Estimation of healthcare expenditure per capita of Turkey using artificial intelligence techniques with genetic algorithm‐based feature selection

2020 ◽  
Author(s):  
Zeynep Ceylan ◽  
Abdulkadir Atalan
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Feature Selection ◽  
Healthcare Expenditure ◽  
Artificial Intelligence Techniques ◽  
Per Capita

scholarly journals Deriving Rules for Forecasting Air Carrier Financial Stress and Insolvency: A Genetic Algorithm Approach

2010 ◽  
Author(s):  
Sergio Davalos ◽  
Richard Gritta ◽  
Bahram Adrangi
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Feature Selection ◽  
Dimensionality Reduction ◽  
Local Optima ◽  
Air Carrier ◽  
Genetic Algorithm Approach ◽  
Genetic Algorithm Model

Statistical and artificial intelligence methods have successfully classified organizational solvency, but are limited in terms of generalization, knowledge on how a conclusion was reached, convergence to a local optima, or inconsistent results. Issues such as dimensionality reduction and feature selection can also affect a model's performance. This research explores the use of the genetic algorithm that has the advantages of the artificial neural network but without its limitations. The genetic algorithm model resulted in a set of easy to understand, if-then rules that were used to assess U.S. air carrier solvency with a 94% accuracy.

scholarly journals Improved Forms for Controlling the Acrobot with Motors of Atypical Size Using Artificial Intelligence Techniques

2017 ◽  
Author(s):  
Gonzalo Mier ◽  
Javier de Lope
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Control Problem ◽  
Inverted Pendulum ◽  
Artificial Intelligence Techniques ◽  
Rest Position ◽  
Planar Robot ◽  
Trajectory Generator

An acrobot is a planar robot with a passive actuator in its first joint. The control problem of the acrobot tries to make it rise from the rest position to the inverted pendulum position. This control problem can be divided in the swing-up problem, when the robot has to rise itself through swinging up as a human acrobat does, and the balancing problem, when the robot has to maintain itself on the inverted pendulum position. We have developed three controllers for the swing-up problem applied to two types of motors: small and big. For small motors, we used the SARSA controller and the PD with a trajectory generator. For big motors, we propose a new controller to control the acrobot, a PWM controller. All controllers except SARSA are tuned using a Genetic Algorithm.

scholarly journals Survey on computational intelligence based image encryption techniques

2020 ◽  
Vol 19 (3) ◽  
pp. 1428
Author(s):  
Thirumalaimuthu Ramanathan ◽  
Md. Jakir Hossen ◽  
Md. Shohel Sayeed ◽  
Joseph Emerson Raja
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Genetic Algorithm ◽  
Image Encryption ◽  
Computational Intelligence ◽  
Visual Cryptography ◽  
Machine Learning Algorithms ◽  
Artificial Intelligence Techniques ◽  
Cryptographic Keys

Image encryption is an important area in visual cryptography that helps in protecting images when shared through internet. There is lot of cryptography algorithms applied for many years in encrypting images. In the recent years, artificial intelligence techniques are combined with cryptography algorithms to support image encryption. Some of the benefits that artificial intelligence techniques can provide are prediction of possible attacks on cryptosystem using machine learning algorithms, generation of cryptographic keys using optimization algorithms, etc. Computational intelligence algorithms are popular in enhancing security for image encryption. The main computational intelligence algorithms used in image encryption are neural network, fuzzy logic and genetic algorithm. In this paper, a review is done on computational intelligence-based image encryption methods that have been proposed in the recent years and the comparison is made on those methods based on their performance on image encryption.

scholarly journals Design of Air Cushion Vehicles Using Artificial Intelligence: Expert System and Genetic Algorithm

2021 ◽  
Author(s):  
Tae-Cheol Jung
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Expert System ◽  
Coast Guard ◽  
Ride Quality ◽  
Successful Implementation ◽  
Artificial Intelligence Techniques ◽  
Initial Design ◽  
Air Cushion ◽  
Thesis Work

In the thesis, initial design of an Air Cushion Vehicle (ACV)is performed with the expert system and its skirt system is further optimized with the genetic algorithm. Both the expert system and genetic algorithm are advanced computerized design techniques of artifical intelligence. Those techniques are specifically developed for the ACVs with programming codes in this thesis. Then the main objective is to show the successful implementation of those techniques in the design of ACVs. The thesis work is divided into two parts. In the first part, the general configuration of ACVs, including the overall dimensions, weight distribution, parametric properties, and several subsystems, is studied and designed by the expert system as an initial design phase. In the second part of the thesis, the skirt system of ACVs is further optimized. In particular, the properties of the bag and finger skirt are optimized for improved ride quality and stability by the genetic algorithm. For the validation of these two artificial intelligence techniques, the CCG (Canadian Coast Guard) 37 ton Waban-Aki and U.S. Navy's 150 ton LCAC (Landing Craft Air Cushion) are selected for the tests. The results of the tests proved that the expert system was successfully implemented and was a powerful tool for the initial design of ACVs. Furthermore, the genetic algorithm optimized the skirt system with significantly improved ride quality and stability. It was also shown that the skirt mass was an important design factor in the heave response of the bag and finger skirt. Hence, this thesis work opened the new possibility of designing ACVs with artificial intelligence techniques.

