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  • Open Access


    Intrusion Detection Method Based on Active Incremental Learning in Industrial Internet of Things Environment

    Zeyong Sun1, Guo Ran2, Zilong Jin1,3,*

    Journal on Internet of Things, Vol.4, No.2, pp. 99-111, 2022, DOI:10.32604/jiot.2022.037416

    Abstract Intrusion detection is a hot field in the direction of network security. Classical intrusion detection systems are usually based on supervised machine learning models. These offline-trained models usually have better performance in the initial stages of system construction. However, due to the diversity and rapid development of intrusion techniques, the trained models are often difficult to detect new attacks. In addition, very little noisy data in the training process often has a considerable impact on the performance of the intrusion detection system. This paper proposes an intrusion detection system based on active incremental learning with the adaptive capability to solve… More >

  • Open Access


    BS-SC Model: A Novel Method for Predicting Child Abuse Using Borderline-SMOTE Enabled Stacking Classifier

    Saravanan Parthasarathy, Arun Raj Lakshminarayanan*

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 1311-1336, 2023, DOI:10.32604/csse.2023.034910

    Abstract For a long time, legal entities have developed and used crime prediction methodologies. The techniques are frequently updated based on crime evaluations and responses from scientific communities. There is a need to develop type-based crime prediction methodologies that can be used to address issues at the subgroup level. Child maltreatment is not adequately addressed because children are voiceless. As a result, the possibility of developing a model for predicting child abuse was investigated in this study. Various exploratory analysis methods were used to examine the city of Chicago’s child abuse events. The data set was balanced using the Borderline-SMOTE technique,… More >

  • Open Access


    Topology Optimization of Strength-Safe Continuum Structures Considering Random Damage

    Jiazheng Du*, Xue Cong, Ying Zhang

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.2, pp. 1091-1120, 2023, DOI:10.32604/cmes.2023.025585

    Abstract Spacecraft in the aerospace field and military equipment in the military field are at risk of being impacted by external objects, which can cause local damage to the structure. The randomness of local damage is a new challenge for structural design, and it is essential to take random damage into account in the conceptual design phase for the purpose of improving structure’s resistance to external shocks. In this article, a random damaged structure is assumed to have damages of the same size and shape at random locations, and the random damage is considered as multiple damage conditions of the structure.… More > Graphic Abstract

    Topology Optimization of Strength-Safe Continuum Structures Considering Random Damage

  • Open Access


    Boosted Stacking Ensemble Machine Learning Method for Wafer Map Pattern Classification

    Jeonghoon Choi1, Dongjun Suh1,*, Marc-Oliver Otto2

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2945-2966, 2023, DOI:10.32604/cmc.2023.033417

    Abstract Recently, machine learning-based technologies have been developed to automate the classification of wafer map defect patterns during semiconductor manufacturing. The existing approaches used in the wafer map pattern classification include directly learning the image through a convolution neural network and applying the ensemble method after extracting image features. This study aims to classify wafer map defects more effectively and derive robust algorithms even for datasets with insufficient defect patterns. First, the number of defects during the actual process may be limited. Therefore, insufficient data are generated using convolutional auto-encoder (CAE), and the expanded data are verified using the evaluation technique… More >

  • Open Access


    GA-Stacking: A New Stacking-Based Ensemble Learning Method to Forecast the COVID-19 Outbreak

    Walaa N. Ismail1,2,*, Hessah A. Alsalamah3,4, Ebtesam Mohamed2

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3945-3976, 2023, DOI:10.32604/cmc.2023.031194

    Abstract As a result of the increased number of COVID-19 cases, Ensemble Machine Learning (EML) would be an effective tool for combatting this pandemic outbreak. An ensemble of classifiers can improve the performance of single machine learning (ML) classifiers, especially stacking-based ensemble learning. Stacking utilizes heterogeneous-base learners trained in parallel and combines their predictions using a meta-model to determine the final prediction results. However, building an ensemble often causes the model performance to decrease due to the increasing number of learners that are not being properly selected. Therefore, the goal of this paper is to develop and evaluate a generic, data-independent… More >

  • Open Access


    Android Malware Detection Using ResNet-50 Stacking

    Lojain Nahhas1, Marwan Albahar1,*, Abdullah Alammari2, Anca Jurcut3

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3997-4014, 2023, DOI:10.32604/cmc.2023.028316

