Hybrid Machine Learning Model of Extreme Learning Machine Radial basis function for Breast Cancer Detection and Diagnosis; a Multilayer Fuzzy Expert System

Mojrian, Sanaz; Pintér, Gergő; Joloudari, Javad Hassannataj; Felde, Imre; Szabo-Gali, Akos; Nádai, László; Mosavi, Amir

doi:10.1109/rivf48685.2020.9140744

Cited by 28 publications

(13 citation statements)

References 40 publications

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“…The benefit of this research is to detect early babies/toddlers who fall into the stunting category by identifying the symptoms experienced by the babies/toddlers and observing them over a certain period of time using an expert system that implements the Mamdani fuzzy method. The difference between this research and research that has been carried out by [23]- [27] is that the method used in this research is a Mamdani fuzzy method which clarifies a problem of uncertainty, inaccuracy and noisy as well as later, the symptoms in the expert system developed will be dynamic in nature and can be added or reduced, adjusted to real conditions in the field [28], [29]. The next update is that the expert system developed can later be used online so that the identification process can be carried out independently without being limited by space and time.…”

Section: Introductionmentioning

confidence: 76%

Fuzzy expert system design for detecting stunting

Wanti,

Somantri,

Adi Prasetya

et al. 2024

IJEECS

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Stunting is a chronic nutritional problem that occurs in toddler due to lack of nutritional intake which results in impaired growth toddler. Usually, toddler who experience stunting are characterized by not increasing weight over a long period of time. Application utilization health which makes it easier for users to access information, one of which can be used to identify toddler who are stunted by selecting symptoms. The symptoms experienced by toddlers go through a system known as the system expert. In this research an expert system will be developed that is capable of early detection developmental disorders in toddlers using the Mamdani fuzzy method. The results obtained from this research are an expert system design for early detection of stunting using the Mamdani fuzzy method. The Mamdani fuzzy method was implemented to group the criteria for toddlers who fall into the stunting category or not from the initial data which is still gray because they are still unsure whether to categorize the toddler as having stunting or not. The detection accuracy rate using the Mamdani fuzzy method is 80.87% compared to expert diagnosis.

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Section: Introductionmentioning

confidence: 76%

Fuzzy expert system design for detecting stunting

Wanti,

Somantri,

Adi Prasetya

et al. 2024

IJEECS

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“…To implement RBF networks, one typically Determine the positions of the radial basis functions known as centroids, assign a width to each radial basis function, determining its in uence on the output, weight assignment, compute the activation of each radial basis function based on the input data, and combine the activations of the radial basis functions to compute the nal output of the network [33].…”

Section: Frameworkmentioning

confidence: 99%

A Novel Data Credibility-based Machine Learning Approach for UAV Capability Evaluation

Zhu,

Yu,

Chang

et al. 2024

Preprint

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The Unmanned Aerial Vehicle (UAV) has become more and more important in both civil use and military operations. The overall reconnaissance capability of the UAV swarm is often affected by multiple signals. A new approach is proposed by recognizing data credibility (DC) using multiple machine learning (ML) techniques, i.e., a novel DCML approach. There are two major components (and major theoretical contributions) of the proposed approach. The first component is the initial identification of less-credible data using a single ML technique. The second component is the cross-identification of less-credible data using multiple ML techniques based on the initial identification results. A practical case is studied for validating the proposed DRML approach. Case study results show that (1) The proposed approach in this paper demonstrates a proficient ability to identify less credible data, (2) The validation with various machine learning methods proves effective, but the efficacy of the method is not necessarily proportional to the quantity of methods employed, (3) The combination of BPNN and GPR yields the most favorable outcomes.

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“…Fuzzy Inference is a method used to analyze the uncertainty of data [31]. Fuzzy is also a logic developed in machine learning that can be used to generate rule patterns in classification [32]. Logical uncertainty can be used as a control process in conducting analysis [33].…”

Section: Fuzzy Inference Enginementioning

confidence: 99%

Machine learning classification analysis model community satisfaction with traditional market facilities as public service

Syahputra

Yanto

Putra

et al. 2023

IJ-AI

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Traditional markets are public service facilities that can be utilized by the community. The market function is used place where sellers and buyers meet in conducting transactions. This study aims to build a machine learning classification analysis model in measuring community satisfaction with traditional market facilities. The analytical methods used include Fuzzy. multiple linear regression (MRL), artificial neural network (ANN), and decision tree (DT). Fuzzy is used to generate a pattern of rules in determining the level of satisfaction. MRL serves to measure and test the correlation of rules that have been formed. The ANN method is used to carry out the classification analysis process based on learning. In the final stage. DT is used to describe the decision tree of the analysis process. This study presents the results of machine learning analysis which is very good in determining satisfaction with an accuracy rate of 99.99%. This result is influenced by fuzzy logic which can develop a classification rule pattern of 32 patterns. MRL also shows a significant correlation level of 81.1% based on the indicator variables. Overall, the machine learning classification analysis model can provide knowledge to be considered in the management of traditional markets as public service facilities.

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Hybrid Machine Learning Model of Extreme Learning Machine Radial basis function for Breast Cancer Detection and Diagnosis; a Multilayer Fuzzy Expert System

Cited by 28 publications

References 40 publications

Fuzzy expert system design for detecting stunting

Fuzzy expert system design for detecting stunting

A Novel Data Credibility-based Machine Learning Approach for UAV Capability Evaluation

Machine learning classification analysis model community satisfaction with traditional market facilities as public service

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