Shancheng JIANG

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Research activity per year

Expertise related to UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):

SDG 7 Affordable and Clean Energy

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Dive into the research topics where Shancheng JIANG is active. These topic labels come from the works of this person. Together they form a unique fingerprint.

1 Similar Profiles

4 RGC 21 - Publication in refereed journal
1 RGC 32 - Refereed conference paper (with host publication)
1 RGC 31B - Invited conference paper (non-refereed items)
1 RGC 33 - Other conference paper

A hybrid DNN-KF model for real-time SOC estimation of lithium-ion batteries under different ambient temperatures
Chen, G., Jiang, S., Xie, M. & Yang, F., 2022, 2022 Global Reliability and Prognostics and Health Management Conference (PHM-Yantai). IEEE, (Global Reliability and Prognostics and Health Management Conference, PHM-Yantai ).
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review
10 Link opens in a new tab Citations (Scopus)
Combining Deep Neural Networks and Classical Time Series Regression Models for Forecasting Patient Flows in Hong Kong
Jiang, S., Xiao, R., Wang, L., Luo, X., Huang, C., Wang, J.-H., Chin, K.-S. & Nie, X., 2019, In: IEEE Access. 7, p. 118965-118974 8808897.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

Open Access
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26 Link opens in a new tab Citations (Scopus)

49 Downloads (CityUHK Scholars)
An integrated machine learning framework for hospital readmission prediction
Jiang, S., Chin, K.-S., Qu, G. & Tsui, K. L., 15 Apr 2018, In: Knowledge-Based Systems. 146, p. 73-90
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
49 Link opens in a new tab Citations (Scopus)
A universal deep learning approach for modeling the flow of patients under different severities
Jiang, S., Chin, K.-S. & Tsui, K. L., Feb 2018, In: Computer Methods and Programs in Biomedicine. 154, p. 191-203
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
47 Link opens in a new tab Citations (Scopus)
Modified genetic algorithm-based feature selection combined with pre-trained deep neural network for demand forecasting in outpatient department
Jiang, S., Chin, K.-S., Wang, L., Qu, G. & Tsui, K. L., 1 Oct 2017, In: Expert Systems with Applications. 82, p. 216-230
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
131 Link opens in a new tab Citations (Scopus)

Data Analytics-based Patient Demand Forecasting for Public Healthcare System
JIANG, S. (Author), CHIN, K. S. (Supervisor), 11 Oct 2017
Student thesis: Doctoral Thesis

Dr. Shancheng JIANG, 姜善成

Personal profile

Expertise related to UN Sustainable Development Goals

Fingerprint

Collaborations from the last five years

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A hybrid DNN-KF model for real-time SOC estimation of lithium-ion batteries under different ambient temperatures

Combining Deep Neural Networks and Classical Time Series Regression Models for Forecasting Patient Flows in Hong Kong

An integrated machine learning framework for hospital readmission prediction

A universal deep learning approach for modeling the flow of patients under different severities

Modified genetic algorithm-based feature selection combined with pre-trained deep neural network for demand forecasting in outpatient department

Data Analytics-based Patient Demand Forecasting for Public Healthcare System

Dr. Shancheng JIANG, 姜善成

Personal profile

Expertise related to UN Sustainable Development Goals

Fingerprint

Collaborations from the last five years

Research output

A hybrid DNN-KF model for real-time SOC estimation of lithium-ion batteries under different ambient temperatures

Combining Deep Neural Networks and Classical Time Series Regression Models for Forecasting Patient Flows in Hong Kong

An integrated machine learning framework for hospital readmission prediction

A universal deep learning approach for modeling the flow of patients under different severities

Modified genetic algorithm-based feature selection combined with pre-trained deep neural network for demand forecasting in outpatient department

Thesis

Data Analytics-based Patient Demand Forecasting for Public Healthcare System