Shihong Zou¹, Huizhong Sun^{1, *}, Guosheng Xu¹, Ruijie Quan²

CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1321-1334, 2020, DOI:10.32604/cmc.2020.09649 - 20 August 2020

Abstract In the information era, the core business and confidential information of enterprises/organizations is stored in information systems. However, certain malicious inside network users exist hidden inside the organization; these users intentionally or unintentionally misuse the privileges of the organization to obtain sensitive information from the company. The existing approaches on insider threat detection mostly focus on monitoring, detecting, and preventing any malicious behavior generated by users within an organization’s system while ignoring the imbalanced ground-truth insider threat data impact on security. To this end, to be able to detect insider threats more effectively, a data… More >

Md. Shakhawat Hossain¹, Md. Daud Hossain¹, Abdul Hannan², Mirza Hasanuzzaman³, Md. Motiar Rohman^4,*

Phyton-International Journal of Experimental Botany, Vol.89, No.3, pp. 541-559, 2020, DOI:10.32604/phyton.2020.010279 - 22 June 2020

Abstract Salinity stress is a major factor limiting plant growth and productivity of many crops including oilseed. The present study investigated the identification of salt tolerant mustard genotypes and better understanding the mechanism of salinity tolerance. Salt stresses significantly reduced relative water content (RWC), chlorophyll (Chl) content, K⁺ and K⁺ /Na⁺ ratio, photosynthetic rate (P_N), transpiration rate (Tr), stomatal conductance (gs), intercellular CO₂ concentration (Ci) and increased the levels of proline (Pro) and lipid peroxidation (MDA) contents, Na⁺ , superoxide (O₂^•− ) and hydrogen peroxide (H₂O₂) in both tolerant and sensitive mustard genotypes. The tolerant genotypes maintained higher Pro and lower MDA… More >

Yong Fang¹, Yunyun Zhang², Cheng Huang^1,*

CMC-Computers, Materials & Continua, Vol.61, No.1, pp. 185-195, 2019, DOI:10.32604/cmc.2019.06144

Abstract In recent years, the rapid development of e-commerce exposes great vulnerabilities in online transactions for fraudsters to exploit. Credit card transactions take a salient role in nowadays’ online transactions for its obvious advantages including discounts and earning credit card points. So credit card fraudulence has become a target of concern. In order to deal with the situation, credit card fraud detection based on machine learning is been studied recently. Yet, it is difficult to detect fraudulent transactions due to data imbalance (normal and fraudulent transactions), for which Smote algorithm is proposed in order to resolve… 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 More >

Menghua Luo^1,2, Ke Wang¹, Zhiping Cai^1,*, Anfeng Liu³, Yangyang Li⁴, Chak Fong Cheang⁵

CMC-Computers, Materials & Continua, Vol.58, No.1, pp. 15-26, 2019, DOI:10.32604/cmc.2019.03708

Abstract The extreme imbalanced data problem is the core issue in anomaly detection. The amount of abnormal data is so small that we cannot get adequate information to analyze it. The mainstream methods focus on taking fully advantages of the normal data, of which the discrimination method is that the data not belonging to normal data distribution is the anomaly. From the view of data science, we concentrate on the abnormal data and generate artificial abnormal samples by machine learning method. In this kind of technologies, Synthetic Minority Over-sampling Technique and its improved algorithms are representative More >

Akram M. Radwan^a,b, Zehra Cataltepe^a,c

Intelligent Automation & Soft Computing, Vol.24, No.4, pp. 777-783, 2018, DOI:10.1080/10798587.2017.1337673

Abstract This paper proposes to apply machine learning techniques to predict students’ performance on two real-world educational data-sets. The first data-set is used to predict the response of students with autism while they learn a specific task, whereas the second one is used to predict students’ failure at a secondary school. The two data-sets suffer from two major problems that can negatively impact the ability of classification models to predict the correct label; class imbalance and class noise. A series of experiments have been carried out to improve the quality of training data, and hence improve… More >

Masoumeh Zareapoor, Jie Yang

Intelligent Automation & Soft Computing, Vol.24, No.4, pp. 721-727, 2018, DOI:10.1080/10798587.2017.1321228

Abstract The design of an efficient credit card fraud detection technique is, however, particularly challenging, due to the most striking characteristics which are; imbalancedness and non-stationary environment of the data. These issues in credit card datasets limit the machine learning algorithm to show a good performance in detecting the frauds. The research in the area of credit card fraud detection focused on detection the fraudulent transaction by analysis of normality and abnormality concepts. Balancing strategy which is designed in this paper can facilitate classification and retrieval problems in this domain. In this paper, we consider the More >

Baogang Tian¹, Yang Shao¹, Zhijiong Wang¹, Jian Li^2,*

Molecular & Cellular Biomechanics, Vol.14, No.3, pp. 187-194, 2017, DOI:10.3970/mcb.2017.014.187

Abstract The purpose of this study was to explore the clinical efficacy and safety of posterior pedicle screw fixation in the treatment of thoracolumbar burst fracture. A total of 120 patients with thoracolumbar burst fractures were selected from January 2014 to December 2016. 60 patients were divided into the study group, and 60 patients were as the control group. The patients in the study group were treated with posterior pedicle screw fixation. The control group was treated with posterior non-traumatic pedicle screw fixation. After treatment, there were six months follow up. The clinical indexes, complications, and… More >

Maroyi A, MT Rasethe

Phyton-International Journal of Experimental Botany, Vol.84, No.2, pp. 288-297, 2015, DOI:10.32604/phyton.2015.84.288

Abstract Documentation of use patterns of plants across national boundaries is of relevance in understanding the importance of plant resources to livelihood strategies of different ethnic groups. Plant resources have gained prominence as a natural asset through which families derive food, firewood, income, medicines and timber, enabling particularly poor communities to achieve self-sufficiency. The objective of this study was to investigate the trends in plant usage in South Africa and Zimbabwe. An ethnobotanical investigation was conducted between January 2012 and January 2013 in the Limpopo Province, South Africa and the Midlands Province, Zimbabwe. The study used… More >

Emrah Yilmaz¹, Hikmet Koyunbakan²

CMES-Computer Modeling in Engineering & Sciences, Vol.99, No.6, pp. 463-471, 2014, DOI:10.3970/cmes.2014.099.463

Abstract In this study, Ambarzumyan’s theorem for quadratic Sturm-Liouville problem is extended to second order differential systems of dimension d ≥ 2. It is shown that if the spectrum is the same as the spectrum belonging to the zero potential, then the matrix valued functions both P(x) and Q(x) are zero by imposing a condition on P(x). In scaler case, this problem was solved in [Koyunbakan, Lesnic and Panakhov (2013)]. More >