Detection and prevention of sinkhole attacks in MANETS based routing protocol using hybrid AdaBoost-Random forest algorithm

Sophie Maria Vincent, Sherril; Duraipandian, N.

doi:10.1016/j.eswa.2024.123765

Expert Systems with Applications

2024

DOI: 10.1016/j.eswa.2024.123765

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Detection and prevention of sinkhole attacks in MANETS based routing protocol using hybrid AdaBoost-Random forest algorithm

Sherril Sophie Maria Vincent,

N. Duraipandian

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Cited by 4 publications

References 49 publications

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Exploring Machine Learning Algorithms for Malicious Node Detection Using Cluster-Based Trust Entropy

Kanthimathi

2024

IEEE Access

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Exploring Machine Learning Algorithms for Malicious Node Detection Using Cluster-Based Trust Entropy

Kanthimathi

2024

IEEE Access

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Incomplete handwritten Dongba character image recognition by multiscale feature restoration

Bi,

Luo

2024

Herit Sci

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Incomplete handwritten Dongba character often appears in heritage documents and its recognition is significant for heritage and philology. However, all previous methods always suppose that a complete Dongba character is used as input, and thus fail to achieve satisfactory performance when applied to incomplete Dongba character recognition. In this paper, an end-to-end network (DB2RNet) is proposed for incomplete handwritten Dongba character image recognition by multiscale feature restoration. Specifically, we first develop datasets that contain different levels of incomplete Dongba characters. A restoration module is proposed to restore the input incomplete Dongba character, and then a recognition module is employed to recognize Dongba character. By introducing an inter-module residual connection between the restoration module and recognition module, the DB2RNet can strengthen feature information transmission and boost the recognition performance. In addition, novel multiscale feature blocks are introduced, which can provide more effective texture and contextual information transmission for Dongba character image restoration, and thus yielding better restoration effects and better recognition results. Extensive experiments are conducted on Dongba character, Chinese character and Oracle character datasets and validate the effectiveness, superiority and robustness of our methods. Experiments results demonstrate that our proposed DB2RNet achieves competitive Dongba character restoration and recognition performance and outperforms the current state-of-the-art methods.

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Application of Big Data Analysis in Model Construction to Prevent Athlete Injury in Training

She

2024

Applied Mathematics and Nonlinear Sciences

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Sports injury is a hot issue in the field of exercise science and sports medicine and a practical problem that needs to be solved urgently. Based on big data analysis, this paper proposes an Ada-RF prediction model that integrates the fusion of the Adaboost algorithm and Random Forest algorithm by studying five machine learning algorithms: logistic regression, k-nearest neighbor, plain Bayes, Adaboost algorithm, and Random Forest and evaluates experiments through the model evaluation criteria. The influence factors of injury risk in adolescent male basketball players were explored in terms of demographic information, training load, subjective perceived health, and assessment of athletic quality. By screening the important factors as independent variables, the risk of athlete injury and illness as dependent variables were applied to the model constructed in this paper and the models built by five traditional machine learning classification algorithms. The prediction effects of multiple models are compared. The experimental results show that the accuracy, recall, specificity, F1 score, sensitivity, and AUC value of the Ada-RF model constructed in this paper are 0.869, 0.885, 0.755, 0.683, 0.754, and 0.789, respectively, which indicate that the Ada-RF integrated model outperforms the single prediction model, and can be used for the early prevention and early treatment of athletes’ injuries and illnesses. It shows that the Ada-RF integrated model outperforms the single prediction model and can provide scientific and accurate auxiliary information for the early prevention and treatment of injuries and diseases in athletes.

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Detection and prevention of sinkhole attacks in MANETS based routing protocol using hybrid AdaBoost-Random forest algorithm

Cited by 4 publications

References 49 publications

Exploring Machine Learning Algorithms for Malicious Node Detection Using Cluster-Based Trust Entropy

Exploring Machine Learning Algorithms for Malicious Node Detection Using Cluster-Based Trust Entropy

Incomplete handwritten Dongba character image recognition by multiscale feature restoration

Application of Big Data Analysis in Model Construction to Prevent Athlete Injury in Training

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