Impact of Swarm Intelligence Techniques in Diabetes Disease Risk Prediction

2016 ◽  
Vol 6 (2) ◽  
pp. 29-43 ◽  
Author(s):  
Sushruta Mishra ◽  
Brojo Kishore Mishra ◽  
Soumya Sahoo ◽  
Bijayalaxmi Panda
Keyword(s):  
Optimization Problems ◽  
Disease Risk ◽  
Optimization Techniques ◽  
Healthcare Sector ◽  
Support Vector ◽  
Vector Machines ◽  
Disease Risks ◽  
Complex Optimization ◽  
Medical And Healthcare ◽  
Timely Treatment

Diabetes has affected over 246 million people worldwide and by 2025 it is expected to rise to over 380 million. With the rise of information technology and its continued advent into the medical and healthcare sector, different symptoms of diabetes are being documented. The techniques inspired from the distributed collective behavior of social colonies have shown worth and excellence in dealing with complex optimization problems and are becoming more popular nowadays. It can be used as an effective problem solving tool for identifying diabetes disease risks. This paper aims at finding solutions to diagnose the disease by analyzing the patterns found in data through various swarm optimization techniques by employing Support Vector Machines and Naïve Bayes algorithms. It proposes a quicker and more efficient technique of diagnosing the disease, leading to timely treatment of the patients.

Impact of Swarm Intelligence Techniques in Diabetes Disease Risk Prediction

2020 ◽  
pp. 1181-1198
Author(s):  
Sushruta Mishra ◽  
Brojo Kishore Mishra ◽  
Soumya Sahoo ◽  
Bijayalaxmi Panda
Keyword(s):  
Optimization Problems ◽  
Disease Risk ◽  
Optimization Techniques ◽  
Healthcare Sector ◽  
Support Vector ◽  
Vector Machines ◽  
Disease Risks ◽  
Complex Optimization ◽  
Medical And Healthcare ◽  
Timely Treatment

Diabetes has affected over 246 million people worldwide and by 2025 it is expected to rise to over 380 million. With the rise of information technology and its continued advent into the medical and healthcare sector, different symptoms of diabetes are being documented. The techniques inspired from the distributed collective behavior of social colonies have shown worth and excellence in dealing with complex optimization problems and are becoming more popular nowadays. It can be used as an effective problem solving tool for identifying diabetes disease risks. This paper aims at finding solutions to diagnose the disease by analyzing the patterns found in data through various swarm optimization techniques by employing Support Vector Machines and Naïve Bayes algorithms. It proposes a quicker and more efficient technique of diagnosing the disease, leading to timely treatment of the patients.

Nondegenerate Piecewise Linear Systems: A Finite Newton Algorithm and Applications in Machine Learning

2012 ◽  
Vol 24 (4) ◽  
pp. 1047-1084 ◽  
Author(s):  
Xiao-Tong Yuan ◽  
Shuicheng Yan
Keyword(s):  
Linear Systems ◽  
Optimization Problems ◽  
Piecewise Linear ◽  
Optimization Methods ◽  
Coefficient Matrix ◽  
Learning Problems ◽  
Support Vector ◽  
Data Sets ◽  
Piecewise Linear Systems ◽  
Vector Machines

We investigate Newton-type optimization methods for solving piecewise linear systems (PLSs) with nondegenerate coefficient matrix. Such systems arise, for example, from the numerical solution of linear complementarity problem, which is useful to model several learning and optimization problems. In this letter, we propose an effective damped Newton method, PLS-DN, to find the exact (up to machine precision) solution of nondegenerate PLSs. PLS-DN exhibits provable semiiterative property, that is, the algorithm converges globally to the exact solution in a finite number of iterations. The rate of convergence is shown to be at least linear before termination. We emphasize the applications of our method in modeling, from a novel perspective of PLSs, some statistical learning problems such as box-constrained least squares, elitist Lasso (Kowalski & Torreesani, 2008 ), and support vector machines (Cortes & Vapnik, 1995 ). Numerical results on synthetic and benchmark data sets are presented to demonstrate the effectiveness and efficiency of PLS-DN on these problems.