scholarly journals Feature Selection based Artificial Intelligence Techniques for the Prediction of COVID like Diseases

2021 ◽  
Vol 1963 (1) ◽  
pp. 012167
Author(s):  
Pijush Dutta ◽  
Shobhandeb Paul ◽  
Ahmed J. Obaid ◽  
Souvik Pal ◽  
Koushik Mukhopadhyay
Keyword(s):  
Artificial Intelligence ◽  
Feature Selection ◽  
Artificial Intelligence Techniques

Application of Artificial Intelligence Techniques to Handle the Uncertainty in the Chemical Process for Environmental Protection

2015 ◽  
pp. 446-477 ◽  
Author(s):  
Tianxing Cai
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Chemical Process ◽  
Chemical Engineering ◽  
Optimization Methods ◽  
Evolutionary Strategy ◽  
Artificial Intelligence Techniques ◽  
Bee Colony ◽  
Degree Of Satisfaction ◽  
Optimization And Control

In the chemical process, the uncertainties are always encountered. Therefore, the algorithm of process modeling, simulation, optimization, and control should have the capability to handle the uncertain parameter. Meta-Heuristics Optimization (MO) techniques are attractive global optimization methods inspired by the various industrial phenomena with uncertainty. These methods have been successfully applied to a wide range of chemical engineering problems with a higher level of degree of satisfaction. In this chapter, the authors introduce multiple artificial intelligence techniques: Genetic Algorithm (GA), Biogeography-Based Optimization (BBO), Differential Evolution (DE), Evolutionary Strategy (ES), Probability-Based Incremental Learning (PBIL), Stud Genetic Algorithm (SGA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), and Fuzzy Logic (FL). It includes the introduction of algorithms and their applications to handle the uncertainty in the chemical process operation.

The Application of Artificial Intelligence in Power System State Estimation with Intelligent Materials

2012 ◽  
Vol 485 ◽  
pp. 131-135 ◽  
Author(s):  
Yun Jing Liu ◽  
Feng Wen Wang
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Power Systems ◽  
Power System ◽  
State Estimation ◽  
Real Time ◽  
System State ◽  
Power System State Estimation ◽  
Artificial Intelligence Techniques

With the development of power systems, the problem of security, stability and economics has become increasingly important. Reliable real-time data base is the foundation of analysis of the systems security and stability. Power system state estimation is used to build reliable real-time model of the power network. It has the on-line security analysis function. Power systems are large, complex systems containing highly nonlinear components. Therefore, traditional approaches often have difficulties in finding the optimal solution efficiently. Artificial intelligence techniques are being applied to a wide range of practical problems in power system. With their ability to some laws of nature and mimic human reasoning, AI techniques such as fuzzy logic and genetic algorithm seem to be more efficient in dealing with large systems and complex problems. Artificial intelligence techniques have been applied in power system applications. This paper presents a method of adaptive genetic algorithm and fuzzy logic applied in phasor measurement placement and bad data identification. And simulation is evaluated on IEEE 22-bus power system.

Application of Artificial Intelligence Techniques to Handle the Uncertainty in the Chemical Process for Environmental Protection

2016 ◽  
pp. 1229-1260
Author(s):  
Tianxing Cai
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Chemical Process ◽  
Chemical Engineering ◽  
Optimization Methods ◽  
Evolutionary Strategy ◽  
Artificial Intelligence Techniques ◽  
Bee Colony ◽  
Degree Of Satisfaction ◽  
Optimization And Control

In the chemical process, the uncertainties are always encountered. Therefore, the algorithm of process modeling, simulation, optimization, and control should have the capability to handle the uncertain parameter. Meta-Heuristics Optimization (MO) techniques are attractive global optimization methods inspired by the various industrial phenomena with uncertainty. These methods have been successfully applied to a wide range of chemical engineering problems with a higher level of degree of satisfaction. In this chapter, the authors introduce multiple artificial intelligence techniques: Genetic Algorithm (GA), Biogeography-Based Optimization (BBO), Differential Evolution (DE), Evolutionary Strategy (ES), Probability-Based Incremental Learning (PBIL), Stud Genetic Algorithm (SGA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), and Fuzzy Logic (FL). It includes the introduction of algorithms and their applications to handle the uncertainty in the chemical process operation.

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