Dental diagnosis from X-Ray images: An expert system based on fuzzy computing

Sơn, Lê Hoàng; Tuấn, Trần Mạnh; Fujita, Hamido; Dey, Nilanjan; Ashour, Amira S.; Ngọc, Võ Trương Như; Anh, Le Quynh; Chu, Dinh‐Toi

doi:10.1016/j.bspc.2017.07.005

Cited by 88 publications

(27 citation statements)

References 32 publications

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“…Using the existing system (i.e., sparse representation), the accuracy of the HR image was 80%, but using the proposed method (global face sparse representation), the accuracy of the HR image was 85-90%. In the future, we will study various methods [32][33][34][35][36][37][38][39][40][41][42][43] and enhance the algorithm on posed face images. The computational complexity of the proposed system was analyzed using the big oh notation O n 2 K M , for the face hallucination methodology.…”

Section: Discussionmentioning

confidence: 99%

A New Integrated Approach Based on the Iterative Super-Resolution Algorithm and Expectation Maximization for Face Hallucination

et al. 2020

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This paper proposed and verified a new integrated approach based on the iterative super-resolution algorithm and expectation-maximization for face hallucination, which is a process of converting a low-resolution face image to a high-resolution image. The current sparse representation for super resolving generic image patches is not suitable for global face images due to its lower accuracy and time-consumption. To solve this, in the new method, training global face sparse representation was used to reconstruct images with misalignment variations after the local geometric co-occurrence matrix. In the testing phase, we proposed a hybrid method, which is a combination of the sparse global representation and the local linear regression using the Expectation Maximization (EM) algorithm. Therefore, this work recovered the high-resolution image of a corresponding low-resolution image. Experimental validation suggested improvement of the overall accuracy of the proposed method with fast identification of high-resolution face images without misalignment.

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Section: Discussionmentioning

confidence: 99%

A New Integrated Approach Based on the Iterative Super-Resolution Algorithm and Expectation Maximization for Face Hallucination

et al. 2020

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“…The results demonstrated the efficiency of the suggested method. Further studies will focus on accelerating computational time of this method by GPU and examining other wheat problems or exploring advanced methods [70][71][72][73][74][75][76][77][78][79][80][81][82].…”

Section: Discussionmentioning

confidence: 99%

Degree Approximation-Based Fuzzy Partitioning Algorithm and Applications in Wheat Production Prediction

Jain

Kapania

et al. 2018

Symmetry

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Recently, prediction modelling has become important in data analysis. In this paper, we propose a novel algorithm to analyze the past dataset of crop yields and predict future yields using regression-based approximation of time series fuzzy data. A framework-based algorithm, which we named DAbFP (data algorithm for degree approximation-based fuzzy partitioning), is proposed to forecast wheat yield production with fuzzy time series data. Specifically, time series data were fuzzified by the simple maximum-based generalized mean function. Different cases for prediction values were evaluated based on two-set interval-based partitioning to get accurate results. The novelty of the method lies in its ability to approximate a fuzzy relation for forecasting that provides lesser complexity and higher accuracy in linear, cubic, and quadratic order than the existing methods. A lesser complexity as compared to dynamic data approximation makes it easier to find the suitable de-fuzzification process and obtain accurate predicted values. The proposed algorithm is compared with the latest existing frameworks in terms of mean square error (MSE) and average forecasting error rate (AFER).

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“…Expert system [27] This is a programming system with expert-level problemsolving capabilities in a specific field. It can effectively use the effective experience and expertise accumulated by experts for many years to solve problems that require experts to solve by simulating the expert's thinking process.…”

Section: Description Advantage Disadvantagementioning

confidence: 99%

Overview of Some Intelligent Control Structures and Dedicated Algorithms

Chang¹,

Chu²,

Lin³

et al. 2021

Automation and Control

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Automatic control refers to the use of a control device to make the controlled object automatically run or keep the state unchanged without the participation of people. The guiding ideology of intelligent control is based on people's way of thinking and ability to solve problems, in order to solve the current methods that require human intelligence. We already know that the complexity of the controlled object includes model uncertainty, high nonlinearity, distributed sensors/actuators, dynamic mutations, multiple time scales, complex information patterns, big data process, and strict characteristic indicators, etc. In addition, the complexity of the environment manifests itself in uncertainty and uncertainty of change. Based on this, various researches continue to suggest that the main methods of intelligent control can include expert control, fuzzy control, neural network control, hierarchical intelligent control, anthropomorphic intelligent control, integrated intelligent control, combined intelligent control, chaos control, wavelet theory, etc. However, it is difficult to want all the intelligent control methods in a chapter, so this chapter focuses on intelligent control based on fuzzy logic, intelligent control based on neural network, expert control and human-like intelligent control, and hierarchical intelligent control and learning control, and provide relevant and useful programming for readers to practice.

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Dental diagnosis from X-Ray images: An expert system based on fuzzy computing

Cited by 88 publications

References 32 publications

A New Integrated Approach Based on the Iterative Super-Resolution Algorithm and Expectation Maximization for Face Hallucination

A New Integrated Approach Based on the Iterative Super-Resolution Algorithm and Expectation Maximization for Face Hallucination

Degree Approximation-Based Fuzzy Partitioning Algorithm and Applications in Wheat Production Prediction

Overview of Some Intelligent Control Structures and Dedicated Algorithms

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