Applied intelligence (Dordrecht, Netherlands)最新文献_第2页

Hashing-based semantic relevance attributed knowledge graph embedding enhancement for deep probabilistic recommendation. 基于哈希的深度概率推荐语义关联属性知识图嵌入增强。

Applied intelligence (Dordrecht, Netherlands) Pub Date : 2023-01-01 DOI: 10.1007/s10489-022-03235-7

Nasrullah Khan, Zongmin Ma, Li Yan, Aman Ullah

{"title":"Hashing-based semantic relevance attributed knowledge graph embedding enhancement for deep probabilistic recommendation.","authors":"Nasrullah Khan, Zongmin Ma, Li Yan, Aman Ullah","doi":"10.1007/s10489-022-03235-7","DOIUrl":"https://doi.org/10.1007/s10489-022-03235-7","url":null,"abstract":"Knowledge graph embedding (KGE) is effectively exploited in providing precise and accurate recommendations from many perspectives in different application scenarios. However, such methods that utilize entire embedded Knowledge Graph (KG) without applying information-relevance regulatory constraints fail to stop the noise penetration into the underlying information. Moreover, higher computational time complexity is a CPU overhead in KG-enhanced systems and applications. The occurrence of these limitations significantly degrade the recommendation performance. Therefore, to cope with these challenges we proposed novel KGEE (Knowledge Graph Embedding Enhancement) approach of Hashing-based Semantic-relevance Attributed Graph-embedding Enhancement (H-SAGE) to model semantically-relevant higher-order entities and relations into the unique Meta-paths. For this purpose, we introduced Node Relevance-based Guided-walk (NRG) modeling technique. Further, to deal with the computational time-complexity, we converted the relevant information to the Hash-codes and proposed Deep-Probabilistic (dProb) technique to place hash-codes in the relevant hash-buckets. Again, we used dProb to generate guided function-calls to maximize the possibility of Hash-Hits in the hash-buckets. In case of Hash-Miss, we applied Locality Sensitive (LS) hashing to retrieve the required information. We performed experiments on three benchmark datasets and compared the empirical as well as the computational performance of H-SAGE with the baseline approaches. The achieved results and comparisons demonstrate that the proposed approach has outperformed the-state-of-the-art methods in the mentioned facets of evaluation.","PeriodicalId":72260,"journal":{"name":"Applied intelligence (Dordrecht, Netherlands)","volume":"53 2","pages":"2295-2320"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9075930/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10485438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

A novel hybrid multi-thread metaheuristic approach for fake news detection in social media. 一种新的混合多线程元启发式社交媒体假新闻检测方法。

Applied intelligence (Dordrecht, Netherlands) Pub Date : 2023-01-01 DOI: 10.1007/s10489-022-03972-9

Gungor Yildirim

{"title":"A novel hybrid multi-thread metaheuristic approach for fake news detection in social media.","authors":"Gungor Yildirim","doi":"10.1007/s10489-022-03972-9","DOIUrl":"https://doi.org/10.1007/s10489-022-03972-9","url":null,"abstract":"In fake news detection, intelligent optimization seems to be a more effective and explainable solution methodology than the black-box methods that have been extensively used in the literature. This study takes the optimization-based method one step further and proposes a novel, multi-thread hybrid metaheuristic approach for fake news detection in social media. The most innovative feature of the proposed method is that it uses a supervisor thread mechanism, which simultaneously monitors and improves the performance and search patterns of metaheuristic algorithms running parallel. With the supervisor thread mechanism, it is possible to analyse different key attribute combinations in the search space. In addition, this study develops a software framework that allows this model to be implemented easily. It tests the performance of the proposed model on three different data sets, respectively containing news about Covid-19, the Syrian War, and daily politics. The proposed method is evaluated in comparison to the results of fifteen different well-known deep models and classification algorithms. Experimental results prove the success of the proposed model and that it can produce competitive results.","PeriodicalId":72260,"journal":{"name":"Applied intelligence (Dordrecht, Netherlands)","volume":"53 9","pages":"11182-11202"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9436741/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9501854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Front-end deep learning web apps development and deployment: a review. 前端深度学习web应用程序的开发和部署：综述。

Applied intelligence (Dordrecht, Netherlands) Pub Date : 2023-01-01 Epub Date: 2022-11-30 DOI: 10.1007/s10489-022-04278-6

