scholarly journals A powerful and efficient evolutionary optimization algorithm based on stem cells algorithm for data clustering

2012 ◽  
Vol 2 (1) ◽  
Author(s):  
Mohammad Taherdangkoo ◽  
Mehran Yazdi ◽  
Mohammad Bagheri
Keyword(s):  
Stem Cells ◽  
Optimization Algorithm ◽  
Data Clustering ◽  
High Speed ◽  
Clustering Algorithms ◽  
Implementation Process ◽  
Pso Algorithm ◽  
High Convergence Rate ◽  
Data Points ◽  
Number Of Classes

AbstractThere are many ways to divide datasets into some clusters. One of most popular data clustering algorithms is K-means algorithm which uses the distance criteria for measuring the data correlation. To do that, we should know in advance the number of classes (K) and choose K data points as an initial set to run the algorithm. However, the choice of initial points is a main problem in this algorithm which may cause the algorithm to converge to a local minimum. Some other data clustering algorithms have been proposed to overcome this problem. The methods are Genetic algorithm (GA), Ant Colony Optimization (ACO), PSO algorithm, and ABC algorithms. In this paper, we employ the Stem Cells Optimization algorithm for data clustering. The algorithm was inspired by behavior of natural stem cells in the human body. We developed a new data clustering based on this new optimization scheme which has the advantages such as high convergence rate and easy implementation process. It also avoids local minimums in an intelligent manner. The experimental results obtained by using the new algorithm on different well-known test datasets compared with those obtained using other mentioned methods demonstrate the better accuracy and high speed of the new algorithm.

An efficient algorithm for function optimization: modified stem cells algorithm

2013 ◽  
Vol 3 (1) ◽  
Author(s):  
Mohammad Taherdangkoo ◽  
Mahsa Paziresh ◽  
Mehran Yazdi ◽  
Mohammad Bagheri
Keyword(s):  
Stem Cells ◽  
Optimization Algorithm ◽  
Optimization Algorithms ◽  
Pso Algorithm ◽  
Function Optimization ◽  
Natural Behavior ◽  
Local Optima ◽  
Bee Colony ◽  
Intelligent Behavior ◽  
Improved Performance

AbstractIn this paper, we propose an optimization algorithm based on the intelligent behavior of stem cell swarms in reproduction and self-organization. Optimization algorithms, such as the Genetic Algorithm (GA), Particle Swarm Optimization (PSO) algorithm, Ant Colony Optimization (ACO) algorithm and Artificial Bee Colony (ABC) algorithm, can give solutions to linear and non-linear problems near to the optimum for many applications; however, in some case, they can suffer from becoming trapped in local optima. The Stem Cells Algorithm (SCA) is an optimization algorithm inspired by the natural behavior of stem cells in evolving themselves into new and improved cells. The SCA avoids the local optima problem successfully. In this paper, we have made small changes in the implementation of this algorithm to obtain improved performance over previous versions. Using a series of benchmark functions, we assess the performance of the proposed algorithm and compare it with that of the other aforementioned optimization algorithms. The obtained results prove the superiority of the Modified Stem Cells Algorithm (MSCA).

scholarly journals A Preference Model on Adaptive Affinity Propagation

2018 ◽  
Vol 8 (3) ◽  
pp. 1805 ◽  
Author(s):  
Rina Refianti ◽  
Achmad Benny Mutiara ◽  
Asep Juarna ◽  
Adang Suhendra
Keyword(s):  
Data Clustering ◽  
Message Passing ◽  
Clustering Algorithms ◽  
Experimental Results ◽  
Data Sets ◽  
Similarity Matrix ◽  
Preference Model ◽  
New Model ◽  
Data Points ◽  
Scanning Algorithm

