Showing 121 - 140 results of 4,166 for search 'features detection algorithms', query time: 0.17s Refine Results
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    A novel hybrid approach for thyroid disease detection: Integrating cuttlefish algorithm and simulated annealing for optimal feature selection by Kapil Shrivastava, Saroj Pandey, Rishav Dubey, Mayank Namdev, Vipin Tiwari, Aditi Sharma

    Published 2025-12-01
    “…This study advances medical diagnostics by combining machine learning algorithms with nature-inspired optimization techniques to detect thyroid illnesses in their early stages. • This article proposes a novel hybrid algorithm that combines the Cuttlefish Optimization Algorithm (CFA) and Simulated Annealing (SA) to find the best features for finding thyroid disease. • The study uses machine-learning models for classification. • The integration of machine learning and nature-inspired optimization significantly enhances the diagnostic capabilities of healthcare systems, enabling prompt diagnosis and treatment planning for thyroid disorders.…”
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  3. 123

    Flaw-YOLOv5s: A Lightweight Potato Surface Defect Detection Algorithm Based on Multi-Scale Feature Fusion by Haitao Wu, Ranhui Zhu, Hengren Wang, Xiangyou Wang, Jie Huang, Shuwei Liu

    Published 2025-03-01
    “…To elevate detection accuracy as well as shorten the computational load of the model, this paper proposes a lightweight Flaw-YOLOv5s algorithm for potato surface defect detection. …”
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    VCNet: Optimized Deep Learning framework with deep feature extraction and genetic algorithm for multiclass rice crop disease detection by Sanam Salman Kazi, Bhakti Palkar, Dhirendra Mishra

    Published 2025-12-01
    “…It also requires fewer parameters and takes minimum training time. • The major contribution of this study is the design of an optimized, efficient and enhanced deep learning technique for multiclass rice crop disease detection embracing with batch normalization, dropout and genetic optimization algorithm to improve generalization power and restrict the overlearning capability for seen and unseen data. • Proposed VCNet, a shallow model with deep feature extraction, employs VGG16 layers for initial extraction fused with custom CNN architecture to correctly detect the challenging classes of diseases like sheath rot in multiclass classification. • The most significant observation is that VCNet accurately predicts the rice disease for each class of diseases under study whereas the existing powerful models largely misclassified for some classes of diseases in multiclass classification.…”
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  10. 130

    LLD-YOLO: A Low-Light Object Detection Algorithm Based on Dynamic Weighted Fusion of Shallow and Deep Features by Wenhao Cai, Yajun Chen, Xiaoyang Qiu, Meiqi Niu, Jianying Li

    Published 2025-01-01
    “…Object detection in low-light scenarios has a wide range of applications, but existing algorithms often struggle to preserve the scarce low-level features in dark environments and exhibit limitations in localization accuracy for blurred edges and occluded objects, leading to suboptimal performance. …”
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    Detecting Malware C&C Communication Traffic Using Artificial Intelligence Techniques by Mohamed Ali Kazi

    Published 2025-01-01
    “…Subsequently, a methodology is proposed for detecting banking malware C&C communication traffic, and this methodology is tested using several feature selection algorithms to determine which feature selection algorithm performs the best. …”
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    Detection of GenAI-produced and student-written C# code: A comparative study of classifier algorithms and code stylometry features by Adewuyi Adetayo Adegbite, Eduan Kotzé

    Published 2025-07-01
    “…This study tested the ability of six classifier algorithms to detect GenAI C# code and to distinguish it from C# code written by students at a South African university. …”
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    Enhanced Peer-to-Peer Botnet Detection Using Differential Evolution for Optimized Feature Selection by Sangita Baruah, Vaskar Deka, Dulumani Das, Utpal Barman, Manob Jyoti Saikia

    Published 2025-05-01
    “…Apart from that, an ensemble learning algorithm is also employed to support and enhance the detection phase, providing a robust defense against the dynamic and sophisticated nature of modern P2P botnets. …”
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    DP-YOLO: A Lightweight Real-Time Detection Algorithm for Rail Fastener Defects by Lihua Chen, Qi Sun, Ziyang Han, Fengwen Zhai

    Published 2025-03-01
    “…To enable accurate and efficient real-time detection of rail fastener defects under resource-constrained environments, we propose DP-YOLO, an advanced lightweight algorithm based on YOLOv5s with four key optimizations. …”
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