Health-aware food recommendation system with dual attention in heterogeneous graphs

Forouzandeh, Saman; Rostami, Mehrdad; Berahmand, Kamal; Sheikhpour, Razieh

doi:10.1016/j.compbiomed.2023.107882

Cited by 8 publications

(2 citation statements)

References 54 publications

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“…Furthermore, Meta-paths are pathways that connect diverse objects, encapsulating their informational relationships. 42 , 43 In HINs, two objects can be linked by multiple meta-paths, each carrying distinct semantics. For instance, within these networks, while both SRS and SCS have attribute type S as their start and end points, the semantics they embody differ: SRS represents a complete process established via a Recipe, whereas SCS denotes a connection between two attributes of type S via the same Channel.…”

Section: Methodsmentioning

confidence: 99%

Clustering on heterogeneous IoT information network based on meta path

Zhao,

Zhang,

et al. 2024

Science Progress

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As the Internet and Internet of Things (IoT) continue to develop, Heterogeneous Information Networks (HIN) have formed complex interaction relationships among data objects. These relationships are represented by various types of edges (meta-paths) that contain rich semantic information. In the context of IoT data applications, the widespread adoption of Trigger-Action Patterns makes the management and analysis of heterogeneous data particularly important. This study proposes a meta-path-based clustering method for heterogeneous IoT data called I-RankClus, which aims to improve the modeling and analysis efficiency of IoT data. By combining ranking with clustering algorithms, the PageRank algorithm was used to calculate the intraclass influence of objects in the network. The HITS algorithm then transfers the influence to the core objects, thereby optimizing the classification of objects during the clustering process. The I-RankClus algorithm does not process each meta-path individually, but instead integrates multiple meta-paths to enhance the interpretability and clustering performance of the model. The experimental results show that the I-RankClus algorithm can process complex IoT datasets more effectively than traditional clustering methods and provide more accurate clustering outcomes. Furthermore, through a detailed analysis of meta-paths, this study explored the influence and importance of different meta-paths, thereby validating the effectiveness of the algorithm. Overall, the research presented in this paper not only improves the application effects of HINs in IoT data analysis but also provides valuable methods and insights for future network data processing.

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Section: Methodsmentioning

confidence: 99%

Clustering on heterogeneous IoT information network based on meta path

Zhao,

Zhang,

et al. 2024

Science Progress

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“…This includes the stages of data processing, feature selection, and model training. Forouzandeh et al [17] designed a Health aware food recommender system (HFRS) using dual attention in heterogeneous graphs. This is effective for unsupervised representation learning on heterogeneous graphstructured data.…”

Section: Introductionmentioning

confidence: 99%

An intelligent time aware food recommender system using support vector machine

Panwar,

Sharma,

Mahela

et al. 2024

IJEECS

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This paper formulated a support vector machine powered time-aware food recommender system (SVMTAFRS) to recommend healthy food for the customers. The rated food item incorporates the user preference (UP) in terms of calories, nutrition factor, and all food contents required for a healthy diet. This also takes into account the user age, time of day and week day while predicting the food rating. The SVMTAFRS involves two steps for computation of user identity document (UID) and predicted food rating (PFR). UID is computed considering the customer age (CA), UP in terms of calories and suitable weight factors. PFR is computed considering the UID and time of day (TOD). PFR for week end day is computed by multiplying the PFR by week end multiplying factor (WEMF). Support vector machine (SVM) is used for recommending the suitable healthy food for customer in terms of correct values of PFR. Efficacy of PFR is tested in terms of mean absolute error (MAE) and root mean squared error (RMSE). This is established that performance of the SVMTAFRS is superior compared to the rule-based food recommender system (RBFRS).

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Employing of machine learning and wearable devices in healthcare system: tasks and challenges

Saad,

Zaki,

Abdelsalam

2024

Neural Comput & Applic

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Disease outbreaks are nowadays a critical issue despite the development and rapid growth of technology. One of the major challenges facing healthcare professionals and healthcare industries is disease prevention and control by technology. Due to busy work schedules, maintaining a healthy lifestyle has become impossible, so the solution to these previous challenges is the intelligent health monitoring system. Over the past few years, a lot of research has been done on the use of Machine learning (ML) techniques in healthcare applications. With wearable devices, ML greatly helps in tracking human activities and vital signs as well as helping to monitor and diagnose patients’ health so it plays a huge role in elderly care. Research and development of more applications for wearable devices has been underway in the past five years, due to significant technological advances in medical sensors. Despite the widespread use of wearable devices, there is little research on machine learning applications of these devices. This paper presents a review of the different areas of recent ML research for healthcare wearable devices. It also discusses the different challenges facing ML applications on wearable devices.

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Health-aware food recommendation system with dual attention in heterogeneous graphs

Cited by 8 publications

References 54 publications

Clustering on heterogeneous IoT information network based on meta path

Clustering on heterogeneous IoT information network based on meta path

An intelligent time aware food recommender system using support vector machine

Employing of machine learning and wearable devices in healthcare system: tasks and challenges

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