Chemical reaction optimization to disease diagnosis by optimizing hyper-planes classifiers

Jalayeri, Somayeh; Abdolrazzagh-Nezhad, Majid

doi:10.1007/s00500-019-03869-9

Cited by 4 publications

(6 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to the complexity of the problem model, we adopted a relatively new intelligent optimization algorithm-chemical reaction optimization algorithm (CRO) [13][14][15] to solve it. This algorithm is a swarm intelligence algorithm based on the collision and energy conversion of molecules in the process of chemical reaction.…”

Section: Optimization Model Of Mobile Sensor Networkmentioning

confidence: 99%

[Retracted] Study of Node Distribution and Density Optimization in Mobile Sensor Network 3D Space

Gao

Lyu

et al. 2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

Mobile sensor network is applied in information collection in emergencies. As the mobile sensor network in real environment is widely deployed with different height and the redundancy of the sensor node needs to be as low as possible, therefore, it is necessary to effectively deploy mobile sensor nodes in the 3D space to have reasonable layout and optimized density. To this end, we established the optimization model of mobile sensor network deployment and solved the model with chemical reaction optimization (CRO). The experimental results have shown that compared with traditional particle swarm optimization (PSO), CRO algorithm can achieve reasonable deployment more rapidly and enhance the network performance evaluation value effectively. The reasonable deployment of mobile sensor network node is very significant to information collecting, postperiod decision-making, and rapid rescuing work in emergencies.

show abstract

Section: Optimization Model Of Mobile Sensor Networkmentioning

confidence: 99%

[Retracted] Study of Node Distribution and Density Optimization in Mobile Sensor Network 3D Space

Gao

Lyu

et al. 2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…Each cuckoo can't lay less than 5 and more than 20 eggs in the iteration. It means cuckoo 𝑖 with ℎ𝑎𝑏𝑖𝑡𝑎𝑡 𝑖 can migrate toward between [5,20] new habitat for laying her eggs. Also, they lay based on the Egg Laying Radius (ELR) which is the maximum laying distance from their habitat.…”

Section: Cuckoo Optimization Algorithm (Coa)mentioning

confidence: 99%

“…It has attractive advantages (+) and significant disadvantages (-) which presented in Table 1. Hybridizations of KNN with meta-heuristic algorithms were proposed to select homogeneous features from the original ones and choose the number of neighbors and the optimal distance 1 Genetic Algorithm 2 Artificial Immune Recognition System 3 Improved Artificial Immune Recognition System 4 Borderline Synthetic Minority and AIRS 5 Gases Brownian Motion Optimization 6 Imperialist Competitive Algorithm Qasem et al…”

mentioning

confidence: 99%

A New Fuzzy Bio-Inspired Based Classification to Cancer Detection

Abdolrazzagh-Nezhad,

Izadpanah

2023

Preprint

Self Cite

View full text Add to dashboard Cite

There are several cancer detection methods with their own disadvantages in flexibility, non-linear complexity and sensitive in imbalance data. In this paper, a new fuzzy bio-inspired based classification method is designed to classify the imbalance medical data. The method consists of a new fuzzy draft of Cuckoo Optimization Algorithm (COA) and separating hyper-planes based on assigning binary codes to separated regions that called Hyper-Planes Classifier (HPC). Based on the technical review is done in the paper, the HPC has a better structural superiority than the other classification algorithms. The Fuzzy Cuckoo Optimization Algorithm (FCOA) which fills up its challenge in proper tuning parameters, is proposed to optimize the weights of the separating hyper-planes with linear complexity time. The FCOA is designed based on a fuzzy inference system for the Egg Laying Radius (ELR) parameter setting to increase the efficiency of the generic COA. The proposed fuzzy bio-inspired based classification method is examined with four famous UCI cancer datasets based on one, two, three and four hyper-planes and compared with more than thirty previous researches. The results show that the proposed method is effective compared to the previous methods and also the COA.

show abstract

“…Actually every one could be become his/her own doctor. Various classification algorithms have been investigated and developed to cancer detection such as Artificial Neural Networks (ANN) [ 1 ] , Decision Trees (DT) [ 2 ] , Bayesian Networks (BN) [ 3 ] , Naïve Bayesian (NB) [ 4 ] , Logistic Regression (LR) [ 5 ] , Support Vector Machine (SVM) [6], Hyper-Plane Classifiers (HPC) [ 7 ] and K-Nearest-Neighbor (KNN) [ 8 ] .…”

Section: Introductionmentioning

confidence: 99%

“…Abdolrazzagh-Nezhad and Radgohar designed a hybridization of SVM and Cultural Algorithm (CA) to cancer detection [ 10 ] . Jalayeri and Abdolrazzagh-Nezhad proposed HPC to fill up the challenges of SVM and optimized the HPC by utilized Chemical Reaction Optimization (CRO) [7]. Wu In 2014, Hui Lin Cheng et al [ 14 ] proposed the optimization algorithm of ad hoc parameters to optimize parameters and SVM feature selection simultaneously, named their method PTVPSO -SVM.…”

Section: Introductionmentioning

confidence: 99%

Improving KNN by Gases Brownian Motion Optimization Algorithm to Breast Cancer Detection

Abdolrazzagh-Nezhad¹,

Mahyabadi

Ebrahimpoor

2020

Data Science: J. of Computing and Appl. Informatics

Self Cite

View full text Add to dashboard Cite

In the last decade, the application of information technology and artificial intelligence algorithms are widely developed in collecting information of cancer patients and detecting them based on proposing various detection algorithms. The K-Nearest-Neighbor classification algorithm (KNN) is one of the most popular of detection algorithms, which has two challenges in determining the value of k and the volume of computations proportional to the size of the data and sample selected for training. In this paper, the Gaussian Brownian Motion Optimization (GBMO) algorithm is utilized for improving the KNN performance to breast cancer detection. To achieve to this aim, each gas molecule contains the information such as a selected subset of features to apply the KNN and k value. The GBMO has lower time-complexity order than other algorithms and has also been observed to perform better than other optimization algorithms in other applications. The algorithm and three well-known meta-heuristic algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Imperialist Competitive Algorithm (ICA) have been implemented on five benchmark functions and compared the obtained results. The GBMO+KNN performed on three benchmark datasets of breast cancer from UCI and the obtained results are compared with other existing cancer detection algorithms. These comparisons show significantly improves this classification accuracy with the proposed detection algorithm.

show abstract

Chemical reaction optimization to disease diagnosis by optimizing hyper-planes classifiers

Cited by 4 publications

References 40 publications

[Retracted] Study of Node Distribution and Density Optimization in Mobile Sensor Network 3D Space

[Retracted] Study of Node Distribution and Density Optimization in Mobile Sensor Network 3D Space

A New Fuzzy Bio-Inspired Based Classification to Cancer Detection

Improving KNN by Gases Brownian Motion Optimization Algorithm to Breast Cancer Detection

Contact Info

Product

Resources

About