Hybrid Multi-layered GMDH-Type Neural Network Using Principal Component-Regression Analysis and Its Application to Medical Image Diagnosis of Lung Cancer

Kondo, Tadashi; Ueno, Junji; Takao, Schoichiro

doi:10.1109/biomedcom.2012.10

Cited by 9 publications

(4 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In previous research by [21] and [24], it was shown that using several neuron architectures (or transfer functions) within a model produced better results than the model which only use one. Nevertheless, increasing the number of transfer functions in a model will increase the model's complexity.…”

Section: Proposed Combined Methodsmentioning

confidence: 99%

“…In this paper, a combination technique using Artificial Bee Colony (ABC) algorithm for finding appropriate weights to combine several Group Method of Data Handling (GMDH) models is proposed. This proposed technique is partially motivated by the work of Kondo et al [21] which implemented three types of neural network architectures in GMDH to automatically organize the GMDH's network architecture in medical imaging. In this paper, instead of implementing three types of architectures in one model, we propose forecasting several GMDH models, each using different types of neural network architectures.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Group Method of Data Handling with Artificial Bee Colony in Combining Forecasts

Yahya

Samsudin²,

Darmawan³

et al. 2018

IJIE

View full text Add to dashboard Cite

In this study, the use of Artificial Bee Colony (ABC) algorithm to combine several time series forecasts is presented. This study is done by combining individual forecasts of Group Method of Data Handling models using the weighted-based combine approach. The weights for each individual model are calculated using ABC algorithm. In order to evaluate the proposed model, this study tested the proposed model on the International Airline Passengers data, and the performances are calculated using mean square error (MSE), mean average error (MAE) and mean average percentage error (MAPE). The accuracy of the proposed model is compared to the individual models and the models implemented in previous literatures. The results revealed that the proposed model produced significantly accurate forecasts.

show abstract

Section: Proposed Combined Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Group Method of Data Handling with Artificial Bee Colony in Combining Forecasts

Yahya

Samsudin²,

Darmawan³

et al. 2018

IJIE

View full text Add to dashboard Cite

show abstract

“…Under the high-speed of information technology, some enterprises have mastered the actual operation time of some stable products through ERP and MES systems, but it is difficult to obtain the operation time of multi-variety and variable-batch mixed-line products. With full consideration of production efficiency, mixed line status, seasonal changes and other factors, such operation time is like stock index, which cannot be directly collected or simply predicted [27][28][29][30]. In the actual project plan management process, the most important thing is to determine the future project plan in advance.…”

Section: Related Workmentioning

confidence: 99%

A Machine Learning Based EMA-DCPM Algorithm for Production Scheduling

Wang¹,

Liu²,

Xia³

et al. 2023

Preprint

View full text Add to dashboard Cite

Being able to complete the production of the product project on time is the prerequisite to win more tasks, many enterprises have begun to study intelligent scheduling algorithms to achieve dynamic control of the total project duration. However, the production operation time of products is highly volatile, and it is impossible to predict the possible operation time of future products. In the actual production environment, we often encounter extreme situations such as mixed production lines, multiple varieties, variable batches, etc. A dynamic optimization CPM model in the ERP/MES system with the attention mechanism (EMA-DCPM) will collect, extract and predict the operation time through artificial intelligence technology, and it can accurately predict the exact operation time of a batch of products that have not yet been produced. Through experiments, our algorithm can realize the production scheduling and get more than 200% better precision than former algorithms and realize dynamic optimization of complex projects.

show abstract

“…But, multi-co linearity occurs and prediction values become unstable. Hybrid multilayered GMDH-type neural network using principal componentregression analysis is proposed [19].…”

Section: Literature Surveymentioning

confidence: 99%

Quantum Computing Based Technique for Cancer Disease Detection System

Jain¹

2014

J Comput Sci Syst Biol

View full text Add to dashboard Cite

In present scenario where various diseases are here and out of them the cancer is one of them most devastating disease. Presently the detection procedure is very much time consuming and the results obtain by them are not so fast, so here in this respect there is a need of more accurate, fast and efficient method through computing technologies. This research work incorporates Quantum computing with the clustering algorithm i.e.

show abstract

Hybrid Multi-layered GMDH-Type Neural Network Using Principal Component-Regression Analysis and Its Application to Medical Image Diagnosis of Lung Cancer

Cited by 9 publications

References 5 publications

Group Method of Data Handling with Artificial Bee Colony in Combining Forecasts

Group Method of Data Handling with Artificial Bee Colony in Combining Forecasts

A Machine Learning Based EMA-DCPM Algorithm for Production Scheduling

Quantum Computing Based Technique for Cancer Disease Detection System

Contact Info

Product

Resources

About