Yang Yu¹, Zeyu Xiong^1,*, Yueshan Xiong¹, Weizi Li²

CMC-Computers, Materials & Continua, Vol.61, No.1, pp. 103-118, 2019, DOI:10.32604/cmc.2019.05154

Abstract Logistic regression is often used to solve linear binary classification problems such as machine vision, speech recognition, and handwriting recognition. However, it usually fails to solve certain nonlinear multi-classification problem, such as problem with non-equilibrium samples. Many scholars have proposed some methods, such as neural network, least square support vector machine, AdaBoost meta-algorithm, etc. These methods essentially belong to machine learning categories. In this work, based on the probability theory and statistical principle, we propose an improved logistic regression algorithm based on kernel density estimation for solving nonlinear multi-classification. We have compared our approach with other methods using non-equilibrium samples,… More >

Kemal Akyol^1,*, Baha Şen²

CMC-Computers, Materials & Continua, Vol.61, No.1, pp. 69-80, 2019, DOI:10.32604/cmc.2019.08143

Abstract The popularity of news, which conveys newsworthy events which occur during day to people, is substantially important for the spectator or audience. People interact with news website and share news links or their opinions. This study uses supervised learning based machine learning techniques in order to predict news popularity in social media sources. These techniques consist of basically two phrases: a) the training data is sent as input to the classifier algorithm, b) the performance of pre-learned algorithm is tested on the testing data. And so, a knowledge discovery from the data is performed. In this context, firstly, twelve datasets… More >

Somayeh Hessam¹, Shaghayegh Vahdat¹, Irvan Masoudi Asl^2,*, Mahnaz Kazemipoor³, Atefeh Aghaei⁴, Shahaboddin Shamshirband,^5,6,*, Timon Rabczuk⁷

CMC-Computers, Materials & Continua, Vol.61, No.1, pp. 11-26, 2019, DOI:10.32604/cmc.2019.06472

Abstract In recent years, Parkinson's Disease (PD) as a progressive syndrome of the nervous system has become highly prevalent worldwide. In this study, a novel hybrid technique established by integrating a Multi-layer Perceptron Neural Network (MLP) with the Biogeography-based Optimization (BBO) to classify PD based on a series of biomedical voice measurements. BBO is employed to determine the optimal MLP parameters and boost prediction accuracy. The inputs comprised of 22 biomedical voice measurements. The proposed approach detects two PD statuses: 0-disease status and 1- good control status. The performance of proposed methods compared with PSO, GA, ACO and ES method. The… More >

Di Shang^1,*, Mohammed Ghriga¹

CMC-Computers, Materials & Continua, Vol.61, No.1, pp. 1-10, 2019, DOI:10.32604/cmc.2019.08217

Abstract In this research, we aim to identify and investigate the impacts of key influencers on community formations and developments. We assess the impacts of key influencers by analyzing the activities and structure of the social media presence of a local community. Results of our analysis show that key influencers play important roles in connecting the community, transferring information, and improving overall sentiment of the community members. Our findings suggest that community practitioners can apply social network analysis to identify value-added influencers and discover strategies for improving the community and keeping leadership roles. More >

Xiangyan Tang¹, Qidong Zheng^1,*, Jieren Cheng^1,2, Victor S. Sheng³, Rui Cao¹, Meizhu Chen¹

CMC-Computers, Materials & Continua, Vol.60, No.3, pp. 1263-1281, 2019, DOI:10.32604/cmc.2019.06173

Abstract The existing network security situation assessment methods cannot effectively assess the Distributed denial-of-service (DDoS) attack situation. In order to solve these problems, we propose a DDoS attack situation assessment method via optimized cloud model based on influence function. Firstly, according to the state change characteristics of the IP addresses which are accessed by new and old user respectively, this paper defines a fusion feature value. Then, based on this value, we establish a V-Support Vector Machines (V-SVM) classification model to analyze network flow for identifying DDoS attacks. Secondly, according to the change of new and old IP addresses, we propose… More >

