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  • Open Access

    ARTICLE

    Attenuate Class Imbalance Problem for Pneumonia Diagnosis Using Ensemble Parallel Stacked Pre-Trained Models

    Aswathy Ravikumar, Harini Sriraman*

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 891-909, 2023, DOI:10.32604/cmc.2023.035848

    Abstract Pneumonia is an acute lung infection that has caused many fatalities globally. Radiologists often employ chest X-rays to identify pneumonia since they are presently the most effective imaging method for this purpose. Computer-aided diagnosis of pneumonia using deep learning techniques is widely used due to its effectiveness and performance. In the proposed method, the Synthetic Minority Oversampling Technique (SMOTE) approach is used to eliminate the class imbalance in the X-ray dataset. To compensate for the paucity of accessible data, pre-trained transfer learning is used, and an ensemble Convolutional Neural Network (CNN) model is developed. The More >

  • Open Access

    ARTICLE

    Combined Effect of Concept Drift and Class Imbalance on Model Performance During Stream Classification

    Abdul Sattar Palli1,6,*, Jafreezal Jaafar1,2, Manzoor Ahmed Hashmani1,3, Heitor Murilo Gomes4,5, Aeshah Alsughayyir7, Abdul Rehman Gilal1

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1827-1845, 2023, DOI:10.32604/cmc.2023.033934

    Abstract Every application in a smart city environment like the smart grid, health monitoring, security, and surveillance generates non-stationary data streams. Due to such nature, the statistical properties of data changes over time, leading to class imbalance and concept drift issues. Both these issues cause model performance degradation. Most of the current work has been focused on developing an ensemble strategy by training a new classifier on the latest data to resolve the issue. These techniques suffer while training the new classifier if the data is imbalanced. Also, the class imbalance ratio may change greatly from… More >

  • Open Access

    ARTICLE

    Enhanced Coyote Optimization with Deep Learning Based Cloud-Intrusion Detection System

    Abdullah M. Basahel1, Mohammad Yamin1, Sulafah M. Basahel2, E. Laxmi Lydia3,*

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4319-4336, 2023, DOI:10.32604/cmc.2023.033497

    Abstract Cloud Computing (CC) is the preference of all information technology (IT) organizations as it offers pay-per-use based and flexible services to its users. But the privacy and security become the main hindrances in its achievement due to distributed and open architecture that is prone to intruders. Intrusion Detection System (IDS) refers to one of the commonly utilized system for detecting attacks on cloud. IDS proves to be an effective and promising technique, that identifies malicious activities and known threats by observing traffic data in computers, and warnings are given when such threats were identified. The… More >

  • Open Access

    ARTICLE

    Class Imbalance Handling with Deep Learning Enabled IoT Healthcare Diagnosis Model

    T. Ragupathi1,*, M. Govindarajan1, T. Priyaradhikadevi2

    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1351-1366, 2022, DOI:10.32604/iasc.2022.025756

    Abstract The rapid advancements in the field of big data, wearables, Internet of Things (IoT), connected devices, and cloud environment find useful to improve the quality of healthcare services. Medical data classification using the data collected by the wearables and IoT devices can be used to determine the presence or absence of disease. The recently developed deep learning (DL) models can be used for several processes such as classification, natural language processing, etc. This study presents a bacterial foraging optimization (BFO) based convolutional neural network-gated recurrent unit (CNN-GRU) with class imbalance handling (CIH) model, named BFO-CNN-GRU-CIH… More >

  • Open Access

    ARTICLE

    Iterative Semi-Supervised Learning Using Softmax Probability

    Heewon Chung, Jinseok Lee*

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5607-5628, 2022, DOI:10.32604/cmc.2022.028154

    Abstract For the classification problem in practice, one of the challenging issues is to obtain enough labeled data for training. Moreover, even if such labeled data has been sufficiently accumulated, most datasets often exhibit long-tailed distribution with heavy class imbalance, which results in a biased model towards a majority class. To alleviate such class imbalance, semi-supervised learning methods using additional unlabeled data have been considered. However, as a matter of course, the accuracy is much lower than that from supervised learning. In this study, under the assumption that additional unlabeled data is available, we propose the More >

