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Search Results (16)
  • Open Access

    ARTICLE

    WebFLex: A Framework for Web Browsers-Based Peer-to-Peer Federated Learning Systems Using WebRTC

    Mai Alzamel1,*, Hamza Ali Rizvi2, Najwa Altwaijry1, Isra Al-Turaiki1

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4177-4204, 2024, DOI:10.32604/cmc.2024.048370

    Abstract Scalability and information personal privacy are vital for training and deploying large-scale deep learning models. Federated learning trains models on exclusive information by aggregating weights from various devices and taking advantage of the device-agnostic environment of web browsers. Nevertheless, relying on a main central server for internet browser-based federated systems can prohibit scalability and interfere with the training process as a result of growing client numbers. Additionally, information relating to the training dataset can possibly be extracted from the distributed weights, potentially reducing the privacy of the local data used for training. In this research paper, we aim to investigate… More >

  • Open Access

    ARTICLE

    Broad Federated Meta-Learning of Damaged Objects in Aerial Videos

    Zekai Li1, Wenfeng Wang2,3,4,5,6,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2881-2899, 2023, DOI:10.32604/cmes.2023.028670

    Abstract We advanced an emerging federated learning technology in city intelligentization for tackling a real challenge— to learn damaged objects in aerial videos. A meta-learning system was integrated with the fuzzy broad learning system to further develop the theory of federated learning. Both the mixed picture set of aerial video segmentation and the 3D-reconstructed mixed-reality data were employed in the performance of the broad federated meta-learning system. The study results indicated that the object classification accuracy is up to 90% and the average time cost in damage detection is only 0.277 s. Consequently, the broad federated meta-learning system is efficient and… More >

  • Open Access

    ARTICLE

    Performance Analysis of Intelligent Neural-Based Deep Learning System on Rank Images Classification

    Muhammad Hameed Siddiqi1,*, Asfandyar Khan2, Muhammad Bilal Khan2, Abdullah Khan2, Madallah Alruwaili1, Saad Alanazi1

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 2219-2239, 2023, DOI:10.32604/csse.2023.040212

    Abstract The use of the internet is increasing all over the world on a daily basis in the last two decades. The increase in the internet causes many sexual crimes, such as sexual misuse, domestic violence, and child pornography. Various research has been done for pornographic image detection and classification. Most of the used models used machine learning techniques and deep learning models which show less accuracy, while the deep learning model ware used for classification and detection performed better as compared to machine learning. Therefore, this research evaluates the performance analysis of intelligent neural-based deep learning models which are based… More >

  • Open Access

    ARTICLE

    A Novel Ensemble Learning System for Cyberattack Classification

    Óscar Mogollón-Gutiérrez*, José Carlos Sancho Núñez, Mar Ávila Vegas, Andrés Caro Lindo

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1691-1709, 2023, DOI:10.32604/iasc.2023.039255

    Abstract Nowadays, IT systems rely mainly on artificial intelligence (AI) algorithms to process data. AI is generally used to extract knowledge from stored information and, depending on the nature of data, it may be necessary to apply different AI algorithms. In this article, a novel perspective on the use of AI to ensure the cybersecurity through the study of network traffic is presented. This is done through the construction of a two-stage cyberattack classification ensemble model addressing class imbalance following a one-vs-rest (OvR) approach. With the growing trend of cyberattacks, it is essential to implement techniques that ensure legitimate access to… More >

  • Open Access

    ARTICLE

    Federated Blockchain Model for Cyber Intrusion Analysis in Smart Grid Networks

    N. Sundareswaran*, S. Sasirekha

    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2129-2143, 2023, DOI:10.32604/iasc.2023.034381

    Abstract Smart internet of things (IoT) devices are used to manage domestic and industrial energy needs using sustainable and renewable energy sources. Due to cyber infiltration and a lack of transparency, the traditional transaction process is inefficient, unsafe and expensive. Smart grid systems are now efficient, safe and transparent owing to the development of blockchain (BC) technology and its smart contract (SC) solution. In this study, federated learning extreme gradient boosting (FL-XGB) framework has been developed along with BC to learn the intrusion inside the smart energy system. FL is best suited for a decentralized BC-enabled system to adapt learning models… More >

