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  • Open Access

    ARTICLE

    Clustering-Aided Supervised Malware Detection with Specialized Classifiers and Early Consensus

    Murat Dener*, Sercan Gulburun

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1235-1251, 2023, DOI:10.32604/cmc.2023.036357

    Abstract One of the most common types of threats to the digital world is malicious software. It is of great importance to detect and prevent existing and new malware before it damages information assets. Machine learning approaches are used effectively for this purpose. In this study, we present a model in which supervised and unsupervised learning algorithms are used together. Clustering is used to enhance the prediction performance of the supervised classifiers. The aim of the proposed model is to make predictions in the shortest possible time with high accuracy and f1 score. In the first stage of the model, the… More >

  • Open Access

    ARTICLE

    MDEV Model: A Novel Ensemble-Based Transfer Learning Approach for Pneumonia Classification Using CXR Images

    Mehwish Shaikh1, Isma Farah Siddiqui1, Qasim Arain1, Jahwan Koo2,*, Mukhtiar Ali Unar3, Nawab Muhammad Faseeh Qureshi4,*

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 287-302, 2023, DOI:10.32604/csse.2023.035311

    Abstract Pneumonia is a dangerous respiratory disease due to which breathing becomes incredibly difficult and painful; thus, catching it early is crucial. Medical physicians’ time is limited in outdoor situations due to many patients; therefore, automated systems can be a rescue. The input images from the X-ray equipment are also highly unpredictable due to variances in radiologists’ experience. Therefore, radiologists require an automated system that can swiftly and accurately detect pneumonic lungs from chest x-rays. In medical classifications, deep convolution neural networks are commonly used. This research aims to use deep pre-trained transfer learning models to accurately categorize CXR images into… More >

  • Open Access

    ARTICLE

    A Deep Learning Ensemble Method for Forecasting Daily Crude Oil Price Based on Snapshot Ensemble of Transformer Model

    Ahmed Fathalla1, Zakaria Alameer2, Mohamed Abbas3, Ahmed Ali4,5,*

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 929-950, 2023, DOI:10.32604/csse.2023.035255

    Abstract The oil industries are an important part of a country’s economy. The crude oil’s price is influenced by a wide range of variables. Therefore, how accurately can countries predict its behavior and what predictors to employ are two main questions. In this view, we propose utilizing deep learning and ensemble learning techniques to boost crude oil’s price forecasting performance. The suggested method is based on a deep learning snapshot ensemble method of the Transformer model. To examine the superiority of the proposed model, this paper compares the proposed deep learning ensemble model against different machine learning and statistical models for… More >

  • Open Access

    ARTICLE

    Detection of Abnormal Network Traffic Using Bidirectional Long Short-Term Memory

    Nga Nguyen Thi Thanh, Quang H. Nguyen*

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 491-504, 2023, DOI:10.32604/csse.2023.032107

    Abstract Nowadays, web systems and servers are constantly at great risk from cyberattacks. This paper proposes a novel approach to detecting abnormal network traffic using a bidirectional long short-term memory (LSTM) network in combination with the ensemble learning technique. First, the binary classification module was used to detect the current abnormal flow. Then, the abnormal flows were fed into the multilayer classification module to identify the specific type of flow. In this research, a deep learning bidirectional LSTM model, in combination with the convolutional neural network and attention technique, was deployed to identify a specific attack. To solve the real-time intrusion-detecting… More >

  • Open Access

    ARTICLE

    Identification Method for Users-Transformer Relationship in Station Area Based on Local Selective Combination in Parallel Outlier Ensembles Algorithm

    Yunlong Ma1, Junwei Niu2,*, Bo Xu3, Xingtao Song2, Wei Huang2, Guoqiang Sun2

    Energy Engineering, Vol.120, No.3, pp. 681-700, 2023, DOI:10.32604/ee.2023.024719