scholarly journals A Survey on Dragonfly Algorithm and its Applications in Engineering

2020 ◽  
Author(s):  
Chnoor M. Rahman ◽  
Tarik A. Rashid
Keyword(s):  
Review Paper ◽  
Optimization Problems ◽  
Optimization Techniques ◽  
Superior Performance ◽  
Test Functions ◽  
Dragonfly Algorithm ◽  
Engineering Problems ◽  
Complex Optimization ◽  
Metaheuristic Techniques ◽  
Benchmark Test Functions

<p></p><p></p><p>Dragonfly algorithm developed in 2016. It is one of the algorithms used by the researchers to optimize an extensive series of uses and applications in various areas. At times, it offers superior performance compared to the most well-known optimization techniques. However, this algorithm faces several difficulties when it is utilized to enhance complex optimization problems. This work addressed the robustness of the method to solve real-world optimization issues, and its deficiency to improve complex optimization problems. This review paper shows a comprehensive investigation of the dragonfly algorithm in the engineering area. First, an overview of the algorithm is discussed. Besides, we also examine the modifications of the algorithm. The merged forms of this algorithm with different techniques and the modifications that have been done to make the algorithm perform better are addressed. Additionally, a survey on applications in the engineering area that used the dragonfly algorithm is offered. A comparison is made between the algorithm and other metaheuristic techniques to show its ability to enhance various problems. The outcomes of the algorithm from the works that utilized the dragonfly algorithm previously and the outcomes of the benchmark test functions proved that in comparison with some techniques, the dragonfly algorithm owns an excellent performance, especially for small to intermediate applications. Moreover, the congestion facts of the technique and some future works are presented. The authors conducted this research to help other researchers who want to study the algorithm and utilize it to optimize engineering problems.</p><br><p></p><p></p>

Optimization of Small Wind Turbines using Genetic Algorithms

2016 ◽  
Vol 7 (4) ◽  
pp. 50-65 ◽  
Author(s):  
Mohammad Hamdan ◽  
Mohammad Hassan Abderrazzaq
Keyword(s):  
Wind Turbines ◽  
Optimization Problems ◽  
Optimization Techniques ◽  
Practical Implementation ◽  
Wind Energy Conversion ◽  
Design Alternatives ◽  
Wide Range ◽  
Small Wind Turbines ◽  
Complex Optimization ◽  
Tower Height

This paper presents a detailed optimization analysis of tower height and rotor diameter for a wide range of small wind turbines using Genetic Algorithm (GA). In comparison with classical, calculus-based optimization techniques, the GA approach is known by its reasonable flexibilities and capability to solve complex optimization problems. Here, the values of rotor diameter and tower height are considered the main parts of the Wind Energy Conversion System (WECS), which are necessary to maximize the output power. To give the current study a practical sense, a set of manufacturer's data was used for small wind turbines with different design alternatives. The specific cost and geometry of tower and rotor are selected to be the constraints in this optimization process. The results are presented for two classes of small wind turbines, namely 1.5kW and 10kW turbines. The results are analyzed for different roughness classes and for two height-wind speed relationships given by power and logarithmic laws. Finally, the results and their practical implementation are discussed.

Parameters of Support Vector Machines Model Optimized Method Based on Genetic Algorithm

2014 ◽  
Vol 989-994 ◽  
pp. 1873-1876
Author(s):  
Yu Zhen Xie ◽  
Zhao Gang Wang ◽  
Xiao Wei Dai
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Support Vector Machine Model ◽  
Optimization Problems ◽  
Support Vector ◽  
Machine Model ◽  
Penalty Factor ◽  
Vector Machines ◽  
Cross Validation Error ◽  
Function Selection

In order to obtain more accurate parameters of support vector machine model, using genetic algorithm to optimize the parameters is an effective method. This paper analyzes the principle of support vector machine for regression, support vector machine kernel function selection, kernel parameters, penalty factor selection and adjustment methods, taking into account genetic algorithm is effective in solving optimization problems, proposed a method using genetic algorithm to optimize the parameters of support vector machine, which uses genetic algorithms to make cross-validation error minimized. The simulation results demonstrate the effectiveness of this method.