Hock-Ann Goh, Chin-Kuan Ho, Fazly Salleh Abas

{"title":"Front-end deep learning web apps development and deployment: a review.","authors":"Hock-Ann Goh, Chin-Kuan Ho, Fazly Salleh Abas","doi":"10.1007/s10489-022-04278-6","DOIUrl":"10.1007/s10489-022-04278-6","url":null,"abstract":"Machine learning and deep learning models are commonly developed using programming languages such as Python, C++, or R and deployed as web apps delivered from a back-end server or as mobile apps installed from an app store. However, recently front-end technologies and JavaScript libraries, such as TensorFlow.js, have been introduced to make machine learning more accessible to researchers and end-users. Using JavaScript, TensorFlow.js can define, train, and run new or existing, pre-trained machine learning models entirely in the browser from the client-side, which improves the user experience through interaction while preserving privacy. Deep learning models deployed on front-end browsers must be small, have fast inference, and ideally be interactive in real-time. Therefore, the emphasis on development and deployment is different. This paper aims to review the development and deployment of these deep-learning web apps to raise awareness of the recent advancements and encourage more researchers to take advantage of this technology for their own work. First, the rationale behind the deployment stack (front-end, JavaScript, and TensorFlow.js) is discussed. Then, the development approach for obtaining deep learning models that are optimized and suitable for front-end deployment is then described. The article also provides current web applications divided into seven categories to show deep learning potential on the front end. These include web apps for deep learning playground, pose detection and gesture tracking, music and art creation, expression detection and facial recognition, video segmentation, image and signal analysis, healthcare diagnosis, recognition, and identification.","PeriodicalId":72260,"journal":{"name":"Applied intelligence (Dordrecht, Netherlands)","volume":"53 12","pages":"15923-15945"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9709375/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9563861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Dynamic stock-decision ensemble strategy based on deep reinforcement learning. 基于深度强化学习的动态股票决策集成策略。

Applied intelligence (Dordrecht, Netherlands) Pub Date : 2023-01-01 DOI: 10.1007/s10489-022-03606-0

Xiaoming Yu, Wenjun Wu, Xingchuang Liao, Yong Han

{"title":"Dynamic stock-decision ensemble strategy based on deep reinforcement learning.","authors":"Xiaoming Yu, Wenjun Wu, Xingchuang Liao, Yong Han","doi":"10.1007/s10489-022-03606-0","DOIUrl":"https://doi.org/10.1007/s10489-022-03606-0","url":null,"abstract":"In a complex and changeable stock market, it is very important to design a trading agent that can benefit investors. In this paper, we propose two stock trading decision-making methods. First, we propose a nested reinforcement learning (Nested RL) method based on three deep reinforcement learning models (the Advantage Actor Critic, Deep Deterministic Policy Gradient, and Soft Actor Critic models) that adopts an integration strategy by nesting reinforcement learning on the basic decision-maker. Thus, this strategy can dynamically select agents according to the current situation to generate trading decisions made under different market environments. Second, to inherit the advantages of three basic decision-makers, we consider confidence and propose a weight random selection with confidence (WRSC) strategy. In this way, investors can gain more profits by integrating the advantages of all agents. All the algorithms are validated for the U.S., Japanese and British stocks and evaluated by different performance indicators. The experimental results show that the annualized return, cumulative return, and Sharpe ratio values of our ensemble strategy are higher than those of the baselines, which indicates that our nested RL and WRSC methods can assist investors in their portfolio management with more profits under the same level of investment risk.","PeriodicalId":72260,"journal":{"name":"Applied intelligence (Dordrecht, Netherlands)","volume":"53 2","pages":"2452-2470"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9082989/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10485437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Interpretable tourism demand forecasting with temporal fusion transformers amid COVID-19. 新冠肺炎期间利用时间融合变压器进行可解释的旅游需求预测。

Applied intelligence (Dordrecht, Netherlands) Pub Date : 2023-01-01 Epub Date: 2022-10-27 DOI: 10.1007/s10489-022-04254-0