    Abstract In the power distribution system, the missing or incorrect file of users-transformer relationship (UTR) in low-voltage station area (LVSA) will affect the lean management of the LVSA, and the operation and maintenance of the distribution network. To effectively improve the lean management of LVSA, the paper proposes an identification method for the UTR based on Local Selective Combination in Parallel Outlier Ensembles algorithm (LSCP). Firstly, the voltage data is reconstructed based on the information entropy to highlight the differences in between. Then, the LSCP algorithm combines four base outlier detection algorithms, namely Isolation Forest (I-Forest), One-Class Support Vector Machine (OC-SVM),… More >

  • Open Access

    ARTICLE

    MI-STEG: A Medical Image Steganalysis Framework Based on Ensemble Deep Learning

    Rukiye Karakis1,2,*

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 4649-4666, 2023, DOI:10.32604/cmc.2023.035881

    Abstract Medical image steganography aims to increase data security by concealing patient-personal information as well as diagnostic and therapeutic data in the spatial or frequency domain of radiological images. On the other hand, the discipline of image steganalysis generally provides a classification based on whether an image has hidden data or not. Inspired by previous studies on image steganalysis, this study proposes a deep ensemble learning model for medical image steganalysis to detect malicious hidden data in medical images and develop medical image steganography methods aimed at securing personal information. With this purpose in mind, a dataset containing brain Magnetic Resonance… More >

  • Open Access

    ARTICLE

    Leveraging Transfer Learning for Spatio-Temporal Human Activity Recognition from Video Sequences

    Umair Muneer Butt1,2,*, Hadiqa Aman Ullah2, Sukumar Letchmunan1, Iqra Tariq2, Fadratul Hafinaz Hassan1, Tieng Wei Koh3

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5017-5033, 2023, DOI:10.32604/cmc.2023.035512

    Abstract Human Activity Recognition (HAR) is an active research area due to its applications in pervasive computing, human-computer interaction, artificial intelligence, health care, and social sciences. Moreover, dynamic environments and anthropometric differences between individuals make it harder to recognize actions. This study focused on human activity in video sequences acquired with an RGB camera because of its vast range of real-world applications. It uses two-stream ConvNet to extract spatial and temporal information and proposes a fine-tuned deep neural network. Moreover, the transfer learning paradigm is adopted to extract varied and fixed frames while reusing object identification information. Six state-of-the-art pre-trained models… More >

  • Open Access

    ARTICLE

    Reducing Dataset Specificity for Deepfakes Using Ensemble Learning

    Qaiser Abbas1, Turki Alghamdi1, Yazed Alsaawy1, Tahir Alyas2,*, Ali Alzahrani1, Khawar Iqbal Malik3, Saira Bibi4

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4261-4276, 2023, DOI:10.32604/cmc.2023.034482

    Abstract The emergence of deep fake videos in recent years has made image falsification a real danger. A person’s face and emotions are deep-faked in a video or speech and are substituted with a different face or voice employing deep learning to analyze speech or emotional content. Because of how clever these videos are frequently, Manipulation is challenging to spot. Social media are the most frequent and dangerous targets since they are weak outlets that are open to extortion or slander a human. In earlier times, it was not so easy to alter the videos, which required expertise in the domain… More >

  • Open Access

    ARTICLE

    An Intelligent Hazardous Waste Detection and Classification Model Using Ensemble Learning Techniques

    Mesfer Al Duhayyim1,*, Saud S. Alotaibi2, Shaha Al-Otaibi3, Fahd N. Al-Wesabi4, Mahmoud Othman5, Ishfaq Yaseen6, Mohammed Rizwanullah6, Abdelwahed Motwakel6

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3315-3332, 2023, DOI:10.32604/cmc.2023.033250

    Abstract Proper waste management models using recent technologies like computer vision, machine learning (ML), and deep learning (DL) are needed to effectively handle the massive quantity of increasing waste. Therefore, waste classification becomes a crucial topic which helps to categorize waste into hazardous or non-hazardous ones and thereby assist in the decision making of the waste management process. This study concentrates on the design of hazardous waste detection and classification using ensemble learning (HWDC-EL) technique to reduce toxicity and improve human health. The goal of the HWDC-EL technique is to detect the multiple classes of wastes, particularly hazardous and non-hazardous wastes.… More >

  • Open Access

    ARTICLE

    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 >

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