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DLRS: Deep Learning-Based Recommender System for Smart Healthcare Ecosystem

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

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DLRS: Deep Learning-Based Recommender System for Smart Healthcare Ecosystem. / Aujla, G.S.; Jindal, A.; Chaudhary, R. et al.
2019 IEEE International Conference on Communications, ICC 2019 - Proceedings. IEEE, 2019. 8761416.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Harvard

Aujla, GS, Jindal, A, Chaudhary, R, Kumar, N, Vashist, S, Sharma, N & Obaidat, MS 2019, DLRS: Deep Learning-Based Recommender System for Smart Healthcare Ecosystem. in 2019 IEEE International Conference on Communications, ICC 2019 - Proceedings., 8761416, IEEE. https://doi.org/10.1109/ICC.2019.8761416

APA

Aujla, G. S., Jindal, A., Chaudhary, R., Kumar, N., Vashist, S., Sharma, N., & Obaidat, M. S. (2019). DLRS: Deep Learning-Based Recommender System for Smart Healthcare Ecosystem. In 2019 IEEE International Conference on Communications, ICC 2019 - Proceedings Article 8761416 IEEE. https://doi.org/10.1109/ICC.2019.8761416

Vancouver

Aujla GS, Jindal A, Chaudhary R, Kumar N, Vashist S, Sharma N et al. DLRS: Deep Learning-Based Recommender System for Smart Healthcare Ecosystem. In 2019 IEEE International Conference on Communications, ICC 2019 - Proceedings. IEEE. 2019. 8761416 doi: 10.1109/ICC.2019.8761416

Author

Aujla, G.S. ; Jindal, A. ; Chaudhary, R. et al. / DLRS : Deep Learning-Based Recommender System for Smart Healthcare Ecosystem. 2019 IEEE International Conference on Communications, ICC 2019 - Proceedings. IEEE, 2019.

Bibtex

@inproceedings{ebbdfaeac67246f5a7effed0dc8fc165,
title = "DLRS: Deep Learning-Based Recommender System for Smart Healthcare Ecosystem",
abstract = "Nowadays, the conventional healthcare domain has witnessed a paradigm shift towards patient-driven healthcare 4.0 ecosystem. In this direction, healthcare recommender systems provide ubiquitous healthcare services to the end users even on the move. However, there are various challenges for the design of patient driven healthcare recommender systems. Some of the major challenges are: a) handling huge amount of data generated by smart devices and sensors, b) dynamic network management for real-time data transmission, and c) lack of knowledge gathering and aggregation methods. For these reasons, in this paper; DLRS: A Deep Learning based Recommender System using software defined networking (SDN) is designed for smart healthcare ecosystem. DLSR works in the following phases: a) a tensor-based dimensionality reduction algorithm is proposed for removing unwanted dimensions in the acquired data, b) a decision tree-based classification scheme is presented for categorization of the patient queries on the basis of different diseases, and c) a convolutional neural network based system is designed for providing recommendations about the patient health. On evaluation, the results obtained prove the superiority of the proposed scheme in contrast to existing competing schemes.",
author = "G.S. Aujla and A. Jindal and R. Chaudhary and N. Kumar and S. Vashist and N. Sharma and M.S. Obaidat",
note = "{\textcopyright}2019 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.",
year = "2019",
month = may,
day = "20",
doi = "10.1109/ICC.2019.8761416",
language = "English",
isbn = "9781538680896",
booktitle = "2019 IEEE International Conference on Communications, ICC 2019 - Proceedings",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - DLRS

T2 - Deep Learning-Based Recommender System for Smart Healthcare Ecosystem

AU - Aujla, G.S.

AU - Jindal, A.

AU - Chaudhary, R.

AU - Kumar, N.

AU - Vashist, S.

AU - Sharma, N.

AU - Obaidat, M.S.

N1 - ©2019 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

PY - 2019/5/20

Y1 - 2019/5/20

N2 - Nowadays, the conventional healthcare domain has witnessed a paradigm shift towards patient-driven healthcare 4.0 ecosystem. In this direction, healthcare recommender systems provide ubiquitous healthcare services to the end users even on the move. However, there are various challenges for the design of patient driven healthcare recommender systems. Some of the major challenges are: a) handling huge amount of data generated by smart devices and sensors, b) dynamic network management for real-time data transmission, and c) lack of knowledge gathering and aggregation methods. For these reasons, in this paper; DLRS: A Deep Learning based Recommender System using software defined networking (SDN) is designed for smart healthcare ecosystem. DLSR works in the following phases: a) a tensor-based dimensionality reduction algorithm is proposed for removing unwanted dimensions in the acquired data, b) a decision tree-based classification scheme is presented for categorization of the patient queries on the basis of different diseases, and c) a convolutional neural network based system is designed for providing recommendations about the patient health. On evaluation, the results obtained prove the superiority of the proposed scheme in contrast to existing competing schemes.

AB - Nowadays, the conventional healthcare domain has witnessed a paradigm shift towards patient-driven healthcare 4.0 ecosystem. In this direction, healthcare recommender systems provide ubiquitous healthcare services to the end users even on the move. However, there are various challenges for the design of patient driven healthcare recommender systems. Some of the major challenges are: a) handling huge amount of data generated by smart devices and sensors, b) dynamic network management for real-time data transmission, and c) lack of knowledge gathering and aggregation methods. For these reasons, in this paper; DLRS: A Deep Learning based Recommender System using software defined networking (SDN) is designed for smart healthcare ecosystem. DLSR works in the following phases: a) a tensor-based dimensionality reduction algorithm is proposed for removing unwanted dimensions in the acquired data, b) a decision tree-based classification scheme is presented for categorization of the patient queries on the basis of different diseases, and c) a convolutional neural network based system is designed for providing recommendations about the patient health. On evaluation, the results obtained prove the superiority of the proposed scheme in contrast to existing competing schemes.

U2 - 10.1109/ICC.2019.8761416

DO - 10.1109/ICC.2019.8761416

M3 - Conference contribution/Paper

SN - 9781538680896

BT - 2019 IEEE International Conference on Communications, ICC 2019 - Proceedings

PB - IEEE

ER -