. Second, a Gated Multi-factor Semantic Sensor operates through a series of stacked gating units, guided by interaction embeddings, to extract features from modal embeddings deeply. Third, a User Preference-Oriented Modality Aligner, leveraging contrastive learning to synchronize user preferences with item features, thus enhancing the expressiveness of embeddings and the overall quality of recommendations. We demonstrate the marked superiority of MMFN in both performance and efficiency compared to traditional collaborative filtering methods and contemporary deep multimodal recommendation systems. Through comprehensive evaluations on the baby, sports, and clothing datasets, MMFN achieves significant gains in Recall@20 metrics, with improvements of 2.49%, 8.79%, and 24.51% over the following best baseline models. Additionally, MMFN also leads in training efficiency, outperforming most competing models. MMFN paves the way for future multimodal recommendation systems, leveraging the full spectrum of deep learning technologies.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 2","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142811092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Global and local information-aware relational graph convolutional network for temporal knowledge graph completion

IF 3.4 2区计算机科学

Applied Intelligence Pub Date : 2024-12-11 DOI: 10.1007/s10489-024-05987-w

Shuo Wang, Shuxu Chen, Zhaoqian Zhong

{"title":"Global and local information-aware relational graph convolutional network for temporal knowledge graph completion","authors":"Shuo Wang, Shuxu Chen, Zhaoqian Zhong","doi":"10.1007/s10489-024-05987-w","DOIUrl":"10.1007/s10489-024-05987-w","url":null,"abstract":"<div><p>Temporal knowledge graph completion (TKGC) focuses on inferring missing facts from temporal knowledge graphs (TKGs) and has been widely studied. While previous models based on graph neural networks (GNNs) have shown noteworthy outcomes, they tend to focus on designing complex modules to learn contextual representations. These complex solutions require a large number of parameters and heavy memory consumption. Additionally, existing TKGC approaches focus on exploiting static feature representation for entities and relationships, which fail to effectively capture the semantic information of contexts. In this paper, we propose a global and local information-aware relational graph convolutional neural network (GLARGCN) model to address these issues. First, we design a sampler, which captures significant neighbors by combining global historical event frequencies with local temporal relative displacements and requires no additional learnable parameters. We then employ a time-aware encoder to model timestamps, relations, and entities uniformly. We perform a graph convolution operation to learn a global graph representation. Finally, our method predicts missing entities using a scoring function. We evaluate the model on four benchmark datasets and one specific dataset with unseen timestamps. The experimental results demonstrate that our proposed GLARGCN model not only outperforms contemporary models but also shows robust performance in scenarios with unseen timestamps.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 2","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142798487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bias reduction via cooperative bargaining in synthetic graph dataset generation

IF 3.4 2区计算机科学

Applied Intelligence Pub Date : 2024-12-11 DOI: 10.1007/s10489-024-05947-4

Axel Wassington, Sergi Abadal

引用次数: 0

Detection and pose measurement of underground drill pipes based on GA-PointNet++

IF 3.4 2区计算机科学

Applied Intelligence Pub Date : 2024-12-11 DOI: 10.1007/s10489-024-05925-w

Jiangnan Luo, Jinyu Cai, Jianping Li, Deyi Zhang, Jiuhua Gao, Yuze Li, Liu Lei, Mengda Hao

{"title":"Detection and pose measurement of underground drill pipes based on GA-PointNet++","authors":"Jiangnan Luo, Jinyu Cai, Jianping Li, Deyi Zhang, Jiuhua Gao, Yuze Li, Liu Lei, Mengda Hao","doi":"10.1007/s10489-024-05925-w","DOIUrl":"10.1007/s10489-024-05925-w","url":null,"abstract":"<div><p>Drilling for gas extraction, a common method in coal mine gas control, involves tedious loading and uploading of drill pipes. This study aims to design a method for detecting and measuring pose drill pipes using point cloud data. We present an experimental platform for acquiring drill pipe point cloud data under various lights. Additionally, we propose a GA-PointNet + + model, enhanced with an adversarial generation network. The pose of the drill pipe was calculated from the segmented pipe and pin point clouds. Results indicate that the intersection-over-union (IoU) values for pipe and pin, based on GA-PointNet + + , are 0.824 and 0.472, respectively. Evaluating the model's performance in recognizing the pin using the ROC curve yielded an AUC of 0.87. The combination of GA-Pointnet + + and RGB-D camera was used to pose drill pipes, achieving an average accuracy of 82.5% under different lighting conditions. Under lighting conditions of 25–35 lx with an added diffuser film and 10–15 lx, the accuracy reaches 90%, with average distance errors of 1.4 cm and 2.5 cm, and average angle errors of 3.5° and 3.7°, respectively. This has significant implications for the use of LED lights in underground environments. Therefore, the proposed drill pipe pose measurement method is of great significance for the intelligentization of coal mine drilling operations.</p><h3>Graphical Abstract</h3><div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 2","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142811017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0