Dunren Che, Professor of Computer Science

About Dunren Che, Professor of Computer Science

Dunren Che, Professor of Computer Science, With an exceptional h-index of 18 and a recent h-index of 14 (since 2020), a distinguished researcher at Southern Illinois University Carbondale, specializes in the field of Database, Data Mining, Machine Learning, Cloud Computing, Scientific Workflows.

His recent articles reflect a diverse array of research interests and contributions to the field:

Cauli-Det: enhancing cauliflower disease detection with modified YOLOv8

Leveraging deep neural networks to uncover unprecedented levels of precision in the diagnosis of hair and scalp disorders

A robust and light-weight transfer learning-based architecture for accurate detection of leaf diseases across multiple plants using less amount of images

Cardiac Failure Forecasting Based on Clinical Data Using a Lightweight Machine Learning Metamodel

Privacy-Preserving On-Screen Activity Tracking and Classification in E-Learning Using Federated Learning

Image-Based Arabian Camel Breed Classification Using Transfer Learning on CNNs

A Generic Efficient Scientific Workflow Engine for the Optimizations of Run-Time Execution

Deep learning-based IoT system for remote monitoring and early detection of health issues in real-time

Dunren Che, Professor of Computer Science Information

University

Position

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Citations(all)

1541

Citations(since 2020)

869

Cited By

1054

hIndex(all)

18

hIndex(since 2020)

14

i10Index(all)

29

i10Index(since 2020)

19

Email

University Profile Page

Google Scholar

Dunren Che, Professor of Computer Science Skills & Research Interests

Database

Data Mining

Machine Learning

Cloud Computing

Scientific Workflows

Top articles of Dunren Che, Professor of Computer Science

Cauli-Det: enhancing cauliflower disease detection with modified YOLOv8

Frontiers in Plant Science

2024/4/18

Leveraging deep neural networks to uncover unprecedented levels of precision in the diagnosis of hair and scalp disorders

Skin Research and Technology

2024/4

A robust and light-weight transfer learning-based architecture for accurate detection of leaf diseases across multiple plants using less amount of images

Frontiers in Plant Science

2024/1/11

Cardiac Failure Forecasting Based on Clinical Data Using a Lightweight Machine Learning Metamodel

Diagnostics

2023/7/31

Privacy-Preserving On-Screen Activity Tracking and Classification in E-Learning Using Federated Learning

IEEE Access

2023/7/27

Image-Based Arabian Camel Breed Classification Using Transfer Learning on CNNs

Applied Sciences

2023/7/14

A Generic Efficient Scientific Workflow Engine for the Optimizations of Run-Time Execution

2023/7/2

Deep learning-based IoT system for remote monitoring and early detection of health issues in real-time

Sensors

2023/5/30

Infrastructure-level support for gpu-enabled deep learning in DATAVIEW

Future Generation Computer Systems

2023/4/1

Addressing uncertainty in imbalanced histopathology image classification of her2 breast cancer: An interpretable ensemble approach with threshold filtered single instance …

IEEE Access

2023/10/26

Gld-det: Guava leaf disease detection in real-time using lightweight deep learning approach based on mobilenet

Agronomy

2023/8/26

Deep-learning-as-a-workflow (DLaaW): An innovative approach to enabling deep learning in scientific workflows

2021/12/15

A novel cluster-based approach for keyphrase extraction from MOOC video lectures

Knowledge and Information Systems

2021/7

SEED: Confidential big data workflow scheduling with intel SGX under deadline constraints

2020

A survey of modern scientific workflow scheduling algorithms and systems in the era of big data

2020/11/7

ZTIMM: A zero-trust-based identity management model for volunteer cloud computing

2020

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