scholarly journals An Extended Approach to Predict Retinopathy in Diabetic Patients Using the Genetic Algorithm and Fuzzy C-Means

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Saeid Jafarzadeh Ghoushchi ◽  
Ramin Ranjbarzadeh ◽  
Amir Hussein Dadkhah ◽  
Yaghoub Pourasad ◽  
Malika Bendechache
Keyword(s):  
Genetic Algorithm ◽  
Diagnostic Method ◽  
Fitness Function ◽  
Jaccard Index ◽  
Diabetic Patients ◽  
Growth Region ◽  
New Approach ◽  
Fuzzy C Means ◽  
Extended Approach ◽  
Selection Of

The present study is developed a new approach using a computer diagnostic method to diagnosing diabetic diseases with the use of fluorescein images. In doing so, this study presented the growth region algorithm for the aim of diagnosing diabetes, considering the angiography images of the patients’ eyes. In addition, this study integrated two methods, including fuzzy C-means (FCM) and genetic algorithm (GA) to predict the retinopathy in diabetic patients from angiography images. The developed algorithm was applied to a total of 224 images of patients’ retinopathy eyes. As clearly confirmed by the obtained results, the GA-FCM method outperformed the hand method regarding the selection of initial points. The proposed method showed 0.78 sensitivity. The comparison of the fuzzy fitness function in GA with other techniques revealed that the approach introduced in this study is more applicable to the Jaccard index since it could offer the lowest Jaccard distance and, at the same time, the highest Jaccard values. The results of the analysis demonstrated that the proposed method was efficient and effective to predict the retinopathy in diabetic patients from angiography images.

scholarly journals Some Studies on Forming Optimization with Genetic Algorithm

2012 ◽  
Vol 2 (2) ◽  
pp. 105-112
Author(s):  
Ganesh Marotrao KAKANDIKAR ◽  
Vilas M. NANDEDKAR
Keyword(s):  
Genetic Algorithm ◽  
Process Parameters ◽  
Wide Spectrum ◽  
Simulation Software ◽  
Flow Conditions ◽  
Element Analysis ◽  
Blank Holder ◽  
New Approach ◽  
Finite Element Analysis Simulation ◽  
Selection Of

Forming is a compression-tension process involving wide spectrum of operations and flow conditions. The result of the process depends on the large number of parameters and their interdependence. The selection of various parameters is still based on trial and error methods. In this paper the authors presents a new approach to optimize the geometry parameters of circular components, process parameters such as blank holder pressure and coefficient of friction etc. The optimization problem has been formulated with the objective of optimizing the maximum forming load required in Forming. Genetic algorithm is used for the optimization purpose to minimize the drawing load and to optimize the process parameters. A finite element analysis simulation software Fast Form Advanced is used for the validations of the results after optimization.

Optimization of Clustering in Wireless Sensor Networks Using Genetic Algorithm

2020 ◽  
pp. 822-836
Author(s):  
Pritee Parwekar ◽  
Sireesha Rodda
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Network ◽  
Sensor Node ◽  
Fitness Function ◽  
Wireless Sensor ◽  
Sleep Mode ◽  
Communication Distance ◽  
Selection Of

The energy of a sensor node is a major factor for life of a network in wireless sensor network. The depletion of the sensor energy is dependent on the communication range from the sink. Clustering is mainly used to prolong the life of a network with energy consumption. This paper proposes optimization of clustering using genetic algorithm which will help to minimize the communication distance. The cluster overhead and the active and sleep mode of a sensor is also considered while calculating the fitness function to form the cluster. This approach helps to prolong the network life of sensor network. The proposed work is tested for different number of nodes and is helping to find the correct solution for the selection of cluster heads.

A new approach for optimum selection of a turbocharger using a genetic algorithm

2014 ◽  
Vol 229 (8) ◽  
pp. 1016-1033 ◽  
Author(s):  
Sepehr Sanaye ◽  
Shahram Sedghi Ghadikolaee ◽  
Mohammad Mehdi Ghasemi ◽  
Golandam Rahimi
Keyword(s):  
Genetic Algorithm ◽  
New Approach ◽  
Optimum Selection ◽  
Selection Of

Genetic Algorithm for Assembly Sequences Planning Based on Heuristic Assembly Knowledge

2010 ◽  
Vol 44-47 ◽  
pp. 3657-3661 ◽  
Author(s):  
Hao Pan ◽  
Wen Jun Hou ◽  
Tie Meng Li
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Graph Model ◽  
Initial Population ◽  
New Approach ◽  
Assembly Sequences ◽  
Connection Graph ◽  
Assembly Sequences Planning

To improve the efficiency of Assembly Sequences Planning (ASP), a new approach based on heuristic assembly knowledge and genetic algorithm was proposed. First, Connection Graph of Assembly (CGA) was introduced, and then, assembly knowledge was described in the form of Assembly Rings, on that basis, the assembly connection graph model containing Assembly Rings was defined, and the formation of initial population algorithm was given. In addition, a function was designed to measure the feasible assembly and then the genetic algorithm fitness function was given. Finally, an example was shown to illustrate the effectiveness of the algorithm.

