K-Means clustering and neural network for object detecting and identifying abnormality of brain tumor

Arunkumar, N.; Mohammed, Mazin Abed; Ghani, Mohd Khanapi Abd; Ibrahim, Dheyaa Ahmed; Abdulhay, Enas; Ramírez-González, Gustavo; Albuquerque, Victor Hugo C. de

doi:10.1007/s00500-018-3618-7

Cited by 118 publications

(48 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This hybrid algorithm was used to solve the problem of the counterfort retaining walls. The k-means technique has been widely used and in recent studies, it has been applied in [59] to bioinformatics for detecting gene expression profile, image segmentation for pest detection [60] in agriculture, and brain tumor identification [61], among others. Particularly the k-means technique has been previously applied in obtaining binary versions of continuous metaheuristics and used to solve the multidimensional knapsack problem [33] and the set covering problem [5] which are NP-hard problems.…”

Section: Hybridizing Metaheuristics With Machine Learningmentioning

confidence: 99%

A Hybrid k-Means Cuckoo Search Algorithm Applied to the Counterfort Retaining Walls Problem

2020

View full text Add to dashboard Cite

The counterfort retaining wall is one of the most frequent structures used in civil engineering. In this structure, optimization of cost and CO2 emissions are important. The first is relevant in the competitiveness and efficiency of the company, the second in environmental impact. From the point of view of computational complexity, the problem is challenging due to the large number of possible combinations in the solution space. In this article, a k-means cuckoo search hybrid algorithm is proposed where the cuckoo search metaheuristic is used as an optimization mechanism in continuous spaces and the unsupervised k-means learning technique to discretize the solutions. A random operator is designed to determine the contribution of the k-means operator in the optimization process. The best values, the averages, and the interquartile ranges of the obtained distributions are compared. The hybrid algorithm was later compared to a version of harmony search that also solved the problem. The results show that the k-mean operator contributes significantly to the quality of the solutions and that our algorithm is highly competitive, surpassing the results obtained by harmony search.

show abstract

Section: Hybridizing Metaheuristics With Machine Learningmentioning

confidence: 99%

A Hybrid k-Means Cuckoo Search Algorithm Applied to the Counterfort Retaining Walls Problem

2020

View full text Add to dashboard Cite

show abstract

“…Clustering adalah proses pengelompokan objek data menjadi beberapa cluster yang terpisah sehingga data yang ada di dalam masing-masing cluster tersebut menjadi sebuah kelompok data yang memiliki kemiripan yang relatif sama [1]. Ada banyak teknik yang dapat digunakan untuk proses clustering seperti Single Linkage, Complete Linkage, Average Linkage, Fuzzy C-Means, Kohonen SOM, LVQ dan K-Means [2].…”

Section: Pendahuluanunclassified

Reduksi Atribut Menggunakan Information Gain untuk Optimasi Cluster Algoritma K-Means

Dinata¹,

Novriando²,

Hasdyna³

et al. 2020

JEPIN

View full text Add to dashboard Cite

Proses clustering dengan algoritma K-Means pada dataset yang memiliki banyak atribut akan mempengaruhi besarnya jumlah iterasi. Pada penelitian ini, metode Information Gain digunakan untuk mereduksi atribut dataset. Dataset yang telah direduksi atribut akan dilanjutkan proses clustering dengan K-Means. Dataset yang dianalisis pada penelitian ini adalah data Hepatitis C Virus yang diperoleh dari UCI Machine Learning Repository, dengan 29 atribut dan 1385 jumlah data. Hasil penelitian ini menunjukkan bahwa rata-rata jumlah iterasi yang diperoleh dari 10 kali pengujian dengan menggunakan K-Means konvensional diperoleh rata-rata sebesar 32 iterasi, sedangkan K-Means dengan reduksi atribut diperoleh rata-rata sebesar 27.7 iterasi. Nilai validitas cluster dihitung menggunakan Davies-Bouldin Index (DBI). Nilai DBI pada K-Means konvensional sebesar 2.1972, sedangkan DBI pada K-Means yang telah direduksi 1 atribut sampai 5 atribut diperoleh nilai rata-rata DBI masing-masing sebesar 2.0290, 1.8771, 1.8641, 1.8389, dan 1.8117.

show abstract

“…In regression setting, MSE was in identifying brain imaging predictions for memory performance (Wang et al, 2011), stimation of kinetic constants from PET data (O'Sullivan and Saha, 1999), for correction partial volume effects in arterial spin labeling MRI (Kim et al, 2018;Asllani et al, 2008), EEG signal classification with neural networks (Kottaimalai et al, 2013). This is also the basis of widely used k-means clustering (Jain et al, 1999) in identifying abnormalities in brain tumors (Arunkumar et al, 2019), modeling state spaces in rsfMRI (Huang et al, 2020b). This loss was also used for image classification of autism spectrum disorder (Heinsfeld et al, 2018), tumor segmentation for MR brain images Mittal et al (2019) among other learning tasks.…”

Section: Losses In Statistics and Machine Learningmentioning

confidence: 99%

Topological Distances Between Brain Networks

Chung

Lee

Solo

et al. 2017

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Many existing brain network distances are based on matrix norms. The element-wise differences may fail to capture underlying topological differences. Further, matrix norms are sensitive to outliers. A few extreme edge weights may severely affect the distance. Thus it is necessary to develop network distances that recognize topology. In this paper, we introduce Gromov-Hausdorff (GH) and Kolmogorov-Smirnov (KS) distances. GH-distance is often used in persistent homology based brain network models. The superior performance of KS-distance is contrasted against matrix norms and GH-distance in random network simulations with the ground truths. The KS-distance is then applied in characterizing the multimodal MRI and DTI study of maltreated children.

show abstract

K-Means clustering and neural network for object detecting and identifying abnormality of brain tumor

Cited by 118 publications

References 32 publications

A Hybrid k-Means Cuckoo Search Algorithm Applied to the Counterfort Retaining Walls Problem

A Hybrid k-Means Cuckoo Search Algorithm Applied to the Counterfort Retaining Walls Problem

Reduksi Atribut Menggunakan Information Gain untuk Optimasi Cluster Algoritma K-Means

Topological Distances Between Brain Networks

Contact Info

Product

Resources

About