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  • Open Access

    ARTICLE

    A Support Vector Machine (SVM) Model for Privacy Recommending Data Processing Model (PRDPM) in Internet of Vehicles

    Ali Alqarni*

    CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 389-406, 2025, DOI:10.32604/cmc.2024.059238 - 03 January 2025

    Abstract Open networks and heterogeneous services in the Internet of Vehicles (IoV) can lead to security and privacy challenges. One key requirement for such systems is the preservation of user privacy, ensuring a seamless experience in driving, navigation, and communication. These privacy needs are influenced by various factors, such as data collected at different intervals, trip durations, and user interactions. To address this, the paper proposes a Support Vector Machine (SVM) model designed to process large amounts of aggregated data and recommend privacy-preserving measures. The model analyzes data based on user demands and interactions with service More >

  • Open Access

    ARTICLE

    Advancing Deepfake Detection Using Xception Architecture: A Robust Approach for Safeguarding against Fabricated News on Social Media

    Dunya Ahmed Alkurdi1,2,*, Mesut Cevik2, Abdurrahim Akgundogdu3

    CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4285-4305, 2024, DOI:10.32604/cmc.2024.057029 - 19 December 2024

    Abstract Deepfake has emerged as an obstinate challenge in a world dominated by light. Here, the authors introduce a new deepfake detection method based on Xception architecture. The model is tested exhaustively with millions of frames and diverse video clips; accuracy levels as high as 99.65% are reported. These are the main reasons for such high efficacy: superior feature extraction capabilities and stable training mechanisms, such as early stopping, characterizing the Xception model. The methodology applied is also more advanced when it comes to data preprocessing steps, making use of state-of-the-art techniques applied to ensure constant… More >

  • Open Access

    ARTICLE

    Cloud-Edge Collaborative Federated GAN Based Data Processing for IoT-Empowered Multi-Flow Integrated Energy Aggregation Dispatch

    Zhan Shi*

    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 973-994, 2024, DOI:10.32604/cmc.2024.051530 - 18 July 2024

    Abstract The convergence of Internet of Things (IoT), 5G, and cloud collaboration offers tailored solutions to the rigorous demands of multi-flow integrated energy aggregation dispatch data processing. While generative adversarial networks (GANs) are instrumental in resource scheduling, their application in this domain is impeded by challenges such as convergence speed, inferior optimality searching capability, and the inability to learn from failed decision making feedbacks. Therefore, a cloud-edge collaborative federated GAN-based communication and computing resource scheduling algorithm with long-term constraint violation sensitiveness is proposed to address these challenges. The proposed algorithm facilitates real-time, energy-efficient data processing by More >

  • Open Access

    ARTICLE

    Sentiment Analysis of Low-Resource Language Literature Using Data Processing and Deep Learning

    Aizaz Ali1, Maqbool Khan1,2, Khalil Khan3, Rehan Ullah Khan4, Abdulrahman Aloraini4,*

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 713-733, 2024, DOI:10.32604/cmc.2024.048712 - 25 April 2024

    Abstract Sentiment analysis, a crucial task in discerning emotional tones within the text, plays a pivotal role in understanding public opinion and user sentiment across diverse languages. While numerous scholars conduct sentiment analysis in widely spoken languages such as English, Chinese, Arabic, Roman Arabic, and more, we come to grappling with resource-poor languages like Urdu literature which becomes a challenge. Urdu is a uniquely crafted language, characterized by a script that amalgamates elements from diverse languages, including Arabic, Parsi, Pashtu, Turkish, Punjabi, Saraiki, and more. As Urdu literature, characterized by distinct character sets and linguistic features,… More >

  • Open Access

    ARTICLE

    A Novel Intrusion Detection Model of Unknown Attacks Using Convolutional Neural Networks

    Abdullah Alsaleh1,2,*

    Computer Systems Science and Engineering, Vol.48, No.2, pp. 431-449, 2024, DOI:10.32604/csse.2023.043107 - 19 March 2024

    Abstract With the increasing number of connected devices in the Internet of Things (IoT) era, the number of intrusions is also increasing. An intrusion detection system (IDS) is a secondary intelligent system for monitoring, detecting and alerting against malicious activity. IDS is important in developing advanced security models. This study reviews the importance of various techniques, tools, and methods used in IoT detection and/or prevention systems. Specifically, it focuses on machine learning (ML) and deep learning (DL) techniques for IDS. This paper proposes an accurate intrusion detection model to detect traditional and new attacks on the… More >

