scholarly journals Coronary Artery Disease Classification Using Deep Neural Network and Ensemble Models Optimized by Particle Swarm Optimization

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Coronary Artery Disease ◽  
Particle Swarm Optimization ◽  
Coronary Artery ◽  
Optimization Technique ◽  
Medical Science ◽  
Research Work ◽  
Particle Swarm ◽  
Optimization Techniques ◽  
Swarm Optimization ◽  
Artery Disease

Nowadays, many people are suffering from several health related issues in which Coronary Artery Disease (CAD) is an important one. Identification, prevention and diagnosis of diseases is a very challenging task in the field of medical science. This paper proposes a new feature optimization technique known as PSO-Ensemble1 to reduce the number of features from CAD datasets. The proposed model is based on Particle Swarm Optimization (PSO) with Ensemble1 classifier as the objective function and is compared with other optimization techniques like PSO-CFSE and PSO-J48 with two benchmark CAD datasets. The main objective of this research work is to classify CAD with the proposed PSO-Ensemble1 model using the Ensemble Technique.

Hybrid particle swarm optimization for rule discovery in the diagnosis of coronary artery disease

2019 ◽  
Vol 38 (1) ◽  
Author(s):  
Mariam Zomorodi‐moghadam ◽  
Moloud Abdar ◽  
Zohreh Davarzani ◽  
Xujuan Zhou ◽  
Pawel Pławiak ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Particle Swarm Optimization ◽  
Coronary Artery ◽  
Particle Swarm ◽  
Rule Discovery ◽  
Swarm Optimization ◽  
Hybrid Particle ◽  
Hybrid Particle Swarm Optimization ◽  
Artery Disease

Parametric Fin Array Optimization for Radial Flux Machines Considering Time Varying Loads

2011 ◽  
Author(s):  
S. Andrew Semidey ◽  
J. Rhett Mayor
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Optimization Techniques ◽  
Time Varying ◽  
Swarm Optimization ◽  
Radial Flux ◽  
Steady State Temperature ◽  
Array Optimization ◽  
Fin Spacing

This work utilizes a novel, generic, thermal model of an electric machine in conjunction with particle swarm optimization to optimize the electric machine’s fin array considering time varying loads. The maximum power rating on radial-flux electrical machines is typically based on the steady state temperature of the windings. This leads to over designs in applications in which only short periods of high power are required. The proposed optimization technique can be used in the design process to reduce the risk of over design therefore leading to reduced material costs for finned frames and increased power density in radial flux machines. Whilst many numerical optimization techniques exist, this paper will consider the application of particle swarm optimization techniques to optimize the fin array parameters. The parameter space to be investigated will consider the fin height (hf), fin width (wf), and fin spacing (sf).

Improved Canny Edges Using Cellular Based Particle Swarm Optimization Technique for Tamil Sign Digital Images

2016 ◽  
Vol 6 (5) ◽  
pp. 2158 ◽  
Author(s):  
M Krishnaveni ◽  
P Subashini ◽  
TT Dhivyaprabha
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Technique ◽  
Research Work ◽  
Particle Swarm ◽  
Threshold Values ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Swarm Optimization ◽  
Hybrid Particle Swarm Optimization ◽  
New Variant

<p>The development of computer based sign language recognition system, for enabling communication with hearing impaired people, is an important research area that faces different challenges in the pre-processing stage of image processing, particularly in boundary detection stage. In edge detection, the possibility of achieving high quality images significantly depends on the fitting threshold values, which are generally selected using canny method, and these threshold values may vary, based on the type of images and the applications chosen. This research work presents a novel idea of establishing a hybrid particle swarm optimization algorithm, which is a combination of PSO with the behavioural pattern of cellular organism in canny method, that defines an objective to find optimal threshold values for the implementation of double thresholding hysteresis method, which is viewed as a non-linear complex problem. The attempt to incorporate the model has minimized the problem of quick convergence of PSO algorithm which has improved the detection of broken edges. The efficiency of the proposed algorithm is proved through the experimental observation, done in Tamil sign images to indicate the better performance of canny operator by introducing new variant based PSO.</p>

scholarly journals Performance evaluation of power optimization in wireless sensor networks using particle swarm optimization

2018 ◽  
Vol 7 (2.7) ◽  
pp. 404
Author(s):  
Chandaluru Mohan Venkata Siva Prasad ◽  
Dr K. RaghavaRao ◽  
D Satish Kumar ◽  
A V. Prabhu
Keyword(s):  
Wireless Sensor Networks ◽  
Particle Swarm Optimization ◽  
Sensor Networks ◽  
Power Consumption ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Optimization Techniques ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Swarm Optimization

Wireless sensor networks are the sensors which are acclimated to sense the environmental condition like temperature, pressure, sultriness, moisture etc, sensing the environment parameters and sending them to the gateway and retrieving the aggregated data from the gateway to the end user. Power is the major constraint in wireless sensor networks. One must need to reduce the power consumption. Wireless sensor networks have sensor nodes in which each node has a processor, antenna and a battery. The batteries consume power so that we require increasing the lifetime of the battery for that some optimization techniques are required to reduce the power consumption. There are some techniques which are inspired from the lifestyle of animals. In this paper proposing an optimization technique which is inspired by the animal demeanor which reduces the power consumption of the sensor nodes which is particle swarm optimization (PSO) technique. PSO is inspired by the convivial demeanor of birds or schooling of fish. By utilizing this bio-inspired technique we can reduce the power consumed by the sensor nodes and at the same time lifetime of the batteries present in the sensor nodes are increased. 