  • Open Access

    ARTICLE

    A Hybrid System for Customer Churn Prediction and Retention Analysis via Supervised Learning

    Soban Arshad1, Khalid Iqbal1,*, Sheneela Naz2, Sadaf Yasmin1, Zobia Rehman2

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4283-4301, 2022, DOI:10.32604/cmc.2022.025442

    Abstract Telecom industry relies on churn prediction models to retain their customers. These prediction models help in precise and right time recognition of future switching by a group of customers to other service providers. Retention not only contributes to the profit of an organization, but it is also important for upholding a position in the competitive market. In the past, numerous churn prediction models have been proposed, but the current models have a number of flaws that prevent them from being used in real-world large-scale telecom datasets. These schemes, fail to incorporate frequently changing requirements. Data… More >

  • Open Access

    ARTICLE

    Optimized Stacked Autoencoder for IoT Enabled Financial Crisis Prediction Model

    Mesfer Al Duhayyim1, Hadeel Alsolai2, Fahd N. Al-Wesabi3,4, Nadhem Nemri3, Hany Mahgoub3, Anwer Mustafa Hilal5, Manar Ahmed Hamza5,*, Mohammed Rizwanullah5

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1079-1094, 2022, DOI:10.32604/cmc.2022.021199

    Abstract Recently, Financial Technology (FinTech) has received more attention among financial sectors and researchers to derive effective solutions for any financial institution or firm. Financial crisis prediction (FCP) is an essential topic in business sector that finds it useful to identify the financial condition of a financial institution. At the same time, the development of the internet of things (IoT) has altered the mode of human interaction with the physical world. The IoT can be combined with the FCP model to examine the financial data from the users and perform decision making process. This paper presents… More >

  • Open Access

    ARTICLE

    Handling Class Imbalance in Online Transaction Fraud Detection

    Kanika1, Jimmy Singla1, Ali Kashif Bashir2, Yunyoung Nam3,*, Najam UI Hasan4, Usman Tariq5

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2861-2877, 2022, DOI:10.32604/cmc.2022.019990

    Abstract With the rise of internet facilities, a greater number of people have started doing online transactions at an exponential rate in recent years as the online transaction system has eliminated the need of going to the bank physically for every transaction. However, the fraud cases have also increased causing the loss of money to the consumers. Hence, an effective fraud detection system is the need of the hour which can detect fraudulent transactions automatically in real-time. Generally, the genuine transactions are large in number than the fraudulent transactions which leads to the class imbalance problem.… More >

  • Open Access

    REVIEW

    Ensemble Learning Models for Classification and Selection of Web Services: A Review

    Muhammad Hasnain1, Imran Ghani2, Seung Ryul Jeong3,*, Aitizaz Ali1

    Computer Systems Science and Engineering, Vol.40, No.1, pp. 327-339, 2022, DOI:10.32604/csse.2022.018300

    Abstract This paper presents a review of the ensemble learning models proposed for web services classification, selection, and composition. Web service is an evolutionary research area, and ensemble learning has become a hot spot to assess web services’ earlier mentioned aspects. The proposed research aims to review the state of art approaches performed on the interesting web services area. The literature on the research topic is examined using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) as a research method. The study reveals an increasing trend of using ensemble learning in the chosen papers More >

  • Open Access

    ARTICLE

    Oversampling Method Based on Gaussian Distribution and K-Means Clustering

    Masoud Muhammed Hassan1, Adel Sabry Eesa1,*, Ahmed Jameel Mohammed2, Wahab Kh. Arabo1

    CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 451-469, 2021, DOI:10.32604/cmc.2021.018280

    Abstract Learning from imbalanced data is one of the greatest challenging problems in binary classification, and this problem has gained more importance in recent years. When the class distribution is imbalanced, classical machine learning algorithms tend to move strongly towards the majority class and disregard the minority. Therefore, the accuracy may be high, but the model cannot recognize data instances in the minority class to classify them, leading to many misclassifications. Different methods have been proposed in the literature to handle the imbalance problem, but most are complicated and tend to simulate unnecessary noise. In this More >

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