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  • Open Access


    Regularised Layerwise Weight Norm Based Skin Lesion Features Extraction and Classification

    S. Gopikha*, M. Balamurugan

    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2727-2742, 2023, DOI:10.32604/csse.2023.028609

    Abstract Melanoma is the most lethal malignant tumour, and its prevalence is increasing. Early detection and diagnosis of skin cancer can alert patients to manage precautions and dramatically improve the lives of people. Recently, deep learning has grown increasingly popular in the extraction and categorization of skin cancer features for effective prediction. A deep learning model learns and co-adapts representations and features from training data to the point where it fails to perform well on test data. As a result, overfitting and poor performance occur. To deal with this issue, we proposed a novel Consecutive Layerwise… More >

  • Open Access


    Short-Term Prediction of Photovoltaic Power Based on Fusion Device Feature-Transfer

    Zhongyao Du1,*, Xiaoying Chen1, Hao Wang2, Xuheng Wang1, Yu Deng1, Liying Sun1

    Energy Engineering, Vol.119, No.4, pp. 1419-1438, 2022, DOI:10.32604/ee.2022.020283

    Abstract To attain the goal of carbon peaking and carbon neutralization, the inevitable choice is the open sharing of power data and connection to the grid of high-permeability renewable energy. However, this approach is hindered by the lack of training data for predicting new grid-connected PV power stations. To overcome this problem, this work uses open and shared power data as input for a short-term PV-power-prediction model based on feature transfer learning to facilitate the generalization of the PV-power-prediction model to multiple PV-power stations. The proposed model integrates a structure model, heat-dissipation conditions, and the loss… More >

  • Open Access


    Improving Association Rules Accuracy in Noisy Domains Using Instance Reduction Techniques

    Mousa Al-Akhras1,2,*, Zainab Darwish2, Samer Atawneh1, Mohamed Habib1,3

    CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 3719-3749, 2022, DOI:10.32604/cmc.2022.025196

    Abstract Association rules’ learning is a machine learning method used in finding underlying associations in large datasets. Whether intentionally or unintentionally present, noise in training instances causes overfitting while building the classifier and negatively impacts classification accuracy. This paper uses instance reduction techniques for the datasets before mining the association rules and building the classifier. Instance reduction techniques were originally developed to reduce memory requirements in instance-based learning. This paper utilizes them to remove noise from the dataset before training the association rules classifier. Extensive experiments were conducted to assess the accuracy of association rules with… More >

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