scholarly journals Machine Learning Prediction of Hospitalization due to COVID-19 based on Self-Reported Symptoms: A Study for Brazil*

2021 ◽  
Author(s):  
Igor Miranda ◽  
Gildeberto Cardoso ◽  
Madhurananda Pahar ◽  
Gabriel Oliveira ◽  
Thomas Niesler
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Support Vector Machines ◽  
Health Professionals ◽  
Human Development Index ◽  
Mobile App ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Vector Machines ◽  
Timely Treatment

Predicting the need for hospitalization due to COVID-19 may help patients to seek timely treatment and assist health professionals to monitor cases and allocate resources. We investigate the use of machine learning algorithms to predict the risk of hospitalization due to COVID-19 using the patient's medical history and self-reported symptoms, regardless of the period in which they occurred. Three datasets containing information regarding 217,580 patients from three different states in Brazil have been used. Decision trees, neural networks, and support vector machines were evaluated, achieving accuracies between 79.1% to 84.7%. Our analysis shows that better performance is achieved in Brazilian states ranked more highly in terms of the official human development index (HDI), suggesting that health facilities with better infrastructure generate data that is less noisy. One of the models developed in this study has been incorporated into a mobile app that is available for public use.

Optimization of Small Wind Turbines Using Genetic Algorithms

2017 ◽  
pp. 1484-1499
Author(s):  
Mohammad Hamdan ◽  
Mohammad Hassan Abderrazzaq
Keyword(s):  
Wind Turbines ◽  
Optimization Problems ◽  
Optimization Techniques ◽  
Practical Implementation ◽  
Wind Energy Conversion ◽  
Design Alternatives ◽  
Wide Range ◽  
Small Wind Turbines ◽  
Complex Optimization ◽  
Tower Height

This paper presents a detailed optimization analysis of tower height and rotor diameter for a wide range of small wind turbines using Genetic Algorithm (GA). In comparison with classical, calculus-based optimization techniques, the GA approach is known by its reasonable flexibilities and capability to solve complex optimization problems. Here, the values of rotor diameter and tower height are considered the main parts of the Wind Energy Conversion System (WECS), which are necessary to maximize the output power. To give the current study a practical sense, a set of manufacturer's data was used for small wind turbines with different design alternatives. The specific cost and geometry of tower and rotor are selected to be the constraints in this optimization process. The results are presented for two classes of small wind turbines, namely 1.5kW and 10kW turbines. The results are analyzed for different roughness classes and for two height-wind speed relationships given by power and logarithmic laws. Finally, the results and their practical implementation are discussed.

scholarly journals Use of support vector machines for disease risk prediction in genome-wide association studies: Concerns and opportunities

2012 ◽  
Vol 33 (12) ◽  
pp. 1708-1718 ◽  
Author(s):  
Florian Mittag ◽  
Finja Büchel ◽  
Mohamad Saad ◽  
Andreas Jahn ◽  
Claudia Schulte ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Risk Prediction ◽  
Disease Risk ◽  
Association Studies ◽  
Genome Wide Association ◽  
Support Vector ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Vector Machines

A Review on Bio-Inspired Migration Optimization Techniques

2015 ◽  
Vol 11 (1) ◽  
pp. 24-35 ◽  
Author(s):  
Jyotsna Verma ◽  
Nishtha Kesswani
Keyword(s):  
Combinatorial Optimization ◽  
Computer Networks ◽  
Optimization Problems ◽  
Optimization Techniques ◽  
Problem Size ◽  
Nature Inspired Computing ◽  
Complex Optimization ◽  
Automated Tools

Nature inspired computing techniques has become a very popular topic in recent years. Number of applications in computer networks, robotics, biology, combinatorial optimization, etc. can be seen in literatures which are based on the bio-inspired techniques. Nature inspired techniques are proven to solve complex optimization problems irrespective of their problem size. This review summarizes various nature inspired migration algorithms and comparison between them, based on the automated tools, evolutionary techniques and applications.

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