Kernel fuzzy C- means clustering with teaching learning based optimization algorithm (TLBO-KFCM)

2021 ◽  
pp. 1-9
Author(s):  
Saumya Singh ◽  
Smriti Srivastava
Keyword(s):  
Genetic Algorithm ◽  
Data Analysis ◽  
Optimization Algorithm ◽  
Clustering Algorithm ◽  
Data Sets ◽  
New Approach ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

In the field of data analysis clustering is considered to be a major tool. Application of clustering in various field of science, has led to advancement in clustering algorithm. Traditional clustering algorithm have lot of defects, while these defects have been addressed but no clustering algorithm can be considered as superior. A new approach based on Kernel Fuzzy C-means clustering using teaching learning-based optimization algorithm (TLBO-KFCM) is proposed in this paper. Kernel function used in this algorithm improves separation and makes clustering more apprehensive. Teaching learning-based optimization algorithm discussed in the paper helps to improve clustering compactness. Simulation using five data sets are performed and the results are compared with two other optimization algorithms (genetic algorithm GA and particle swam optimization PSO). Results show that the proposed clustering algorithm has better performance. Another simulation on same set of data is also performed, and clustering results of TLBO-KFCM are compared with teaching learning-based optimization algorithm with Fuzzy C- Means Clustering (TLBO-FCM).

Land Use and Transportation Planning for Twin Cities Using a Genetic Algorithm

2000 ◽  
Vol 1722 (1) ◽  
pp. 67-74 ◽  
Author(s):  
Richard J. Balling ◽  
John Taber ◽  
Kirsten Day ◽  
Scott Wilson
Keyword(s):  
Genetic Algorithm ◽  
Land Use ◽  
Transportation Planning ◽  
Fitness Function ◽  
High Growth ◽  
Pareto Set ◽  
Decision Makers ◽  
Twin Cities ◽  
New Approach ◽  
Future Plans

A new approach to future land use and transportation planning for high-growth cities is presented. The approach employs a genetic algorithm to efficiently search through hundreds of thousands of possible future plans. A new fitness function is developed to guide the genetic algorithm toward a Pareto set of plans for the multiple competing objectives that are involved. This set may be placed before decision makers. A Pareto set scanner also is described that assists decision makers in shopping through the Pareto set to select a plan. Some of the differences between simultaneous planning and separate planning of highly coupled twin cities also are examined.

Optimization of Clustering in Wireless Sensor Networks Using Genetic Algorithm

2017 ◽  
Vol 8 (4) ◽  
pp. 84-98 ◽  
Author(s):  
Pritee Parwekar ◽  
Sireesha Rodda
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Network ◽  
Sensor Node ◽  
Fitness Function ◽  
Wireless Sensor ◽  
Sleep Mode ◽  
Communication Distance ◽  
Selection Of

The energy of a sensor node is a major factor for life of a network in wireless sensor network. The depletion of the sensor energy is dependent on the communication range from the sink. Clustering is mainly used to prolong the life of a network with energy consumption. This paper proposes optimization of clustering using genetic algorithm which will help to minimize the communication distance. The cluster overhead and the active and sleep mode of a sensor is also considered while calculating the fitness function to form the cluster. This approach helps to prolong the network life of sensor network. The proposed work is tested for different number of nodes and is helping to find the correct solution for the selection of cluster heads.

scholarly journals Automatic Segmentation of Medical Images Using Fuzzy c-Means and the Genetic Algorithm

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Omid Jamshidi ◽  
Abdol Hamid Pilevar
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Fitness Function ◽  
Automatic Segmentation ◽  
Brain Images ◽  
Fuzzy C Means ◽  
Mr Brain Images ◽  
Magnetic Resonance Imaging Mri ◽  
The Right ◽  
Number Of Segments

Magnetic resonance imaging (MRI) segmentation is a complex issue. This paper proposes a new method for estimating the right number of segments and automatic segmentation of human normal and abnormal MR brain images. The purpose of automatic diagnosis of the segments is to find the number of divided image areas of an image according to its entropy and with correctly diagnose of the segment of an image also increased the precision of segmentation. Regarding the fact that guessing the number of image segments and the center of segments automatically requires algorithm test many states in order to solve this problem and to have a high accuracy, we used a combination of the genetic algorithm and the fuzzy c-means (FCM) method. In this method, it has been tried to change the FCM method as a fitness function for combination of it in genetic algorithm to do the image segmentation more accurately. Our experiment shows that the proposed method has a significant improvement in the accuracy of image segmentation in comparison to similar methods.

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