Daojian Zeng^1,2, Yao Xiao^1,2, Jin Wang^2,*, Yuan Dai^1,2, Arun Kumar Sangaiah³

CMC-Computers, Materials & Continua, Vol.60, No.3, pp. 1251-1261, 2019, DOI:10.32604/cmc.2019.06100

Abstract Recently, many researchers have concentrated on distant supervision relation extraction (DSRE). DSRE has solved the problem of the lack of data for supervised learning, however, the data automatically labeled by DSRE has a serious problem, which is class imbalance. The data from the majority class obviously dominates the dataset, in this case, most neural network classifiers will have a strong bias towards the majority class, so they cannot correctly classify the minority class. Studies have shown that the degree of separability between classes greatly determines the performance of imbalanced data. Therefore, in this paper we propose a novel model, which… More >

Wenjie Liu^1,2,*, Yong Xu², James C. N. Yang³, Wenbin Yu^1,2, Lianhua Chi⁴

CMC-Computers, Materials & Continua, Vol.60, No.3, pp. 1237-1250, 2019, DOI:10.32604/cmc.2019.03551

Abstract Privacy-preserving computational geometry is the research area on the intersection of the domains of secure multi-party computation (SMC) and computational geometry. As an important field, the privacy-preserving geometric intersection (PGI) problem is when each of the multiple parties has a private geometric graph and seeks to determine whether their graphs intersect or not without revealing their private information. In this study, through representing Alice’s (Bob’s) private geometric graph G_A (G_B) as the set of numbered grids S_A (S_B), an efficient privacy-preserving quantum two-party geometric intersection (PQGI) protocol is proposed. In the protocol, the oracle operation O_A (O_B) is firstly utilized… More >

Yue Zhao¹, Jianjian Yue¹, Wei Song^1,*, Xiaona Xu¹, Xiali Li¹, Licheng Wu¹, Qiang Ji²

CMC-Computers, Materials & Continua, Vol.60, No.3, pp. 1223-1235, 2019, DOI:10.32604/cmc.2019.05636

Abstract Tibetan language has very limited resource for conventional automatic speech recognition so far. It lacks of enough data, sub-word unit, lexicons and word inventories for some dialects. And speech content recognition and dialect classification have been treated as two independent tasks and modeled respectively in most prior works. But the two tasks are highly correlated. In this paper, we present a multi-task WaveNet model to perform simultaneous Tibetan multi-dialect speech recognition and dialect identification. It avoids processing the pronunciation dictionary and word segmentation for new dialects, while, in the meantime, allows training speech recognition and dialect identification in a single… More >

Zhiliang Wang^1,*, Youpeng Huang¹, Feng Xiong¹

CMC-Computers, Materials & Continua, Vol.60, No.3, pp. 1189-1206, 2019, DOI:10.32604/cmc.2019.04972

Abstract This article reports numerical results investigating the damage evolution and spatial distribution characteristics of intact and jointed rockmass subjected to blast loading. The behaviors of rock material are described by the Holmquist- Johnson-Cook (HJC) constitutive model incorporated in the finite element software LS-DYNA. Results indicate that the damage distribution shows a reverse S-shape attenuation with the increase of the distance from borehole, and a better goodness of fit with the Logistic function is observed. In the single-hole blasting of jointed rockmass, there are two types of regions around the intersection of borehole and joint in which the damage degree is… More >

Yu Wang^1,2,*, Yan Cao², Liancheng Zhang², Hongtao Zhang³, Roxana Ohriniuc⁴, Guodong Wang⁵, Ruosi Cheng⁶

CMC-Computers, Materials & Continua, Vol.60, No.3, pp. 1171-1187, 2019, DOI:10.32604/cmc.2019.05575

Abstract Network traffic anomaly detection has gained considerable attention over the years in many areas of great importance. Traditional methods used for detecting anomalies produce quantitative results derived from multi-source information. This makes it difficult for administrators to comprehend and deal with the underlying situations. This study proposes another method to yet determine traffic anomaly (YATA), based on the cloud model. YATA adopts forward and backward cloud transformation algorithms to fuse the quantitative value of acquisitions into the qualitative concept of anomaly degree. This method achieves rapid and direct perspective of network traffic. Experimental results with standard dataset indicate that using… More >