P2P context awareness based sensibility design recommendation using color and bio-signal analysis

Jung, Hoill; Chung, Kyungyong

doi:10.1007/s12083-015-0398-z

Cited by 15 publications

(2 citation statements)

References 21 publications

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“…Regarding the research trend of the sensibility-engineering design recommendation, a sensibility-engineering design-recommendation system is applied to fashion, automobiles, textile, bags, shoes, makeup, bicycles, and glasses so that personalized services can be provided based on the user sensibility [14,15]. Sensibility consists of sense-based psychological experiences and the perceptions of physical stimuli, and it is generated using a combination of various feelings including all five senses, comfort, discomfort, and inconvenience.…”

Section: Fig 1 Heterogeneous Big Data Integration Configurationmentioning

confidence: 99%

Deep Learning-based Evolutionary Recommendation Model for Heterogeneous Big Data Integration

2020

KSII TIIS

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This study proposes a deep learning-based evolutionary recommendation model for heterogeneous big data integration, for which collaborative filtering and a neural-network algorithm are employed. The proposed model is used to apply an individual's importance or sensory level to formulate a recommendation using the decision-making feedback. The evolutionary recommendation model is based on the Deep Neural Network (DNN), which is useful for analyzing and evaluating the feedback data among various neural-network algorithms, and the DNN is combined with collaborative filtering. The designed model is used to extract health information from data collected by the Korea National Health and Nutrition Examination Survey, and the collaborative filtering-based recommendation model was compared with the deep learning-based evolutionary recommendation model to evaluate its performance. The RMSE is used to evaluate the performance of the proposed model. According to the comparative analysis, the accuracy of the deep learning-based evolutionary recommendation model is superior to that of the collaborative filtering-based recommendation model.

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Section: Fig 1 Heterogeneous Big Data Integration Configurationmentioning

confidence: 99%

Deep Learning-based Evolutionary Recommendation Model for Heterogeneous Big Data Integration

2020

KSII TIIS

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“…The paper, by Jung et al [9]., purposes a P2P context awareness based sensibility design recommendation using the color and bio-signal analysis. The proposed method is P2P network-based collaborative filtering that uses a rules-based, context-aware, cluster, P2P network, as well as learning where the user has already evaluated preferences for recommendation.…”

mentioning

confidence: 99%

Convergence P2P context awareness

Kim

Han

2015

Peer-to-Peer Netw. Appl.

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Welcome to this special issue of Peer-to-Peer Networking and Applications on "Convergence P2P Context Awareness," which contains fifteen of the best papers submitted to the issue. The purpose of this special issue is to publish current and relevant research papers from academia and practitioners in various industries on this emerging topic.In the IT convergence environment, various sensors and computing resources, including mobile devices and networks in users' everyday lives, should be aware of the users' intentions and the surrounding environment and should provide services based on that awareness. Providing personalized services for users requires convergence peer-to-peer (P2P) context awareness that can efficiently share various forms of information collected by computing resources ubiquitous in users' daily spaces, and that can automatically recognize and process context information about users and their surrounding environments [14][15][16]. This special issue covers some of the hottest topics in Convergence P2P Context Awareness, including: Hybrid P2P networking system; P2P Knowledge based Applications and Management; P2P Multicasting and multimedia delivery; P2P Overlay networks; P2P Security issues; P2P Wireless and mobile networks; P2P Data and index structures; P2P Modeling, performance, and robustness; Semantic routing and search in P2P; P2P Architectures and protocols; Convergence/co-existence of P2P and other networks.The paper, by Ji et al. [1]., proposes mining students activities from a computer supported collaborative learning system based on peer to peer network. Our proposed method is a learner activity analysis model based on learner activity information generated in a computer-supported collaborative learning (CSCL) system that students, teachers, and administrators can use to conduct a learner activity analysis that is easy to understand and effective. As for the proposed learner activity model, it classifies learning activities in a CSCL system into three categories (vivacity, learning, and relationship) and then creates quotients to represent them accordingly. The paper, by Kim et al. [2]., proposes the efficient peer-to-peer context awareness data forwarding scheme based on the devices location in emergency situations. This efficient P2P data forwarding scheme (EP-DFS) can be applied to various fields that are needed for P2P communications and networking. To support P2P transmission in the proposed EP-DFS, each intermediate device located in the transmission path establishes a synchronized path. The user can communicate with other people, and relay the message to the outside world in emergency mode.The paper, by Li et al. [3]., shows the effects of social media usage on country image and purchase intention from social P2P network perspective. The proposed method examines the impact of social media usage on a country's image, and the relationship between social media usage and the images of a country's economy, politics, nationality, and culture.

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