scholarly journals A decision support system for hybrid corn classification

2021 ◽  
Vol 911 (1) ◽  
pp. 012033
Author(s):  
Bunyamin Zainuddin ◽  
F. Tabri ◽  
N. N. Andayani ◽  
Roy Efendi ◽  
Suwardi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Decision Support ◽  
Decision Support System ◽  
Seed Production ◽  
Support System ◽  
Accuracy Score ◽  
Flowering Stage ◽  
Corn Seed ◽  
Cross Pollination ◽  
Highly Correlated

Abstract High yielding corn is primarily derived from a cross-pollination among superior appearing male and female plants. Cross-pollination is closely linked at the tasseling/flowering stage, marked by the emergence of tassel for 5-10 days. With the advancement of machine learning, there are opportunities to apply deep learning models to control the purity of plants. The research aims to develop a decision support system based on deep learning to enable earlier identification and removal of contamination/off-type plants during seed production. The datasets containing 1,587 tassel images taken by high resolution camera. The results of the training and the validation sequence indicated a highly correlated accuracy score. A quite contrasting tassel morphology makes it easier for the model to distinguish on and off-type plants. The loss value during the training and the validation stages was 0.05 and 0.1 respectively. A stand-alone graphical user interface (GUI) was deployed to support the early detection of tassels in the field. This tool can be used to support national corn seed production programs.

scholarly journals A data-driven approach for chemotherapy recommendation model based on deep learning for patients with colorectal cancer in Korea

2020 ◽  
Author(s):  
Jinhyeok Park ◽  
Kang-Yoon Lee ◽  
Jeong-Heum Baek ◽  
Youngho Lee
Keyword(s):  
Colorectal Cancer ◽  
Deep Learning ◽  
Decision Support ◽  
Decision Support System ◽  
Cancer Treatment ◽  
Support System ◽  
Treatment Guideline ◽  
Concordance Rate ◽  
Actual Data ◽  
Nccn Guidelines

Abstract Background: Recently, the Clinical Decision Support System (CDSS) has attracted attention as a method for minimizing medical errors. To overcome the limitation that existing CDSS does not reflect actual data, we proposed CDSS based on deep learning. Methods: We proposed Colorectal Cancer Chemotherapy Recommender (C3R), a deep learning-based chemotherapy recommendation model. This supplements the limitation that the existing CDSS is difficult to support data-based decision making. It is configured to study the clinical data generated at Gachon Gil Medical Center and recommend appropriate chemotherapy. To validate the model, we compared the diagnosis concordance rate with the NCCN Guidelines, a representative cancer treatment guideline, and the results of the Gachon Gil Medical Center’s Colorectal Cancer Treatment Protocol (GCCTP). Results: The diagnosis concordance rates of the C3R model with the NCCN guidelines were 70.5% for the Top-1 Accuracy and 84% for the Top-2 Accuracy. Also, the diagnosis concordance rate with the GCCTP were 57.9% for the Top-1 Accuracy and 77.8 for the Top-2 Accuracy. Conclusions: This model is meaningful in that it is Korea’s first colon cancer treatment method decision support system that reflects actual data. In the future, if sufficient data is secured through multi-organization, more reliable results can be obtained.

Leveraging Fog Computing and Deep Learning for Building a Secure Individual Health-Based Decision Support System to Evade Air Pollution

2020 ◽  
pp. 380-406
Author(s):  
Chitra P. ◽  
Abirami S.
Keyword(s):  
Air Pollution ◽  
Deep Learning ◽  
Decision Support ◽  
Health Status ◽  
Air Quality ◽  
Decision Support System ◽  
Support System ◽  
Quality Index ◽  
Air Quality Index ◽  
Individual Health

Globalization has led to critical influence of air pollution on individual health status. Insights to the menace of air pollution on individual's health can be achieved through a decision support system, built based on air pollution status and individual's health status. The wearable internet of things (wIoT) devices along with the air pollution monitoring sensors can gather a wide range of data to understand the effect of air pollution on individual's health. The high-level feature extraction capability of deep learning can extract productive patterns from these data to predict the future air quality index (AQI) values along with their amount of risks in every individual. The chapter aims to develop a secure decision support system that analyzes the events adversity by calculating the temporal health index (THI) of the individual and the effective air quality index (AQI) of the location. The proposed architecture utilizes fog paradigm to offload security functions by adopting deep learning algorithms to detect the malicious network traffic patterns from the benign ones.

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