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Search Results (5)
  • Open Access

    ARTICLE

    Explainable Diabetic Retinopathy Detection Using a Distributed CNN and LightGBM Framework

    Pooja Bidwai1,2, Shilpa Gite1,3, Biswajeet Pradhan4,*, Abdullah Almari5

    CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2645-2676, 2025, DOI:10.32604/cmc.2025.061018 - 03 July 2025

    Abstract Diabetic Retinopathy (DR) is a critical disorder that affects the retina due to the constant rise in diabetics and remains the major cause of blindness across the world. Early detection and timely treatment are essential to mitigate the effects of DR, such as retinal damage and vision impairment. Several conventional approaches have been proposed to detect DR early and accurately, but they are limited by data imbalance, interpretability, overfitting, convergence time, and other issues. To address these drawbacks and improve DR detection accurately, a distributed Explainable Convolutional Neural network-enabled Light Gradient Boosting Machine (DE-ExLNN) is… More >

  • Open Access

    ARTICLE

    A Study on the Inter-Pretability of Network Attack Prediction Models Based on Light Gradient Boosting Machine (LGBM) and SHapley Additive exPlanations (SHAP)

    Shuqin Zhang1, Zihao Wang1,*, Xinyu Su2

    CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5781-5809, 2025, DOI:10.32604/cmc.2025.062080 - 19 May 2025

    Abstract The methods of network attacks have become increasingly sophisticated, rendering traditional cybersecurity defense mechanisms insufficient to address novel and complex threats effectively. In recent years, artificial intelligence has achieved significant progress in the field of network security. However, many challenges and issues remain, particularly regarding the interpretability of deep learning and ensemble learning algorithms. To address the challenge of enhancing the interpretability of network attack prediction models, this paper proposes a method that combines Light Gradient Boosting Machine (LGBM) and SHapley Additive exPlanations (SHAP). LGBM is employed to model anomalous fluctuations in various network indicators,… More >

  • Open Access

    ARTICLE

    Copy Move Forgery Detection Using Novel Quadsort Moth Flame Light Gradient Boosting Machine

    R. Dhanya1,*, R. Kalaiselvi2

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1577-1593, 2023, DOI:10.32604/csse.2023.031319 - 03 November 2022

    Abstract A severe problem in modern information systems is Digital media tampering along with fake information. Even though there is an enhancement in image development, image forgery, either by the photographer or via image manipulations, is also done in parallel. Numerous researches have been concentrated on how to identify such manipulated media or information manually along with automatically; thus conquering the complicated forgery methodologies with effortlessly obtainable technologically enhanced instruments. However, high complexity affects the developed methods. Presently, it is complicated to resolve the issue of the speed-accuracy trade-off. For tackling these challenges, this article put… More >

  • Open Access

    ARTICLE

    An Intrusion Detection System for SDN Using Machine Learning

    G. Logeswari*, S. Bose, T. Anitha

    Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 867-880, 2023, DOI:10.32604/iasc.2023.026769 - 06 June 2022

    Abstract Software Defined Networking (SDN) has emerged as a promising and exciting option for the future growth of the internet. SDN has increased the flexibility and transparency of the managed, centralized, and controlled network. On the other hand, these advantages create a more vulnerable environment with substantial risks, culminating in network difficulties, system paralysis, online banking frauds, and robberies. These issues have a significant detrimental impact on organizations, enterprises, and even economies. Accuracy, high performance, and real-time systems are necessary to achieve this goal. Using a SDN to extend intelligent machine learning methodologies in an Intrusion… More >

  • Open Access

    ARTICLE

    Coal Rock Condition Detection Model Using Acoustic Emission and Light Gradient Boosting Machine

    Jing Li1, Yong Yang2, *, Hongmei Ge1, Li Zhao3, Ruxue Guo3, 4

    CMC-Computers, Materials & Continua, Vol.63, No.1, pp. 151-162, 2020, DOI:10.32604/cmc.2020.05649 - 30 March 2020

    Abstract Coal rock mass instability fracture may result in serious hazards to underground coal mining. Acoustic emissions (AE) stimulated by internal structure fracture should carry lots of favorable information about health condition of rock mass. AE as a sensitive non-destructive test method is gradually utilized to detect anomaly conditions of coal rock. This paper proposes an improved multi-resolution feature to extract AE waveform at different frequency resolutions using Coilflet Wavelet Transform method (CWT). It is further adopt an efficient Light Gradient Boosting Machine (LightGBM) by several cascaded sub weak classifier models to merge AE features at More >

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