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Background: With the rapid development of advanced artificial intelligence technologies which have been applied in varying types of applications, especially in the medical field. Cancer is one of the biggest problems in medical sciences. If cancer can be detected and treated early, the possibility of a cure will be greatly increased. Malignant skin cancer is one of the cancers with the highest mortality rate, which cannot be diagnosed in time only through doctors’ experience. We can employ artificial intelligence algorithms to detect skin cancer at an early stage, for example, patients are determined whether suffering from skin cancer by detecting skin damage or spots. Objective: We use the real HAM10000 image dataset to analyze and predict skin cancer. Methods: (1) We introduce a lightweight attention module to discover the relationships between features, and we fine-tune the pre-trained model (i.e., ResNet-50) on the HAM10000 dataset to extract the hidden high-level features from the images; (2) we integrate these high-level features with generic statistical features, and use the SMOTE oversampling technique to augment samples from the minority classes; and (3) we input the augmented samples into the XGBoost model for training and predicting. Results: The experimental results show that the accuracy, sensitivity, and specificity of the proposed SkinDet (Skin cancer detector based on transfer learning and feature fusion) model reached 98.24%, 97.84%, and 98.13%. The proposed model has stronger classification capability for the minority classes, such as dermato fibroma and actinic keratoses. result: The experimental results show that the accuracy, sensitivity and specificity of the proposed SkinDet (Skin cancer detector based on transfer learning and feature fusion) model reached 98.24%, 97.84% and 98.13%. The proposed model has stronger classification capability for the minority classes, such as dermato fibroma and actinic keratoses. Conclusion: SkinDet contains a lightweight attention module and can extract the hidden high-level features of the images by fine-tuning the pretrained model on the skin cancer dataset. In particular, SkinDet integrates high-level features with statistical features and augments samples of these minority classes. Importantly, SkinDet can be applied to classify the samples into minority classes.

When we mine long sequential pattern and discover knowledge by the PrefixSpan algorithm in Web Usage Mining (WUM).The elements and the suffix sequences are much more may cause the problem of the calculation, such as the space explosion. To further solve the problem a more effective way is that. Firstly, a server session-based server log file format is proposed. Then the improved algorithm on the PrefixSpan based on server session constraint is discussed for mining frequent Sequential patterns on the website. Finally, the validity and superiority of the method are presented by the experiment in the paper.