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  • Open Access


    Hybrid Deep Learning Based Attack Detection for Imbalanced Data Classification

    Rasha Almarshdi1,2,*, Laila Nassef1, Etimad Fadel1, Nahed Alowidi1

    Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 297-320, 2023, DOI:10.32604/iasc.2023.026799

    Abstract Internet of Things (IoT) is the most widespread and fastest growing technology today. Due to the increasing of IoT devices connected to the Internet, the IoT is the most technology under security attacks. The IoT devices are not designed with security because they are resource constrained devices. Therefore, having an accurate IoT security system to detect security attacks is challenging. Intrusion Detection Systems (IDSs) using machine learning and deep learning techniques can detect security attacks accurately. This paper develops an IDS architecture based on Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) deep learning… More >

  • Open Access


    Imbalanced Classification in Diabetics Using Ensembled Machine Learning

    M. Sandeep Kumar1, Mohammad Zubair Khan2,*, Sukumar Rajendran1, Ayman Noor3, A. Stephen Dass1, J. Prabhu1

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4397-4409, 2022, DOI:10.32604/cmc.2022.025865

    Abstract Diabetics is one of the world’s most common diseases which are caused by continued high levels of blood sugar. The risk of diabetics can be lowered if the diabetic is found at the early stage. In recent days, several machine learning models were developed to predict the diabetic presence at an early stage. In this paper, we propose an embedded-based machine learning model that combines the split-vote method and instance duplication to leverage an imbalanced dataset called PIMA Indian to increase the prediction of diabetics. The proposed method uses both the concept of over-sampling and More >

  • Open Access


    AMDnet: An Academic Misconduct Detection Method for Authors’ Behaviors

    Shihao Zhou1, Ziyuan Xu3,4, Jin Han1,*, Xingming Sun1,2, Yi Cao5

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5995-6009, 2022, DOI:10.32604/cmc.2022.023316

    Abstract In recent years, academic misconduct has been frequently exposed by the media, with serious impacts on the academic community. Current research on academic misconduct focuses mainly on detecting plagiarism in article content through the application of character-based and non-text element detection techniques over the entirety of a manuscript. For the most part, these techniques can only detect cases of textual plagiarism, which means that potential culprits can easily avoid discovery through clever editing and alterations of text content. In this paper, we propose an academic misconduct detection method based on scholars’ submission behaviors. The model… More >

  • Open Access


    A New Fuzzy Adaptive Algorithm to Classify Imbalanced Data

    Harshita Patel1, Dharmendra Singh Rajput1,*, Ovidiu Petru Stan2, Liviu Cristian Miclea2

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 73-89, 2022, DOI:10.32604/cmc.2022.017114

    Abstract Classification of imbalanced data is a well explored issue in the data mining and machine learning community where one class representation is overwhelmed by other classes. The Imbalanced distribution of data is a natural occurrence in real world datasets, so needed to be dealt with carefully to get important insights. In case of imbalance in data sets, traditional classifiers have to sacrifice their performances, therefore lead to misclassifications. This paper suggests a weighted nearest neighbor approach in a fuzzy manner to deal with this issue. We have adapted the ‘existing algorithm modification solution’ to learn… More >

  • Open Access


    A New Random Forest Applied to Heavy Metal Risk Assessment

    Ziyan Yu1, Cong Zhang1,*, Naixue Xiong2, Fang Chen1

    Computer Systems Science and Engineering, Vol.40, No.1, pp. 207-221, 2022, DOI:10.32604/csse.2022.018301

    Abstract As soil heavy metal pollution is increasing year by year, the risk assessment of soil heavy metal pollution is gradually gaining attention. Soil heavy metal datasets are usually imbalanced datasets in which most of the samples are safe samples that are not contaminated with heavy metals. Random Forest (RF) has strong generalization ability and is not easy to overfit. In this paper, we improve the Bagging algorithm and simple voting method of RF. A W-RF algorithm based on adaptive Bagging and weighted voting is proposed to improve the classification performance of RF on imbalanced datasets.… More >

  • Open Access


    An Approach Using Fuzzy Sets and Boosting Techniques to Predict Liver Disease

    Pushpendra Kumar1,2,*, Ramjeevan Singh Thakur3

    CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3513-3529, 2021, DOI:10.32604/cmc.2021.016957

    Abstract The aim of this research is to develop a mechanism to help medical practitioners predict and diagnose liver disease. Several systems have been proposed to help medical experts by diminishing error and increasing accuracy in diagnosing and predicting diseases. Among many existing methods, a few have considered the class imbalance issues of liver disorder datasets. As all the samples of liver disorder datasets are not useful, they do not contribute to learning about classifiers. A few samples might be redundant, which can increase the computational cost and affect the performance of the classifier. In this… More >

  • Open Access


    Dealing with Imbalanced Dataset Leveraging Boundary Samples Discovered by Support Vector Data Description

    Zhengbo Luo1, Hamïd Parvïn2,3,4,*, Harish Garg5, Sultan Noman Qasem6,7, Kim-Hung Pho8, Zulkefli Mansor9

    CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2691-2708, 2021, DOI:10.32604/cmc.2021.012547

    Abstract These days, imbalanced datasets, denoted throughout the paper by ID, (a dataset that contains some (usually two) classes where one contains considerably smaller number of samples than the other(s)) emerge in many real world problems (like health care systems or disease diagnosis systems, anomaly detection, fraud detection, stream based malware detection systems, and so on) and these datasets cause some problems (like under-training of minority class(es) and over-training of majority class(es), bias towards majority class(es), and so on) in classification process and application. Therefore, these datasets take the focus of many researchers in any science… More >

  • Open Access


    A Rasterized Lightning Disaster Risk Method for Imbalanced Sets Using Neural Network

    Yan Zhang1,2, Jin Han1,2,*, Chengsheng Yuan1,2, Shuo Yang3, Chuanlong Li1,2, Xingming Sun1,2

    CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 563-574, 2021, DOI:10.32604/cmc.2020.012502

    Abstract Over the past 10 years, lightning disaster has caused a large number of casualties and considerable economic loss worldwide. Lightning poses a huge threat to various industries. In an attempt to reduce the risk of lightning-caused disaster, many scholars have carried out in-depth research on lightning. However, these studies focus primarily on the lightning itself and other meteorological elements are ignored. In addition, the methods for assessing the risk of lightning disaster fail to give detailed attention to regional features (lightning disaster risk). This paper proposes a grid-based risk assessment method based on data from… More >

  • Open Access


    Ensemble Strategy for Insider Threat Detection from User Activity Logs

    Shihong Zou1, Huizhong Sun1, *, Guosheng Xu1, Ruijie Quan2

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

    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 >

  • Open Access


    A Novel Strategy for Mining Highly Imbalanced Data in Credit Card Transactions

    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 >

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