  • Open Access

    REVIEW

    Broad Learning System for Tackling Emerging Challenges in Face Recognition

    Wenjun Zhang1, Wenfeng Wang2,3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.3, pp. 1597-1619, 2023, DOI:10.32604/cmes.2022.020517

    Abstract Face recognition has been rapidly developed and widely used. However, there is still considerable uncertainty in the computational intelligence based on human-centric visual understanding. Emerging challenges for face recognition are resulted from information loss. This study aims to tackle these challenges with a broad learning system (BLS). We integrated two models, IR3C with BLS and IR3C with a triplet loss, to control the learning process. In our experiments, we used different strategies to generate more challenging datasets and analyzed the competitiveness, sensitivity, and practicability of the proposed two models. In the model of IR3C with BLS, the recognition rates for… More >

  • Open Access

    ARTICLE

    Generating Intelligent Remedial Materials with Genetic Algorithms and Concept Maps

    Che-Chern Lin*, Chien-Chun Pan

    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1333-1349, 2022, DOI:10.32604/iasc.2022.025387

    Abstract This study proposes an intelligent remedial learning framework to improve students’ learning effectiveness. Basically, this framework combines a genetic algorithm with a concept map in order to select a set of remedial learning units according to students’ weaknesses of learning concepts. In the proposed algorithm, a concept map serves to represent the knowledge structure of learning concepts, and a genetic algorithm performs an iteratively evolutionary procedure in order to establish remedial learning materials based on students’ understanding of these learning concepts. This study also conducted simulations in order to validate the proposed framework using artificially generated data sets, and problematic… More >

  • Open Access

    ARTICLE

    Securing Fog Computing For E-Learning System Using Integration of Two Encryption Algorithms

    Hind A. Alshambri1,* Fawaz Alassery2

    Journal of Cyber Security, Vol.3, No.3, pp. 149-166, 2021, DOI:10.32604/jcs.2021.022112

    Abstract Currently, the majority of institutions have made use of information technologies to improve and develop their diverse educational methods to attract more learners. Through information technologies, e-learning and learning-on-the go have been adopted by the institutions to provide affordability and flexibility of educational services. Most of the educational institutes are offering online teaching classes using the technologies like cloud computing, networking, etc. Educational institutes have developed their e-learning platforms for the online learning process, through this way they have paved the way for distance learning. But e-learning platform has to face a lot of security challenges in terms of cyberattacks… More >

  • Open Access

    ARTICLE

    Student Behavior Modeling for an E-Learning System Offering Personalized Learning Experiences

    K. Abhirami1,*, M. K. Kavitha Devi2

    Computer Systems Science and Engineering, Vol.40, No.3, pp. 1127-1144, 2022, DOI:10.32604/csse.2022.020013

    Abstract With the advent of computing and communication technologies, it has become possible for a learner to expand his or her knowledge irrespective of the place and time. Web-based learning promotes active and independent learning. Large scale e-learning platforms revolutionized the concept of studying and it also paved the way for innovative and effective teaching-learning process. This digital learning improves the quality of teaching and also promotes educational equity. However, the challenges in e-learning platforms include dissimilarities in learner’s ability and needs, lack of student motivation towards learning activities and provision for adaptive learning environment. The quality of learning can be… More >

  • Open Access

    ARTICLE

    I-Quiz: An Intelligent Assessment Tool for Non-Verbal Behaviour Detection

    B. T. Shobana1,*, G. A. Sathish Kumar2

    Computer Systems Science and Engineering, Vol.40, No.3, pp. 1007-1021, 2022, DOI:10.32604/csse.2022.019523

    Abstract Electronic learning (e-learning) has become one of the widely used modes of pedagogy in higher education today due to the convenience and flexibility offered in comparison to traditional learning activities. Advancements in Information and Communication Technology have eased learner connectivity online and enabled access to an extensive range of learning materials on the World Wide Web. Post covid-19 pandemic, online learning has become the most essential and inevitable medium of learning in primary, secondary and higher education. In recent times, Massive Open Online Courses (MOOCs) have transformed the current education strategy by offering a technology-rich and flexible form of online… More >

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