  • Open Access

    ARTICLE

    Multi-Source Data Privacy Protection Method Based on Homomorphic Encryption and Blockchain

    Ze Xu, Sanxing Cao*

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 861-881, 2023, DOI:10.32604/cmes.2023.025159 - 05 January 2023

    Abstract Multi-Source data plays an important role in the evolution of media convergence. Its fusion processing enables the further mining of data and utilization of data value and broadens the path for the sharing and dissemination of media data. However, it also faces serious problems in terms of protecting user and data privacy. Many privacy protection methods have been proposed to solve the problem of privacy leakage during the process of data sharing, but they suffer from two flaws: 1) the lack of algorithmic frameworks for specific scenarios such as dynamic datasets in the media domain;… More > Graphic Abstract

    Multi-Source Data Privacy Protection Method Based on Homomorphic Encryption and Blockchain

  • Open Access

    ARTICLE

    Remote Sensing Data Processing Process Scheduling Based on Reinforcement Learning in Cloud Environment

    Ying Du1,2, Shuo Zhang1,2, Pu Cheng3,*, Rita Yi Man Li4, Xiao-Guang Yue5,6

    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.3, pp. 1965-1979, 2023, DOI:10.32604/cmes.2023.024871 - 23 November 2022

    Abstract Task scheduling plays a crucial role in cloud computing and is a key factor determining cloud computing performance. To solve the task scheduling problem for remote sensing data processing in cloud computing, this paper proposes a workflow task scheduling algorithm---Workflow Task Scheduling Algorithm based on Deep Reinforcement Learning (WDRL). The remote sensing data process modeling is transformed into a directed acyclic graph scheduling problem. Then, the algorithm is designed by establishing a Markov decision model and adopting a fitness calculation method. Finally, combine the advantages of reinforcement learning and deep neural networks to minimize make-time More >

  • Open Access

    ARTICLE

    Big Data Analytics Using Graph Signal Processing

    Farhan Amin1, Omar M. Barukab2, Gyu Sang Choi1,*

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 489-502, 2023, DOI:10.32604/cmc.2023.030615 - 22 September 2022

    Abstract The networks are fundamental to our modern world and they appear throughout science and society. Access to a massive amount of data presents a unique opportunity to the researcher’s community. As networks grow in size the complexity increases and our ability to analyze them using the current state of the art is at severe risk of failing to keep pace. Therefore, this paper initiates a discussion on graph signal processing for large-scale data analysis. We first provide a comprehensive overview of core ideas in Graph signal processing (GSP) and their connection to conventional digital signal… More >

  • Open Access

    ARTICLE

    A Survey of Machine Learning for Big Data Processing

    Reem Almutiri*, Sarah Alhabeeb, Sarah Alhumud, Rehan Ullah Khan

    Journal on Big Data, Vol.4, No.2, pp. 97-111, 2022, DOI:10.32604/jbd.2022.028363 - 31 October 2022

    Abstract Today’s world is a data-driven one, with data being produced in vast amounts as a result of the rapid growth of technology that permeates every aspect of our lives. New data processing techniques must be developed and refined over time to gain meaningful insights from this vast continuous volume of produced data in various forms. Machine learning technologies provide promising solutions and potential methods for processing large quantities of data and gaining value from it. This study conducts a literature review on the application of machine learning techniques in big data processing. It provides a More >

  • Open Access

    ARTICLE

    Cuckoo Optimized Convolution Support Vector Machine for Big Health Data Processing

    Eatedal Alabdulkreem1, Jaber S. Alzahrani2, Majdy M. Eltahir3, Abdullah Mohamed4, Manar Ahmed Hamza5,*, Abdelwahed Motwakel5, Mohamed I. Eldesouki6, Mohammed Rizwanullah5

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3039-3055, 2022, DOI:10.32604/cmc.2022.029835 - 16 June 2022

    Abstract Big health data collection and storing for further analysis is a challenging task because this knowledge is big and has many features. Several cloud-based IoT health providers have been described in the literature previously. Furthermore, there are a number of issues related to time consumed and overall network performance when it comes to big data information. In the existing method, less performed optimization algorithms were used for optimizing the data. In the proposed method, the Chaotic Cuckoo Optimization algorithm was used for feature selection, and Convolutional Support Vector Machine (CSVM) was used. The research presents… More >

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