U. Venkateshkumar^1,*, S. Ramakrishnan²

Intelligent Automation & Soft Computing, Vol.26, No.4, pp. 817-830, 2020, DOI:10.32604/iasc.2020.010116

Abstract A hybrid form of the genetic algorithm and the modified K-Means cluster algorithm forms as a Gencluster to detect a spectrum hole among n-number of primary users (PUs) is present in the cooperative spectrum sensing model. The fusion center (FC), applies the genetic algorithm to identify the best chromosome, which contains many PUs cluster centers and by applying the modified K-Means cluster algorithm identifies the cluster with the PU vacant spectrum showing high accuracy, and maximum probability of detection with minimum false alarm rates are achieved. The graphical representation of the performance metric of the system model shows 95% accuracy… More >

Kun Liu^*

Intelligent Automation & Soft Computing, Vol.26, No.4, pp. 749-754, 2020, DOI:10.32604/iasc.2020.010109

Abstract In the English network examination system, the big data distribution is highly coupled, the cost of data query is large, and the precision is not good. In order to improve the ability of the data classification and query in the English network examination system, a method of data classification and query in the English network examination system is proposed based on the grid region clustering and frequent itemset feature extraction of the association rules. Using the grid image analysis to improve the statistical analysis of the English performance analysis, the collection and storage structure analysis of the information resource data… More >

Sun-Taag Choe, We-Duke Cho^*, Jai-Hoon Kim, and Ki-Hyung Kim

Intelligent Automation & Soft Computing, Vol.26, No.4, pp. 679-691, 2020, DOI:10.32604/iasc.2020.010102

Abstract Recent research on activity recognition in wearable devices has identified a key challenge: k-nearest neighbors (k-NN) algorithms have a high operational time complexity. Thus, these algorithms are difficult to utilize in embedded wearable devices. Herein, we propose a method for reducing this complexity. We apply a clustering algorithm for learning data and assign labels to each cluster according to the maximum likelihood. Experimental results show that the proposed method achieves effective operational levels for implementation in embedded devices; however, the accuracy is slightly lower than that of a traditional k-NN algorithm. Additionally, our method provides the advantage of controlling the… More >

LU GAN, XIAOSHU DENG, YAN LIU, ANCAI LUO, JIAO CHEN, JING XIANG, ZHENGWU ZHAO^*

BIOCELL, Vol.44, No.3, pp. 451-459, 2020, DOI:10.32604/biocell.2020.08007

Abstract The present study focused on the segregation of the percentage of grains with chalkiness (PGWC), using Huanghuazhan as the female parent and CS197 as the male parent to construct the hybrid rice F₂ population. Molecular markers were used for genotype analysis among the extremely low and extremely high PGWC individuals from the F₂ population. The results revealed that the genotypes of 10 extremely low PGWC individuals were 80.00% and 3.33%, which is identical to Huanghuazhan and CS197, respectively. The heterozygotes accounted for 16.67%. On the contrary, the genotypes of 10 extremely high PGWC individuals were 37.78% and 32.22%, which is… More >

Xing Deng^1,2, Haijian Shao^1,2,*

Energy Engineering, Vol.117, No.5, pp. 279-287, 2020, DOI:10.32604/EE.2020.011619

Abstract Recurrent neural networks (RNNs) as one of the representative deep learning methods, has restricted its generalization ability because of its indigestion hidden-layer information presentation. In order to properly handle of hidden-layer information, directly reduce the risk of over-fitting caused by too many neuron nodes, as well as realize the goal of streamlining the number of hidden layer neurons, and then improve the generalization ability of RNNs, the hidden-layer information of RNNs is precisely analyzed by using the unsupervised clustering methods, such as Kmeans, Kmeans++ and Iterative self-organizing data analysis (Isodata), to divide the similarity of raw data points, and maps… More >

Peng Tang^∗, Yu Wang, Ning Shen

Computer Systems Science and Engineering, Vol.35, No.4, pp. 237-246, 2020, DOI:10.32604/csse.2020.35.237

Abstract In today’s modern society, the physical fitness of college students is gradually declining. In this paper, a prediction model for college students’ physical fitness is established, in which support vector regression (SVR) and k-means clustering are combined together for the prediction of college students’ fitness. Firstly, the physical measurement data of college students are classified according to gender and class characteristics. Then, the k-means clustering method is used to classify the physical measurement data of college students. Next, the physical characteristics of college students are extracted by SVR to establish the prediction model of physical indicators, and the model for… More >

Swathypriyadharsini P^1,∗, K.Premalatha^2,†

Computer Systems Science and Engineering, Vol.35, No.1, pp. 39-50, 2020, DOI:10.32604/csse.2020.35.039

Abstract Microarrays have reformed biotechnological research in the past decade. Deciphering the hidden patterns in gene expression data proffers a prodigious preference to strengthen the understanding of functional genomics. The complexity of biological networks with larger volume of genes also increases the challenges of comprehending and interpretation of the resulting mass of data. Clustering addresses these challenges, which is essential in the data mining process to reveal natural structures and identify interesting patterns in the underlying data. The clustering of gene expression data has been proven to be useful in making known the natural structure inherent in gene expression data, understanding… More >

Ahmed A. M. Jamel^1,∗, Bahriye Akay^2,†

Computer Systems Science and Engineering, Vol.34, No.5, pp. 259-281, 2019, DOI:10.32604/csse.2019.34.259

Abstract Parallel processing has turned into one of the emerging fields of machine learning due to providing consistent work by performing several tasks simultaneously, enhancing reliability (the presence of more than one device ensures the workflow even if some devices disrupted), saving processing time and introducing low cost and high-performance computation units. This research study presents a survey of parallel K-means and Fuzzy-c-means clustering algorithms based on their implementations in parallel environments such as Hadoop, MapReduce, Graphical Processing Units, and multi-core systems. Additionally, the enhancement in parallel clustering algorithms is investigated as hybrid approaches in which K-means and Fuzzy-c-means clustering algorithms… More >

Meng Wei^*, Ningxin Zhang, Yuan Tong, Yu Song

Intelligent Automation & Soft Computing, Vol.26, No.3, pp. 597-607, 2020, DOI:10.32604/iasc.2020.013938

Abstract The traditional radar signal detection mode of the analog digital converter (ADC) has a low prediction efficiency. Therefore, the advanced prediction model of the tunnel geological radar based on the cluster computing was designed. The completeness factor of the detection radar signal was calculated by the computer cluster effect, and then the information extraction and information integration of the radar pulse for the radar detection signal was determined. Moreover, the multi-order nonlinear regression forecasting model restructured the received signal. Thus, the prediction of the radar detection signal was achieved. In order to ensure the effectiveness of the design, the simulation… More >

Wei Li^{1, 2, 3}, Min Zhou^{1, *}, Hairong Dong¹

Intelligent Automation & Soft Computing, Vol.26, No.3, pp. 531-538, 2020, DOI:10.32604/iasc.2020.013930

Abstract Different types of stations have different functional roles in the urban rail transit network. Firstly, based on the characteristics of the urban rail transit network structure, the time series features and passenger flow features of the station smart card data are extracted. Secondly, we use the principal component analysis method to select the suitable clustering variables. Finally, we propose a station classification model based on the two-step cluster method. The effectiveness of the proposed method is verified in the Beijing subway. The results show that the proposed model can successfully identify the types of urban rail transit stations, clarify the… More >