Binrong Wu, Lin Wang, Yu-Rong Zeng

{"title":"Interpretable tourism demand forecasting with temporal fusion transformers amid COVID-19.","authors":"Binrong Wu, Lin Wang, Yu-Rong Zeng","doi":"10.1007/s10489-022-04254-0","DOIUrl":"10.1007/s10489-022-04254-0","url":null,"abstract":"An innovative ADE-TFT interpretable tourism demand forecasting model was proposed to address the issue of the insufficient interpretability of existing tourism demand forecasting. This model effectively optimizes the parameters of the Temporal Fusion Transformer (TFT) using an adaptive differential evolution algorithm (ADE). TFT is a brand-new attention-based deep learning model that excels in prediction research by fusing high-performance prediction with time-dynamic interpretable analysis. The TFT model can produce explicable predictions of tourism demand, including attention analysis of time steps and the ranking of input factors' relevance. While doing so, this study adds something unique to the literature on tourism by using historical tourism volume, monthly new confirmed cases of travel destinations, and big data from travel forums and search engines to increase the precision of forecasting tourist volume during the COVID-19 pandemic. The mood of travelers and the many subjects they spoke about throughout off-season and peak travel periods were examined using a convolutional neural network model. In addition, a novel technique for choosing keywords from Google Trends was suggested. In other words, the Latent Dirichlet Allocation topic model was used to categorize the major travel-related subjects of forum postings, after which the most relevant search terms for each topic were determined. According to the findings, it is possible to estimate tourism demand during the COVID-19 pandemic by combining quantitative and emotion-based characteristics.","PeriodicalId":72260,"journal":{"name":"Applied intelligence (Dordrecht, Netherlands)","volume":"53 11","pages":"14493-14514"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9607734/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9921219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DC-CNN: Dual-channel Convolutional Neural Networks with attention-pooling for fake news detection. DC-CNN:带有注意力池的双通道卷积神经网络用于假新闻检测。

Applied intelligence (Dordrecht, Netherlands) Pub Date : 2023-01-01 DOI: 10.1007/s10489-022-03910-9

Kun Ma, Changhao Tang, Weijuan Zhang, Benkuan Cui, Ke Ji, Zhenxiang Chen, Ajith Abraham

{"title":"DC-CNN: Dual-channel Convolutional Neural Networks with attention-pooling for fake news detection.","authors":"Kun Ma, Changhao Tang, Weijuan Zhang, Benkuan Cui, Ke Ji, Zhenxiang Chen, Ajith Abraham","doi":"10.1007/s10489-022-03910-9","DOIUrl":"https://doi.org/10.1007/s10489-022-03910-9","url":null,"abstract":"Fake news detection mainly relies on the extraction of article content features with neural networks. However, it has brought some challenges to reduce the noisy data and redundant features, and learn the long-distance dependencies. To solve the above problems, Dual-channel Convolutional Neural Networks with Attention-pooling for Fake News Detection (abbreviated as DC-CNN) is proposed. This model benefits from Skip-Gram and Fasttext. It can effectively reduce noisy data and improve the learning ability of the model for non-derived words. A parallel dual-channel pooling layer was proposed to replace the traditional CNN pooling layer in DC-CNN. The Max-pooling layer, as one of the channels, maintains the advantages in learning local information between adjacent words. The Attention-pooling layer with multi-head attention mechanism serves as another pooling channel to enhance the learning of context semantics and global dependencies. This model benefits from the learning advantages of the two channels and solves the problem that pooling layer is easy to lose local-global feature correlation. This model is tested on two different COVID-19 fake news datasets, and the experimental results show that our model has the optimal performance in dealing with noisy data and balancing the correlation between local features and global features.","PeriodicalId":72260,"journal":{"name":"Applied intelligence (Dordrecht, Netherlands)","volume":"53 7","pages":"8354-8369"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9340725/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9488404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

MA-Net:Mutex attention network for COVID-19 diagnosis on CT images MA-Net:用于CT图像诊断COVID-19的互斥关注网络

Applied intelligence (Dordrecht, Netherlands) Pub Date : 2022-04-09 DOI: 10.1007/s10489-022-03431-5

Bingbing Zheng, Yu Zhu, Qin Shi, Dawei Yang, Yanmei Shao, Tao Xu

引用次数: 3

Driving maneuver classification from time series data: a rule based machine learning approach 基于时间序列数据的驾驶机动分类:基于规则的机器学习方法

Applied intelligence (Dordrecht, Netherlands) Pub Date : 2022-03-28 DOI: 10.1007/s10489-022-03328-3

M. Haque, Supriya Sarker, M. A. Dewan

引用次数: 3

Improving exchange rate forecasting via a new deep multimodal fusion model 通过一种新的深度多模态融合模型改进汇率预测

Applied intelligence (Dordrecht, Netherlands) Pub Date : 2022-03-25 DOI: 10.1007/s10489-022-03342-5

Edmure Windsor, Wei Cao

引用次数: 9

An edge-driven multi-agent optimization model for infectious disease detection 边缘驱动的传染病检测多智能体优化模型

Applied intelligence (Dordrecht, Netherlands) Pub Date : 2022-03-07 DOI: 10.1007/s10489-021-03145-0

Y. Djenouri, Gautam Srivastava, A. Yazidi, Jerry Chun‐wei Lin

引用次数: 2