In recent years, two new data clustering algorithms have been proposed. One of them isAffinity Propagation (AP). AP is a new data clustering technique that use iterative message passing and consider all data points as potential exemplars. Two important inputs of AP are a similarity matrix (SM) of the data and the parameter ”preference” p. Although the original AP algorithm has shown much success in data clustering, it still suffer from one limitation: it is not easy to determine the value of the parameter ”preference” p which can result an optimal clustering solution. To resolve this limitation, we propose a new model of the parameter ”preference” p, i.e. it is modeled based on the similarity distribution. Having the SM and p, Modified Adaptive AP (MAAP) procedure is running. MAAP procedure means that we omit the adaptive p-scanning algorithm as in original Adaptive-AP (AAP) procedure. Experimental results on random non-partition and partition data sets show that (i) the proposed algorithm, MAAP-DDP, is slower than original AP for random non-partition dataset, (ii) for random 4-partition dataset and real datasets the proposed algorithm has succeeded to identify clusters according to the number of dataset’s true labels with the execution times that are comparable with those original AP. Beside that the MAAP-DDP algorithm demonstrates more feasible and effective than original AAP procedure.

scholarly journals Performance Analysis of Whale optimization based Data Clustering

2020 ◽  
Vol 5 (2) ◽  
pp. 117-127
Author(s):  
Ahamed Shafeeq ◽  
Keyword(s):  
Data Clustering ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Whale Optimization ◽  
Silhouette Index ◽  
Data Points ◽  
Feature Diagram ◽  
Exploratory Data ◽  
Exploratory Data Mining

Data clustering is the method of gathering of data points so that the more similar points will be in the same group. It is a key role in exploratory data mining and a popular technique used in many fields to analyze statistical data. Quality clusters are the key requirement of the cluster analysis result. There will be tradeoffs between the speed of the clustering algorithm and the quality of clusters it produces. Both the quality and speed criteria must be considered for the state-of-the-art clustering algorithm for applications. The Bio-inspired technique has ensured that the process is not trapped in local minima, which is the main bottleneck of the traditional clustering algorithm. The results produced by the bio-inspired clustering algorithms are better than the traditional clustering algorithms. The newly introduced Whale optimization-based clustering is one of the promising algorithms from the bio-inspired family. The quality of clusters produced by Whale optimization-based clustering is compared with k-means, Kohonen self-organizing feature diagram, Grey wolf optimization. Popular quality measures such as the Silhouette index, Davies-Bouldin index, and Calianski-Harabasz index are used in the evaluation.

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.

scholarly journals Fully Automated Cultivation of Adipose-Derived Stem Cells in the StemCellDiscovery—A Robotic Laboratory for Small-Scale, High-Throughput Cell Production Including Deep Learning-Based Confluence Estimation

Processes ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 575
Author(s):  
Jelena Ochs ◽  
Ferdinand Biermann ◽  
Tobias Piotrowski ◽  
Frederik Erkens ◽  
Bastian Nießing ◽  
...  
Keyword(s):  
Stem Cells ◽  
Deep Learning ◽  
High Throughput ◽  
High Speed ◽  
New Technologies ◽  
Human Mesenchymal Stem Cells ◽  
Simulation Software ◽  
System Capacity ◽  
Small Scale ◽  
Production Environment

Laboratory automation is a key driver in biotechnology and an enabler for powerful new technologies and applications. In particular, in the field of personalized therapies, automation in research and production is a prerequisite for achieving cost efficiency and broad availability of tailored treatments. For this reason, we present the StemCellDiscovery, a fully automated robotic laboratory for the cultivation of human mesenchymal stem cells (hMSCs) in small scale and in parallel. While the system can handle different kinds of adherent cells, here, we focus on the cultivation of adipose-derived hMSCs. The StemCellDiscovery provides an in-line visual quality control for automated confluence estimation, which is realized by combining high-speed microscopy with deep learning-based image processing. We demonstrate the feasibility of the algorithm to detect hMSCs in culture at different densities and calculate confluences based on the resulting image. Furthermore, we show that the StemCellDiscovery is capable of expanding adipose-derived hMSCs in a fully automated manner using the confluence estimation algorithm. In order to estimate the system capacity under high-throughput conditions, we modeled the production environment in a simulation software. The simulations of the production process indicate that the robotic laboratory is capable of handling more than 95 cell culture plates per day.

scholarly journals A novel bidirectional clustering algorithm based on local density

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Baicheng Lyu ◽  
Wenhua Wu ◽  
Zhiqiang Hu
Keyword(s):  
Clustering Algorithm ◽  
Local Density ◽  
Clustering Algorithms ◽  
Cluster Number ◽  
Denoising Method ◽  
Number Of Clusters ◽  
Data Points ◽  
Cutoff Distance ◽  
Large Clusters ◽  
Small Clusters