    Abstract There has been an increase in attacks on mobile devices, such as smartphones and tablets, due to their growing popularity. Mobile malware is one of the most dangerous threats, causing both security breaches and financial losses. Mobile malware is likely to continue to evolve and proliferate to carry out a variety of cybercrimes on mobile devices. Mobile malware specifically targets Android operating system as it has grown in popularity. The rapid proliferation of Android malware apps poses a significant security risk to users, making static and manual analysis of malicious files difficult. Therefore, efficient identification and classification of Android malicious… More >

  • Open Access


    Stacking Ensemble Learning-Based Convolutional Gated Recurrent Neural Network for Diabetes Miletus

    G. Geetha1,2,*, K. Mohana Prasad1

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 703-718, 2023, DOI:10.32604/iasc.2023.032530

    Abstract Diabetes mellitus is a metabolic disease in which blood glucose levels rise as a result of pancreatic insulin production failure. It causes hyperglycemia and chronic multiorgan dysfunction, including blindness, renal failure, and cardiovascular disease, if left untreated. One of the essential checks that are needed to be performed frequently in Type 1 Diabetes Mellitus is a blood test, this procedure involves extracting blood quite frequently, which leads to subject discomfort increasing the possibility of infection when the procedure is often recurring. Existing methods used for diabetes classification have less classification accuracy and suffer from vanishing gradient problems, to overcome these… More >

  • Open Access


    Airstacknet: A Stacking Ensemble-Based Approach for Air Quality Prediction

    Amel Ksibi1, Amina Salhi1, Ala Saleh Alluhaidan1,*, Sahar A. El-Rahman2

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2073-2096, 2023, DOI:10.32604/cmc.2023.032566

    Abstract The quality of the air we breathe during the courses of our daily lives has a significant impact on our health and well-being as individuals. Unfortunately, personal air quality measurement remains challenging. In this study, we investigate the use of first-person photos for the prediction of air quality. The main idea is to harness the power of a generalized stacking approach and the importance of haze features extracted from first-person images to create an efficient new stacking model called AirStackNet for air pollution prediction. AirStackNet consists of two layers and four regression models, where the first layer generates meta-data from… More >

  • Open Access


    Intrusion Detection Using Ensemble Wrapper Filter Based Feature Selection with Stacking Model

    D. Karthikeyan1,*, V. Mohan Raj2, J. Senthilkumar2, Y. Suresh2

    Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 645-659, 2023, DOI:10.32604/iasc.2023.027039

    Abstract The number of attacks is growing tremendously in tandem with the growth of internet technologies. As a result, protecting the private data from prying eyes has become a critical and tough undertaking. Many intrusion detection solutions have been offered by researchers in order to decrease the effect of these attacks. For attack detection, the prior system has created an SMSRPF (Stacking Model Significant Rule Power Factor) classifier. To provide creative instance detection, the SMSRPF combines the detection of trained classifiers such as DT (Decision Tree) and RF (Random Forest). Nevertheless, it does not generate any accurate findings that are adequate.… More >

  • Open Access


    Phishing Websites Detection by Using Optimized Stacking Ensemble Model

    Zeyad Ghaleb Al-Mekhlafi1, Badiea Abdulkarem Mohammed1,2,*, Mohammed Al-Sarem3, Faisal Saeed3, Tawfik Al-Hadhrami4, Mohammad T. Alshammari1, Abdulrahman Alreshidi1, Talal Sarheed Alshammari1

    Computer Systems Science and Engineering, Vol.41, No.1, pp. 109-125, 2022, DOI:10.32604/csse.2022.020414

    Abstract Phishing attacks are security attacks that do not affect only individuals’ or organizations’ websites but may affect Internet of Things (IoT) devices and networks. IoT environment is an exposed environment for such attacks. Attackers may use thingbots software for the dispersal of hidden junk emails that are not noticed by users. Machine and deep learning and other methods were used to design detection methods for these attacks. However, there is still a need to enhance detection accuracy. Optimization of an ensemble classification method for phishing website (PW) detection is proposed in this study. A Genetic Algorithm (GA) was used for… More >

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