scholarly journals Improved Canny Edges Using Cellular Based Particle Swarm Optimization Technique for Tamil Sign Digital Images

2016 ◽  
Vol 6 (5) ◽  
pp. 2158
Author(s):  
M Krishnaveni ◽  
P Subashini ◽  
TT Dhivyaprabha
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Technique ◽  
Research Work ◽  
Particle Swarm ◽  
Threshold Values ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Swarm Optimization ◽  
Hybrid Particle Swarm Optimization ◽  
New Variant

<p>The development of computer based sign language recognition system, for enabling communication with hearing impaired people, is an important research area that faces different challenges in the pre-processing stage of image processing, particularly in boundary detection stage. In edge detection, the possibility of achieving high quality images significantly depends on the fitting threshold values, which are generally selected using canny method, and these threshold values may vary, based on the type of images and the applications chosen. This research work presents a novel idea of establishing a hybrid particle swarm optimization algorithm, which is a combination of PSO with the behavioural pattern of cellular organism in canny method, that defines an objective to find optimal threshold values for the implementation of double thresholding hysteresis method, which is viewed as a non-linear complex problem. The attempt to incorporate the model has minimized the problem of quick convergence of PSO algorithm which has improved the detection of broken edges. The efficiency of the proposed algorithm is proved through the experimental observation, done in Tamil sign images to indicate the better performance of canny operator by introducing new variant based PSO.</p>

A Comparative Study of Genetic and Particle Swarm Optimization Algorithms and Their Hybrid Method in Water Flooding Optimization

2018 ◽  
Vol 140 (10) ◽  
Author(s):  
Majid Siavashi ◽  
Mohsen Yazdani
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Newton Method ◽  
Oil Recovery ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Optimization Techniques ◽  
Water Flooding ◽  
Swarm Optimization

Optimization of oil production from petroleum reservoirs is an interesting and complex problem which can be done by optimal control of well parameters such as their flow rates and pressure. Different optimization techniques have been developed yet, and metaheuristic algorithms are commonly employed to enhance oil recovery projects. Among different metaheuristic techniques, the genetic algorithm (GA) and the particle swarm optimization (PSO) have received more attention in engineering problems. These methods require a population and many objective function calls to approach more the global optimal solution. However, for a water flooding project in a reservoir, each function call requires a long time reservoir simulation. Hence, it is necessary to reduce the number of required function evaluations to increase the rate of convergence of optimization techniques. In this study, performance of GA and PSO are compared with each other in an enhanced oil recovery (EOR) project, and Newton method is linked with PSO to improve its convergence speed. Furthermore, hybrid genetic algorithm-particle swarm optimization (GA-PSO) as the third optimization technique is introduced and all of these techniques are implemented to EOR in a water injection project with 13 decision variables. Results indicate that PSO with Newton method (NPSO) is remarkably faster than the standard PSO (SPSO). Also, the hybrid GA-PSO method is more capable of finding the optimal solution with respect to GA and PSO. In addition, GA-PSO, NPSO, and GA-NPSO methods are compared and, respectively, GA-NPSO and NPSO showed excellence over GA-PSO.

scholarly journals Comprehensive learning particle swarm optimization for sizing and placement of distributed generation for network loss reduction

2020 ◽  
Vol 20 (1) ◽  
pp. 16 ◽  
Author(s):  
Eshan Karunarathne ◽  
Jagadeesh Pasupuleti ◽  
Janaka Ekanayake ◽  
Dilini Almeida
Keyword(s):  
Particle Swarm Optimization ◽  
Distributed Generation ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Distribution Networks ◽  
Optimization Techniques ◽  
Premature Convergence ◽  
Optimal Placement ◽  
Swarm Optimization ◽  
Comparison Results

With the technological advancements, Distributed Generation (DG) has become a common method of overwhelming the issues like power losses and voltage drops which accompanies with the leaf of the feeders of radial distribution networks. Many researchers have used several optimization techniques and tools which could be used to locate and size the DG units in the system. Particle Swarm Optimization (PSO) is one of the famous optimization techniques. However, the premature convergence is identified as a fundamental adverse effect of this optimization technique. Therefore, the optimization problem can direct the objective function to a local minimum. This paper presents a variant of PSO techniques, “Comprehensive Learning Particle Swarm Optimization (CLPSO)” to determine the optimal placement and sizing of the DGs, which uses a novel learning strategy whereby all other particles’ historical best information and learning probability value are used to update a particle’s velocity. The CLPSO particles learn from one exampler for few iterations, instead of learing from global and personal best values in every iteration in PSO and this technique retains the swarm's variability to avoid premature convergence. A detailed analysis was conducted for the IEEE 33 bus system. The comparison results have revealed a higher convergence and an accuracy than the PSO.

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