AbstractWith the widely application of cluster analysis, the number of clusters is gradually increasing, as is the difficulty in selecting the judgment indicators of cluster numbers. Also, small clusters are crucial to discovering the extreme characteristics of data samples, but current clustering algorithms focus mainly on analyzing large clusters. In this paper, a bidirectional clustering algorithm based on local density (BCALoD) is proposed. BCALoD establishes the connection between data points based on local density, can automatically determine the number of clusters, is more sensitive to small clusters, and can reduce the adjusted parameters to a minimum. On the basis of the robustness of cluster number to noise, a denoising method suitable for BCALoD is proposed. Different cutoff distance and cutoff density are assigned to each data cluster, which results in improved clustering performance. Clustering ability of BCALoD is verified by randomly generated datasets and city light satellite images.

scholarly journals Multibyte Electromagnetic Analysis Based on Particle Swarm Optimization Algorithm

2021 ◽  
Vol 11 (2) ◽  
pp. 839
Author(s):  
Shaofei Sun ◽  
Hongxin Zhang ◽  
Xiaotong Cui ◽  
Liang Dong ◽  
Muhammad Saad Khan ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Communication Systems ◽  
Optimization Problems ◽  
Classical Method ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Electromagnetic Analysis ◽  
Swarm Optimization ◽  
Test Board

This paper focuses on electromagnetic information security in communication systems. Classical correlation electromagnetic analysis (CEMA) is known as a powerful way to recover the cryptographic algorithm’s key. In the classical method, only one byte of the key is used while the other bytes are considered as noise, which not only reduces the efficiency but also is a waste of information. In order to take full advantage of useful information, multiple bytes of the key are used. We transform the key into a multidimensional form, and each byte of the key is considered as a dimension. The problem of the right key searching is transformed into the problem of optimizing correlation coefficients of key candidates. The particle swarm optimization (PSO) algorithm is particularly more suited to solve the optimization problems with high dimension and complex structure. In this paper, we applied the PSO algorithm into CEMA to solve multidimensional problems, and we also add a mutation operator to the optimization algorithm to improve the result. Here, we have proposed a multibyte correlation electromagnetic analysis based on particle swarm optimization. We verified our method on a universal test board that is designed for research and development on hardware security. We implemented the Advanced Encryption Standard (AES) cryptographic algorithm on the test board. Experimental results have shown that our method outperforms the classical method; it achieves approximately 13.72% improvement for the corresponding case.

High-Speed Tracking of Murine Cardiac Stem Cells by Harmonic Nanodoublers

Small ◽  
2012 ◽  
Vol 8 (17) ◽  
pp. 2752-2756 ◽  
Author(s):  
Thibaud Magouroux ◽  
Jerome Extermann ◽  
Pernilla Hoffmann ◽  
Yannick Mugnier ◽  
Ronan Le Dantec ◽  
...  
Keyword(s):  
Stem Cells ◽  
High Speed ◽  
Cardiac Stem Cells ◽  
Speed Tracking

The Optimization of Project Time-Cost Based on Particle Swarm Optimization Algorithm

2013 ◽  
Vol 791-793 ◽  
pp. 1423-1426
Author(s):  
Hai Min Wei ◽  
Rong Guang Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm Optimization Algorithm ◽  
Cost Optimization ◽  
Particle Swarm ◽  
Implementation Process ◽  
Pso Algorithm ◽  
Project Schedule ◽  
Swarm Optimization ◽  
Schedule Optimization ◽  
Schedule Management

Project schedule management is the management to each stage of the degree of progress and project final deadline in the project implementation process. Its purpose is to ensure that the project can meet the time constraints under the premise of achieving its overall objectives.When the progress of schedule found deviation in the process of schedule management ,the progress of the plan which have be advanced previously need to adjust.This article mainly discussed to solve the following two questions:establish the schedule optimization model by using the method of linear;discuss the particle swarm optimization (PSO) algorithm and its parameters which have effect on the algorithm:Particle swarm optimization (PSO) algorithm is presented in the time limited project and the application of a cost optimization.

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