scholarly journals Correction to “Comparative Research of Swarm Intelligence Clustering Algorithms for Analyzing Medical Data”

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 110251-110251
Author(s):  
Xueyuan Gong ◽  
Liansheng Liu ◽  
Simon Fong ◽  
Qiwen Xu ◽  
Tingxi Wen ◽  
...  
Keyword(s):  
Swarm Intelligence ◽  
Comparative Research ◽  
Clustering Algorithms ◽  
Medical Data

scholarly journals Comparative Research of Swarm Intelligence Clustering Algorithms for Analyzing Medical Data

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 137560-137569 ◽  
Author(s):  
Xueyuan Gong ◽  
Liansheng Liu ◽  
Simon Fong ◽  
Qiwen Xu ◽  
Tingxi Wen ◽  
...  
Keyword(s):  
Swarm Intelligence ◽  
Comparative Research ◽  
Clustering Algorithms ◽  
Medical Data

scholarly journals A General Framework for Mixed and Incomplete Data Clustering Based on Swarm Intelligence Algorithms

Mathematics ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 786
Author(s):  
Yenny Villuendas-Rey ◽  
Eley Barroso-Cubas ◽  
Oscar Camacho-Nieto ◽  
Cornelio Yáñez-Márquez
Keyword(s):  
Swarm Intelligence ◽  
Data Clustering ◽  
Incomplete Data ◽  
Missing Values ◽  
Clustering Algorithms ◽  
Bat Algorithm ◽  
Hybrid Features ◽  
Bee Colony ◽  
Learning Tasks ◽  
Clustering Data

Swarm intelligence has appeared as an active field for solving numerous machine-learning tasks. In this paper, we address the problem of clustering data with missing values, where the patterns are described by mixed (or hybrid) features. We introduce a generic modification to three swarm intelligence algorithms (Artificial Bee Colony, Firefly Algorithm, and Novel Bat Algorithm). We experimentally obtain the adequate values of the parameters for these three modified algorithms, with the purpose of applying them in the clustering task. We also provide an unbiased comparison among several metaheuristics based clustering algorithms, concluding that the clusters obtained by our proposals are highly representative of the “natural structure” of data.

Swarm Intelligence-Based Clustering Algorithms: A Survey

2016 ◽  
pp. 303-341 ◽  
Author(s):  
Tülin İnkaya ◽  
Sinan Kayalıgil ◽  
Nur Evin Özdemirel
Keyword(s):  
Swarm Intelligence ◽  
Clustering Algorithms

Psoriasis Lesion Detection Using Hybrid Seeker Optimization Based Image Clustering

2021 ◽  
Vol 17 ◽  
Author(s):  
Manoranjan Dash ◽  
Narendra Digambar Londhe ◽  
Subhojit Ghosh ◽  
Ritesh Raj ◽  
Rajendra Sonawane
Keyword(s):  
Swarm Intelligence ◽  
Clustering Algorithms ◽  
Optimization Technique ◽  
Evolutionary Optimization ◽  
Superior Performance ◽  
Initial Population ◽  
Psoriatic Lesion ◽  
Lesion Segmentation ◽  
Evolutionary Optimization Technique ◽  
Psoriasis Lesion

Background: In recent years, there has been a massive increase in the number of people suffering from psoriasis. For proper psoriasis diagnosis, psoriasis lesion segmentation is a pre-requisite for quantifying the severity of this disease. However, segmentation of psoriatic lesion cannot be evaluated just by visual inspection as they exhibit inter and intra variability among the severity classes. Most of the approaches currently pursued by dermatologists are subjective in nature. The existing conventional clustering algorithm for objective segmentation of psoriasis lesion suffers from limitations of premature local convergence. Objective: An alternative method for psoriatic lesion segmentation with the objective analysis is sought in the present work. The present work aims at obtaining optimal lesion segmentation by adopting an evolutionary optimization technique which possesses a higher probability of global convergence for psoriasis lesion segmentation. Method: A hybrid evolutionary optimization technique based on the combination of two swarm intelligence algorithms; namely Artificial Bee Colony and Seeker Optimization algorithm has been proposed. The initial population for the hybrid technique is obtained from the two conventional local-based approaches i.e. Fuzzy C-means and K-means clustering algorithms. Results: The initial population selection from the convergence of classical techniques reduces the effect of population dynamics on the final solution and hence yields precise lesion segmentation with Jaccard Index of 0.91 from 720 psoriasis images. Conclusion: The performance comparison reflects the superior performance of the proposed algorithm over other swarm intelligence and conventional clustering algorithms.

scholarly journals Analysis And Detection of Diabetes Using Data Mining Techniques – Efficiency Comparison

2021 ◽  
pp. 73-79
Author(s):  
G. Ramadevi ◽  
Srujitha Yeruva ◽  
P. Sravanthi ◽  
P. Eknath Vamsi ◽  
S. Jaya Prakash
Keyword(s):  
Data Mining ◽  
Clustering Algorithms ◽  
Medical Data ◽  
Healthcare Sector ◽  
Data Mining Techniques ◽  
Data Mining Algorithms ◽  
Use Of Data ◽  
Efficiency Comparison ◽  
Using Data ◽  
Mining Algorithms

In a digitized world, data is growing exponentially and it is difficult to analyze the data and give the results. Data mining techniques play an important role in healthcare sector - BigData. By making use of Data mining algorithms it is possible to analyze, detect and predict the presence of disease which helps doctors to detect the disease early and in decision making. The objective of data mining techniques used is to design an automated tool that notifies the patient’s treatment history disease and medical data to doctors. Data mining techniques are very much useful in analyzing medical data to achieve meaningful and practical patterns. This project works on diabetes medical data, classification and clustering algorithms like (OPTICS, NAIVEBAYES, and BRICH) are implemented and the efficiency of the same is examined.

scholarly journals Auto Encoder and Deep Auto Encoders Based Deep Learning on Medical Data

2019 ◽  
Vol 8 (4) ◽  
pp. 3832-3835
Keyword(s):  
Neural Networks ◽  
Feature Extraction ◽  
Artificial Neural Networks ◽  
Deep Learning ◽  
Clustering Algorithms ◽  
Medical Data ◽  
Machine Learning Techniques ◽  
Knowledge Generation ◽  
Generation Process ◽  
Artificial Neural

In rapid growth of medical informatics, patient data need to be organized and used for medical diagnosis and other uses such as disease prediction and drug discovery. There are many more traditional methods used for text based information such as K-NN, K-Means and other clustering algorithms, but image based medical data (or) signals based medical data is needed. So there is a need of new approaches for efficient classification and knowledge generation process. Artificial neural network based methods are mostly suited for deep learning, since there are many more approaches available in artificial neural networks. Deep learning and Machine learning techniques requires efficient pattern or feature extraction and pattern identification. Auto encoders and deep auto encoders works based on artificial neural networks and most suitable multimodal data feature extraction and identification. In this paper we have to show deep learning methods such as auto encoder and deep auto encoders for classifying multimodal medical data.

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