Medical Image Interpolation Using Recurrent Type-2 Fuzzy Neural Network

Tavoosi, Jafar; Zhang, Chunwei; Mohammadzadeh, Ardashir; Mobayen, Saleh; Mosavi, Amir

doi:10.3389/fninf.2021.667375

Cited by 20 publications

(7 citation statements)

References 26 publications

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“…To ensure the validity and fairness of the experiments (Chen et al, 2021 ; Moayedi and Mosavi, 2021d ; Nosratabadi et al, 2021 ; Yang et al, 2021 ), all the algorithms involved in the comparisons were conducted under the same experimental conditions. Such a setting is one of the most crucial rules in the artificial intelligence community (Song et al, 2020 ; Thaher et al, 2020 ; Mousavi et al, 2021 ; Tavoosi et al, 2021 ). The population size was set to 20, the maximum number of evaluations MaxFEs was uniformly set to 100, and all the algorithms were tested 30 times independently to reduce the influence of random conditions.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Multi-Threshold Image Segmentation of Maize Diseases Based on Elite Comprehensive Particle Swarm Optimization and Otsu

et al. 2021

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Maize is a major global food crop and as one of the most productive grain crops, it can be eaten; it is also a good feed for the development of animal husbandry and essential raw material for light industry, chemical industry, medicine, and health. Diseases are the main factor limiting the high and stable yield of maize. Scientific and practical identification is a vital link to reduce the damage of diseases and accurate segmentation of disease spots is one of the fundamental techniques for disease identification. However, one single method cannot achieve a good segmentation effect to meet the diversity and complexity of disease spots. In order to solve the shortcomings of noise interference and oversegmentation in the Otsu segmentation method, a non-local mean filtered two-dimensional histogram was used to remove the noise in disease images and a new elite strategy improved comprehensive particle swarm optimization (PSO) method was used to find the optimal segmentation threshold of the objective function in this study. The experimental results of segmenting three kinds of maize foliar disease images show that the segmentation effect of this method is better than other similar algorithms and it has better convergence and stability.

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Section: Experiments and Resultsmentioning

confidence: 99%

Multi-Threshold Image Segmentation of Maize Diseases Based on Elite Comprehensive Particle Swarm Optimization and Otsu

et al. 2021

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“…Accurate mathematical modeling regarding constraints and requirements of problems is a crucial step for any real-world problem in optimization and machine learning. [77][78][79][80][81][82] Hence, SMA is not an exception as well. More details about this algorithm are delineated below:…”

Section: Slime Mould Algorithmmentioning

confidence: 99%

“…SMA is a new optimizer that has been developed by Li et al, 71 and the oscillation mode of slime mould inspires it. Accurate mathematical modeling regarding constraints and requirements of problems is a crucial step for any real‐world problem in optimization and machine learning 77–82 . Hence, SMA is not an exception as well.…”

Section: Slime Mould Algorithmmentioning

confidence: 99%

Adaptive slime mould algorithm for optimal design of photovoltaic models

Lin

Ahmadianfar²,

Golilarz

et al. 2022

Energy Science & Engineering

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Solar energy is becoming more popular as it is a clean source of electricity. The design of photovoltaic (PV) cells has therefore captivated experts worldwide.The two key issues are the lack of an excellent model to define solar cells and the lack of data regarding PV cells. This scenario even impacts solar module performance (panels). The behavior of solar cells is described by the current versus voltage. Considering these values, the design challenge entails solving complicated nonlinear multimodal objectives. Different methods to figure out the parameters of the PV cells and panels have been suggested. They do not come up with the best solutions most of the time. Hence, a powerful and reliable optimizer is needed to derive the optimal parameters of these models.To this end, this study has developed an adaptive slime mould algorithm (ASMA) as a robust and precise optimization method. To implement the ASMA, four improvements are proposed: (1) a trigonometric-based mutation and a double-based best mutation are introduced to promote the global and local search; (2) a suitable mechanism to adaptively select the control parameters of the SMA; (3) a local escaping strategy; (4) an opposition-based learning operator to improve the best solution. The ASMA is employed to derive optimal parameters of PV models and assessed utilizing a total number of eight well-known optimization algorithms. The findings show that the ASMA is very competitive in terms of accuracy and convergence speed and that this is supported by a wealth of evidence. As a result, when assessing the parameters of the PV model, ASMA is a very efficient and robust optimizer.

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“…AI procedures are generally based on machine learning (ML), pattern recognition (PR) and deep learning (DL), which further consist of artificial neural networks (ANNs), fuzzy logic, genetic programming (GP), etc. [ 16 , 17 , 31 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 ]. The application of these AI techniques in structural engineering are traced back to the early 1980s, where they were first used in the compliance checking of design codes [ 51 , 52 ] and expert interactive design of concrete columns (EIDOCC) [ 53 , 54 ].…”

Section: Introductionmentioning

confidence: 99%

Predicting the Lateral Load Carrying Capacity of Reinforced Concrete Rectangular Columns: Gene Expression Programming

et al. 2022

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This research presents a novel approach of artificial intelligence (AI) based gene expression programming (GEP) for predicting the lateral load carrying capacity of RC rectangular columns when subjected to earthquake loading. To achieve the desired research objective, an experimental database assembled by the Pacific Earthquake Engineering Research (PEER) center consisting of 250 cyclic tested samples of RC rectangular columns was employed. Seven input variables of these column samples were utilized to develop the coveted analytical models against the established capacity outputs. The selection of these input variables was based on the linear regression and cosine amplitude method. Based on the GEP modelling results, two analytical models were proposed for computing the flexural and shear capacity of RC rectangular columns. The performance of both these models was evaluated based on the four key fitness indicators, i.e., coefficient of determination (R2), root mean squared error (RMSE), mean absolute error (MAE), and root relative squared error (RRSE). From the performance evaluation results of these models, R2, RMSE, MAE, and RRSE were found to be 0.96, 53.41, 38.12, and 0.20, respectively, for the flexural capacity model, and 0.95, 39.47, 28.77, and 0.22, respectively, for the shear capacity model. In addition to these fitness criteria, the performance of the proposed models was also assessed by making a comparison with the American design code of concrete structures ACI 318-19. The ACI model reported R2, RMSE, MAE, and RRSE to be 0.88, 101.86, 51.74, and 0.39, respectively, for flexural capacity, and 0.87, 238.74, 183.66, and 1.35, respectively, for shear capacity outputs. The comparison depicted a better performance and higher accuracy of the proposed models as compared to that of ACI 318-19.

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Medical Image Interpolation Using Recurrent Type-2 Fuzzy Neural Network

Cited by 20 publications

References 26 publications

Multi-Threshold Image Segmentation of Maize Diseases Based on Elite Comprehensive Particle Swarm Optimization and Otsu

Multi-Threshold Image Segmentation of Maize Diseases Based on Elite Comprehensive Particle Swarm Optimization and Otsu

Adaptive slime mould algorithm for optimal design of photovoltaic models

Predicting the Lateral Load Carrying Capacity of Reinforced Concrete Rectangular Columns: Gene Expression Programming

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